diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..cce4c8e --- /dev/null +++ b/.gitignore @@ -0,0 +1,12 @@ +MCOR_error_messages.log +.DS_Store +.cache +.idea +__pycache__ +venv/ +data/* +main_files/* +output/* +solar_data/* +creds.yaml +tests/system_tests/data/solar_data/* \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..6bfaf6f --- /dev/null +++ b/LICENSE @@ -0,0 +1,13 @@ +MCOR: Microgrid Component Optimization for Resilience + +Copyright © 2022, Battelle Memorial Institute + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index cf904e9..4133f24 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,157 @@ -# MCOR -Microgrid Component Optimization for Resiliency Tool +# Microgrid Component Optimization for Resilience (MCOR) Tool + +The goal of the MCOR tool is to provide several viable microgrid configurations that can meet +the resilience goals of a site and maximize economic benefits. This means meeting a set of +critical loads for a specified period of time, without any power supply from the electrical +grid. The tool simulates power dispatch for many different system configurations, and ranks +the systems according to the goals of the site. + +## Getting Started + +### Prerequisites + +* Python 3 +* pvlib: + https://github.com/pvlib/pvlib-python.git +* numba + Install numba to speed up solar power calculation: + http://numba.pydata.org/#installing + +To install required packages: +``` pip install -r requirements.txt``` + +### Set-up +You will need to create your own credentials file with an NREL API key in order to download +historical solar data from the NSRDB. To do so: +1. Sign up for an API key here: https://developer.nrel.gov/signup/ +2. Create a file called creds.yaml in the root directory and then add your email address and +API key to the file. +The file should have the following format: +```yaml +nrel_api_key: +nrel_api_email: +``` + +### How to run MCOR +To run MCOR, update the parameter values in the first half of main.py (or your site-specific +copy of main.py) under the section titled "Define simulation parameters here". Then run +main.py in either a terminal or your IDE of choice. + +## Contents + +### data/ +Includes input data such as component costs, generator specs, and validation requirements. + +### main_files/main.py +Script for running MCOR from the command line. This file must be copied and +modified to include site parameters. + +### output/ +Output data from an MCOR run is saved here (Excel and pkl files). + +### solar_data/ +Includes downloaded NREL solar data and generated solar profile files that are created when +running MCOR. + +### testing/ +Unit and integration tests. + +### alternative_solar_profiles.py +Alternative Solar Profiles (ASP) algorithm used for solar forecasting: +Original author in MATLAB: James Follum and Trevor Hardy + +#### File contents: +Classes: +* AlternativeSolarProfiles + +Standalone functions: +* date_parser + +### creds.py +Includes credentials for NREL api key. + +### config.py +Includes repository paths. + +### generate_solar_profile.py +Calls the ASP code and calculates AC power production. + +Solar power calculations carried out with pvlib-python: + https://github.com/pvlib/pvlib-python + +For an explanation of the pvlib power calculation: + http://nbviewer.jupyter.org/github/pvlib/pvlib-python/blob/master/docs/tutorials/pvsystem.ipynb + +#### File contents: +Classes: +* SolarProfileGenerator + +Standalone functions: +* download_solar_data +* calc_pv_prod +* calc_night_duration +* parse_himawari_tmy + +### microgrid_optimizer.py +Optimization class for simulating, filtering and ranking microgrid systems. + +#### File contents: +Classes: +* Optimizer +* GridSearchOptimizer (inherits from Optimizer) + +Standalone functions: +* get_electricity_rate + +### microgrid_simulator.py +Microgrid simulator class. Includes the core of the system dispatch algorithm. + +#### File Contents: +Classes: +* Simulator +* PVBattGenSimulator (inherits from Simulator) + +Standalone functions: +* calculate_load_duration + +### microgrid_system.py +Class structure for microgrid system and its components. + +#### File contents: +Classes: +* Component +* PV (inherits from Component) +* Battery (inherits from Component) +* SimpleLiIonBattery (inherits from Battery) +* Generator (inherits from Component) +* FuelTank (inherits from Component) +* MicrogridSystem +* SimpleMicrogridSystem (inherits from MicrogridSystem) + +### validation.py +Classes: +* Error +* ParamValidationError (inherits from Error) + +Standalone functions: +* log_error +* validate_all_parameters +* validate_parameter +* ...various custom validation functions + +## Contact +For questions, please reach out to sarah.newman@pnnl.gov + +## Disclaimer Notice +This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. +Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. +
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+operated by
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diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/alternative_solar_profiles.py b/alternative_solar_profiles.py new file mode 100644 index 0000000..bb85b02 --- /dev/null +++ b/alternative_solar_profiles.py @@ -0,0 +1,1098 @@ +# -*- coding: utf-8 -*- +""" +Alternative Solar Profiles (ASP) algorithm used for solar forecasting: +Original author in MATLAB: James Follum and Trevor Hardy + +The algorithm creates a probabilistic model for the solar "state" (defined by the ghi, dni, +cloud type, and temperature values) for each month on daily and hourly timescales based on +historical data downloaded from NREL's NRSDB: https://nsrdb.nrel.gov/ + +To create solar trials, the algorithm randomly samples the model for a given month and hour +given the probability of transitioning from one "state" to another "state". States are first +created for each day of a trial, and then hourly states are generated iteratively until a set +of hourly states is found that matches the daily state or the maximum number of iterations +is reached. This maximum number is set to 200 by default, as testing showed that for 85% of +days, 300 iterations was sufficient to reach agreement. + +File contents: + Classes: + AlternativeSolarProfiles + + Standalone functions: + date_parser + +""" + +import numpy as np +import pandas as pd +import random +import math +import os +import multiprocessing +from scipy import stats +import datetime as dt +from validation import validate_all_parameters, log_error +from config import SOLAR_DATA_DIR + + +class AlternativeSolarProfiles: + """ + Class to create solar profiles using a probabilistic model based on historical solar data. + + Parameters + ---------- + + longitude: Site longitude in degrees + + latitude: Site latitude in degrees + + num_trials: Number of solar profiles to create + + length_trials: Length of solar profiles in days + + start_year: Start year for solar data download + + end_year: End year for solar data download + + num_hourly_ghi_states: Number of discrete GHI states for hourly model + + num_hourly_dni_states: Number of discrete DNI states for hourly model + + num_daily_ghi_states: Number of discrete GHI states for daily model + + num_daily_dni_states: Number of discrete DNI states for daily model + + cloud_hours: Tuple containing maximum and minimum hours of the day to be used to set + the day's cloud state + + temp_bins: Temperature bins + + max_iter: The maximum number of iterations allowed for trying to match hourly and + daily states. After this number is reached the best set of hourly states generated + thus far is used. + + multithreading: Whether to use multithreading to speed up the calculation. This is + set to True by default, but should be set to False for debugging + + Methods + ---------- + + create_state_transition_matrices: Creates monthly state transition matrices from + historical NREL solar data to hold the probability of transitioning from one solar + state to another. + + load_nrel_data: Loads pre-downloaded NREL solar data from csv files. + + bin_hourly_data: Bins hourly historical solar data into buckets based on ghi, dni, + cloud type, and temperature values. + + bin_daily_data: Bins daily historical solar data into buckets based on ghi, dni, cloud + type, and temperature values. + + clean_temperature: Replaces consecutive 0s in historical temperature data with nans, + with a threshold at 4 or more 0s in a row. + + create_hourly_state_transition_matrices: Creates an hourly state transition matrix, + based on historical solar data. The transition matrix specifies the probability of + transition from one solar state (determined by GHI, DNI, cloud cover type, and + temperature) to another, for each hour and month of the year. + + create_daily_state_transition_matrices: Creates a daily state transition matrix, based + on historical solar data. The transition matrix specifies the probability of + transition from one solar state (determined by GHI, DNI, and cloud cover type) to + another, for each month of the year. + + create_trial_data: Simulates solar profiles based on hourly and daily transition + matrices, which specify the probability of transitioning from a given solar state + (determined by GHI, DNI,temperature, and cloud cover type) to another for each + hour and month of the year. + + calc_solar_params_from_states: Calculates solar params (ghi, dni, temp) from hourly + states and clear sky data. + + generate_trial_date_ranges: Generates date_ranges for each trial and removes leap + days. + + preprocess_states: Groups state dataframes to speed up creation of trials. + + generate_random_state_daily: Generates random daily states for a solar trial. + + compare_hourly_daily_states: Iteratively generates hourly states, checking for + consistency with the daily state. + + generate_random_state_hourly: Generates random hourly states for a solar trial. + + aggregate_hourly_states: Aggregates hourly states to a daily state for comparison. + + Calculated Attributes + ---------- + + nrel_data_df: Dataframe with historical data downloaded from NREL's NRSDB + + hourly_states: Dataframe with binned hourly historical solar data + + daily_states: Dataframe with binned daily historical solar data + + simple_prob_hourly: Dataframe containing every unique set of month, hour, and + to-states, along with the probability of getting to that to-state for that + month/hour, independent of a from-state. + + state_prob_hourly: Dataframe containing every unique set of month, hour, from- + (previous hour) states and to- (current hour) states, along with counts and + probabilities. This is the probability of getting a certain to-state given the + from-state. + + simple_prob_daily: Dataframe containing every unique set of month and to-states, along + with the probability of getting to that to-state for that month, independent of a + from-state. + + state_prob_daily: Dataframe containing every unique set of month, from- (previous + hour) states, and to- (current hour) states, along with counts and probabilities. + This is the probability of getting a certain to-state given the from-state. + + num_iters: Keeps track of the number of iterations required for the daily and hourly + states to match. + + state_prob_hourly_grouped: Dictionary containing state_prob_hourly data grouped by + month, hour, night state, and from-state. This is stored as a dictionary to speed + up the calculation. + + state_prob_daily_grouped: Dictionary containing state_prob_daily data grouped by month + and from-state. + + simple_prob_hourly_grouped: Dictionary containing simple_prob_hourly data grouped by + month, hour, and night state. + + simple_prob_daily_grouped: Dictionary containing simple_prob_daily data grouped by + month. + + solar_trials: Holds generated trial data. + + """ + + def __init__(self, latitude, longitude, num_trials, length_trials, start_year=1998, + end_year=2016, num_hourly_ghi_states=11, num_hourly_dni_states=11, + num_daily_ghi_states=11, num_daily_dni_states=11, cloud_hours=(10, 17), + temp_bins=range(-30, 50, 3), max_iter=200, multithreading=True, + validate=True): + + # Assign parameters from arguments + self.latitude = latitude + self.longitude = longitude + self.num_trials = int(num_trials) + self.length_trials = int(length_trials) + self.start_year = int(start_year) + self.end_year = int(end_year) + self.num_hourly_ghi_states = num_hourly_ghi_states + self.num_hourly_dni_states = num_hourly_dni_states + self.num_daily_ghi_states = num_daily_ghi_states + self.num_daily_dni_states = num_daily_dni_states + self.cloud_hours = cloud_hours + self.temp_bins = temp_bins + self.max_iter = max_iter + self.multithreading = multithreading + + # Initialize other attributes + self.nrel_data_df = None + self.hourly_states = None + self.daily_states = None + self.simple_prob_hourly = None + self.state_prob_hourly = None + self.simple_prob_daily = None + self.state_prob_daily = None + self.num_iters = [] + self.state_prob_hourly_grouped = None + self.state_prob_daily_grouped = None + self.simple_prob_hourly_grouped = None + self.simple_prob_daily_grouped = None + self.solar_trials = [] + + # Validate input arguments + if validate: + # List of initialized parameters to validate + args_dict = {'latitude': self.latitude, + 'longitude': self.longitude, + 'num_trials': self.num_trials, + 'length_trials': self.length_trials, + 'start_year': self.start_year, + 'end_year': self.end_year, + 'num_ghi_states': self.num_hourly_ghi_states, + 'num_dni_states': self.num_hourly_dni_states, + 'num_daily_ghi_states': self.num_daily_ghi_states, + 'num_daily_dni_states': self.num_daily_dni_states, + 'cld_hours': self.cloud_hours, + 'temp_bins': self.temp_bins, + 'max_iter': self.max_iter, + 'multithreading': self.multithreading} + + # Validate input parameters + validate_all_parameters(args_dict) + + def create_state_transition_matrices(self): + """ + Create monthly state transition matrices from historical NREL solar data. + + inputs + self.latitude: Site latitude in degrees + self.longitude: Site longitude in degrees + self.start_year: Start year for solar data download + self.end_year: End year for solar data download + self.num_hourly_ghi_states: Number of discrete GHI states for hourly model + self.num_hourly_dni_states: Number of discrete DNI states for hourly model + self.temp_bins: Bins for temperature data + self.num_daily_ghi_states: Number of discrete GHI states for daily model + self.num_daily_dni_states: Number of discrete DNI states for daily model + self.cloud_hours: Hours of the day (range) used to set the day's cloud state + + outputs + self.state_prob_hourly: Dataframe containing every unique set of month, hour, + from- (previous hour) states and to- (current hour) states, along with counts + and probabilities. This is the probability of getting a certain to-state given + the from-state. + self.simple_prob_hourly: Dataframe containing every unique set of month, hour, and + to-states, along with the probability of getting that to-state for that + month/hour, independent of a from-state. + self.state_prob_daily: Dataframe containing every unique set of month, from- + (previous hour) states and to- (current hour) states, along with counts and + probabilities. This is the probability of getting a certain to-state given + the from-state. + self.simple_prob_daily: Dataframe containing every unique set of month and + to-states, along with the probability of getting that to-state for that + month, independent of a from-state. + """ + + # Read in historical NREL solar data + self.load_nrel_data() + + # Clean data + self.clean_temperature() + self.nrel_data_df.loc[self.nrel_data_df['cloud_type'] < 0, 'cloud_type'] = \ + self.nrel_data_df.loc[self.nrel_data_df['cloud_type'] < 0, 'cloud_type'].apply( + lambda x: random.randrange(11)) + + # Bin data into buckets + self.bin_hourly_data() + self.bin_daily_data() + + # Create state transition matrices + self.create_hourly_state_transition_matrices() + self.create_daily_state_transition_matrices() + + def load_nrel_data(self): + """ + Load pre-downloaded NREL solar data from csv files. + + inputs + self.latitude: Site latitude in degrees + self.longitude: Site longitude in degrees + self.start_year: Start year for solar data download + self.end_year: End year for solar data download + + outputs + self.nrel_data_df: Dataframe with historical data downloaded from NREL's NRSDB + + """ + # Read in historical NREL solar data + filedir = os.path.join(SOLAR_DATA_DIR, 'nrel', + '{}_{}'.format(self.latitude, self.longitude)) + + # Check that files exist in directory + if '{}_{}_{}.csv'.format(self.latitude, self.longitude, + self.start_year) not in os.listdir(filedir): + message = 'NREL data not found, check the latitude and longitude '\ + 'or run get_solar_data() first.' + log_error(message) + raise Exception(message) + + # Read in files and add to dataframe + self.nrel_data_df = pd.DataFrame() + for year in range(self.start_year, self.end_year+1): + self.nrel_data_df = pd.concat([self.nrel_data_df, pd.read_csv( + os.path.join(filedir, '{}_{}_{}.csv'.format( + self.latitude, self.longitude, year)), + usecols=['Year', 'Month', 'Day', 'Hour', 'DNI', 'GHI', + 'Clearsky DNI', 'Clearsky GHI', 'Cloud Type', + 'Solar Zenith Angle', 'Temperature'])]) + self.nrel_data_df['datetime'] = self.nrel_data_df.apply( + lambda x: date_parser(x['Month'], x['Day'], x['Year'], x['Hour']), axis=1) + self.nrel_data_df.columns = ['year', 'month', 'day', + 'hour', 'dni', 'ghi', 'clear_sky_dni', + 'clear_sky_ghi', 'cloud_type', + 'solar_zenith_angle', 'temperature', 'datetime'] + + # Recast month, hour, year, day datatypes - this doesn't work with + # parse_dates + self.nrel_data_df['year'] = self.nrel_data_df['year'].astype(np.int64) + self.nrel_data_df['month'] = self.nrel_data_df['month'].astype(np.int64) + self.nrel_data_df['day'] = self.nrel_data_df['day'].astype(np.int64) + self.nrel_data_df['hour'] = self.nrel_data_df['hour'].astype(np.int64) + + def bin_hourly_data(self): + """ + Bin hourly historical solar data into buckets based on ghi, dni, and temperature + values. + + inputs + self.nrel_data_df: Downloaded historical solar data + self.num_hourly_ghi_states: Number of discrete GHI states for hourly model + self.num_hourly_dni_states: Number of discrete DNI states for hourly model + self.temp_bins: Bins for temperature data + + outputs + self.hourly_states: Dataframe with binned hourly historical solar data + + """ + + # Calculate states (bins) for ghi and dni + hourly_data = self.nrel_data_df.copy(deep=True) + hourly_data['ghi_state'] = np.round( + (hourly_data['clear_sky_ghi'] - hourly_data['ghi']) / + hourly_data['clear_sky_ghi'] * + (self.num_hourly_ghi_states - 1)).fillna(math.exp(1)) + hourly_data['dni_state'] = np.round( + (hourly_data['clear_sky_dni'] - hourly_data['dni']) / + hourly_data['clear_sky_dni'] * + (self.num_hourly_dni_states - 1)).fillna(math.exp(1)) + + # Gets rid of any negative states arising from, e.g., dni > clear sky dni + hourly_data.loc[hourly_data['ghi_state'] < 0, 'ghi_state'] = 0 + hourly_data.loc[hourly_data['dni_state'] < 0, 'dni_state'] = 0 + + # Calculate states for temperature data + hourly_data['temp_state'] = hourly_data['temperature'].apply( + lambda x: np.digitize(x, self.temp_bins)) + hourly_data.loc[np.isnan(hourly_data['temperature']), 'temp_state'] = np.nan + + # Clean up columns and index + self.hourly_states = hourly_data[['year', 'month', 'day', 'hour', + 'ghi_state', 'dni_state', + 'cloud_type', 'temp_state']] + self.hourly_states.index = range(len(self.hourly_states)) + + def bin_daily_data(self): + """ + Bin daily historical solar data into buckets based on ghi, dni, cloud type, and + temperature values. Bins represent the percentage (as a decimal) of the available + radiation that was observed during the day. + + inputs + self.nrel_data_df: Downloaded historical solar data + self.num_daily_ghi_states: Number of discrete GHI states for daily model + self.num_daily_dni_states: Number of discrete DNI states for daily model + self.cloud_hours: Hours of the day (range) used to set the day's cloud state + + outputs + self.daily_states: Dataframe with binned daily historical solar data + + """ + + # Aggregate historical data to daily values + daily_data = self.nrel_data_df.groupby(['year', 'month', 'day']).sum().reset_index() + + # Calculate states (bins) for daily ghi and dni data + daily_data['ghi_state'] = np.round(daily_data['ghi'] / + daily_data['clear_sky_ghi'] * + (self.num_daily_ghi_states-1)) + 1 + daily_data['dni_state'] = np.round(daily_data['dni'] / + daily_data['clear_sky_dni'] * + (self.num_daily_dni_states-1)) + 1 + + # Daily cloud data: get most common cloud type for each day from daylight hours + daily_cloud_type = self.nrel_data_df.loc[ + (self.nrel_data_df['hour'] >= self.cloud_hours[0]) & + (self.nrel_data_df['hour'] < self.cloud_hours[1])].groupby( + ['year', 'month', 'day'])['cloud_type'].apply( + lambda x: stats.mode(x).mode[0]) + daily_data = daily_data.merge( + daily_cloud_type.reset_index(name='cloud_state'), + left_on=['year', 'month', 'day'], + right_on=['year', 'month', 'day']) + self.daily_states = daily_data[['year', 'month', 'day', 'ghi_state', + 'dni_state', 'cloud_state']] + + def clean_temperature(self): + """ + Replaces consecutive 0s in historical temperature data with nans, with a threshold at + 4 or more 0s in a row. + + inputs + self.nrel_data_df + + outputs + self.nrel_data_df + + """ + + # Get number of 0s to keep + temp = self.nrel_data_df['temperature'].copy(deep=True) + temp.index = range(len(temp)) + + # Get 0s from temperature array + temp_zeros_index = temp[temp == 0].index + if not len(temp_zeros_index): + return + + # Get the start and end index of each group of consecutive 0s + diff_index = temp_zeros_index[1:] - temp_zeros_index[:-1] + group_end_index = np.where(diff_index > 1)[0] + group_start_index = np.append([0], group_end_index+1) + group_end_index = np.append(group_end_index, len(temp_zeros_index)-1) + + # Get the length of each group of 0s + group_lengths = group_end_index - group_start_index + 1 + + # Sort groups of 0s based on length + groups_lengths_df = pd.Series(group_lengths).sort_values(ascending=False) + groups_sorted = groups_lengths_df.values + group_start_index = group_start_index[groups_lengths_df.index] + group_end_index = group_end_index[groups_lengths_df.index] + + # Replace all groups of 4 or more 0s + last_group = np.where(groups_sorted < 4)[0][0] + + # For each group to replace, set 0s to NaNs + for group_index in range(last_group): + temp.iloc[temp_zeros_index[group_start_index[group_index]]: + temp_zeros_index[group_end_index[group_index]]+1] = np.nan + self.nrel_data_df['temperature'] = temp.values + + def create_hourly_state_transition_matrices(self): + """ + Creates an hourly state transition matrix, based on historical solar data. The + transition matrix specifies the probability of transition from one solar state + (determined by GHI, DNI, cloud cover type, and temperature) to another, for each + hour and month of the year. + + inputs: + self.hourly_states: ghi, dni, cloud, and temperature states (binned values) for + each hour from the historical data + + outputs: + self.state_prob_hourly: dataframe containing every unique set of month, hour, + from- (previous hour) states and to- (current hour) states, along with counts + and probabilities. This is the probability of getting a certain to-state given + the from-state. + self.simple_prob_hourly: dataframe containing every unique set of month, hour, and + to-states, along with the probability of getting that to-state for that + month/hour, independent of a from-state. + + """ + + # Create dataframe with from- and to-states for each hour + from_and_to_states = self.hourly_states.iloc[1:] + from_and_to_states.index = range(len(from_and_to_states)) + from_and_to_states = from_and_to_states.merge( + self.hourly_states.iloc[:-1][['ghi_state', 'dni_state', + 'cloud_type', 'temp_state']], + right_index=True, left_index=True, suffixes=['_to', '_from']) + + # Group by month, hour, from-, and to-states to get probabilities + state_prob = from_and_to_states.groupby( + ['month', 'hour', 'ghi_state_to', 'dni_state_to', 'cloud_type_to', + 'temp_state_to', 'ghi_state_from', 'dni_state_from', + 'cloud_type_from', 'temp_state_from'])['year'].count().to_frame( + name='count').reset_index() + + # Divide by the total number of to-states for each from-state to get a probability + sum_of_to_states_for_each_from_state = state_prob.groupby( + ['month', 'hour', 'ghi_state_from', 'dni_state_from', + 'cloud_type_from', 'temp_state_from'])['count'].sum().reset_index() + state_prob = state_prob.merge( + sum_of_to_states_for_each_from_state, + left_on=['month', 'hour', 'ghi_state_from', 'dni_state_from', + 'cloud_type_from', 'temp_state_from'], + right_on=['month', 'hour', 'ghi_state_from', 'dni_state_from', + 'cloud_type_from', 'temp_state_from'], + suffixes=['', '_sum']) + state_prob['prob'] = state_prob['count'] / state_prob['count_sum'] + + # Get a list of all to-states for each month and hour to calculate the simple + # probability of transitioning to a given to-state independent of the from-state + simple_prob = from_and_to_states.groupby( + ['month', 'hour', 'ghi_state_to', 'dni_state_to', 'cloud_type_to', + 'temp_state_to'])['year'].count().to_frame(name='count').reset_index() + sum_of_to_states_for_each_hour_month = simple_prob.groupby( + ['month', 'hour'])['count'].sum().reset_index() + simple_prob = simple_prob.merge( + sum_of_to_states_for_each_hour_month, + left_on=['month', 'hour'], right_on=['month', 'hour'], + suffixes=['', '_sum']) + simple_prob['prob'] = simple_prob['count'] / simple_prob['count_sum'] + + # Add a column to each probabilty dataframe indicating if it is night + simple_prob['night_state'] = ((simple_prob['ghi_state_to'] == math.exp(1)) | + (simple_prob['dni_state_to'] == math.exp(1))) + state_prob['night_state'] = ((state_prob['ghi_state_to'] == math.exp(1)) | + (state_prob['dni_state_to'] == math.exp(1))) + + # Save to class attributes + self.simple_prob_hourly = simple_prob + self.state_prob_hourly = state_prob + + def create_daily_state_transition_matrices(self): + """ + Creates a daily state transition matrix, based on historical solar data. The + transition matrix specifies the probability of transition from one solar state + (determined by GHI, DNI, and cloud cover type) to another, for each month of the + year. + + inputs: + self.daily_states: ghi, dni, and cloud states (binned values) for each day from + the historical data + + outputs: + self.state_prob_daily: dataframe containing every unique set of month, from- + (previous hour) states and to- (current hour) states, along with counts and + probabilities. This is the probability of getting a certain to-state given the + from-state. + self.simple_prob_daily: dataframe containing every unique set of month and + to-states, along with the probability of getting that to-state for that + month, independent of a from-state. + + """ + + # Create dataframe with from- and to-states for each day + from_and_to_states = self.daily_states.iloc[1:] + from_and_to_states.index = range(len(from_and_to_states)) + from_and_to_states = from_and_to_states.merge( + self.daily_states.iloc[:-1][['ghi_state', 'dni_state', 'cloud_state']], + right_index=True, left_index=True, suffixes=['_to', '_from']) + + # Group by month, from-, and to-states to get probabilities + state_prob = from_and_to_states.groupby( + ['month', 'ghi_state_to', 'dni_state_to', 'cloud_state_to', + 'ghi_state_from', 'dni_state_from', 'cloud_state_from'])[ + 'year'].count().to_frame(name='count').reset_index() + + # Divide by the total number of to-states for each from-state to get a probability + sum_of_to_states_for_each_from_state = state_prob.groupby( + ['month', 'ghi_state_from', 'dni_state_from', + 'cloud_state_from'])['count'].sum().reset_index() + state_prob = state_prob.merge( + sum_of_to_states_for_each_from_state, + left_on=['month', 'ghi_state_from', 'dni_state_from', 'cloud_state_from'], + right_on=['month', 'ghi_state_from', 'dni_state_from', 'cloud_state_from'], + suffixes=['', '_sum']) + state_prob['prob'] = state_prob['count'] / state_prob['count_sum'] + + # Get a list of all to-states for each month to calculate the simple probability of + # transitioning to a given to-state independent of the from-state + simple_prob = from_and_to_states.groupby( + ['month', 'ghi_state_to', 'dni_state_to', 'cloud_state_to'])[ + 'year'].count().to_frame(name='count').reset_index() + sum_of_to_states_for_each_month = simple_prob.groupby( + ['month'])['count'].sum().reset_index() + simple_prob = simple_prob.merge( + sum_of_to_states_for_each_month, left_on=['month'], + right_on=['month'], suffixes=['', '_sum']) + simple_prob['prob'] = simple_prob['count'] / simple_prob['count_sum'] + + # Save to class attributes + self.simple_prob_daily = simple_prob + self.state_prob_daily = state_prob + + def create_trial_data(self, start_datetimes=None, validate=True): + """ + Simulates solar profiles based on hourly and daily transition matrices, which specify + the probability of transitioning from a given solar state (determined by GHI, DNI, + temperature, and cloud cover type) to another for each hour and month of the year. + + inputs: + self.nrel_data_df: historical solar data + self.simple_prob_hourly: dataframe holding the probabilities of transitioning to a + given hourly state, independent of the from-state. + self.simple_prob_daily: dataframe holding the probabilities of transitioning to a + given daily state, independent of the from-state. + self.state_prob_hourly: dataframe holding the probabilities of transition to a + given hourly state, given the from-state. + self.state_prob_daily: dataframe holding the probabilities of transition to a + given daily state, given the from-state. + self.num_trials: number of solar profiles to generate + self.length_trials: length of profiles in days + + outputs: + self.solar_trials: dict containing a dataframe for each trial + + """ + + # Validate input arguments + if validate and start_datetimes is not None: + # List of initialized parameters to validate + args_dict = {'num_trials': self.num_trials, + 'start_year': self.start_year, + 'end_year': self.end_year, + 'start_datetimes': start_datetimes} + + # Validate input parameters + validate_all_parameters(args_dict) + + # Generate date ranges for each trial + date_ranges = self.generate_trial_date_ranges(start_datetimes) + + # Group state dataframes to speed up trial creation + self.preprocess_states() + + # Generate daily states + input_list = [] + for date_range in date_ranges: + daily_state = self.generate_random_state_daily(date_range) + clear_sky_data = self.nrel_data_df.merge( + date_range.to_frame(), left_on='datetime', right_on=0, + how='inner').set_index('datetime') + input_list += [(daily_state, date_range, clear_sky_data)] + + # Iterate through each day and create hourly states + hourly_states_list = [] + + # ...with parallelization + if self.multithreading: + # Create list to hold processes and Queue for multithreading + procs = [] + queue = multiprocessing.Queue() + + for daily_state, date_range, clear_sky_data in input_list: + proc = multiprocessing.Process( + target=self.compare_hourly_daily_states, + args=(daily_state, date_range, clear_sky_data, queue)) + procs.append(proc) + proc.start() + + # Unpack results from Queue + for proc in procs: + hourly_states = queue.get() + hourly_states_list.append(hourly_states) + + # Wait until all processes have finished + for proc in procs: + proc.join() + + # ...without parallelization + else: + for daily_state, date_range, clear_sky_data in input_list: + hourly_states_list += [self.compare_hourly_daily_states( + daily_state, date_range, clear_sky_data)] + + # Calculate hourly ghi, dni, and temperature values from states + for hourly_states, clear_sky_data in hourly_states_list: + self.solar_trials += [self.calc_solar_params_from_states( + hourly_states, clear_sky_data)] + + def calc_solar_params_from_states(self, hourly_states, clear_sky_data): + """ + Calculate solar params (ghi, dni, temp) from hourly states and clear sky data. + + inputs: + hourly_states: Dataframe with hourly state data for a given trial + clear_sky_data: historical solar data for the same time period as the hourly + states + self.num_hourly_ghi_states: Number of discrete GHI states for hourly model + self.num_hourly_dni_states: Number of discrete DNI states for hourly model + self.temp_bins: Bins for temperature data + + outputs: + hourly_states: Dataframe with hourly ghi, dni, cloud type, and temperature values + for a given trial + + """ + + # Merge hourly states with clearsky data from the same period + hourly_states = hourly_states.merge(clear_sky_data[[ + 'clear_sky_ghi', 'clear_sky_dni']], right_index=True, left_index=True) + + # Calculate solar params from clear sky data and solar states + hourly_states['ghi'] = hourly_states['clear_sky_ghi'] * \ + (1 - hourly_states['ghi_state'] / (self.num_hourly_ghi_states - 1)) + hourly_states['dni'] = hourly_states['clear_sky_dni'] * \ + (1 - hourly_states['dni_state'] / (self.num_hourly_dni_states - 1)) + hourly_states['temp'] = np.diff(self.temp_bins)[0] * \ + (hourly_states['temp_state'] - 0.5) + self.temp_bins[0] + return hourly_states[['ghi', 'dni', 'cloud_state', 'temp']] + + def generate_trial_date_ranges(self, start_datetimes=None): + """ + Generate date_ranges for each trial and remove leap days. Allows for an input argument + specifying the start datetimes to use. + + inputs + start_datetimes: list of datetime objects. If set as None (default), these are + generated randomly. + self.nrel_data_df: historical solar data + self.length_trials: number of days in each trial + self.num_trials: number of trials to generate + + outputs + date_ranges: list of Pandas date_range objects + + """ + + # Randomly generate start times for each trial + if start_datetimes is None: + start_datetimes = self.nrel_data_df.iloc[:-self.length_trials * 24].sample( + self.num_trials)['datetime'].values + + # Create a date range object for each start datetime + date_ranges = [pd.date_range(start=start_date, + periods=self.length_trials * 24, + freq='H') + for start_date in start_datetimes] + + # Remove any leap days and pad the date_range accordingly + for i, date_range in enumerate(date_ranges): + replace_timesteps = len(date_range[(date_range.month == 2) & + (date_range.day == 29)]) + if replace_timesteps: + # Remove 2/29 + date_range = date_range[date_range.date != dt.date(date_range[0].year, 2, 29)] + + # Add more timesteps + date_ranges[i] = date_range.append(pd.date_range( + date_range[-1] + dt.timedelta(hours=1), + periods=replace_timesteps, freq='H')) + + return date_ranges + + def preprocess_states(self): + """ + Group state dataframes to speed up creation of trials. + + inputs + self.state_prob_hourly: Dataframe containing every unique set of month, hour, + from- (previous hour) states and to- (current hour) states, along with counts + and probabilities. + self.simple_prob_hourly: Dataframe containing every unique set of month, hour, + and to- (current hour) states, along with counts and probabilities. + self.simple_prob_hourly: Dataframe containing every unique set of month, from- + (previous hour) states and to- (current day) states, along with counts and + probabilities. + self.simple_prob_daily: Dataframe containing every unique set of month and to- + (current day) states, along with counts and probabilities. + + outputs + (Dictionaries containing groupings of the input parameters) + self.state_prob_hourly_grouped: Dict keys: (month, hour, night_state, ghi_state, + dni_state, cloud_type, temp_state) + self.simple_prob_hourly_grouped: Dict keys: (month, hour, night_state) + self.state_prob_daily_grouped: Dict keys: (month, ghi_state, dni_state, + cloud_type) + self.simple_prob_daily_grouped: Dict keys: (month) + + """ + + # Create pandas DataFrameGroupBy objects + state_prob_hourly_groups = self.state_prob_hourly.groupby([ + 'month', 'hour', 'night_state', 'ghi_state_from', + 'dni_state_from', 'cloud_type_from', 'temp_state_from']) + simple_prob_hourly_groups = self.simple_prob_hourly.groupby([ + 'month', 'hour', 'night_state']) + state_prob_daily_groups = self.state_prob_daily.groupby([ + 'month', 'ghi_state_from', 'dni_state_from', 'cloud_state_from']) + simple_prob_daily_groups = self.simple_prob_daily.groupby([ + 'month']) + + # Parse into dictionaries for faster access + self.state_prob_hourly_grouped = { + label: dataframe for label, dataframe in state_prob_hourly_groups} + self.simple_prob_hourly_grouped = { + label: dataframe for label, dataframe in simple_prob_hourly_groups} + self.state_prob_daily_grouped = { + label: dataframe for label, dataframe in state_prob_daily_groups} + self.simple_prob_daily_grouped = { + label: dataframe for label, dataframe in simple_prob_daily_groups} + + def generate_random_state_daily(self, date_range): + """ + Generate random daily states for a solar trial. + + inputs: + date_range: datetimeindex object for the trial + self.length_trials: length of profiles in days + self.state_prob_daily_grouped: dataframe holding the probabilities of transition + to a given daily state, given the from-state, grouped by month and from-state. + self.simple_prob_daily_grouped: dataframe holding the probabilities of + transitioning to a given daily state, independent of the from-state, grouped + by month. + + outputs: + dataframe with daily trial states + + """ + + # Create lists to hold generated states and the current state + current_state = [None, None, None] + state_list = [] + + # Create daily states iteratively + sorted_dates = np.sort(list(set(date_range.date))) + for date in sorted_dates: + try: + # Get the numpy values from the subset of states with the corresponding month + # and from-states. While the state data is stored in a pandas dataframe, the + # following operations are carried out on numpy arrays to speed up the + # calculation. + # Grouped daily states have labels of the form: + # (month, ghi_state_from, dni_state_from, cloud_state_from) + states_subset = self.state_prob_daily_grouped[ + tuple([date.month] + list(current_state))].values + + # Except statement catches cases where state subset is empty (i.e. if the + # from-state does not exist in the corresponding month) or the first day in the + # trial, in which case it uses the simple probability dataframe which doesn't + # require a from-state. + except KeyError: + # Same as above, but for simple probabilities + states_subset = self.simple_prob_daily_grouped[ + date.month].values + + # Get the probabilities for this subset of states + probs = states_subset[:, -1].astype(float) + + # Randomly select a state based on the probabilities + sample_index = np.random.choice(range(len(states_subset)), + p=probs / np.sum(probs)) + + # Assign the current state and add to list + current_state = states_subset[sample_index, 1:4] + state_list += [current_state] + + return pd.DataFrame(data=state_list, index=sorted_dates, + columns=['ghi_state', 'dni_state', 'cloud_state']) + + def compare_hourly_daily_states(self, daily_states, date_range, + clear_sky_data, queue=None): + """ + Iteratively generates hourly states, checking for consistency with the daily state. + An hourly state is chosen if the aggregated ghi, dni, and cloud state values match + that ofthe daily state or the maximum number of iterations is reached. If the + latter, the generated state which matches the most daily state values is used. + + The hourly states are only checked for consistency with the daily state if the current + day contains all of the "cloud hours", i.e. if a trial starts at 2pm, this check + is not run for the first day. + + inputs: + daily_states: Dataframe with daily states for a trial + date_range: Pandas date_range object for the trial + clear_sky_data: historical solar data for the same dates as the trial + self.max_iter: maximum number of iterations for generating hourly states + self.cloud_hours: Tuple containing maximum and minimum hours of the day to be used + to set the day's cloud state + self.num_iters: List containing the number of iterations required to generate + hourly states for each day and each trial + + outputs: + Dataframe with hourly states for the given trial + + """ + + # Initialize list to hold hourly states + all_hourly_states = [] + current_state = [None, None, None, None] + + # Loop through days in the trial + for date_time, daily_state in daily_states.iterrows(): + + # Set up parameters for while loop + num_iter = 0 + best_num_matches = 0 + best_states = None + + # Filter date_range to only include this day + one_day_date_range = date_range[date_range.date == date_time] + + # Get clearsky data from date range and get night states, which indicate if it is + # day or night time - this is used to filter which states are considered. + one_day_clear_sky_data = clear_sky_data[ + clear_sky_data.index.date == date_time] + night_states = one_day_clear_sky_data['clear_sky_ghi'] == 0 + + # Keep generating hourly states until a set is found which matches the daily + # states or the maximum number of iterations is reached + while num_iter < self.max_iter: + # Generate hourly states + hourly_states = self.generate_random_state_hourly( + one_day_date_range, night_states, current_state) + + # Check if this day contains all daylight hours, via the cloud hours param - + # the daily/hourly check should not be performed for the beginning or end of + # a trial with only a partial day of generated hourly states + if not len(set(range(self.cloud_hours[0], self.cloud_hours[1])) - + set(one_day_date_range.hour)): + + # Aggregate hourly states to daily + agg_ghi_state, agg_dni_state, cloud_modes = \ + self.aggregate_hourly_states(one_day_clear_sky_data, hourly_states) + + # Check if states match + num_matches = np.sum([ + agg_ghi_state == daily_state['ghi_state'], + agg_dni_state == daily_state['dni_state'], + daily_state['cloud_state'] in cloud_modes]) + + # If all states match, this is an acceptable state, if not, save the state + # if it exceeds the previous number of best matches + if num_matches == 3: + best_states = hourly_states + break + + elif num_matches > best_num_matches: + best_num_matches = num_matches + best_states = hourly_states + else: + best_states = hourly_states + break + num_iter += 1 + + # If the maximum number of iterations has been reached and no acceptable state has + # been found, just use the current state + if best_states is None: + best_states = hourly_states + + # Record the number of iterations required to find a match + # Note: this only works without multi-threading + self.num_iters += [num_iter] + + # Add to hourly states list + all_hourly_states += list(best_states.values) + current_state = best_states.iloc[-1].values + + if queue is None: + # Without multithreading, return hourly states dataframe + return pd.DataFrame( + data=all_hourly_states, index=date_range, + columns=['ghi_state', 'dni_state', 'cloud_state', + 'temp_state']), clear_sky_data + else: + # With multithreading, add dataframe to Queue + queue.put([pd.DataFrame( + data=all_hourly_states, index=date_range, + columns=['ghi_state', 'dni_state', 'cloud_state', + 'temp_state']), clear_sky_data]) + + def generate_random_state_hourly(self, hourly_date_range, night_states, + current_state): + """ + Generate random hourly states for a solar trial. + + inputs: + hourly_date_range: date_range object with the hours for the current day to be + generated + night_states: list containing a Boolean indicating if it is night-time for each + hour in the day + current_state: array with hourly state from previous hour + self.length_trials: length of profiles in days + self.nrel_data_df: historical solar data + self.state_prob_hourly_grouped: dataframe holding the probabilities of transition + to a given daily state, given the from-state, grouped by month, hour, + night_state and from-states. + self.simple_prob_hourly_grouped: dataframe holding the probabilities of + transitioning to a given daily state, independent of the from-state, grouped + by month, hour, and night_state. + + outputs: + dataframe with hourly trial states + + """ + + # Create list to hold generated states + state_list = [] + + # Create hourly states iteratively + for date_time, night_state in zip(hourly_date_range, night_states): + try: + # Get the numpy values from the subset of states with the corresponding month, + # hour, and from-states. While the state data is stored in a pandas + # dataframe, the following operations are carried out on numpy arrays to + # speed up the calculation. + # Grouped hourly states have labels of the form: + # (month, hour, night_state, ghi_state_from, dni_state_from, + # cloud_type_from, temp_state_from) + states_subset = self.state_prob_hourly_grouped[ + tuple([date_time.month, date_time.hour, night_state] + + list(current_state))].values + # Except statement catches cases where state subset is empty (i.e. if the + # from-state does not exist in the corresponding month) or the first day in the + # trial, in which case it uses the simple probability dataframe which doesn't + # require a from-state. + except KeyError: + # Same as above, but for simple probabilities + # Grouped hourly simple states have labels of the form: + # (month, hour, night_state) + states_subset = self.simple_prob_hourly_grouped[ + (date_time.month, date_time.hour, night_state)].values + + # Get the probabilities for this subset of states + probs = states_subset[:, -2].astype(float) + + # Randomly select a state based on the probabilities + sample_index = np.random.choice(range(len(states_subset)), + p=probs / np.sum(probs)) + + # Assign the current state and add to list + current_state = states_subset[sample_index, 2:6] + state_list += [current_state] + + # return state_list + return pd.DataFrame(data=state_list, index=hourly_date_range, + columns=['ghi_state', 'dni_state', 'cloud_state', 'temp_state']) + + def aggregate_hourly_states(self, one_day_clear_sky_data, hourly_states): + """ + Aggregate hourly states to a daily state. + + inputs + one_day_clear_sky_data: historical solar data for a single day + hourly_states: DataFrame with hourly states for a single day + self.num_hourly_ghi_states: Number of discrete GHI states for hourly model + self.num_hourly_dni_states: Number of discrete DNI states for hourly model + self.num_daily_ghi_states: Number of discrete GHI states for daily model + self.num_daily_dni_states: Number of discrete DNI states for daily model + self.cloud_hours: Tuple containing maximum and minimum hours of the day to be used + to set the day's cloud state + + outputs + agg_ghi_state: Hourly GHI states aggregated to a daily value + agg_dni_state: Hourly DNI states aggregated to a daily value + cloud_modes: Modes of hourly cloud types + + """ + + # Calculate the percent of available clearsky radiation that was observed for this day + ghi_percent = (one_day_clear_sky_data['clear_sky_ghi'] * + (1 - hourly_states['ghi_state'] / + (self.num_hourly_ghi_states - 1))).sum() / \ + one_day_clear_sky_data['clear_sky_ghi'].sum() + dni_percent = (one_day_clear_sky_data['clear_sky_dni'] * + (1 - hourly_states['dni_state'] / + (self.num_hourly_dni_states - 1))).sum() / \ + one_day_clear_sky_data['clear_sky_dni'].sum() + + # Calculate aggregated daily state based on hourly states + agg_ghi_state = round(ghi_percent * (self.num_daily_ghi_states - 1)) + 1 + agg_dni_state = round(dni_percent * (self.num_daily_dni_states - 1)) + 1 + + # Calculate the modes of the hourly cloud states + cloud_value_counts = hourly_states.loc[hourly_states.index.hour.isin( + list(range(self.cloud_hours[0], self.cloud_hours[1]))), + 'cloud_state'].value_counts() + cloud_modes = list(cloud_value_counts[ + cloud_value_counts == cloud_value_counts.iloc[0]].index) + + return agg_ghi_state, agg_dni_state, cloud_modes + + +def date_parser(month, day, year, hour): + """ Function to parse datetimes for historical solar data. """ + return pd.to_datetime('{}/{}/{} {}:00'.format(int(month), int(day), int(year), int(hour))) + + +if __name__ == "__main__": + # Used for testing + t = dt.datetime.utcnow() + asp = AlternativeSolarProfiles(latitude=46.34, longitude=-119.28, + num_trials=10, length_trials=14, + max_iter=200, multithreading=False) + asp.create_state_transition_matrices() + print('time for setup: {}'.format(dt.datetime.utcnow()-t)) + t = dt.datetime.utcnow() + asp.create_trial_data() + print('time for trial creation: {}'.format(dt.datetime.utcnow() - t)) diff --git a/config.py b/config.py new file mode 100644 index 0000000..088fb83 --- /dev/null +++ b/config.py @@ -0,0 +1,14 @@ +""" +Configuration and file paths. +""" + +import os + +ROOT_DIR = os.path.dirname(os.path.realpath(__file__)) +DATA_DIR = os.path.join(ROOT_DIR, 'data') +MAIN_DIR = os.path.join(ROOT_DIR, 'main_dir') +TEST_DIR = os.path.join(ROOT_DIR, 'tests') +SYS_TESTS_DIR = os.path.join(TEST_DIR, 'system_tests') +SOLAR_DATA_DIR = os.path.join(ROOT_DIR, 'solar_data') +OUTPUT_DIR = os.path.join(ROOT_DIR, 'output') +UNIT_TESTS_DIR = os.path.join(TEST_DIR, 'unit_tests') diff --git a/data/MCOR Prices.xlsx b/data/MCOR Prices.xlsx new file mode 100644 index 0000000..63f34d9 Binary files /dev/null and b/data/MCOR Prices.xlsx differ diff --git a/data/parameter_validation.csv b/data/parameter_validation.csv new file mode 100644 index 0000000..277ce44 --- /dev/null +++ b/data/parameter_validation.csv @@ -0,0 +1,145 @@ +Parameter,Type,Min,Max,Enums,Size,Custom,Custom Arguments,Custom Message +add_additional_generator,bool,,,,,,, +albedo,"int,float",0,1,,,,, +altitude,"int,float",0,3000,,,,, +annual_load_profile,pandas.core.series.Series,,,,,check_annual_load_profile,duration,"The annual load profile must have a datetime index, contain values for each day and hour (or the corresponding interval if duration is not 1 hour) of the year, and have non-negative values." +off_grid_load_profile,pandas.core.series.Series,,,,,check_off_grid_load_profile,duration,"The off grid load profile must have a datetime index, contain values for each day and hour (or the corresponding interval if duration is not 1 hour) of the year, and have non-negative values." +azimuth,"int,float",-180,180,,,,, +base_power_profile,pandas.core.series.Series,,,,,,, +batt_capacity,"int,float",0,,,,,, +batt_cost_per_Wh,"int,float",0,10,,,,, +batt_percent,"int,float",,,,,,, +battery_params,dict,,,,,check_battery_params,,"battery_params must be a dictionary with the following keys and value datatypes: {'battery_power_to_energy': float, 'initial_soc': float, 'one_way_battery_efficiency': float, 'one_way_inverter_efficiency': float, 'soc_upper_limit': float, 'soc_lower_limit': float, 'init_soc_lower_limit': float}. The initial SOC must be between the upper and lower limits." +battery_power_to_energy,"int,float",0,100,,,,, +capital_cost,"int,float",0,,,,,, +category,string,,,"pv,battery,generator",,,, +cld_hours,tuple,,,,2,check_cld_hours,,each element in cld_hours must be between 0 and 23 +component,microgrid_system.Component,,,,,,, +dispatch_strategy,str,,,"night_const_batt,night_dynamic_batt",,,, +duration,int,1,86400,,,,, +net_metering_rate,"int,float",0,1,,,,, +electricity_rate,"int,float",0,1,,,,, +end_year,int,1998,2020,,,,, +existing,bool,,,,,,, +existing_components,dict,,,,,check_existing_components,,"Existing components can only include PV, Battery, Generator, or FuelTank objects, and have keys 'pv', 'batt', 'gen', or 'fuel_tank'." +filter_constraints,list,,,,,check_filter_constraints,,"The filter_constraints list must contain dictionaries with the format: {parameter, type, value} where parameter can be any of the following: capital_cost_usd, pv_area_ft2, annual_benefits_usd, simple_payback_yr, fuel_used_gal mean, fuel_used_gal most-conservative, pv_capacity, or pv_percent mean and type can be [max, min] and value is the maximum or minimum allowable value." +fuel_curve_degree,int,1,3,,,,, +fuel_curve_model,dict,,,,,check_fuel_curve_model,,"The generator fuel curve model must be of the form {'1/4 Load (gal/hr)': val, '1/2 Load (gal/hr)': val, '3/4 Load (gal/hr)': val, + 'Full Load (gal/hr)': val}" +fuel_used_gal,"int,float",0,,,,,, +gen_percent,"int,float",,,,,,, +gen_power_percent,tuple,,,,,check_gen_power_percent,,Each value in gen_power_percent must be between 0 and 100. +generator,microgrid_system.Generator,,,,,,, +generator_buffer,"int,float",0.9,2,,,,, +generator_capital_cost,"int,float",0,,,,,, +generator_costs,pandas.core.frame.DataFrame,,,,,check_generator_costs,,"Generator costs must include the following columns: 'Power (kW)', '1/4 Load (gal/hr)', '1/2 Load (gal/hr)', '3/4 Load (gal/hr)', 'Full Load (gal/hr)', 'Cost (USD)'" +generator_power_kW,"int,float",0,,,,,, +grouped_load,pandas.core.frame.DataFrame,,,,,check_grouped_load,,Grouped load must be a DataFrame with the columns 'num_hours' and 'binned_load'. +ideal_minimum_load,"int,float",0,1,,,,, +include_batt,tuple,,,,,check_include_batt,,"Include batt must be a list of tuples of the form (battery capacity, battery power)" +include_pv,tuple,,,,,check_include_pv,,Include pv must be a list of pv sizes in kW +initial_soc,"int,float",0,1,,,check_initial_soc,"soc_upper_limit,soc_lower_limit",The initial SOC must be within the specified SOC upper and lower limits. +inverter_cost_per_W,"int,float",0,10,,,,, +is_night,bool,,,,,,, +latitude,"int,float",-90,90,,,,, +length_trials,"int,float",1,365,,,,, +load_duration,pandas.core.frame.DataFrame,,,,,,, +load_profile,pandas.core.series.Series,,,,,check_load_profile,,Load profiles must have a datetime index and non-negative values. +loading_level_to_add_unit,"int,float",0,1,,,,, +loading_level_to_remove_unit,"int,float",0,1,,,,, +location,dict,,,,,check_location,,"Location must be a dictionary with the following keys and value datatypes: {'longitude': float, 'latitude': float, 'timezone': string, 'altitude': float}" +longitude,"int,float",-180,180,,,,, +module_area_in2,"int,float",0,,,,,, +module_capacity,"int,float",0,1,,,,, +net_load,float,,,,,,, +net_metering_limits,dict,,,,,check_net_metering_limits,,"Net metering limits must have the form: {type: ['capacity_cap' or 'percent_of_load'], value: [ or ]}." +new_system,microgrid_system.MicrogridSystem,,,,,,, +night_duration,"int,float",,,,,check_night_duration,is_night,The night duration must be > 0 +night_profile,pandas.core.frame.DataFrame,,,,,check_night_profile,,"The night profile does not have the necessary columns or index, make sure you are using a DataFrame produced by calc_night_duration." +night_profiles,list,,,,,check_night_profiles,,"One more more of the night profiles does not have the necessary columns or index, make sure you are using a list produced by get_night_duration." +num_daily_dni_states,"int,float",1,100,,,,, +num_daily_ghi_states,"int,float",1,100,,,,, +num_dni_states,"int,float",1,100,,,,, +num_ghi_states,"int,float",1,100,,,,, +num_seconds,int,1,86400,,,,, +num_systems,int,1,,,,,, +num_trials,"int,float",1,1000,,,,, +num_units,int,1,,,,,, +one_way_battery_efficiency,"int,float",0,1,,,,, +one_way_inverter_efficiency,"int,float",0,1,,,,, +outputs,dict,,,,,check_outputs,,Something is wrong with the aggregated system outputs. +path,str,,,,,check_path,,"Invalid path, check that the specified path exists in your working directory." +perc,"int, float",0,100,,,,, +percent_at_night,"int,float",0,1,,,,, +plot_per_fig,int,1,9,,,,, +power,"int,float",0,,,,,, +power_profile,pandas.core.series.Series,,,,,check_power_profile,,"The power profile does not have the necessary columns or index, make sure you are using a DataFrame produced by calc_pv_prod." +power_profiles,list,,,,,check_power_profiles,,"One or more of the power profiles does not have the necessary columns or index, make sure you are using a list produced by get_power_profiles." +pv_capacity,"int,float",0,,,,,, +pv_cost_per_W,"int,float",0,10,,,,, +pv_params,dict,,,,,check_pv_params,,"pv_params must be a dictionary with the following keys and value datatypes: {'tilt': float, 'azimuth': float, 'module_capacity': float, 'module_area': float (in square inches),'spacing_buffer': float, 'advanced_inputs': dict, 'pv_tracking': string, 'pv_racking': string}." +pv_percent,"int,float",,,,,,, +racking,str,,,"open_rack_cell_glassback,roof_mount_cell_glassback,open_rack_cell_polymerback,insulated_back_polymerback, open_rack_polymer_thinfilm_steel,22x_concentrator_tracker",,,, +ranking_criteria,list,,,,,check_ranking_criteria,,"The ranking_criteria list must include dictionaries with the format: {parameter, order_type} where parameter can be any of the following: capital_cost_usd, annual_benefits_usd, simple_payback_yr, fuel_used_gal mean, or fuel_used_gal most-conservative and order_type can be [ascending, descending]." +rated_power,"int,float",0,,,,,, +read_from_file,bool,,,,,,, +resilience_percent,"int,float",0,100,,,,, +sim,microgrid_simulator.Simulator,,,,,,, +sitename,str,,,,,check_sitename,"path,start_year,end_year","The NREL data files for this location and year can not be found, make sure you have the correct sitename and path, and that get_solar_data has already been run for this location." +soc_at_initial_hour_of_night,"int,float",0,1,,,,, +soc_lower_limit,"int,float",0,1,,,,, +soc_upper_limit,"int,float",0,1,,,,, +solar_profile,pandas.core.frame.DataFrame,,,,,check_solar_profile,,"The solar profile does not have the necessary columns or index, make sure you are using a DataFrame produced by get_solar_profiles." +spacing_buffer,"int,float",0,10,,,,, +start_year,int,1998,2020,,,,, +storage_recovery_percent,"int,float",,,,,,, +strings,dict,,,,,check_strings,,The strings parameter much include both mods_per_string and strings_per_inv. +system,microgrid_system.MicrogridSystem,,,,,,, +temp_bins,range,,,,,,, +temp_profile,pandas.core.frame.DataFrame,,,,,check_temp_profile,,"The temperature profile does not have the necessary columns or index, make sure you are using a DataFrame produced by get_solar_profiles." +temp_profiles,list,,,,,check_temp_profiles,,"One or more of the temperature profiles does not have the necessary columns or index, make sure you are using a list produced by get_solar_profiles." +tilt,"int,float",0,90,,,,, +timezone,str,,,,,check_timezone,,The timezone must be a valid pytz timezone. For all options type: pytz.all_timezones. +TMY,bool,,,,,,, +tmy_solar,pandas.core.series.Series,,,,8760,,, +unmet_load,pandas.core.frame.DataFrame,,,,,check_unmet_load,,Unmet load must be a DataFrame with a DateTimeIndex +wind_speed,"int,float",0,,,,,, +soiling,"int,float",0,100,,,,, +shading,"int,float",0,100,,,,, +snow,"int,float",0,100,,,,, +mismatch,"int,float",0,100,,,,, +wiring,"int,float",0,100,,,,, +connections,"int,float",0,100,,,,, +lid,"int,float",0,100,,,,, +nameplate_rating,"int,float",0,100,,,,, +age,"int,float",0,100,,,,, +availability,"int,float",0,100,,,,, +spg_advanced_inputs,dict,,,,,check_spg_advanced_inputs,, +module,dict,,,,,check_module,,"Module must be a dictionary with the following keys and valid datatype: {'database': string, 'model': string, 'capacity': float, 'area_in2': float}" +module_database,str,,,"CECMod,SandiaMod",,,, +inverter,dict,,,,,check_inverter,,"Inverter must be a dictionary with the following keys and value datatypes: {'database': string, 'model': string}." +inverter_database,str,,,CECInverter,,,, +mods_per_string,int,1,,,,,, +strings_per_inv,int,1,,,,,, +module_name,str,,,,,check_module_name,,PV module not in database. Please check pvlib module options in pvlib/data/. +inverter_name,str,,,,,check_inverter_name,,PV inverter not in database. Please check pvlib inverter options in pvlib/data/. +max_iter,int,1,1000,,,,, +multithreading,bool,,,,,,, +start_datetimes,list,,,,,check_start_datetimes,"num_trials,start_year,end_year","start_datetimes must have length num_trials, consist of datetime objects, and be within the range of the start and end years" +annual_production,pandas.core.series.Series,,,,,check_annual_production,,"The annual production profile must have a datetime index, contain values for each day and hour of the year, and have non-negative values." +temperature,pandas.core.series.Series,,,,,check_temperature,,"The annual temperature profile must have a datetime index, contain values for each day and hour of the year, and have non-negative values." +batt_sizing_method,str,,,"longest_night,no_pv_export",,,, +system_costs,dict,,,,,check_system_costs,,"system_costs must be a dictionary with the following keys and value datatypes: {'battery_costs': pd.DataFrame, 'generator_costs': pd.DataFrame, 'pv_costs': pd.DataFrame , 'fuel_tank_costs': pd.DataFrame, 'om_costs': pd.DataFrame}." +battery_costs,pandas.core.frame.DataFrame,,,,,check_battery_costs,,"Battery_Cost must include the following columns: 'Battery System', 'Inverter', and 'BOS'" +pv_costs,pandas.core.frame.DataFrame,,,,,check_pv_costs,,"PV_Cost must include the following columns: '10', '100', '500', '1000 ', '3500'" +fuel_tank_costs,pandas.core.frame.DataFrame,,,,,check_fuel_tank_costs,,Fuel tanks must include the following columns: 'Costs' +om_costs,pandas.core.frame.DataFrame,,,,,check_om_costs,,"O&M must include the following columns: 'Generator', 'Battery', 'PV_ground;fixed', 'PV_ground;single_axis ', 'PV_roof;fixed', 'PV_carport;fixed'" +pv_tracking,str,,,"fixed,single_axis",,,, +pv_racking,str,,,"ground,roof,carport",,,, +fuel_tank_cost,"int,float",0,10000000,,,,, +fuel_tank_size,"int,float",0,10000000,,,,, +om_cost,"int,float",0,500,,,,, +demand_rate_list,"int,float,list",,,,,check_demand_rate_list,,Demand charges must be in the form of a number or a list with 12 elements. +demand_rate,"int,float",0,50,,,,, +solar_source,str,,,"nsrdb,himawari",,check_solar_source,"start_year,end_year",The NSRDB dataset only contains data from 1998-2016. The Himawari dataset only contains data from 2016-2020 +scenario_criteria,str,,,"pv,gen",,,, \ No newline at end of file diff --git a/data/sample_load_profile.csv b/data/sample_load_profile.csv new file mode 100644 index 0000000..e7ed551 --- /dev/null +++ b/data/sample_load_profile.csv @@ -0,0 +1,8761 @@ +Datetime,Load +1/1/17 0:00,58.52399272 +1/1/17 1:00,89.2812415 +1/1/17 2:00,84.03058525 +1/1/17 3:00,103.4387596 +1/1/17 4:00,89.62370657 +1/1/17 5:00,108.1606474 +1/1/17 6:00,92.36682722 +1/1/17 7:00,112.8242398 +1/1/17 8:00,87.54663213 +1/1/17 9:00,78.68893453 +1/1/17 10:00,51.18515444 +1/1/17 11:00,53.67420795 +1/1/17 12:00,41.06645885 +1/1/17 13:00,40.99465219 +1/1/17 14:00,34.28578001 +1/1/17 15:00,35.82993622 +1/1/17 16:00,30.38378755 +1/1/17 17:00,33.22559377 +1/1/17 18:00,44.15039486 +1/1/17 19:00,61.20182127 +1/1/17 20:00,60.25325934 +1/1/17 21:00,73.55564824 +1/1/17 22:00,68.41239699 +1/1/17 23:00,83.98426444 +1/2/17 0:00,75.68003657 +1/2/17 1:00,95.05743027 +1/2/17 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15:00,23.23689854 +12/31/17 16:00,22.45169075 +12/31/17 17:00,24.12144637 +12/31/17 18:00,37.26624726 +12/31/17 19:00,42.54186349 +12/31/17 20:00,42.30090116 +12/31/17 21:00,50.12753341 +12/31/17 22:00,49.3970282 +12/31/17 23:00,57.6132208 \ No newline at end of file diff --git a/generate_solar_profile.py b/generate_solar_profile.py new file mode 100644 index 0000000..bcccfe9 --- /dev/null +++ b/generate_solar_profile.py @@ -0,0 +1,1134 @@ +# -*- coding: utf-8 -*- +""" +Runs the Alternative Solar Profiles (ASP) algorithm used for solar forecasting and uses +pvlib-python to calculate AC power for each profile. + +Install numba to speed up solar power calculation: + http://numba.pydata.org/#installing + +Solar power calculations carried out with pvlib-python: + https://github.com/pvlib/pvlib-python + +For an explanation of the pvlib power calculation: + http://nbviewer.jupyter.org/github/pvlib/pvlib-python/blob/master/docs/tutorials/pvsystem.ipynb + +Default solar module selected by choosing a new module similar to the top U.S. installed panel +according to OpenPV: + https://openpv.nrel.gov + SunPower SPR-327NE-WHT-D (from 2011) + +Default inverter choosen as reasonable size (5kW) from top U.S. inverter manufacturer +(SMA - manufacture U.S. inverters in Colorado) + +The default modules per string is 7, with two strings in parallel, corresponding to a total of +5.04kW, with a voltage of 490V for a 5kW inverter. + +File contents: + Classes: + SolarProfileGenerator + + Standalone functions: + download_solar_data + simulate_solar_profiles + calc_pv_prod + calc_night_duration + +""" + +import os +import pandas as pd +import numpy as np +import math +import datetime +import pytz +import matplotlib.pyplot as plt +import io +import requests +import json +import warnings +import yaml + +from pvlib.temperature import sapm_cell, TEMPERATURE_MODEL_PARAMETERS +from pvlib import solarposition, irradiance, atmosphere, pvsystem, tracking +from validation import validate_all_parameters, log_error, strings_warnings +from alternative_solar_profiles import AlternativeSolarProfiles +from config import SOLAR_DATA_DIR, ROOT_DIR + +PV_DEFAULTS = {'albedo': 0.12, + 'module': {'database': 'CECMod', + 'model': 'SunPower_SPR_X22_360_C_AC', + 'capacity': 0.360, 'area_in2': 2525.56}, + 'inverter': { + 'database': 'CECInverter', + 'model': 'SMA_America__SB5000US__240V_'}, + 'strings': {'mods_per_string': 7, 'strings_per_inv': 2}, + } + + +class SolarProfileGenerator: + """ + Class to download NREL solar data, create solar profiles using the ASP model, and + calculate power profiles using pvlib. + + Parameters + ---------- + + longitude: Site longitude in degrees + + latitude: Site latitude in degrees + + timezone: US timezone, options: + US/Alaska, US/Aleutian, US/Arizona, US/Central, US/East-Indiana, US/Eastern, + US/Hawaii, US/Indiana-Starke, US/Michigan, US/Mountain, US/Pacific, + US/Pacific-New, US/Samoa + + altitude: Site altitude in meters + + tilt: Panel tilt in degrees + + azimuth: Panel azimuth in degrees + + num_trials: Number of solar profiles to create + + length_trials: Length of solar profiles in days + + pv_racking: Type of pv racking (options: [roof, ground, carport]) + Default = ground + + pv_tracking: Type of tracking (options: [fixed, single_axis]) + Default = fixed + + max_track_angle: Maximum rotation angle (in degrees) for single-axis tracking + + backtrack: Whether or not backtracking is allowed for single-axis tracking + + start_year: Start year for solar data download + + end_year: End year for solar data download + + solar_source: The source of the solar data to download. The available options are: + nsrdb: NREL's NSRDB -- CONUS, Central America, and parts of South America and + Canada + himawari: Himawari -- Pacific Island and East Asia locations + Default = nsrdb + + num_ghi_states: Number of discrete GHI states for hourly model + + num_dni_states: Number of discrete DNI states for hourly model + + cld_hours: Hours of the day (range) used to set the day's cloud state + + temp_bins: Temperature bins + + num_daily_ghi_states: Number of discrete GHI states for daily model + + num_daily_dni_states: Number of discrete DNI states for daily model + + max_iter: The maximum number of iterations allowed for trying to match hourly and + daily states in the ASP code. + + multithreading: Whether to use multithreading to speed up the ASP calculation. This is + set to True by default, but should be set to False for debugging + + advanced_inputs: Dictionary specifying advanced pv system inputs. + These could include: + albedo, racking, module, inverter, strings, soiling, shading, snow, mismatch, + wiring, connections, lid, nameplate_rating, age, availability + + suppress_warnings: Boolean specifying whether or not warnings should be printed to the + console with relevant plots. + + Methods + ---------- + + get_solar_data: Downloads solar data from NREL + + get_wind_speed: Gets average wind speed from TMY data + + get_solar_profiles: Calculates simulated solar profiles based on a solar state + probability matrix + + get_power_profiles: Calculates the output AC power for a 1kW system for each solar and + temperature profile. + + get_power_profiles_from_upload: Creates power profiles from uploaded 8760 production + data. + + get_night_duration: For each power profile, gets the hours that are at night, and the + duration of each night + + get_pv_params: Gets the module capacity and area for calculating array size + + crop_timeline: Used to crop the profiles to a period of less than 1 day + + pv_checks: Creates several plots to verify that the pv power calculation went OK + + get_dc_to_ac: Returns the DC to AC ratio + + get_losses: Returns system power losses + + Calculated Attributes + ---------- + + wind_speed: median wind speed in m/s + + solar_profiles: list of Pandas dataframes with solar profiles + + temp_profiles: list of Pandas dataframes with temperature profiles + + power_profiles: list of Pandas series' with PV power profiles for a 1kW system + + night_profiles: list of Pandas dataframes with info on whether it is night + + tmy_solar_profile: TMY solar profile + + tmy_power_profile: TMY power profile for a 1kW system + + constraints: Pandas DataFrame holding constraints for input parameters + + """ + + def __init__(self, latitude, longitude, timezone, altitude, tilt, azimuth, num_trials, + length_trials, pv_racking='ground', pv_tracking='fixed', max_track_angle=90, + backtrack=True, start_year=1998, end_year=2020, solar_source='nsrdb', + num_ghi_states=11., num_dni_states=11., cld_hours=(10, 17), + temp_bins=range(-30, 49), num_daily_ghi_states=11., + num_daily_dni_states=11., max_iter=200, multithreading=True, + advanced_inputs={}, validate=True, suppress_warnings=False): + + # Assign parameters + self.latitude = latitude + self.longitude = longitude + self.timezone = timezone + self.altitude = altitude + self.tilt = tilt + self.azimuth = azimuth + self.num_trials = num_trials + self.length_trials = length_trials + self.pv_racking = pv_racking + self.pv_tracking = pv_tracking + self.max_track_angle = max_track_angle + self.backtrack = backtrack + self.start_year = start_year + self.end_year = end_year + self.solar_source = solar_source + self.num_ghi_states = num_ghi_states + self.num_dni_states = num_dni_states + self.cld_hours = cld_hours + self.temp_bins = temp_bins + self.num_daily_ghi_states = num_daily_ghi_states + self.num_daily_dni_states = num_daily_dni_states + self.max_iter = max_iter + self.multithreading = multithreading + self.advanced_inputs = advanced_inputs + self.suppress_warnings = suppress_warnings + self.wind_speed = None + self.solar_profiles = [] + self.temp_profiles = [] + self.power_profiles = [] + self.night_profiles = [] + self.tmy_solar_profile = None + self.tmy_power_profile = None + + # Add PV_DEFAULTS to advanced inputs if not already included + for key in PV_DEFAULTS: + if key not in self.advanced_inputs: + self.advanced_inputs[key] = PV_DEFAULTS[key] + + if validate: + # List of initialized parameters to validate + args_dict = {'latitude': self.latitude, + 'longitude': self.longitude, + 'timezone': self.timezone, + 'altitude': self.altitude, + 'tilt': self.tilt, + 'azimuth': self.azimuth, + 'num_trials': self.num_trials, + 'length_trials': self.length_trials, + 'pv_racking': self.pv_racking, + 'pv_tracking': self.pv_tracking, + 'start_year': self.start_year, + 'end_year': self.end_year, + 'num_ghi_states': self.num_ghi_states, + 'num_dni_states': self.num_dni_states, + 'cld_hours': self.cld_hours, + 'temp_bins': self.temp_bins, + 'num_daily_ghi_states': self.num_daily_ghi_states, + 'num_daily_dni_states': self.num_daily_dni_states, + 'max_iter': self.max_iter, + 'multithreading': self.multithreading, + 'spg_advanced_inputs': self.advanced_inputs, + 'solar_source': self.solar_source} + + # Validate input parameters + validate_all_parameters(args_dict) + + def get_solar_data(self): + """ Downloads solar data from NREL """ + + # Check that directory exists to hold nrel solar csv files + if '{}_{}'.format(self.latitude, self.longitude) not in \ + os.listdir(os.path.join(SOLAR_DATA_DIR, 'nrel')): + os.mkdir(os.path.join(SOLAR_DATA_DIR, 'nrel', '{}_{}'.format( + self.latitude, self.longitude))) + + # Download nrel files + download_solar_data(self.latitude, self.longitude, + os.path.join( + SOLAR_DATA_DIR, + 'nrel', + f'{self.latitude}_{self.longitude}'), + start_year=self.start_year, + end_year=self.end_year, + validate=False, + source=self.solar_source) + + download_solar_data(self.latitude, self.longitude, os.path.join( + SOLAR_DATA_DIR, 'nrel', '{}_{}'.format(self.latitude, + self.longitude)), + TMY=True, validate=False, source=self.solar_source) + + # Load each file and fill any nans + filedir = os.path.join(SOLAR_DATA_DIR, 'nrel', '{}_{}'.format( + self.latitude, self.longitude)) + files = os.listdir(filedir) + for file in files: + # skip files that aren't csv files (ex: .DS_Store) + if not file.split('.')[-1] == '.csv': + continue + df = pd.read_csv(os.path.join(filedir, file)) + df.fillna(0, inplace=True) + + # Check that dataframe is not empty + if not len(df): + message = 'NREL solar data empty. Check that you are using ' \ + 'valid parameters to access the NREL api.' + log_error(message) + raise Exception(message) + + df.to_csv(os.path.join(filedir, file), index=False) + + def get_wind_speed(self): + """ Get average wind speed from TMY data. """ + + self.wind_speed = self.tmy_solar_profile['Wind Speed'].median() + + def get_solar_profiles(self, validate=True): + """ Calculates simulated solar profiles based on a solar state probability matrix. """ + + # Create AlternativeSolarProfiles object + asp = AlternativeSolarProfiles( + self.latitude, self.longitude, self.num_trials, self.length_trials, + self.start_year, self.end_year, self.num_ghi_states, + self.num_dni_states, self.num_daily_ghi_states, + self.num_daily_dni_states, self.cld_hours, self.temp_bins, + self.max_iter, self.multithreading, validate=validate) + + # Create state transition matrices + asp.create_state_transition_matrices() + + # Generate trials + asp.create_trial_data() + + # Create directory to hold data + if '{}_{}_{}d_{}t'.format( + self.latitude, self.longitude, int(self.length_trials), + int(self.num_trials)) not in \ + os.listdir(os.path.join(SOLAR_DATA_DIR, 'solar_profiles')): + os.mkdir(os.path.join( + SOLAR_DATA_DIR, 'solar_profiles', '{}_{}_{}d_{}t'.format( + self.latitude, self.longitude, int(self.length_trials), + int(self.num_trials)))) + + # Extract trial data + for i, solar_profile in enumerate(asp.solar_trials): + + # Recreate index with timezone + try: + solar_profile.index = pd.date_range( + start=solar_profile.index[0], periods=len(solar_profile), + freq='H', tz=self.timezone) + + # NREL historical data is not daylight savings time aware, so we need to shift + # it for compatibility with the pvlib library + # Check if daylight savings time is in effect during 1st hour + if solar_profile.index[0].tzinfo._dst.seconds > 0: + # If it is, shift the index to account for it + solar_profile.index = solar_profile.index + datetime.timedelta(minutes=60) + + # Check for a timezone error caused by the first timestep falling on the 'skipped' + # hour of dst + except pytz.exceptions.NonExistentTimeError: + # Shift the start time to account for it + solar_profile.index = pd.date_range( + start=solar_profile.index[0] + datetime.timedelta(hours=1), + periods=len(solar_profile), freq='H', tz=self.timezone) + + # Save to file + solar_profile.to_csv(os.path.join( + SOLAR_DATA_DIR, 'solar_profiles', '{}_{}_{}d_{}t'.format( + self.latitude, self.longitude, int(self.length_trials), + int(self.num_trials)), + '{}_{}_solar_trial_{}.csv'.format(self.latitude, + self.longitude, i))) + + def get_power_profiles(self): + """ + Calculate the output AC power for a 1kW system for each solar and temperature profile. + + If read_from_file is True, reads the solar and temperature data from csv, allowing + for faster lookup rather than re-running get_solar_data and get_solar_profiles. + + """ + + # For each solar and temperature profile, calculate PV production + # Load the solar and temperature data from csv + for i in range(int(self.num_trials)): + try: + solar = pd.read_csv(os.path.join( + SOLAR_DATA_DIR, 'solar_profiles', '{}_{}_{}d_{}t'.format( + self.latitude, self.longitude, int(self.length_trials), + int(self.num_trials)), + '{}_{}_solar_trial_{}.csv'.format(self.latitude, self.longitude, i)), + index_col=0, parse_dates=[0]) + + # Allow for backward compatibility with solar profiles generated before ASP + # code was converted to Python + if 'temp' not in solar.columns: + solar['temp'] = pd.read_csv(os.path.join( + SOLAR_DATA_DIR, 'solar_profiles', '{}_{}_{}d_{}t'.format( + self.latitude, self.longitude, + int(self.length_trials), + int(self.num_trials)), + '{}_{}_temp_trial_{}.csv'.format(self.latitude, self.longitude, i)), + index_col=0, parse_dates=[0]).values + + except FileNotFoundError: + message = 'Solar profile csvs not found. Please check that you have entered' \ + ' the longitude, latitude, number, and length of trials for a ' \ + 'site with previously generated solar profiles.' + log_error(message) + raise Exception(message) + + # Fix timezone + try: + solar.index = solar.index.tz_convert(self.timezone) + except AttributeError: + # Deal with pandas issue creating datetime index from timeseries including + # daylight savings time shift + solar.index = pd.to_datetime(solar.index, utc=True).tz_convert(self.timezone) + + # Check that the solar data and index are not misaligned (e.g. the sun is up + # during the day) + # Get median solar start hour + solar['date'] = solar.index.date + median_hour = solar.groupby('date').apply( + lambda x: x[x['ghi'] > 0].iloc[0].name.hour + if len(x[x['ghi'] > 0]) else 0).median() + if not 4 < median_hour < 12: + message = 'The solar profiles are mismatched with the index (e.g. the sun ' \ + 'is not shining at the expected times). Make sure your solar ' \ + 'profiles are valid.' + log_error(message) + raise Exception(message) + + self.solar_profiles += [solar] + self.temp_profiles += [solar['temp'].to_frame(name='temp_celcius')] + + # Read raw TMY file + self.tmy_solar_profile = pd.read_csv( + os.path.join(SOLAR_DATA_DIR, + 'nrel', + '{}_{}'.format(self.latitude, self.longitude), + '{}_{}_tmy.csv'.format(self.latitude, self.longitude))) + + # Get average wind speed + self.get_wind_speed() + + # Calculate PV production for each solar profile + for solar, temp in zip(self.solar_profiles, self.temp_profiles): + self.power_profiles += [calc_pv_prod( + solar, temp, self.latitude, self.longitude, self.altitude, + self.tilt, self.azimuth, self.wind_speed, + self.advanced_inputs['albedo'], + self.pv_racking, + self.advanced_inputs['module'], + self.advanced_inputs['inverter'], + self.advanced_inputs['strings'], + pv_tracking=self.pv_tracking, + max_track_angle=self.max_track_angle, + backtrack=self.backtrack, validate=False, + suppress_warnings=self.suppress_warnings, + advanced_inputs=self.advanced_inputs)] + + # Get TMY solar PV power + # Parse index + tmy_solar = self.tmy_solar_profile + tmy_solar.index = pd.to_datetime(tmy_solar[['Year', 'Month', 'Day', + 'Hour', 'Minute']]) + + # Add timezone + # If using the himawari dataset, get the timezone for the closest station + if self.solar_source == 'himawari': + tmy_meta_name = f'{self.latitude}_{self.longitude}_tmy.json' + tmy_meta_path = os.path.join( + SOLAR_DATA_DIR, 'nrel', '{}_{}'.format(self.latitude, self.longitude), + tmy_meta_name) + with open(tmy_meta_path, 'r') as f: + tmy_meta = json.load(f) + if tmy_meta['tz'] < 0: + tmy_tz = f'Etc/GMT{int(tmy_meta["tz"])}' + else: + tmy_tz = f'Etc/GMT+{int(tmy_meta["tz"])}' + tmy_solar.index = tmy_solar.index.tz_localize('UTC').tz_convert(tmy_tz) + else: + tmy_solar.index = tmy_solar.index.tz_localize('UTC').tz_convert(self.timezone) + + # If TMY data is listed as on the hour, add 30 minutes. This is due to a bug in the + # NREL NSRDB api that was corrected sometime in mid-2019, so data downloaded before + # that has to be corrected. + if tmy_solar.index[0].minute == 0: + tmy_solar.index = tmy_solar.index + datetime.timedelta(minutes=30) + + # Un-shift from timezone conversion so it starts at the beginning of the year + if tmy_solar.index[0].month == 1: + tmy_solar.index = tmy_solar.index - \ + datetime.timedelta(hours=tmy_solar.index[0].hour) + else: + tmy_solar.index = tmy_solar.index + \ + datetime.timedelta(hours=24 - tmy_solar.index[0].hour) + + # Rename columns + tmy_solar.rename(columns={'DHI': 'dhi', 'DNI': 'dni', 'GHI': 'ghi'}, inplace=True) + + # Get power profile + self.tmy_power_profile = calc_pv_prod( + tmy_solar, tmy_solar['Temperature'].to_frame(name='temp_celcius'), + self.latitude, self.longitude, self.altitude, self.tilt, + self.azimuth, self.wind_speed, self.advanced_inputs['albedo'], + self.pv_racking, self.advanced_inputs['module'], + self.advanced_inputs['inverter'], self.advanced_inputs['strings'], + pv_tracking=self.pv_tracking, max_track_angle=self.max_track_angle, + backtrack=self.backtrack, validate=False, + suppress_warnings=self.suppress_warnings, + advanced_inputs=self.advanced_inputs) + + def get_power_profiles_from_upload(self, annual_production, temperature=None, + validate=True): + """ + Create power profiles from uploaded 8760 production data. + + inputs: + annual_production: Pandas Series with 8760 production for a 1kW array. + temperature: Pandas series with 8760 temperature data. If it is set to None, + create a Series with all 0s - this parameter is a placeholder for future + battery models that incorporate temperature. + + """ + + # Validate inputs + if validate: + # Use the annual load profile checks + args_dict = {'annual_production': annual_production} + + if temperature is not None: + args_dict['temperature'] = temperature + + # Validate input parameters + validate_all_parameters(args_dict) + + # Randomly create start dates + annual_production.index = pd.date_range( + start='1/1/2017', end='1/1/2018', freq='H')[:-1] + start_datetimes = annual_production.sample( + int(self.num_trials)).index.values + + # Create a date range object for each start datetime + date_ranges = [pd.date_range(start=start_date, + periods=self.length_trials * 24, + freq='H') + for start_date in start_datetimes] + + # Create 2-year annual profile to allow for profiles with year-end overlap + twoyear_profile = annual_production.append(annual_production) + twoyear_profile.index = pd.date_range( + start='1/1/2017', end='1/1/2019', freq='H')[:-1] + + # Apply to temperature data + if temperature is None: + twoyear_temperature_profile = twoyear_profile * 0 + else: + twoyear_temperature_profile = temperature.append(temperature) + twoyear_temperature_profile.index = pd.date_range( + start='1/1/2017', end='1/1/2019', freq='H')[:-1] + + # Loop over each date range and sample profile data + for date_range in date_ranges: + self.power_profiles += [twoyear_profile.loc[date_range]] + self.temp_profiles += [twoyear_temperature_profile.loc[date_range + ].to_frame(name='temp_celcius')] + + # Set TMY solar profile + self.tmy_power_profile = annual_production + + def get_night_duration(self, percent_at_night=0, validate=True): + """ + For each power profile, get the hours that are at night, and the duration of each + night. + + If percent_at_night is specified, it is considered nighttime when the pv power is at + max(power_profile) * percent_at_night, allowing a buffer before sundown. + """ + + # Validate parameters + if validate: + args_dict = {'percent_at_night': percent_at_night} + validate_all_parameters(args_dict) + + for power_profile in self.power_profiles: + self.night_profiles += [calc_night_duration( + power_profile, percent_at_night, validate=False)] + + def get_pv_params(self): + """ Get the module capacity and area for calculating array size. """ + + return self.advanced_inputs['module'] + + def crop_timeline(self, num_seconds, validate=True): + """ Used to crop the profiles to a period of less than 1 day + + num_seconds is the number of seconds of the new outage + period + """ + + # Validate parameters + if validate: + args_dict = {'num_seconds': num_seconds} + validate_all_parameters(args_dict) + + # For each profile in solar_profiles, power_profiles, temp_profiles and + # night_profiles, crop to the specified number of seconds, rounding down to the + # nearest timestep + for i in range(len(self.solar_profiles)): + self.solar_profiles[i] = \ + self.solar_profiles[i].loc[ + :self.solar_profiles[i].index[0] + datetime.timedelta( + seconds=num_seconds - 1)] + + for i in range(len(self.power_profiles)): + self.power_profiles[i] = \ + self.power_profiles[i].loc[ + :self.power_profiles[i].index[0] + datetime.timedelta( + seconds=num_seconds - 1)] + + for i in range(len(self.temp_profiles)): + self.temp_profiles[i] = \ + self.temp_profiles[i].loc[ + :self.temp_profiles[i].index[0] + datetime.timedelta( + seconds=num_seconds - 1)] + + for i in range(len(self.night_profiles)): + self.night_profiles[i] = \ + self.night_profiles[i].loc[ + :self.night_profiles[i].index[0] + datetime.timedelta( + seconds=num_seconds - 1)] + + def pv_checks(self): + """ Several checks to make sure the pv profiles look OK. """ + + # Get the profiles with the min and max PV energy + total_energy = [prof.sum() for prof in self.power_profiles] + max_profile_num = np.where(total_energy == max(total_energy))[0][0] + min_profile_num = np.where(total_energy == min(total_energy))[0][0] + + # Plot the profiles with min and max pv energy + fig = plt.figure() + ax1 = fig.add_subplot(121) + self.power_profiles[max_profile_num].plot( + ax=ax1, title='Profile with max energy generation') + ax1.set_ylabel('Power (kW)') + ax2 = fig.add_subplot(122) + self.power_profiles[min_profile_num].plot( + ax=ax2, title='Profile with min energy generation') + ax2.set_ylabel('Power (kW)') + + # Plot the TMY profile + fig = plt.figure() + temp_profile = self.tmy_power_profile.copy(deep=True) + temp_profile.index = temp_profile.index.map( + lambda x: x.replace(year=2017)) + temp_profile.plot(title='TMY power profile') + plt.ylabel('Power (kW)') + + def get_dc_to_ac(self): + """ Returns the dc to ac ratio. """ + + # Get total DC power per inverter + dc = self.advanced_inputs['module']['capacity'] * \ + self.advanced_inputs['strings']['mods_per_string'] * \ + self.advanced_inputs['strings']['strings_per_inv'] * 1000 + + # Get inverter AC power + inverter_list = pvsystem.retrieve_sam( + self.advanced_inputs['inverter']['database']) + inverter = inverter_list[self.advanced_inputs['inverter']['model']] + ac = inverter['Paco'] + + return dc / ac + + def get_losses(self): + """ Returns the system losses. """ + + # Calculate losses + params = {key: val for key, val in self.advanced_inputs.items() + if key in ['soiling', 'shading', 'snow', 'mismatch', + 'wiring', 'connections', 'lid', + 'nameplate_rating', 'age', 'availability']} + losses = pvsystem.pvwatts_losses(**params) + + return losses + + +def download_solar_data(latitude=46.34, longitude=-119.28, path='.', TMY=False, + start_year=1998, end_year=2020, validate=True, source='nsrdb'): + """ + Downloads hourly solar data for each year in the NREL NRSDB or the NREL Himawari dataset + and formats into pandas dataframes contained in solar_dict + """ + + # Put arguments in a dict + args_dict = {'latitude': latitude, 'longitude': longitude, 'path': path, + 'TMY': TMY, 'start_year': start_year, 'end_year': end_year} + + # Validate all parameters + if validate: + validate_all_parameters(args_dict) + + # Set up parameters to send to NREL API + try: + with open(os.path.join(ROOT_DIR, 'creds.yaml'), 'r') as f: + creds_dict = yaml.safe_load(f) + except FileNotFoundError: + message = "creds.yaml file not found. Please create this file with your NREL API " \ + "key. See instructions in README." + log_error(message) + raise FileNotFoundError(message) + try: + key = creds_dict['nrel_api_key'] + email = creds_dict['nrel_api_email'] + except KeyError as e: + message = '{} not in creds file. Make sure you format the file as specified in the ' \ + 'README'.format(e) + log_error(message) + raise KeyError(message) + + solar_dict = {} + + # Only download TMY + if TMY: + names = ['tmy'] + attributes = '' + + # Download each year in the dataset + else: + names = range(start_year, end_year + 1) + attributes = 'attributes=dhi,dni,ghi,clearsky_dhi,clearsky_dni,clearsky_ghi,' \ + 'cloud_type,solar_zenith_angle,air_temperature,&' + + # Download data + for name in names: + # Try multiple attempts if request times out + success = 0 + count = 0 + while success == 0: + try: + if name == 'tmy': + dataset = 'psm3-tmy-download' + interval = 60 + elif source == 'himawari': + dataset = 'himawari-download' + interval = 30 + else: + dataset = 'psm3-download' + interval = 30 + url = f'https://developer.nrel.gov/api/nsrdb/v2/solar/' \ + f'{dataset}.csv?api_key={key}&email={email}' + payload = f'wkt=POINT({longitude}+{latitude})&names={name}&{attributes}' \ + f'leap_day=false&utc=false&interval={interval}' + + if source == 'himawari' and TMY: + url = f'https://developer.nrel.gov/api/pvwatts/v6.json?api_key={key}' + payload = f'lat={latitude}&lon={longitude}&dataset=intl&' \ + f'system_capacity=1&module_type=0&losses=5&array_type=0&' \ + f'tilt=0&azimuth=0&timeframe=hourly&radius=0' + + response = requests.get(f'{url}&{payload}') + + # Check response status code + if response.status_code == 200: + if source == 'himawari' and TMY: + parse_himawari_tmy(response=response, path=path, + longitude=longitude, + latitude=latitude) + success = 1 + + else: + skiprows = 2 + + solar_dict[name] = pd.read_csv(io.StringIO( + response.content.decode('utf-8')), skiprows=skiprows) + success = 1 + else: + message = f'Error downloading NSRDB data > {response.text}' + raise Exception(message) + + except TimeoutError: + count += 1 + if count > 5: + message = 'Failed to download solar data file, too many timeouts.' + log_error(message) + raise IOError(message) + pass + except IOError: + message = 'Failed to download solar data file, check url: {}'. \ + format(f'{url}&{payload}') + log_error(message) + raise IOError(message) + + # Save as csv files + for key, val in solar_dict.items(): + # Remove times on the half-hour for non-TMY profiles + if key != 'tmy': + val = val[val['Minute'] == 0] + + # Save to csv + val.to_csv(os.path.join(path, '{}_{}_{}.csv'.format( + latitude, longitude, key)), index=False) + + +def parse_himawari_tmy(response, path, latitude, longitude): + """ + Converts the TMY data downloaded from the PVWatts tool (downloaded when + solar_source='himawari') to the same format as the TMY data dowloaded from NREL's + NSRDB. + + The Himawari dataset does not include TMY data, so the TMY data for East Asia / + Pacific Island locations is downloaded from the international station TMY data + provided by PVWatts. + + The TMY data is saved to the same path as the annual solar data. A file containing the + metadata for the station in also saved, which includes the station's location, + elevation, and distance (in meters) from the specified point location. + """ + + # Read the response and convert to a pandas dataframe + response = json.loads(response.content.decode('utf-8')) + + # Data to include: + # dn - DNI, df - DHI, tamb - Temperature, wspd - Wind Speed + tmy_data = pd.DataFrame.from_dict( + {k: response['outputs'][k] for k in ['dn', 'df', 'tamb', 'wspd']}) + tmy_data.columns = ['DNI', 'DHI', 'Temperature', 'Wind Speed'] + + # Read in the station metadata (location, distance for selected point, etc) + tmy_meta = response['station_info'] + + # Generate year, month, day, hour, and minute columns + tmy_data['datetime'] = pd.date_range(start='1/1/2021 0:00:00', periods=8760, freq='H') + tmy_data['Year'] = tmy_data['datetime'].apply(lambda x: x.year) + tmy_data['Month'] = tmy_data['datetime'].apply(lambda x: x.month) + tmy_data['Day'] = tmy_data['datetime'].apply(lambda x: x.day) + tmy_data['Hour'] = tmy_data['datetime'].apply(lambda x: x.hour) + tmy_data['Minute'] = tmy_data['datetime'].apply(lambda x: x.minute) + tmy_data = tmy_data.drop(columns='datetime') + + # Save the file and station info file + tmy_name = f'{latitude}_{longitude}_tmy.csv' + tmy_meta_name = f'{latitude}_{longitude}_tmy.json' + tmy_path = os.path.join(path, tmy_name) + tmy_meta_path = os.path.join(path, tmy_meta_name) + tmy_data.to_csv(tmy_path, index=False) + with open(tmy_meta_path, 'w') as f: + json.dump(tmy_meta, f) + + # Display a message to the user telling them how far away the TMY station is from the + # point location they specified. + distance = float(tmy_meta['distance']) / 1000 + print(f'TMY station is {int(distance)} km from site, at {tmy_meta["city"]}, ' + f'{tmy_meta["state"]} in timezone: {tmy_meta["tz"]}') + + +def calc_pv_prod(solar_profile, temp_profile, latitude, longitude, altitude, tilt, azimuth, + wind_speed, albedo, pv_racking, module_name, inverter_name, strings, + pv_tracking='fixed', max_track_angle=90, backtrack=True, validate=True, + suppress_warnings=False, advanced_inputs={}): + """ Calculates the PV production from a solar profile using pvlib. """ + + if validate: + # Put arguments in a dict + args_dict = {'solar_profile': solar_profile, + 'temp_profile': temp_profile, 'latitude': latitude, + 'longitude': longitude, 'altitude': altitude, + 'tilt': tilt, 'azimuth': azimuth, + 'wind_speed': wind_speed, 'albedo': albedo, + 'pv_racking': pv_racking, 'module': module_name, + 'inverter': inverter_name, 'strings': strings} + + # Validate all parameters + validate_all_parameters(args_dict) + + # Get solar position + # Try using numba if installed to speed up calculation + # Catch UserWarnings from pvlib/numba + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=UserWarning) + try: + solpos = solarposition.get_solarposition(solar_profile.index, latitude, + longitude, altitude, method='nrel_numba') + except: + solpos = solarposition.get_solarposition(solar_profile.index, latitude, longitude, + altitude) + + # Calculate extraterrestrial irradiance + # try/except block to allow for old or new versions of pvlib-python + try: # new version + dni_extra = irradiance.get_extra_radiation(solar_profile.index) + except: # old version + dni_extra = irradiance.extraradiation(solar_profile.index) + + # Calculate dhi or ghi if not included (not calculated by ASP code) + zenith_angle = np.array([elem if elem < 90 else 90 + for elem in solpos['apparent_zenith'].values]) + if 'dhi' not in solar_profile: + solar_profile['dhi'] = solar_profile['ghi'] - \ + (solar_profile['dni'] * np.cos(zenith_angle * math.pi / 180)) + if 'ghi' not in solar_profile: + solar_profile['ghi'] = solar_profile['dhi'] + \ + (solar_profile['dni'] * np.cos(zenith_angle * math.pi / 180)) + + # Make sure dhi not negative + plot_power = False + if len(solar_profile[solar_profile['dhi'] < 0]): + message = 'Warning: dhi value is negative at the following times:{}, check to make ' \ + 'sure the ghi and dni values are consistent with the timestamp.'.format( + solar_profile[solar_profile['dhi'] < 0].index) + log_error(message) + if not suppress_warnings: + plot_power = True + print(message) + solar_profile.loc[solar_profile['dhi'] < 0, 'dhi'] = 0 + + # Deal with issue of poa_sky_diffuse blowing up if solar_zenith too close to 90 + solpos.loc[(solpos['apparent_zenith'] > 87) & + (solpos['apparent_zenith'] <= 90), + 'apparent_zenith'] = 87. + solpos.loc[(solpos['apparent_zenith'] > 90) & + (solpos['apparent_zenith'] < 93), + 'apparent_zenith'] = 93. + + # If single-axis tracking is used, calculate new tilt, azimuth, and aoi + if pv_tracking == 'single_axis': + tracker_data = tracking.singleaxis(solpos['apparent_zenith'], + solpos['azimuth'], axis_tilt=tilt, + axis_azimuth=azimuth, + max_angle=max_track_angle, + backtrack=backtrack) + tracker_data.fillna(0, inplace=True) + surface_tilt = tracker_data['surface_tilt'] + surface_azimuth = tracker_data['surface_azimuth'] + aoi = tracker_data['aoi'] + else: + surface_tilt = tilt + surface_azimuth = azimuth + aoi = irradiance.aoi(tilt, azimuth, solpos['apparent_zenith'], solpos['azimuth']) + + # Calculate plane of array diffuse sky radiation using the Hay Davies model + poa_sky_diffuse = irradiance.haydavies(surface_tilt, surface_azimuth, + solar_profile['dhi'], + solar_profile['dni'], dni_extra, + solpos['apparent_zenith'], + solpos['azimuth']) + + # try/except block to allow for old or new versions of pvlib-python + try: # new + # Calculate ground diffuse + poa_ground_diffuse = irradiance.get_ground_diffuse( + surface_tilt, solar_profile['ghi'], albedo=albedo) + + # Calculate POA total + poa_irrad = irradiance.poa_components(aoi, solar_profile['dni'], + poa_sky_diffuse, + poa_ground_diffuse) + except: # old + # Calculate ground diffuse + poa_ground_diffuse = irradiance.grounddiffuse(surface_tilt, + solar_profile['ghi'], + albedo=albedo) + + # Calculate POA total + poa_irrad = irradiance.globalinplane(aoi, solar_profile['dni'], + poa_sky_diffuse, + poa_ground_diffuse) + + # Get pvsystem racking type based on racking parameter + racking_dict = {'ground': 'open_rack_glass_glass', + 'roof': 'close_mount_glass_glass', + 'carport': 'open_rack_glass_glass'} + + # Model parameters from pvlib.temperature + temp_model_params = TEMPERATURE_MODEL_PARAMETERS['sapm'][racking_dict[pv_racking]] + + # Calculate cell and module temperature + pvtemps = sapm_cell(poa_irrad['poa_global'], wind_speed=wind_speed, + temp_air=temp_profile['temp_celcius'], + **temp_model_params) + + # Select module and inverter + try: + module_list = pvsystem.retrieve_sam(module_name['database']) + module = module_list[module_name['model']] + except (ValueError, KeyError): + message = 'PV module not in database. Please check pvlib module options.' + log_error(message) + raise Exception(message) + try: + inverter_list = pvsystem.retrieve_sam(inverter_name['database']) + inverter = inverter_list[inverter_name['model']] + except (ValueError, KeyError): + message = 'Inverter not in database. Please check pvlib module options.' + log_error(message) + raise Exception(message) + + # Calculate DC power + # try/except block to account for pvlib-python update which modified function arguments + try: + photocurrent, saturation_current, resistance_series, \ + resistance_shunt, nNsVth = pvsystem.calcparams_desoto( + poa_irrad['poa_global'], temp_cell=pvtemps['temp_cell'], + alpha_isc=module['alpha_sc'], module_parameters=module, + EgRef=1.121, dEgdT=-0.0002677) + except: + photocurrent, saturation_current, resistance_series, \ + resistance_shunt, nNsVth = pvsystem.calcparams_desoto( + poa_irrad['poa_global'], pvtemps, + module['alpha_sc'], module['a_ref'], module['I_L_ref'], + module['I_o_ref'], + module['R_sh_ref'], module['R_s']) + # Properly catch RuntimeWarnings not caught in pvlib + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + dc_power = pvsystem.singlediode(photocurrent, saturation_current, resistance_series, + resistance_shunt, nNsVth) + + # If dc power exceeds the rated module power by more than 110% print out a warning + if len(dc_power[dc_power['p_mp'] > module_name['capacity'] * 1000 * 1.1]): + message = 'Warning: DC power exceeds module rating by more than ' \ + '110% for the following timesteps.' + log_error(message) + if not suppress_warnings: + print(message) + print((dc_power.loc[dc_power['p_mp'] > module_name['capacity'] + * 1000 * 1.1, 'p_mp'] / + module_name['capacity'] / 1000 * 100).to_frame().rename( + columns={'p_mp': 'percentage of module power'})) + + # Calculate losses + params = {key: val for key, val in advanced_inputs.items() + if key in ['soiling', 'shading', 'snow', 'mismatch', 'wiring', + 'connections', 'lid', 'nameplate_rating', 'age', 'availability']} + losses = pvsystem.pvwatts_losses(**params) + dc_power['p_mp'] = dc_power['p_mp'] * (1 - losses / 100) + + # If dhi was < 0 at any point, plot power to make sure it looks reasonable + if plot_power: + plt.figure() + dc_power['p_mp'].plot(title='DC Power for {:.0f}W system'.format( + module_name['capacity'] * 1000)) + + # Create strings of modules for conversion to AC power + dc_power['v_mp'] *= strings['mods_per_string'] + dc_power['p_mp'] *= strings['mods_per_string'] * strings['strings_per_inv'] + + # Calculate AC power + ac_power = pvsystem.inverter.sandia(dc_power.v_mp, dc_power.p_mp, inverter) + + # Force values less than 0 to 0 + ac_power[ac_power < 0] = 0 + + # Divide by the total DC power to get the power (in kW) per 1kW of solar + return ac_power / (module_name['capacity'] * 1000 * + strings['mods_per_string'] * strings['strings_per_inv']) + + +def calc_night_duration(power_profile, percent_at_night=0, validate=True): + """ + For each timestep in a solar or power profile, determine if it is night (or there is no PV + generation), if it is the first timestep of the night and the total night duration. + + If percent_at_night is specified, it is considered nighttime when the pv power is at + max(power_profile) * percent_at_night, allowing a buffer before sundown. It is allowed + to range from 0 to 1. + + """ + + if validate: + # Put arguments in a dict + args_dict = {'percent_at_night': percent_at_night} + + # Validate all parameters + validate_all_parameters(args_dict) + + # Create dataframe to hold night info for each timestep + night_df = pd.DataFrame(index=power_profile.index) + + # Determine which timesteps are during the nighttime, add to dataframe + night_df['is_night'] = power_profile.values <= power_profile.max() * percent_at_night + + # Calculate duration for each night (or 0 PV period) + temp1 = night_df['is_night'].cumsum().value_counts() + temp2 = np.array(temp1[temp1 > 1].sort_index().index) + if night_df.iloc[-1, -1]: + temp2 = np.append(temp2, np.array(temp1.sort_index().index[-1])) + temp2 = temp2[np.where(temp2 > 0)] + night_lengths = [temp2[0]] + list(temp2[1:] - temp2[:-1]) + + # Find the first hour of each night (or 0 PV period) + night_df['is_first_hour_of_night'] = False + night_df['night_duration'] = np.nan + for i, _ in enumerate(night_df.iterrows()): + if (i == 0 and night_df.iloc[0, -3]) or \ + (i > 0 and night_df.iloc[i, -3] + and not night_df.iloc[i - 1, -3]): + night_df.iloc[i, -2] = True + night_df.iloc[i, -1] = night_lengths.pop(0) + night_df['night_duration'].fillna(method='ffill', inplace=True) + night_df.loc[night_df['is_night'] == False, 'night_duration'] = 0 + + return night_df + + +if __name__ == "__main__": + # Used for testing + # Create a SolarProfileGenerator object + latitude = 46.34 + longitude = -119.28 + timezone = 'US/Pacific' + spg = SolarProfileGenerator(latitude, longitude, timezone, 0, 0, 0, 5, 14, + pv_tracking='fixed', validate=True, + start_year=1998, end_year=2020, + solar_source='nsrdb') + print('generation successful') + + # Download NREL profiles + spg.get_solar_data() + print('downloaded data') + + # Get solar profiles from ASP code + spg.get_solar_profiles() + print('generated profiles') + + # Get power profiles using pvlib-python + spg.get_power_profiles() + print('calculated power') + + spg.pv_checks() diff --git a/main_files/__init__.py b/main_files/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/main_files/main.py b/main_files/main.py new file mode 100644 index 0000000..e6b3fce --- /dev/null +++ b/main_files/main.py @@ -0,0 +1,240 @@ +# -*- coding: utf-8 -*- +""" +Script for running microgrid sizing tool from command line. + +Created on Wed May 9 16:34:12 2018 + +@author: Sarah Newman +""" + +import sys +import os +import pickle +import pandas as pd +from generate_solar_profile import SolarProfileGenerator +from microgrid_optimizer import GridSearchOptimizer +from microgrid_system import PV, SimpleLiIonBattery, Generator, FuelTank + +if __name__ == "__main__": + + ########################################################################### + # Define simulation parameters here + ########################################################################### + + # Go back to main working directory + os.chdir('..') + + # System level + latitude = 46.34 + longitude = -119.28 + timezone = 'US/Pacific' + # common options = [US/Alaska, US/Aleutian, US/Arizona, US/Central, + # US/East-Indiana, US/Eastern, US/sHawaii, US/Indiana-Starke, + # US/Michigan, US/Mountain, US/Pacific, US/Pacific-New, US/Samoa] + altitude = 0 + num_trials = 200 + length_trials = 14 + + # PV + tilt = 20 + azimuth = 180. + spacing_buffer = 2 + pv_racking = 'ground' # options: [ground, roof, or carport] + pv_tracking = 'fixed' # fixed or single_axis + # define where the solar data is downloaded from. Valid options are: + # ['nsrdb', 'himawari']. The default value ('nsrdb') pulls data from + # NREL's NSRDB which covers CONUS, Central America, and parts of Canada + # and South America. It has data from 1998-2021. The Himawari dataset + # covers East Asia and the Pacific Islands. It has data from 2016-2020. + solar_data_source = 'nsrdb' + solar_data_start_year = 1998 # the first year of historical solar data + solar_data_end_year = 2020 # the last year of historical solar data + + # Battery + battery_power_to_energy = 0.25 + initial_soc = 1 + one_way_battery_efficiency = 0.9 + one_way_inverter_efficiency = 0.95 + soc_upper_limit = 1 + soc_lower_limit = 0.2 + batt_sizing_method = 'longest_night' # 'longest_night' or 'no_pv_export' + + # The fraction of maximum PV power below which it is considered night, + # for the purposes of battery discharging + percent_at_night = 0.1 + + # Load (in kW) + annual_load_profile = pd.read_csv( + os.path.join('data', 'sample_load_profile.csv'), index_col=0)['Load'] + off_grid_load_profile = None + save_filename = 'project_name' + + # Utility rate in $/kWh + utility_rate = 0.03263 + + # Demand rate in $/kW (optional) + # The demand rate can either be a single number or a list with a rate + # for each month. + demand_rate = None + + # Component costs and generator options + system_costs = pd.read_excel('data/MCOR Prices.xlsx', sheet_name=None, + index_col=0) + + # Determine if asp multithreading should be used + multithreading = False + + # Filtering constraints + # The constraints list contains dictionaries with the format: + # {parameter, type, value} + # where parameter can be any of the following: + # capital_cost_usd, pv_area_ft2, annual_benefits_usd, simple_payback_yr, + # fuel_used_gal mean, fuel_used_gal most-conservative, pv_percent mean, + # pv_capacity + # and type can be [max, min] + # and value is the maximum or minimum allowable value + constraints = [] + + # Ranking criteria + # The ranking_criteria list is ordered from most to least important, + # and includes dictionaries with the format: + # {parameter, order_type} + # where parameter can be any of the following: + # capital_cost_usd, annual_benefits_usd, simple_payback_yr, + # fuel_used_gal mean, fuel_used_gal most-conservative + # and order_type can be [ascending, descending] + ranking_criteria = [{'parameter': 'simple_payback_yr', + 'order_type': 'ascending'}] + + # Settings for dispatch plots + # To plot the scenarios with min/max pv, set 'scenario_criteria' to 'pv', to plot + # scenarios with min/max fuel consumption, set 'scenario_criteria' to 'gen', or to plot + # a specific scenario number, set the scenario_num parameter + scenario_criteria = 'pv' + scenario_num = None + + # Existing components - this should be used if no smaller sizes are to be + # considered and equipment costs should not be included, otherwise use + # the include_pv option below. + existing_components = {} + # Uncomment the following to specify existing components + # pv = PV(existing=True, pv_capacity=100, tilt=tilt, azimuth=azimuth, + # module_capacity=0.360, module_area=3, spacing_buffer=2, + # pv_tracking='fixed', pv_racking='ground') + # existing_components = {'pv': pv} + + # Including specific PV and battery sizes + include_pv = () + include_batt = () + # Uncomment the following to specify specific pv and battery sizes + # include_pv = (500, 400) + # include_batt = ((1000, 100), (1000, 500)) + + # Net-metering options: + # (1) To specify a net-metering rate different from the utility rate, + # set net_metering_rate below + # (2) To specify no net-metering, set net_metering_rate to 0 + # (3) To enforce an installed capacity cap, use the constraints + # parameter above with a pv_capacity limit + # (4) To enforce an instantaneous export power cap (in kW), set the + # net_metering_limits parameter below, e.g. + # net_metering_limits = {'type': 'capacity_cap', 'value': 100} + # (5) To enforce a cap as a percentage of load, set: + # net_metering_limits = {'type': 'percent_of_load', 'value': 100} + # (6) In the case where there is no net-metering, but the battery is + # sized to capture all excess pv generation (i.e. if + # batt_sizing_method = 'no_pv_export'), and you want to estimate + # the revenue from using the battery during normal operation to + # capture and use this excess generation, set the + # net_metering_limits parameter below, e.g. + # net_metering_limits = {'type': 'no_nm_use_battery'} + # *Note*: this will not include any additional costs due to wearing + # out the battery more quickly from using it daily and does not + # capture revenue from using the battery for grid services. It + # also assumes that the battery is sized with the no_pv_export + # method. + net_metering_limits = None + net_metering_rate = None + + ########################################################################### + + # Get solar and power profiles + print('Creating solar profiles...') + spg = SolarProfileGenerator(latitude, longitude, timezone, altitude, tilt, + azimuth, float(num_trials), + float(length_trials), + start_year=solar_data_start_year, + end_year=solar_data_end_year, + pv_racking=pv_racking, + pv_tracking=pv_tracking, + suppress_warnings=False, + multithreading=multithreading, + solar_source=solar_data_source) + + # Note: it is strongly recommended to comment out the following two lines + # after running them once for a particular site. This will save a lot + # of time by not having to regenerate the solar profiles + spg.get_solar_data() + spg.get_solar_profiles() + print('Calculating power profiles...') + spg.get_power_profiles() + spg.get_night_duration(percent_at_night=percent_at_night) + tmy_solar = spg.tmy_power_profile + module_params = spg.get_pv_params() + + # Set up parameter dictionaries + location = {'longitude': longitude, 'latitude': latitude, + 'timezone': timezone, 'altitude': altitude} + pv_params = {'tilt': tilt, 'azimuth': azimuth, + 'module_capacity': module_params['capacity'], + 'module_area': module_params['area_in2'], + 'spacing_buffer': spacing_buffer, 'advanced_inputs': {}, + 'pv_racking': pv_racking, 'pv_tracking': pv_tracking} + battery_params = {'battery_power_to_energy': battery_power_to_energy, + 'initial_soc': initial_soc, + 'one_way_battery_efficiency': one_way_battery_efficiency, + 'one_way_inverter_efficiency': + one_way_inverter_efficiency, + 'soc_upper_limit': soc_upper_limit, + 'soc_lower_limit': soc_lower_limit} + + # Create an optimizer object + print('Running system optimization...') + optim = GridSearchOptimizer(spg.power_profiles, spg.temp_profiles, + spg.night_profiles, annual_load_profile, + location, tmy_solar, pv_params, battery_params, + system_costs, + electricity_rate=utility_rate, + net_metering_rate=net_metering_rate, + demand_rate=demand_rate, + existing_components=existing_components, + output_tmy=True, + validate=True, + net_metering_limits=net_metering_limits, + off_grid_load_profile=off_grid_load_profile, + batt_sizing_method=batt_sizing_method, + gen_power_percent=()) + + # Create a grid of systems + optim.define_grid(include_pv=include_pv, include_batt=include_batt) + + # Get load profiles for the corresponding solar profile periods + optim.get_load_profiles() + + # Run all simulations + optim.run_sims_par() + + # Filter and rank results + optim.parse_results() + optim.filter_results(constraints) + optim.rank_results(ranking_criteria) + + # Check pv profiles for calculation errors + spg.pv_checks() + + # Plot dispatch graphs + optim.plot_best_system(scenario_criteria=scenario_criteria, scenario_num=scenario_num) + + # Save results + optim.save_results_to_file(spg, save_filename) + pickle.dump(optim, open('output/{}.pkl'.format(save_filename), 'wb')) diff --git a/microgrid_optimizer.py b/microgrid_optimizer.py new file mode 100644 index 0000000..35e41a4 --- /dev/null +++ b/microgrid_optimizer.py @@ -0,0 +1,1791 @@ +# -*- coding: utf-8 -*- +""" + +Optimization class for simulating, filtering and ranking microgrid + systems. + +File contents: + Classes: + Optimizer + GridSearchOptimizer (inherits from Optimizer) + + Standalone functions: + get_electricity_rate + +""" +import json +import multiprocessing +import os +import urllib + +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +import pandas as pd +import tabulate +from geopy.geocoders import Nominatim + +from generate_solar_profile import SolarProfileGenerator +from microgrid_simulator import PVBattGenSimulator +from microgrid_system import PV, SimpleLiIonBattery, SimpleMicrogridSystem +from validation import validate_all_parameters, log_error, annual_load_profile_warnings + + +class Optimizer: + """ Parent optimization class """ + + def next_system(self): + pass + + def run_sims(self): + pass + + +class GridSearchOptimizer(Optimizer): + """ + Simulates a grid of microgrid systems, and for each system, runs all + solar profiles. + + Parameters + ---------- + + power_profiles: list of Pandas series' with PV power profiles + for a 1kW system + + temp_profiles: list of Pandas dataframes with temperature + profiles + + night_profiles: list of Pandas dataframes with info on whether + it is night + + annual_load_profile: Pandas series with a full year-long load + profile. It must have a DateTimeIndex with a timezone. + + location: dictionary with the following keys and value + datatypes: + {'longitude': float, 'latitude': float, 'timezone': string, + 'altitude': float} + + tmy_solar: TMY solar pv production time series in kwh + + pv_params: dictionary with the following keys and value + datatypes: + {'tilt': float, 'azimuth': float, 'module_capacity': float, + 'module_area': float (in square inches), + 'pv_racking': string (options: [roof, ground, carport]), + Default = ground, + 'pv_tracking': string (options: [fixed, single_axis]), + 'advanced_inputs': dict (currently does nothing)} + + battery_params: dictionary with the following keys and value + datatypes: + {'battery_power_to_energy': float, 'initial_soc': float, + 'one_way_battery_efficiency': float, + 'one_way_inverter_efficiency': float, + 'soc_upper_limit': float, 'soc_lower_limit': float, + 'init_soc_lower_limit': float} + + system_costs: dictionary containing the following Pandas + dataframes: + pv_costs: cost of PV per Watt, with the upper limit for pv + sizes as the columns: + 100, 5000, 100000 + and the pv racking and tracking type as the rows: + ground;fixed: 2.78, 1.64, 0.83 + ground;single_axis: 2.97, 1.75, 0.89 + roof;fixed: 2.65, 1.56, 0.83 + carport;fixed: 3.15, 1.86, 0.83 + + om_costs: operations and maintenance costs for the following + components: + Generator ($/kW-yr): scalar - 102.65, exponent - 0.669 + Battery ($/kW-yr): 2.41 + PV_ground;fixed ($/kW-yr): 15 + PV_ground;single_axis ($/kW-yr): 18 + PV_roof;fixed ($/kW-yr): 17.50 + PV_carport;fixed ($/kW-yr): 12.50 + + battery_costs: costs for the battery: + Battery System ($/Wh): 0.521 + Inverter and BOS ($/W): 0.401 + + fuel_tank_costs: costs for fuel tanks based on size (gal) + + generator_costs: list of possible generators, with the + following columns: + Power: Generator rated power + 1/4 Load (gal/hr), 1/2 Load (gal/hr), 3/4 Load (gal/hr), Full Load (gal/hr): + loading levels for the generator fuel curve + Cost (USD): cost for specific generator size + + duration: Timestep duration in seconds. + Default: 3600 (1 hour) + + dispatch_strategy: determines the battery dispatch strategy. + Options include: + night_const_batt (constant discharge at night) + night_dynamic_batt (updates the discharge rate based on + remaining available capacity) + Default: night_dynamic_batt + + batt_sizing_method: method for sizing the battery. Options are: + - longest_night + - no_pv_export + Default = longest_night + + electricity_rate: Local electricity rate in $/kWh. If it is set + to None, the rate is determined by looking up the average + state rate found here: + https://www.eia.gov/electricity/state/ + Default = None + + net_metering_rate: Rate in $/kWh used for calculating exported + PV revenue. If it is set to None, the rate is assumed to be + the same as the electricity rate. + Default = None + + demand_rate: Demand charge rate in $/kW used for calculating PV + system revenue. Can be either a number or a list of 12 + numbers, one for each month. If it is set to None, no demand + charge savings are calculated. + + net_metering_limits: Dictionary specifying local limits for the + net-metering policy in the form of: + {type: ['capacity_cap' or 'percent_of_load'], + value: [ or ]} + Default = None + + generator_buffer: Buffer between chosen generator size and + maximum required power. E.g. a buffer of 1.1 means a + generator has to be sized 10% larger than the maximum power. + Default = 1.1 + + gen_power_percent: tuple specifying which power levels to report + from the generator load duration curve, as a percent of the + maximum generator size (which includes a buffer). + Default = () + + existing_components: Dictionary containing Component objects for + equipment already on site in the form: + {'pv': , 'generator': } + + filter: lists any filtering criteria that have been applied to + results. + Default = None + + rank: lists any ranking criteria that have been applied to + results. + Default = None + + off_grid_load_profile: load profile to be used for off-grid + operation. If this parameter is not set to None, the + annual_load_profile is used to size the PV system and + calculate annual revenue, and this profile is used to size + the battery and generator and calculate resilience metrics. + Default = None + + Methods + ---------- + + size_PV_for_netzero: Sizes PV system according to net zero and + incrementally smaller sizes + + size_batt_by_longest_night: Sizes the battery system according + to the longest night of the year. + + size_batt_for_no_pv_export: Sizes the battery such that no + excess PV is exported to the grid during normal operation. + + create_new_system: Create a new SimpleMicrogridSystem to add to + the system grid + + define_grid: Defines the grid of system sizes to consider + + print_grid: Prints out the sizes in a grid of system + configurations + + get_load_profiles: For each solar profile, extracts the load + profile from annual_load_profile for the corresponding + dates/times + + next_system: Pops and returns the next system in the + input_system_grid list + + run_sims: Runs the simulations for all systems and profiles + + run_sims_par: Run the simulations for all systems and profiles + using Python's multiprocessing package + + aggregate_by_system: Runs the simulation for a given system + configuration for multiple solar/temp profiles and + aggregates the results + + parse_results: Parses simulation results into a dataframe + + filter_results: Filters the results_grid dataframe by specified + constraints + + rank_results: Ranks the results_grid dataframe by specified + ranking criteria + + print_systems_results: Returns info about each of the systems + from the results grid + + plot_system_dispatch: Displays dispatch plots for each of the + systems in the results grid + + plot_best_system: Displays dispatch and load duration plots for + 3 systems (best in terms of ranking, best with battery, + and system with least fuel usage) + + add_system: Add a specific system to the input list + + get_system: Return a specific system based on its name + + get_input_systems: Returns the dictionary of input systems + + get_output_systems: Returns the dictionary of output systems + + format_inputs: Formats the inputs into dicts for writing to file + + save_results_to_file: Saves inputs, assumptions, and results to + an excel file + + Calculated Attributes + ---------- + + load_profiles: List of load profiles for the corresponding + outage periods + + input_system_grid: Dictionary of MicrogridSystem objects to + simulate + + output_system_grid: Dictionary of already simulated + MicrogridSystem objects + + results_grid: Pandas dataframe containing output results from + the simulations, with one row per system + + """ + + def __init__(self, power_profiles, temp_profiles, night_profiles, + annual_load_profile, location, tmy_solar, pv_params, + battery_params, system_costs, duration=3600, + dispatch_strategy='night_dynamic_batt', + batt_sizing_method='longest_night', electricity_rate=None, + net_metering_rate=None, demand_rate=None, + net_metering_limits=None, + generator_buffer=1.1, + gen_power_percent=(), existing_components={}, + off_grid_load_profile=None, + output_tmy=False, validate=True): + + self.power_profiles = power_profiles + self.temp_profiles = temp_profiles + self.night_profiles = night_profiles + self.annual_load_profile = annual_load_profile + self.location = location + self.tmy_solar = tmy_solar + self.pv_params = pv_params + self.battery_params = battery_params + self.system_costs = system_costs + self.duration = duration + self.dispatch_strategy = dispatch_strategy + self.batt_sizing_method = batt_sizing_method + self.electricity_rate = electricity_rate + self.net_metering_rate = net_metering_rate + self.demand_rate = demand_rate + self.net_metering_limits = net_metering_limits + self.generator_buffer = generator_buffer + self.gen_power_percent = gen_power_percent + self.existing_components = existing_components + self.off_grid_load_profile = off_grid_load_profile + self.output_tmy = output_tmy + self.load_profiles = [] + self.input_system_grid = {} # Dict of MicrogridSystem objects + self.output_system_grid = {} + self.results_grid = None + self.filter = None + self.rank = None + + if validate: + # List of initialized parameters to validate + args_dict = {'power_profiles': power_profiles, + 'temp_profiles': temp_profiles, + 'night_profiles': night_profiles, + 'annual_load_profile': annual_load_profile, + 'location': location, 'tmy_solar': tmy_solar, + 'pv_params': pv_params, + 'battery_params': battery_params, + 'duration': duration, + 'gen_power_percent': gen_power_percent, + 'dispatch_strategy': dispatch_strategy, + 'batt_sizing_method': batt_sizing_method, + 'system_costs': system_costs} + if electricity_rate is not None: + args_dict['electricity_rate'] = electricity_rate + if net_metering_rate is not None: + args_dict['net_metering_rate'] = net_metering_rate + if demand_rate is not None: + args_dict['demand_rate_list'] = demand_rate + if net_metering_limits is not None: + args_dict['net_metering_limits'] = net_metering_limits + if len(existing_components): + args_dict['existing_components'] = existing_components + if off_grid_load_profile is not None: + args_dict['off_grid_load_profile'] = off_grid_load_profile + + # Validate input parameters + validate_all_parameters(args_dict) + + # De-localize timezones from profiles + for profile in self.power_profiles: + profile.index = profile.index.map(lambda x: x.tz_localize(None)) + for profile in self.temp_profiles: + profile.index = profile.index.map(lambda x: x.tz_localize(None)) + for profile in self.night_profiles: + profile.index = profile.index.map(lambda x: x.tz_localize(None)) + tmy_solar.index = tmy_solar.index.map(lambda x: x.tz_localize(None)) + + # Fix annual load profile index + self.annual_load_profile.index = pd.date_range( + start='1/1/2017', end='1/1/2018', + freq='{}S'.format(int(self.duration)))[:-1] + if self.off_grid_load_profile is not None: + self.off_grid_load_profile.index = self.annual_load_profile.index + + if validate: + # Check for warnings + annual_load_profile_warnings(self.annual_load_profile) + if self.off_grid_load_profile is not None: + annual_load_profile_warnings(self.off_grid_load_profile) + + # Get electricity rate data if a rate is not specified + if self.electricity_rate is None: + self.electricity_rate = get_electricity_rate(self.location, + validate=False) + + def size_PV_for_netzero(self): + """ + Sizes PV system according to net zero and incrementally smaller + sizes. + + The maximum PV size is determined by the net-zero case + (annual solar production = annual load) plus solar in excess + of load times the system RTE losses, with smaller sizes + calculated as % of net zero size: + - Maximum size = net-zero + excess*(1-RTE) + - Net-zero size + - Net-zero * 50% + - Net-zero * 25% + - 0 PV + """ + + # Get the total annual load and solar energy produced + total_annual_load = self.annual_load_profile.sum() + total_annual_solar = self.tmy_solar.sum() + net_zero = total_annual_load / total_annual_solar + + # Calculate round-trip efficiency based on battery and inverter + # efficiency + system_rte = self.battery_params['one_way_battery_efficiency'] ** 2 \ + * self.battery_params['one_way_inverter_efficiency'] ** 2 + + # Calculate the amount of pv energy lost through + # charging/discharging batteries at the net zero capacity + losses = pd.Series(self.tmy_solar.values * net_zero - + self.annual_load_profile.values) + losses.loc[losses < 0] = 0 + total_lost = losses.sum() * (1 - system_rte) + + # Maximum (net-zero) solar size is based on scaling total annual + # solar to equal annual load plus losses + max_cap = (total_annual_load + total_lost) / total_annual_solar + + # Create grid based on max, min and standard intervals + return [max_cap, net_zero, net_zero * 0.5, net_zero * 0.25] + + def size_batt_by_longest_night(self, load_profile): + """ + Sizes the battery system according to the longest night of the + year. + + The maximum battery capacity is determined by the load of the + highest load night, with the power determined by a fixed + power to energy ratio. Smaller sizes are calculated as a + fraction of the maximum size, also with a fixed power to + energy ratio: + - Maximum size = capacity for longest night + - Maximum size * 75% + - Maximum size * 50% + - Maximum size * 25% + - O ES + """ + + # Get nighttime load based on TMY pv power + night_df = load_profile.to_frame(name='load') + night_df['pv_power'] = self.tmy_solar.values + + # Set daytime load to 0 + night_df.loc[night_df['pv_power'] > 0, 'load'] = 0 + + # Get date (without hour) for each timestep + night_df['day'] = night_df.index.day + night_df['month'] = night_df.index.month + + # Add up the energy for each night (since this is done by + # calendar date, these aren't technically continuous nights, + # but this whole calculation is an approximation anyway) + max_nightly_energy = night_df.groupby(['month', 'day'])['load']. \ + sum().max() + + # Maximum battery capacity = max nightly load / RTE + system_rte = self.battery_params['one_way_battery_efficiency'] ** 2 \ + * self.battery_params['one_way_inverter_efficiency'] ** 2 + max_cap = max_nightly_energy / system_rte + max_pow = max_cap * self.battery_params['battery_power_to_energy'] + + return [(max_cap, max_pow), + (max_cap * 0.75, max_pow * 0.75), + (max_cap * 0.5, max_pow * 0.5), + (max_cap * 0.25, max_pow * 0.25), + (0, 0)] + + def size_batt_for_no_pv_export(self, pv_sizes, load_profile): + """ + Sizes the battery such that no excess PV is exported to the grid + during normal operation. + """ + + # Calculate excess PV production for each PV size + excess_pv = load_profile.to_frame(name='load') + excess_pv['pv_base'] = self.tmy_solar.values + for size in pv_sizes: + excess_pv[int(size)] = excess_pv['pv_base'] * size + excess_pv['{}_exported'.format(int(size))] = excess_pv[int(size)]\ + - excess_pv['load'] + excess_pv[excess_pv < 0] = 0 + + # Calculate battery power as the maximum exported PV power + power = excess_pv.max() + + # Calculate capacity as the maximum daily exported PV energy + excess_pv['day'] = excess_pv.index.date + cap = excess_pv.groupby('day').sum().max() + + return [(round(cap['{}_exported'.format(int(size))] * + self.battery_params['one_way_inverter_efficiency'] * + self.battery_params['one_way_battery_efficiency'], 2), + round(power['{}_exported'.format(int(size))] * + self.battery_params['one_way_inverter_efficiency'], 2)) + for size in pv_sizes] + + def create_new_system(self, pv_size, battery_size): + """ + Create a new SimpleMicrogridSystem to add to the system grid. + """ + + # Create PV object + pv = PV('pv' in self.existing_components, pv_size, + self.pv_params['tilt'], self.pv_params['azimuth'], + self.pv_params['module_capacity'], + self.pv_params['module_area'], + self.pv_params['spacing_buffer'], + self.pv_params['pv_tracking'], + self.pv_params['pv_racking'], + self.pv_params['advanced_inputs'], validate=False) + + # Create Battery object + batt = SimpleLiIonBattery( + 'battery' in self.existing_components, battery_size[1], + battery_size[0], self.battery_params['initial_soc'], + self.battery_params['one_way_battery_efficiency'], + self.battery_params['one_way_inverter_efficiency'], + self.battery_params['soc_upper_limit'], + self.battery_params['soc_lower_limit'], validate=False) + + # Determine system name + system_name = 'pv_{:.1f}kW_batt_{:.1f}kW_{:.1f}kWh'.format( + pv_size, battery_size[1], battery_size[0]) + + # Create system object + system = SimpleMicrogridSystem(system_name) + + # Add components to system + system.add_component(pv, validate=False) + system.add_component(batt, validate=False) + + return system_name, system + + def define_grid(self, include_pv=(), include_batt=(), validate=True): + """ + Defines the grid of system sizes to consider. + + Parameters: + + include_pv: list of pv sizes to be added to the grid (in kW) + + include_batt: list of battery sizes to be added to the grid + in the form of a tuple: + (batt capacity, batt power) in (kWh, kW) + + """ + + if validate: + # List of initialized parameters to validate + args_dict = {} + if len(include_pv): + args_dict['include_pv'] = include_pv + if len(include_batt): + args_dict['include_batt'] = include_batt + + if len(args_dict): + # Validate input parameters + validate_all_parameters(args_dict) + + # Size the pv system based on load and pv power + pv_range = self.size_PV_for_netzero() + + # Add any sizes in include_pv + for size in include_pv: + pv_range += [size] + + # If there is an existing pv system, use this to inform ranges + if 'pv' in self.existing_components: + # Use the current PV size as the minimum + min_cap = self.existing_components['pv'].pv_capacity + + # If it is not currently in the range, add it + if self.existing_components['pv'].pv_capacity not in pv_range: + pv_range += [self.existing_components['pv'].pv_capacity] + else: + min_cap = 0 + + # Get rid of any pv sizes smaller than the minimum (e.g. from + # existing system) + pv_range = [elem for elem in pv_range if elem >= min_cap] + + # Determine which method to use for sizing the battery + if self.batt_sizing_method == 'longest_night': + # Determine which load profile to use for sizing the battery + if self.off_grid_load_profile is None: + batt_sizing_load = self.annual_load_profile.copy(deep=True) + else: + batt_sizing_load = self.off_grid_load_profile.copy(deep=True) + + # Determine maximum battery size based on "worst" night (night + # with highest load) + batt_range = self.size_batt_by_longest_night(batt_sizing_load) + + elif self.batt_sizing_method == 'no_pv_export': + # Size battery to capture all excess PV generation + batt_range = self.size_batt_for_no_pv_export( + pv_range, self.annual_load_profile.copy(deep=True)) + + else: + # Add error about wrong label + message = 'Invalid battery sizing method' + log_error(message) + raise Exception(message) + + # Add any sizes in include_batt + # Note: this will not have an effect for the no pv export battery + # sizing methodology + for size in include_batt: + batt_range += [size] + + # Create MicrogridSystem objects for each system + if self.batt_sizing_method == 'longest_night': + for pv_size in pv_range: + for battery_size in batt_range: + # Add system to input system dictionary + system_name, system = self.create_new_system(pv_size, + battery_size) + self.input_system_grid[system_name] = system + elif self.batt_sizing_method == 'no_pv_export': + for pv_size, battery_size in zip(pv_range, batt_range): + system_name, system = self.create_new_system(pv_size, + battery_size) + self.input_system_grid[system_name] = system + + # Add a system with 0 PV and 0 batteries + system_name, system = self.create_new_system(0, [0, 0]) + self.input_system_grid[system_name] = system + + # If there is an existing generator, add to each system + if 'generator' in self.existing_components: + for system in self.input_system_grid.values(): + system.add_component(self.existing_components['generator'], + validate=False) + + def print_grid(self): + """ Print out the sizes in a grid of system configurations. """ + + # Iterate through both the input and output grids and print + # sizes for each system + for system in np.sort(list(self.input_system_grid.keys()) + + list(self.output_system_grid.keys())): + print(system) + + def get_system(self, system_name): + """ Return a specific system based on its name. """ + if system_name in self.input_system_grid: + return self.input_system_grid[system_name] + elif system_name in self.output_system_grid: + return self.output_system_grid[system_name] + else: + print('Please enter a valid system name') + + def get_load_profiles(self): + """ + For each solar profile, extract the load profile from the + corresponding date/times. + + """ + + # If the user specified an off-grid load profile, use this for the + # resiliency simulations, otherwise use the annual load profile + if self.off_grid_load_profile is None: + load_profile = self.annual_load_profile + else: + load_profile = self.off_grid_load_profile + + # Create 2-year load profile to allow for profiles with + # year-end overlap + twoyear_load_profile = pd.concat([load_profile, load_profile]) + twoyear_load_profile.index = pd.date_range( + start='1/1/2017', end='1/1/2019', + freq='{}S'.format(int(self.duration)))[:-1] + + # Loop over each solar profile + for power_profile in self.power_profiles: + + # Get the first timestamp for the solar profile + start_time = power_profile.index[0] + + # If it is February 29, decrease by one day + if start_time.day == 29 and start_time.month == 2: + start_time = start_time.replace(day=28) + + # Change the start time to year 2017 (same as load profile) + start_time = start_time.replace(year=2017) + + # Create a datetime index + temp_index = pd.date_range(start=start_time, + periods=len(power_profile), + freq='{}S'.format(int(self.duration))) + + # Get the load profile values at the corresponding + # date/times + self.load_profiles += [twoyear_load_profile.loc[temp_index]] + + def next_system(self): + """ + For the gridsearch optimizer, this function just returns the + next system in the grid. + + """ + try: + return self.input_system_grid.popitem() + except KeyError: + message = 'There are no more systems in the input system grid.' + log_error(message) + raise Exception(message) + + def run_sims(self): + """ Runs the simulations """ + + # Loop over each system using next_system() + while len(self.input_system_grid): + system_name, system = self.next_system() + print('Running system: {}'.format(system_name)) + + # Call aggregate_by_system to run multiple solar profiles + # per system + results, _ = self.aggregate_by_system(system, validate=False) + + # Save the results in the MicrogridSystem object + system.load_duration = results.pop('load_duration') + system.outputs = results + + # Calculate the annual benefits + system.calc_annual_pv_benefits(self.tmy_solar, + self.annual_load_profile, + self.duration, + self.electricity_rate, + self.net_metering_rate, + self.demand_rate, + self.batt_sizing_method, + self.battery_params[ + 'one_way_battery_efficiency'], + self.battery_params[ + 'one_way_inverter_efficiency'], + self.net_metering_limits, + self.existing_components, + validate=False) + + # Calculate the required fuel tank size and number + system.size_fuel_tank(self.system_costs['fuel_tank_costs'], + self.existing_components, + max(system.outputs['fuel_used_gal'])) + + # Calculate system costs and payback + system.calc_costs(self.system_costs, + self.existing_components, validate=False) + system.calc_payback() + + # Calculate PV area + system.get_pv_area() + + # Add to output_system_grid list + self.output_system_grid[system_name] = system + + def run_sims_par(self): + """ Runs the simulations using the multiprocessing toolbox """ + + # Create list of systems to pass to multiprocessing pool + input_list = [] + while len(self.input_system_grid): + input_list += [(self.input_system_grid.popitem()[1], False)] + output_list = [] + + # Create multiprocessing pool + with multiprocessing.Pool(os.cpu_count()) as pool: + # Run processes + results = pool.starmap(self.aggregate_by_system, input_list) + + for result, system in results: + print('Running system: {}'.format(system.get_name())) + + # Save the results in the MicrogridSystem object + system.load_duration = result.pop('load_duration') + system.outputs = result + + # Add to output_system_grid list + output_list += [system] + + for system in output_list: + # Calculate the annual benefits + system.calc_annual_pv_benefits(self.tmy_solar, + self.annual_load_profile, + self.duration, + self.electricity_rate, + self.net_metering_rate, + self.demand_rate, + self.batt_sizing_method, + self.battery_params[ + 'one_way_battery_efficiency'], + self.battery_params[ + 'one_way_inverter_efficiency'], + self.net_metering_limits, + self.existing_components, + validate=False) + + # Calculate the required fuel tank size and number + system.size_fuel_tank(self.system_costs['fuel_tank_costs'], + self.existing_components, + max(system.outputs['fuel_used_gal'])) + + # Calculate system costs and payback + system.calc_costs(self.system_costs, self.existing_components, + validate=False) + system.calc_payback() + + # Calculate PV area + system.get_pv_area() + + # Add to output system grid dict + self.output_system_grid[system.get_name()] = system + + def aggregate_by_system(self, system, validate=True): + """ + Runs the simulation for a given system configuration for + multiple solar/temp profiles and aggregates the results. + + """ + + if validate: + # List of initialized parameters to validate + args_dict = {'system': system} + + # Validate input parameters + validate_all_parameters(args_dict) + + # Create lists to hold dispatch outputs + results_summary = {'pv_percent': [], 'batt_percent': [], + 'gen_percent': [], 'storage_recovery_percent': [], + 'fuel_used_gal': [], 'generator_power_kW': []} + + # Create empty load duration dictionaries to hold hours, kWh, and + # kW not met at each generator size + if self.off_grid_load_profile is None: + load_profile = self.annual_load_profile + else: + load_profile = self.off_grid_load_profile + hours_not_met = {} + kWh_not_met = {} + max_kW_not_met = {} + + # For each solar/temp profile, create a simulator object + for i, (power_profile, temp_profile, load_profile, night_profile) in \ + enumerate(zip(self.power_profiles, self.temp_profiles, + self.load_profiles, self.night_profiles)): + # Reset battery state + system.components['battery'].reset_state() + + # Set simulation name + simulation_name = 'system_{}_profile_{}'.format(system.get_name(), i) + + # Create simulation object + simulation = PVBattGenSimulator( + simulation_name, power_profile, temp_profile, load_profile, + night_profile, system, self.location, self.duration, + self.dispatch_strategy, + generator_buffer=self.generator_buffer, validate=False) + + # Run the simulation for each object + simulation.scale_power_profile() + simulation.calc_dispatch() + + # Check length to make sure there was not a merging error + if len(simulation.dispatch_df) != len(power_profile): + message = 'Error in dispatch calculation: dispatch ' \ + 'dataframe wrong size - check for merging error.' + log_error(message) + raise Exception(message) + + # Size and dispatch generator + simulation.size_single_generator( + self.system_costs['generator_costs'], validate=False) + + # Add the results to the lists + results_summary['pv_percent'] += \ + [simulation.get_load_breakdown()['pv'] * 100] + results_summary['batt_percent'] += \ + [simulation.get_load_breakdown()['battery'] * 100] + results_summary['gen_percent'] += \ + [simulation.get_load_breakdown()['generator'] * 100] + results_summary['storage_recovery_percent'] += \ + [simulation.get_storage_recovery_percent()] + results_summary['fuel_used_gal'] += \ + [simulation.get_fuel_used()] + results_summary['generator_power_kW'] += \ + [simulation.get_generator_power()] + + # Add load duration data + grouped_load = simulation.get_load_duration_df() + hours_not_met['sim_{}'.format(i)] = grouped_load['num_hours_above'] + kWh_not_met['sim_{}'.format(i)] = grouped_load[ + 'energy_not_met_above_load_level'] + max_kW_not_met['sim_{}'.format(i)] = grouped_load[ + 'max_percent_not_met'] + + # Add the simulation to the system simulations dictionary + system.add_simulation(i, simulation, validate=False) + + # Convert load duration dictionaries to dataframes + hours_not_met = pd.concat(hours_not_met, axis=1) + kWh_not_met = pd.concat(kWh_not_met, axis=1) + max_kW_not_met = pd.concat(max_kW_not_met, axis=1) + + # Get mean and max for each of the load_duration metrics + results_summary['load_duration'] = pd.DataFrame() + results_summary['load_duration']['hours_not_met_max'] = \ + hours_not_met.fillna(0).max(axis=1) + results_summary['load_duration']['hours_not_met_average'] = \ + hours_not_met.fillna(0).mean(axis=1) + results_summary['load_duration']['scenarios_not_met'] = \ + hours_not_met[hours_not_met > 0].count(axis=1) + results_summary['load_duration']['kWh_not_met_average'] = \ + kWh_not_met.fillna(0).mean(axis=1) + results_summary['load_duration']['kWh_not_met_max'] = \ + kWh_not_met.fillna(0).max(axis=1) + results_summary['load_duration']['max_%_kW_not_met_average'] = \ + max_kW_not_met.fillna(0).mean(axis=1) + results_summary['load_duration']['max_%_kW_not_met_max'] = \ + max_kW_not_met.fillna(0).max(axis=1) + + # Find the simulation with the largest generator and add that + # generator object to the system + max_gen_sim_num = \ + np.where(results_summary['generator_power_kW'] + == max(results_summary['generator_power_kW']))[0][0] + max_gen_sim = system.get_simulation(max_gen_sim_num) + system.add_component(max_gen_sim.generator_obj, validate=False) + + return results_summary, system + + def parse_results(self): + """ Parse simulation results into a dataframe """ + + # If simulations have not been run, run them now + if not len(self.output_system_grid): + self.run_sims() + + # Results columns + metrics = ['pv_capacity', 'battery_capacity', 'battery_power', + 'generator_power', 'fuel_tank_size_gal', + 'capital_cost_usd', 'pv_capital', 'battery_capital', + 'generator_capital', 'fuel_tank_capital', 'pv_o&m', + 'battery_o&m', 'generator_o&m', 'pv_area_ft2', + 'annual_benefits_usd', 'demand_benefits_usd', + 'simple_payback_yr', 'pv_percent mean', + 'batt_percent mean', 'gen_percent mean', + 'generator_power_kW mean', 'generator_power_kW std', + 'generator_power_kW most-conservative', + 'fuel_used_gal mean', 'fuel_used_gal std', + 'fuel_used_gal most-conservative'] + + # Add columns for displaying information about smaller generator sizes + for perc in self.gen_power_percent: + metrics += ['{}%_smaller_gen_size'.format(perc), + '{}%_smaller_gen_typical_fuel_gal'.format(perc), + '{}%_smaller_gen_conservative_fuel_gal'.format(perc), + '{}%_smaller_gen_cost'.format(perc), + '{}%_smaller_gen_scenarios_not_met'.format(perc), + '{}%_smaller_gen_hours_not_met_average'.format(perc), + '{}%_smaller_gen_hours_not_met_max'.format(perc), + '{}%_smaller_gen_kWh_not_met_average'.format(perc), + '{}%_smaller_gen_kWh_not_met_max'.format(perc), + '{}%_smaller_gen_max_%_kW_not_met_average'.format(perc), + '{}%_smaller_gen_max_%_kW_not_met_max'.format(perc)] + + # Create dataframe to hold results + self.results_grid = pd.DataFrame(columns=metrics) + + # Iterate through each simulation and add to dataframe + for system_name, system in self.output_system_grid.items(): + + # Get system outputs + outputs = system.outputs + + # For each metric, calculate mean and standard deviation + results_summary = {key: {'mean': np.mean(val), 'std': np.std(val)} + for key, val in outputs.items()} + + # Calculate worst-case scenario metric for generator size + results_summary['generator_power_kW']['most-conservative'] = \ + max(outputs['generator_power_kW']) + + # Calculate worst-case scenario metric for fuel use + results_summary['fuel_used_gal']['most-conservative'] = \ + max(outputs['fuel_used_gal']) + + # Save to system object + system.set_outputs(results_summary, validate=False) + + # Turn results into a dataframe + system_row = pd.DataFrame.from_dict(results_summary).transpose().\ + stack() + system_row.index = [' '.join(col).strip() + for col in system_row.index.values] + + # Add load duration info for smaller generator sizes + # specified by gen_power_percent + for perc in self.gen_power_percent: + # Pad the load duration curve with 0s up to the generator size + for row in range(system.load_duration.index[-1] + 1, + int(system.generator_power_kW[ + 'most-conservative']) + 1): + system.load_duration.loc[row] = [0, 0, 0, 0, 0, 0, 0] + + # Find the % smallest generator size from the worst-case + system_row = pd.concat([system_row, + system.calculate_smaller_generator_metrics( + perc, self.system_costs['generator_costs'], + validate=False)]) + + # Add static outputs (ones that don't vary between + # simulations) + system_row = pd.concat([system_row, + pd.Series(system.get_outputs( + ['capital_cost_usd', 'pv_area_ft2', + 'annual_benefits_usd', 'demand_benefits_usd', + 'simple_payback_yr']))]) + + # Get component sizes and costs + system_row = pd.concat([system_row, + pd.Series( + {'pv_capacity': system.components['pv'].pv_capacity, + 'battery_capacity': + system.components['battery'].batt_capacity, + 'battery_power': system.components['battery'].power, + 'generator_power': + system.components['generator'].rated_power + * system.components['generator'].num_units, + 'fuel_tank_size_gal': + system.components['fuel_tank'].tank_size + * system.components['fuel_tank'].num_units})]) + system_row = pd.concat([system_row, pd.Series(system.costs_usd)]) + + # Add results to dataframe + self.results_grid.loc[system_name] = system_row + + def filter_results(self, filter_constraints, validate=True): + """ + Filters the system results by defined constraints. + + The filter_constraints list contains dictionaries with the + format: + {parameter, type, value} + where parameter can be any of the following: + capital_cost_usd, pv_area_ft2, annual_benefits_usd, + simple_payback_yr, fuel_used_gal, + pv_percent, gen_percent + and type can be [max, min] + and value is the maximum or minimum allowable value + + """ + + if validate: + # List of initialized parameters to validate + args_dict = {'filter_constraints': filter_constraints} + + # Validate input parameters + validate_all_parameters(args_dict) + + # If simulations have not been run, run them now + if self.results_grid is None: + self.parse_results() + + # Iterate through each constraint + for constraint in filter_constraints: + # Filter out any systems that do not meet the constraint + if constraint['type'] == 'min': + self.results_grid = self.results_grid.loc[ + self.results_grid[constraint['parameter']] >= + constraint['value']] + else: + self.results_grid = self.results_grid.loc[ + self.results_grid[constraint['parameter']] <= + constraint['value']] + + # Set filter parameter + self.filter = filter_constraints + + def rank_results(self, ranking_criteria, validate=True): + """ + Ranks the system results by defined ranking criteria. + + The ranking_criteria list is ordered from most to least + important, and includes dictionaries with the format: + {parameter, order_type} + where parameter can be any of the following: + capital_cost_usd, annual_benefits_usd, simple_payback_yr, + fuel_used_gal + and order_type can be [ascending, descending] + + """ + + if validate: + # List of initialized parameters to validate + args_dict = {'ranking_criteria': ranking_criteria} + + # Validate input parameters + validate_all_parameters(args_dict) + + if not len(ranking_criteria): + return + + # If simulations have not been run, run them now + if self.results_grid is None: + self.parse_results() + + # Reformat ranking criteria into lists + ranking_params = [criteria['parameter'] for criteria + in ranking_criteria] + ranking_order = [criteria['order_type'] == 'ascending' for criteria + in ranking_criteria] + + # Sort the results grid + self.results_grid.sort_values(by=ranking_params, + ascending=ranking_order, inplace=True) + + # Set rank parameter + self.rank = ranking_criteria + + def print_systems_results(self, num_systems=None, validate=True): + """ + Returns info about each of the systems from the results grid. If + num_systems is specified, only return up to that many + systems. + + """ + + if validate and num_systems is not None: + # List of initialized parameters to validate + args_dict = {'num_systems': num_systems} + + # Validate input parameters + validate_all_parameters(args_dict) + + # Only return top systems if there is a limit + if num_systems is None: + num_systems = len(self.results_grid) + + # Iterate through (presumably) filtered and ordered results + # dataframe + for system_name, row in self.results_grid.iloc[ + :min(num_systems, + len(self.results_grid))].iterrows(): + # Print system info + print('\n') + print(system_name) + + # Print results info + print(tabulate.tabulate(row.to_frame(), floatfmt='.1f')) + + def plot_best_system(self, scenario_criteria='pv', scenario_num=None, validate=True): + """ + Displays dispatch and load duration plots for three systems: + (1) The top system (as ranked) + (2) The top system (as ranked) with a battery + (3) The system with the least fuel consumption + + Parameters + scenario_criteria: the criteria for identifying the best and worst scenarios, + options=['pv', 'gen'] with 'pv' showing the scenarios with the least and most + solar irradiance and 'gen' showing the scenarios with the least and most + generator fuel use + scenario_num: the number of a specific scenario to plot + """ + + if validate: + # List of initialized parameters to validate + args_dict = {'scenario_criteria': scenario_criteria} + + # Validate input parameters + validate_all_parameters(args_dict) + + # Identify systems to run + systems = {} + try: + systems['Most Optimal System'] = \ + self.get_system(self.results_grid.index[0]) + except IndexError: + message = 'No systems in results grid. Check that filtering ' \ + 'constraint is not too restrictive.' + log_error(message) + raise Exception(message) + systems['Most Optimal System with Batteries'] = \ + self.get_system(self.results_grid[ + self.results_grid['battery_capacity'] > 0]. + index[0]) + systems['System with Least Fuel Consumption'] = \ + self.get_system(self.results_grid.sort_values( + by='fuel_used_gal most-conservative').index[0]) + + # Create figures to hold plots + for i, (system_name, system) in enumerate(systems.items()): + # Determine which scenarios to plot + if scenario_criteria == 'pv': + # Find the outage periods with the max and min PV + criteria_dict = {key: val.sum() for key, val + in enumerate(self.power_profiles)} + scenario_label = 'Solar Irradiance Scenario' + else: + # Find the outage periods with the max and min generator runtime + criteria_dict = {key: val for key, val + in enumerate(system.outputs['fuel_used_gal'])} + scenario_label = 'Fuel Consumption Scenario' + max_scenario = max(criteria_dict, key=criteria_dict.get) + min_scenario = min(criteria_dict, key=criteria_dict.get) + + if scenario_num is None: + # Create figure + fig = plt.figure(figsize=[15, 10]) + + # Plot the maximum PV outage dispatch + ax = fig.add_subplot(121) + system.plot_dispatch(max_scenario, ax=ax) + ax.legend(['Load', 'PV', 'Battery', 'Generator'], loc=1, + fontsize=8) + ax.set_title('{} Max {}\n {} generator {}kW'.format( + system_name, scenario_label, system.get_name().replace('_', ' '), + system.components['generator'].rated_power)) + + # Plot the minimum PV outage dispatch + ax = fig.add_subplot(122) + system.plot_dispatch(min_scenario, ax=ax) + ax.legend(['Load', 'PV', 'Battery', 'Generator'], loc=1, + fontsize=8) + ax.set_title('{} Min {}\n {} generator {}kW'.format( + system_name, scenario_label, system.get_name().replace('_', ' '), + system.components['generator'].rated_power)) + plt.tight_layout() + + else: + # Plot individual scenario + fig = plt.figure(figsize=[8, 10]) + ax = fig.add_subplot(111) + system.plot_dispatch(scenario_num, ax=ax) + ax.legend(['Load', 'PV', 'Battery', 'Generator'], loc=1, + fontsize=8) + ax.set_title('{} \n {} generator {}kW'.format( + system_name, system.get_name().replace('_', ' '), + system.components['generator'].rated_power)) + plt.tight_layout() + + # Plot load duration curve + fig = plt.figure(figsize=[12, 10]) + ax = fig.add_subplot(111) + system.load_duration[['hours_not_met_max', + 'hours_not_met_average']].plot(ax=ax) + ax.set_xlabel('Generator Power (kW)') + ax.set_ylabel('Number of Hours Not Met') + ax.legend(['Maximum of Scenarios', 'Average of Scenarios']) + ax.set_title('{} Load Duration\n {} generator {}kW'.format( + system_name, system.get_name().replace('_', ' '), + system.components['generator'].rated_power)) + + def plot_system_dispatch(self, num_systems=None, plot_per_fig=3, + validate=True): + """ + Displays dispatch plots for each of the systems in the results + grid. If num_systems is specified, only plots up to that + many systems. + + Includes plots for the outage periods with the most and least + pv. + + """ + + if validate: + # List of initialized parameters to validate + args_dict = {'plot_per_fig': plot_per_fig} + if num_systems is not None: + args_dict['num_systems'] = num_systems + + # Validate input parameters + validate_all_parameters(args_dict) + + # Set the number of systems if not specified + if num_systems is None or num_systems > len(self.results_grid): + num_systems = len(self.results_grid) + + # Find the outage periods with the max and min PV + sim_pv = {key: val.sum() for key, val + in enumerate(self.power_profiles)} + max_pv = max(sim_pv, key=sim_pv.get) + min_pv = min(sim_pv, key=sim_pv.get) + + # Create figures to hold plots + # Only include plot_per_fig systems per figure + for fig_num in range(int(np.ceil(num_systems / plot_per_fig))): + + # Get the number of subplots in this figure + num_plots = min(num_systems - fig_num * plot_per_fig, + plot_per_fig) + + # Create figure + fig = plt.figure(figsize=[12, 10]) + + # Iterate through each system + for plot_num, (system_name, _) in enumerate( + self.results_grid.iloc[ + fig_num * plot_per_fig:fig_num * plot_per_fig + num_plots]. + iterrows()): + # Plot the maximum PV outage dispatch + ax = fig.add_subplot(num_plots, 2, plot_num * 2 + 1) + self.get_system(system_name).plot_dispatch(max_pv, ax=ax) + ax.legend(['Load', 'PV', 'Battery', 'Generator'], loc=1, + fontsize=8) + ax.set_title('{} max PV'.format(system_name)) + + # Plot the minimum PV outage dispatch + ax = fig.add_subplot(num_plots, 2, plot_num * 2 + 2) + self.get_system(system_name).plot_dispatch(min_pv, ax=ax) + ax.legend(['Load', 'PV', 'Battery', 'Generator'], loc=1, + fontsize=8) + ax.set_title('{} min PV'.format(system_name)) + + plt.tight_layout() + + def add_system(self, new_system, validate=True): + """ Add a specific system to the input list """ + + if validate: + # List of initialized parameters to validate + args_dict = {'system': new_system} + + # Validate input parameters + validate_all_parameters(args_dict) + + # Check that the name is unique + system_name = new_system.get_name() + if system_name in self.input_system_grid.keys() or system_name \ + in self.output_system_grid.keys(): + message = 'Could not add system, name not unique' + log_error(message) + raise Exception(message) + + # Add the new system to the input list + self.input_system_grid[system_name] = new_system + + def get_input_systems(self): + return self.input_system_grid + + def get_output_systems(self): + return self.output_system_grid + + def format_inputs(self, spg): + """ + Formats the inputs into dicts for writing to file. + + Parameters + ---------- + spg: SolarProfileGenerator object + + Returns + ---------- + inputs: dictionary holding input variables + assumptions: dictionary holding assumption variables + + """ + + # Store input variables + inputs = {} + inputs['Location'] = pd.DataFrame.from_dict(self.location, + orient='index') + inputs['Location'].loc['altitude'] = \ + '{}m'.format(inputs['Location'].loc['altitude'].values[0]) + inputs['Simulation Info'] = \ + pd.DataFrame.from_dict({ + '# scenarios': int(spg.num_trials), + 'scenario length': spg.length_trials, + 'scenario filters': 'None', 'scenario ranking': 'None'}, + orient='index') + if self.filter is not None: + inputs['Simulation Info'].loc['scenario filters'] = \ + ', '.join(['{}: {}'.format(constraint['parameter'], + constraint['value']) + for constraint in self.filter]) + if self.rank is not None: + inputs['Simulation Info'].loc['scenario ranking'] = \ + ', '.join([constraint['parameter'] for constraint in self.rank]) + inputs['PV System'] = \ + pd.DataFrame.from_dict({ + 'tilt': spg.tilt, 'azimuth': spg.azimuth, + 'spacing_buffer': self.pv_params['spacing_buffer'], + 'pv_tracking': self.pv_params['pv_tracking'], + 'pv_racking': self.pv_params['pv_racking']}, + orient='index') + inputs['Battery System'] = pd.DataFrame.from_dict({ + key.replace('_', ' '): val for key, val in + self.battery_params.items()}, orient='index') + inputs['Battery System'].loc['battery sizing method'] = self.batt_sizing_method + inputs['Existing Equipment'] = pd.DataFrame.from_dict( + {'PV': 'None', 'Battery': 'None', 'Generator': 'None', 'FuelTank': 'None'}, + orient='index') + if 'pv' in self.existing_components: + inputs['Existing Equipment'].loc['PV'] = \ + '{}kW'.format(self.existing_components['pv'].pv_capacity) + if 'generator' in self.existing_components: + inputs['Existing Equipment'].loc['Generator'] = \ + '{} units of {}kW'.format( + self.existing_components['generator'].num_units, + self.existing_components['generator'].rated_power) + if 'battery' in self.existing_components: + inputs['Existing Equipment'].loc['Battery'] = \ + '{}kW, {}kWh'.format( + self.existing_components['battery'].power, + self.existing_components['battery'].batt_capacity) + if 'fuel_tank' in self.existing_components: + inputs['Existing Equipment'].loc['FuelTank'] = \ + '{}gal'.format(self.existing_components['fuel_tank'].tank_size) + + # Store assumptions + assumptions = {} + assumptions['PV System'] = pd.DataFrame.from_dict( + {'albedo': spg.advanced_inputs['albedo'], + 'dc to ac ratio': spg.get_dc_to_ac(), + 'losses': spg.get_losses(), 'net-metering limits': 'None'}, + orient='index') + if self.net_metering_limits is not None: + if self.net_metering_limits['type'] == 'capacity_cap': + assumptions['PV System'].loc['net-metering limits'] = \ + '{}kW capacity cap'.format(self.net_metering_limits['value']) + elif self.net_metering_limits['type'] == 'percent_of_load': + assumptions['PV System'].loc['net-metering limits'] = \ + '{}% of annual load'.format(self.net_metering_limits['value']) + + assumptions['Cost'] = pd.DataFrame.from_dict( + {'utility rate': '${}/kWh'.format(self.electricity_rate), + 'net-metering rate': '${}/kWh'.format(self.net_metering_rate)}, + orient='index') + + assumptions['Generator'] = pd.DataFrame.from_dict( + {'sizing buffer': '{:.0f}%'.format( + self.generator_buffer*100-100)}, orient='index') + + return inputs, assumptions + + def save_results_to_file(self, spg, filename='simulation_results'): + """ + Saves inputs, assumptions and results to an excel file. + + Parameters + ---------- + spg: SolarProfileGenerator object + + filename: filename for results spreadsheet, without an + extension + + """ + + # Get dictionaries of inputs and assumptions + inputs, assumptions = self.format_inputs(spg) + + # Parse results if not already done + if self.results_grid is None: + self.parse_results() + + # Re-format column and index names + format_results = self.results_grid.copy(deep=True) + + # Re-order columns + format_results = format_results[ + ['pv_capacity', 'battery_capacity', 'battery_power', + 'generator_power_kW mean', + 'generator_power_kW most-conservative', 'fuel_tank_size_gal', + 'pv_area_ft2', 'capital_cost_usd', + 'pv_capital', 'battery_capital', 'generator_capital', + 'fuel_tank_capital', 'pv_o&m', 'battery_o&m', 'generator_o&m', + 'annual_benefits_usd', 'demand_benefits_usd', + 'simple_payback_yr', 'pv_percent mean', 'batt_percent mean', + 'gen_percent mean', 'fuel_used_gal mean', + 'fuel_used_gal most-conservative'] + list( + format_results.columns[26:])] + + # Rename columns + format_results.rename(columns= + {'pv_capacity': 'PV Capacity', + 'battery_capacity': 'Battery Capacity', + 'battery_power': 'Battery Power', + 'generator_power_kW mean': 'Generator Power (typical scenario)', + 'generator_power_kW most-conservative': + 'Generator Power (conservative scenario)', + 'fuel_tank_size_gal': 'Total Fuel Tank Capacity', + 'pv_area_ft2': 'PV Area', 'capital_cost_usd': 'Capital Cost', + 'pv_capital': 'PV Capital', 'battery_capital': 'Battery Capital', + 'generator_capital': 'Generator Capital', + 'fuel_tank_capital': 'Fuel Tank Capital', + 'pv_o&m': 'PV O&M', 'battery_o&m': 'Battery O&M', + 'generator_o&m': 'Generator O&M', + 'annual_benefits_usd': 'Annual PV Net-meter Revenue', + 'demand_benefits_usd': 'Annual PV Demand Savings', + 'simple_payback_yr': 'Simple Payback', + 'pv_percent mean': 'PV Percent', + 'batt_percent mean': 'Battery Percent', + 'gen_percent mean': 'Generator Percent', + 'fuel_used_gal mean': 'Fuel used (average scenario)', + 'fuel_used_gal most-conservative': 'Fuel used (conservative scenario)'}, + inplace=True) + + # Add units + units = ['kW', 'kWh', 'kW', 'kW', 'kW', 'gallons', 'ft^2', '$', '$', + '$', '$', '$', '$/year', '$/year', '$/year', '$/year', + '$/year', 'years', '%', '%', '%', 'gallons', 'gallons'] + for _ in self.gen_power_percent: + units += ['kW', 'gallons', 'gallons', '$', '', '', '', 'kWh', + 'kWh', 'kW', 'kW'] + format_results.loc['units'] = units + + format_results.columns = [col.replace('_', ' ').capitalize() + for col in format_results.columns] + format_results["temp"] = range(1, len(format_results) + 1) + format_results.loc['units', 'temp'] = 0 + format_results = format_results.sort_values("temp").drop('temp', + axis=1) + + # Create workbook + writer = pd.ExcelWriter('output/{}.xlsx'.format(filename), + engine='xlsxwriter') + workbook = writer.book + + # Create formatting + bold_bottomborder = workbook.add_format({'bold': True, 'bottom': True, + 'align': 'center'}) + bold = workbook.add_format({'bold': True, 'border': 0}) + index_format = workbook.add_format({'align': 'left', 'border': 0, + 'bold': False}) + dollars = workbook.add_format({'num_format': '$#,##0'}) + one_fp = workbook.add_format({'num_format': '0.0'}) + no_fp = workbook.add_format({'num_format': 0x01}) + perc = workbook.add_format({'num_format': 0x01}) + + # Determine format for each column + formats = [one_fp, one_fp, one_fp, one_fp, one_fp, one_fp, no_fp, + dollars, dollars, dollars, dollars, dollars, dollars, + dollars, dollars, dollars, dollars, one_fp, perc, perc, + perc, one_fp, one_fp] + for _ in self.gen_power_percent: + formats += [one_fp, one_fp, one_fp, dollars, no_fp, one_fp, no_fp, + no_fp, no_fp, one_fp, one_fp] + + # Write results sheet + format_results.reset_index(drop=True).to_excel(writer, + sheet_name='Results', + index=False) + results_sheet = writer.sheets['Results'] + + # Format results sheet + results_sheet.set_row(0, None, bold) + results_sheet.set_row(1, None, bold_bottomborder) + for i, formatting in enumerate(formats): + results_sheet.set_column(i, i, len(format_results.columns[i]), + formatting) + + # Write load profile sheet + if self.off_grid_load_profile is None: + lp = self.annual_load_profile.reset_index() + lp.columns = ['Datetime', 'Load (kW)'] + lp.to_excel(writer, sheet_name='Load Profile', index=False) + load_sheet = writer.sheets['Load Profile'] + load_sheet.set_column(0, 1, 25, None) + else: + lp = self.annual_load_profile.reset_index() + lp['off-grid'] = self.off_grid_load_profile.values + lp.columns = ['Datetime', 'Annual Load (kW)', 'Off-Grid Load (kW)'] + lp.to_excel(writer, sheet_name='Load Profile', index=False) + load_sheet = writer.sheets['Load Profile'] + load_sheet.set_column(0, 1, 25, None) + load_sheet.set_column(0, 2, 25, None) + + # Write TMY solar sheet + if self.output_tmy: + sp = self.tmy_solar.reset_index() + sp.columns = ['Datetime', 'PV Power for a 1kW array (kW)'] + sp['Datetime'] = self.annual_load_profile.index + sp.to_excel(writer, sheet_name='TMY PV Profile', index=False) + load_sheet = writer.sheets['TMY PV Profile'] + load_sheet.set_column(0, 1, 25, None) + + # Write inputs sheet + # Location variables + inputs['Location'].reset_index().to_excel(writer, + sheet_name='Input Variables', + index=False) + inputs_sheet = writer.sheets['Input Variables'] + inputs_sheet.write(0, 0, 'Location', bold_bottomborder) + inputs_sheet.write(0, 1, '', bold_bottomborder) + + # Simulation variables + inputs['Simulation Info'].reset_index().to_excel( + writer, sheet_name='Input Variables', startrow=6, index=False) + inputs_sheet.write(6, 0, 'Simulation Info', bold_bottomborder) + inputs_sheet.write(6, 1, '', bold_bottomborder) + + # PV variables + inputs['PV System'].reset_index().to_excel( + writer, sheet_name='Input Variables', startrow=12, index=False) + inputs_sheet.write(12, 0, 'PV System', bold_bottomborder) + inputs_sheet.write(12, 1, '', bold_bottomborder) + + # Battery variables + inputs['Battery System'].reset_index().to_excel( + writer, sheet_name='Input Variables', startrow=19, index=False) + inputs_sheet.write(19, 0, 'Battery System', bold_bottomborder) + inputs_sheet.write(19, 1, '', bold_bottomborder) + + # Existing equipment variables + inputs['Existing Equipment'].reset_index().to_excel( + writer, sheet_name='Input Variables', startrow=28, index=False) + inputs_sheet.write(28, 0, 'Existing Equipment', bold_bottomborder) + inputs_sheet.write(28, 1, '', bold_bottomborder) + inputs_sheet.set_column(0, 1, 30, index_format) + + # Write assumptions sheet + assumptions['PV System'].reset_index().to_excel( + writer, sheet_name='Assumptions', index=False) + assumptions_sheet = writer.sheets['Assumptions'] + assumptions_sheet.write(0, 0, 'PV System', bold_bottomborder) + assumptions_sheet.write(0, 1, '', bold_bottomborder) + assumptions['Cost'].reset_index().to_excel( + writer, sheet_name='Assumptions', startrow=7, index=False) + assumptions_sheet.write(7, 0, 'Costs', bold_bottomborder) + assumptions_sheet.write(7, 1, '', bold_bottomborder) + assumptions['Generator'].reset_index().to_excel( + writer, sheet_name='Assumptions', startrow=12, index=False) + assumptions_sheet.write(12, 0, 'Generator', bold_bottomborder) + assumptions_sheet.write(12, 1, '', bold_bottomborder) + assumptions_sheet.set_column(0, 1, 30, index_format) + + writer.save() + + def plot_compare_metrics(self, x_var='simple_payback_yr', + y_var='capital_cost_usd', cmap='BuGn_r'): + """ + Compares different systems by plotting metrics against each + other. Default x and y parameters are payback and total + capital cost, respectively. + + """ + + # Parse results if not already done + if self.results_grid is None: + self.parse_results() + + # Check that vars exist in results grid + if x_var not in self.results_grid.columns or y_var not in self.results_grid.columns: + return('ERROR: {} or {} are not valid output metrics, please ' + 'choose one of the following options: {}'.format( + x_var, y_var, ', '.join(self.results_grid.columns.values))) + + # Make pv and batt sizes categorical, so exact sizes are shown + # in the legend + results_mod = self.results_grid.copy() + results_mod['pv_capacity'] = results_mod['pv_capacity'].apply( + lambda x: str(int(x))+'kW') + pv_order = [str(elem2)+'kW' for elem2 in np.flipud( + np.sort([int(elem[:-2]) for elem in results_mod['pv_capacity'].unique()]))] + results_mod['battery_power'] = results_mod['battery_power'].apply( + lambda x: str(int(x))+'kW') + batt_order = [str(elem2)+'kW' for elem2 in np.flipud( + np.sort([int(elem[:-2]) for elem in results_mod['battery_power'].unique()]))] + + # Create plot + fig, ax = plt.subplots(figsize=[8, 6], subplot_kw={'position': (0.1, 0.1, 0.6, 0.75)}) + sns.scatterplot(data=results_mod, x=x_var, y=y_var, + size='battery_power', hue='pv_capacity', palette=cmap, + size_order=batt_order, hue_order=pv_order, ax=ax, + edgecolor='#0c2c84') + + # Adjust legend params + plt.gca().legend(loc=7, fontsize=10, bbox_to_anchor=(1.4, 0.5), scatterpoints=1) + + # Add title + plt.title('Comparison of {} and \n{} across system sizes'. + format(x_var, y_var), position=(0.5, 1.05)) + + def plot_compare_sizes(self, var='simple_payback_yr', cmap='BuGn_r'): + """ + Compares different systems by plotting sizes against each other + with color determined by an output metric. + Default metric is payback. + + """ + + # Parse results if not already done + if self.results_grid is None: + self.parse_results() + + # Check that var exists in results grid + if var not in self.results_grid.columns: + return ('ERROR: {} is not a valid output metric, please choose ' + 'one of the following options: {}' + .format(var, ', '.join(self.results_grid.columns.values))) + + # Convert to heatmap data structure + fig, ax = plt.subplots(figsize=[8, 6], subplot_kw={'position': (0.1, 0.1, 0.6, 0.75)}) + results_heatmap = self.results_grid.pivot( + index='pv_capacity', columns='battery_power', values=var) + results_heatmap.sort_index(ascending=False, inplace=True) + sns.heatmap(results_heatmap, cmap=cmap, annot=True, fmt='.1f', + ax=ax, cbar_kws={'label': var}, + xticklabels=results_heatmap.columns.values.round(1), + yticklabels=results_heatmap.index.values.round(1)) + plt.title('Comparison of {} across system sizes'.format(var)) + + +def get_electricity_rate(location, validate=True): + """ + Get the state-averaged electricity rate based on a location with the + format {'latitude': , 'longitude': } + + """ + + if validate: + # List of initialized parameters to validate + args_dict = {'location': location} + + # Validate input parameters + validate_all_parameters(args_dict) + + # Pull the state rates from EIA + try: + rates = pd.read_html('https://www.eia.gov/electricity/state/', flavor='html5lib')[0] + assert len({'Name', 'Average retail price (cents/kWh)', + 'Net summer capacity (MW)', 'Net generation (MWh)', + 'Total retail sales (MWh)'} - set(rates.columns)) == 0 + except (ValueError, AssertionError, urllib.error.URLError): + message = 'Warning: Could not load rates from EIA, using saved ' \ + 'rates August 2018.' + log_error(message) + print(message) + rates = pd.read_csv(os.path.join('data', 'electricity_rates_08.2018.csv')) + + try: + # Get the state via reverse geocoding + locator = Nominatim(user_agent='mcor') + loc = locator.reverse(f"{location['latitude']}, {location['longitude']}") + state = loc.address.split(', ')[-3] + + # Return the electricity rate for that state in $/kWh + return rates.set_index('Name').loc[ + state, 'Average retail price (cents/kWh)'] / 100 + + except Exception as e: + # If there is an error, return the median electricity rate + print('Reverse Geocoding fail, using median U.S. electricity rate') + return rates['Average retail price (cents/kWh)'].median() / 100 + + +if __name__ == "__main__": + # Used for testing + multiprocessing.freeze_support() + + # Load in costs + system_costs = pd.read_excel('data/MCOR Prices.xlsx', sheet_name=None, index_col=0) + + # Set up solar profiles + latitude = 46.34 + longitude = -119.28 + timezone = 'US/Pacific' + spg = SolarProfileGenerator(latitude, longitude, timezone, 265.176, 20, -180, + 200., 14., validate=False) + spg.get_power_profiles() + spg.get_night_duration(percent_at_night=0.1, validate=False) + module_params = spg.get_pv_params() + tmy_solar = spg.tmy_power_profile + + # Set up load profile + annual_load_profile = pd.read_csv(os.path.join('data', 'sample_load_profile.csv'), + index_col=0)['Load'] + + # Set up parameter dictionaries + location = {'longitude': longitude, 'latitude': latitude, + 'timezone': timezone, 'altitude': 0} + pv_params = {'tilt': 20, 'azimuth': -180, + 'module_capacity': module_params['capacity'], + 'module_area': module_params['area_in2'], + 'spacing_buffer': 2, + 'pv_racking': 'ground', + 'pv_tracking': 'fixed', + 'advanced_inputs': {}} + battery_params = {'battery_power_to_energy': 0.25, 'initial_soc': 1, + 'one_way_battery_efficiency': 0.9, + 'one_way_inverter_efficiency': 0.95, + 'soc_upper_limit': 1, 'soc_lower_limit': 0.1} + + # Create optimization object + optim = GridSearchOptimizer(spg.power_profiles, spg.temp_profiles, + spg.night_profiles, annual_load_profile, + location, tmy_solar, pv_params, battery_params, + system_costs, electricity_rate=None, + net_metering_limits=None, generator_buffer=1.1, + existing_components={}, output_tmy=False, + validate=True) + + # Create a grid of systems + optim.define_grid() + + # Get load profiles for the corresponding solar profile periods + optim.get_load_profiles() + optim.run_sims() + + # Filter and rank + optim.parse_results() + ranking_criteria = [{'parameter': 'simple_payback_yr', + 'order_type': 'ascending'}] + optim.rank_results(ranking_criteria) diff --git a/microgrid_simulator.py b/microgrid_simulator.py new file mode 100644 index 0000000..c667a66 --- /dev/null +++ b/microgrid_simulator.py @@ -0,0 +1,603 @@ +# -*- coding: utf-8 -*- +""" + +Microgrid simulator class. Includes the core of the system dispatch algorithm. + +File Contents: + Classes: + Simulator + PVBattGenSimulator (inherits from Simulator) + + Standalone functions: + calculate_load_duration + +""" + +import numpy as np +import pandas as pd +from copy import deepcopy + +from microgrid_system import Generator +from microgrid_system import PV, SimpleLiIonBattery, SimpleMicrogridSystem +from generate_solar_profile import SolarProfileGenerator +from validation import validate_all_parameters, log_error + +# Suppress pandas warnings +pd.options.mode.chained_assignment = None + + +class Simulator: + """ + Runs the core algorithm of the simulation given one system + configuration and one solar/temp/load profile. + + Parameters + ---------- + + load_profile: Pandas series with the load profile for a given + simulation period + + system: MicrogridSystem object + + location: dictionary with the following keys and value + datatypes: + {'longitude': float, 'latitude': float, 'timezone': string, + 'altitude': float} + + name: unique simulator name + + Methods + ---------- + + get_name: return simulator name + + """ + + def __init__(self, name, load_profile, system, location, validate=True): + self.name = name + self.load_profile = load_profile + self.system = system + self.location = location + + # Validate input parameters + if validate: + args_dict = {'load_profile': load_profile, 'system': system, + 'location': location} + validate_all_parameters(args_dict) + + def get_name(self): + return self.name + + +class PVBattGenSimulator(Simulator): + """ + Simulates a system with PV, a battery and a backup generator + + Parameters + ---------- + + name: unique simulator name + + base_power_profile: Pandas series with a PV power profile for a 1kW system + + temp_profile: Pandas dataframe with temperature profile + + load_profile: Pandas series with the load profile for a given simulation period + + night_profile: Pandas dataframe with info on whether it is night + + system: MicrogridSystem object + + location: dictionary with the following keys and value + datatypes: + {'longitude': float, 'latitude': float, 'timezone': string, + 'altitude': float} + + duration: Timestep duration in seconds + + dispatch_strategy: determines the battery dispatch strategy. + Options include: + night_const_batt (constant discharge at night) + night_dynamic_batt (updates the discharge rate based on remaining available + capacity) + + generator_buffer: Buffer between chosen generator size and + maximum required power. E.g. a buffer of 1.1 means a + generator has to be sized 10% larger than the maximum power. + Default = 1.1 + + Methods + ---------- + + scale_power_profile: Scale power profile by capacity of PV system + + calc_dispatch: Runs the battery dispatch algorithm + + calc_timestep_dispatch: Calculates battery dispatch for an individual timestep + + size_single_generator: Size the generator(s) based on load not met by PV and + batteries, given several different generator models + + calc_existing_generator_dispatch: Determines how an existing generator meets the load + and consumes fuel. An additional generator may be added if the existing one cannot + meet the load at all timesteps. + + get_load_breakdown: Returns load_breakdown attribute + + get_storage_recovery_percent: Returns storage_recovery_percent attribute + + get_fuel_used: Returns fuel_used_gal attribute + + get_generator_power: Returns generator_power_kW attribute + + get_load_duration_df: Returns load_duration+df attribute + + Calculated Attributes + ---------- + + scaled_power_profile: PV power profile scaled to PV system capacity + + soc_at_initial_hour_of_night: Tracks the SOC at the beginning of each night to + determine the nightly battery discharge rate + + load_breakdown: The fraction of load met by each component + + storage_recovery_percent: The percentage of unused PV that is recovered by the battery + + fuel_used_gal: The total fuel used by the generator(s) in gallons + + generator_power_kW: The rated power of the chosen generator used to calculate fuel + consumption + + generator_obj: The generator object for the chosen generator + + dispatch_df: Pandas dataframe containing dispatch info for each timestep. + Includes the columns: + ['load', 'pv_power', 'battery_soc', 'delta_battery_power', + 'excess_PV', 'load_not_met'] + + load_duration_df: Pandas dataframe containing load duration curve, with columns: + [load_bin, num_hours, num_hours_at_or_below] + + """ + + def __init__(self, name, base_power_profile, temp_profile, load_profile, night_profile, + system, location, duration, dispatch_strategy, generator_buffer=1.1, + validate=True): + self.name = name + self.base_power_profile = base_power_profile + self.scaled_power_profile = None + self.temp_profile = temp_profile + self.load_profile = load_profile + self.night_profile = night_profile + self.system = system + self.location = location + self.duration = duration + self.dispatch_strategy = dispatch_strategy + self.soc_at_initial_hour_of_night = 0 + self.generator_buffer = generator_buffer + self.load_breakdown = {} + self.storage_recovery_percent = None + self.fuel_used_gal = None + self.generator_power_kW = None + self.generator_obj = None + self.dispatch_df = None + self.load_duration_df = None + self.night_hours_left = 0 + + # Validate input parameters + if validate: + args_dict = {'base_power_profile': base_power_profile, + 'temp_profile': temp_profile, + 'load_profile': load_profile, + 'night_profile': night_profile, 'system': system, + 'location': location, 'duration': duration, + 'generator_buffer': generator_buffer, + 'dispatch_strategy': dispatch_strategy} + validate_all_parameters(args_dict) + + # Check that all profiles have the same index (although the year will differ) + if ((self.base_power_profile.index[0].month, + self.base_power_profile.index[0].day, + self.base_power_profile.index[0].hour) != + (self.load_profile.index[0].month, + self.load_profile.index[0].day, + self.load_profile.index[0].hour)) or \ + ((self.base_power_profile.index[0].month, + self.base_power_profile.index[0].day, + self.base_power_profile.index[0].hour) != + (self.temp_profile.index[0].month, + self.temp_profile.index[0].day, + self.temp_profile.index[0].hour)) or \ + ((self.base_power_profile.index[0].month, + self.base_power_profile.index[0].day, + self.base_power_profile.index[0].hour) != + (self.night_profile.index[0].month, + self.night_profile.index[0].day, + self.night_profile.index[0].hour)): + + message = 'The pv power, load, temperature, and night ' \ + 'profiles must all have the same index.' + log_error(message) + raise Exception(message) + + def scale_power_profile(self): + """ Scale power profile by capacity of PV system """ + + self.scaled_power_profile = self.base_power_profile \ + * self.system.components['pv'].pv_capacity + + def calc_dispatch(self): + """ + Runs dispatch algorithm + + The dataframe dispatch_df holds the information on the system for each timestep, with + the following columns: + - load: load in kW + - pv_power: AC power produced by PV minus efficiency and inverter losses + - battery_soc: the battery state of charge at the end of the timestep (as a + fraction) + - delta_battery_power: the amount of power charged or discharged from the battery + minus efficiency and inverter losses + + """ + + # Create dataframe to hold dispatch info for each timestep + self.dispatch_df = pd.DataFrame(index=self.scaled_power_profile.index, + columns=['load', 'pv_power']) + self.dispatch_df['load'] = self.load_profile.values + self.dispatch_df['pv_power'] = self.scaled_power_profile + self.dispatch_df = pd.concat([self.dispatch_df, self.night_profile], axis=1) + + # Calculate battery SOC and power change at each timestep + battery_state_df = pd.DataFrame(list(self.dispatch_df.apply( + lambda x: self.calc_timestep_dispatch( + x['load'], x['pv_power'], None, self.duration, x['is_night'], + x['is_first_hour_of_night'], x['night_duration']), + axis=1).values), columns=['battery_soc', 'delta_battery_power'], + index=self.dispatch_df.index) + self.dispatch_df = pd.concat([self.dispatch_df, battery_state_df], axis=1) + + # Calculate battery change in power, soc, and excess PV at each timestep + self.dispatch_df['delta_battery_power'] = \ + self.dispatch_df['delta_battery_power'].astype('float') + self.dispatch_df['battery_soc'] = \ + self.dispatch_df['battery_soc'].astype('float') + self.dispatch_df['excess_PV'] = \ + self.dispatch_df['pv_power'] - self.dispatch_df['load'] + + # Calculate load not met + self.dispatch_df['load_not_met'] = \ + self.dispatch_df['load'] - self.dispatch_df['pv_power'] \ + - self.dispatch_df['delta_battery_power'] + self.dispatch_df.loc[self.dispatch_df['load_not_met'] < 0, 'load_not_met'] = 0 + + # Calculate load breakdown by each component + self.load_breakdown['pv'] = 1 + self.dispatch_df.loc[ + self.dispatch_df['excess_PV'] <= 0, 'excess_PV'].sum() \ + / self.dispatch_df['load'].sum() + self.load_breakdown['battery'] = self.dispatch_df.loc[ + self.dispatch_df['delta_battery_power'] >= 0, + 'delta_battery_power'].sum() / self.dispatch_df['load'].sum() + self.load_breakdown['generator'] = \ + self.dispatch_df['load_not_met'].sum() / self.dispatch_df['load'].sum() + + # Calculate ES recovery percent + # If there is no PV, this will cause a RuntimeWarning, so set to 0 (try/except won't + # catch Warnings) + if len(self.dispatch_df.loc[self.dispatch_df['excess_PV'] > 0]): + self.storage_recovery_percent = \ + np.abs(self.dispatch_df.loc[ + self.dispatch_df['delta_battery_power'] < 0, + 'delta_battery_power'].sum() + / self.dispatch_df.loc[ + self.dispatch_df['excess_PV'] > 0, + 'excess_PV'].sum() * 100) + else: + self.storage_recovery_percent = 0 + + def calc_timestep_dispatch(self, load, pv_power, temperature, duration, + is_night, is_first_hour_of_night, + night_duration): + """ Calculates dispatch for an individual timestep. """ + + # Get current battery state + initial_soc, voltage, cycles, time_since_last_used = \ + self.system.components['battery'].get_state() + + # Get net load after PV applied + net_load = load - pv_power + + # If first hour of night, update soc_at_initial_hour_of_night + if is_first_hour_of_night: + self.soc_at_initial_hour_of_night = deepcopy(initial_soc) + self.night_hours_left = night_duration + elif self.night_hours_left > 0: + self.night_hours_left -= 1 + elif is_night and self.night_hours_left <= 0: + message = 'Night-time with no hours left at night: ' \ + 'night_hours_left {}.'.format(self.night_hours_left) + log_error(message) + raise Exception(message) + + # Call battery update model + delta_power = self.system.components['battery'].update_state( + net_load, duration, temperature, is_night, night_duration, + self.night_hours_left, self.soc_at_initial_hour_of_night, + self.dispatch_strategy) + + # Check for errors + if delta_power is None: + print("Error message: net load: {}, initial soc: {}" + "".format(net_load, initial_soc)) + + # Return initial SOC and power charged or discharged + return initial_soc, delta_power + + def size_single_generator(self, generator_options, validate=True): + """ + Size the generator(s) based on load not met by PV and batteries + and several different generator models. + + """ + + # Validate input parameters + if validate: + args_dict = {'generator_costs': generator_options} + validate_all_parameters(args_dict) + + # Calculate generator usage and fuel required to meet load not met + # Total rated power (including multiple units together) based on max unmet power + max_power = self.dispatch_df['load_not_met'].max() + + # Find the smallest generator(s) with sufficient rated power, assumes generators are + # sorted from smallest to largest. If no single generator is large enough, try + # multiple gensets of the same size + gen = None + num_gen = 1 + while gen is None: + # Find an appropriately sized generator + best_gen = generator_options[generator_options.index + * num_gen >= max_power + * self.generator_buffer] + + # If no single generator is large enough, increase the number of generators + if not len(best_gen): + num_gen += 1 + else: + # Create generator object + best_gen = best_gen.iloc[0] + self.generator_power_kW = best_gen.name*num_gen + gen = Generator(existing=False, + rated_power=best_gen.name, + num_units=num_gen, + fuel_curve_model=best_gen[ + ['1/4 Load (gal/hr)', '1/2 Load (gal/hr)', + '3/4 Load (gal/hr)', 'Full Load (gal/hr)']].to_dict(), + capital_cost=best_gen['Cost (USD)'], + validate=False) + self.generator_obj = gen + + # Calculate the load duration and total fuel used + grouped_load, self.fuel_used_gal = gen.calculate_fuel_consumption( + self.dispatch_df[['load_not_met']], self.duration, validate=False) + self.load_duration_df = calculate_load_duration(grouped_load, validate=False) + + def calc_existing_generator_dispatch(self, generator_options, + add_additional_generator=True, + validate=True): + """ + If there is an existing generator, determine how it meets the load and consumes fuel. + + If add_additional_generator is set to True, an additional generator may be dispatched + to meet any unmet load, and the load duration curve for that additional generator + is returned along with the total fuel used. If it is set to False an empty load + duration curve is returned. + + Note: this function is currently not used + + """ + + # Validate input parameters + if validate: + args_dict = {'generator_options': generator_options, + 'add_additional_generator': add_additional_generator} + validate_all_parameters(args_dict) + + # Get info from existing generator + gen = self.system.components['generator'] + self.generator_power_kW = gen.rated_power + + # Create a temporary dataframe to hold load not met cropped at the existing generator + # rated power to calculate fuel used by existing generator + temp_df = self.dispatch_df.copy() + temp_df.loc[temp_df['load_not_met'] > gen.rated_power, + 'load_not_met'] = gen.rated_power + grouped_load, existing_gen_fuel_used = gen.calculate_fuel_consumption( + temp_df[['load_not_met']], self.duration, validate=False) + temp_load_duration_curve = calculate_load_duration(grouped_load, validate=False) + + # Determine if unmet load can be supplied by existing generator + self.dispatch_df['load_not_met_by_generator'] = \ + self.dispatch_df['load_not_met'] - gen.rated_power + self.dispatch_df.loc[self.dispatch_df['load_not_met_by_generator'] < 0, + 'load_not_met_by_generator'] = 0 + + # If the load cannot be fully met by the existing generator + if self.dispatch_df['load_not_met_by_generator'].sum() > 0: + + # If another generator can be added + if add_additional_generator: + + # Total rated power (including multiple units together) based on max unmet + # power + max_power = self.dispatch_df['load_not_met_by_generator'].max() + + # Find the smallest generator with sufficent rated power, assumes generators + # are sorted from smallest to largest + addl_gen = None + num_gen = 1 + while addl_gen is None: + # Find an appropriately sized generator + best_gen = generator_options[generator_options.index + * num_gen >= max_power + * self.generator_buffer] + + # If no single generator is large enough, increase the number of + # generators + if not len(best_gen): + num_gen += 1 + else: + # Create generator object + best_gen = best_gen.iloc[0] + self.generator_power_kW += best_gen.name*num_gen + addl_gen = Generator( + existing=False, rated_power=best_gen.name, + num_units=num_gen, + fuel_curve_model=best_gen[ + ['1/4 Load (gal/hr)', '1/2 Load (gal/hr)', + '3/4 Load (gal/hr)', 'Full Load (gal/hr)']].to_dict(), + capital_cost=best_gen['Cost (USD)'], + validate=False) + self.generator_obj = addl_gen + + # Calculate the load duration curve and fuel consumption for the additional + # generator + grouped_load, addl_fuel_used = \ + addl_gen.calculate_fuel_consumption( + self.dispatch_df[['load_not_met_by_generator']], + self.duration, validate=False) + self.load_duration_df = calculate_load_duration(grouped_load, + validate=False) + self.fuel_used_gal = existing_gen_fuel_used + addl_fuel_used + + # If another generator cannot be dispatched + else: + self.load_duration_df = pd.DataFrame( + 0, index=temp_load_duration_curve.index, + columns=temp_load_duration_curve.columns) + self.fuel_used_gal = existing_gen_fuel_used + + # If the existing generator can meet load, use empty load duration curve and existing + # fuel used + else: + self.load_duration_df = pd.DataFrame( + 0, index=temp_load_duration_curve.index, + columns=temp_load_duration_curve.columns) + self.fuel_used_gal = existing_gen_fuel_used + + def get_load_breakdown(self): + return self.load_breakdown + + def get_storage_recovery_percent(self): + return self.storage_recovery_percent + + def get_fuel_used(self): + return self.fuel_used_gal + + def get_generator_power(self): + return self.generator_power_kW + + def get_load_duration_df(self): + return self.load_duration_df + + +def calculate_load_duration(grouped_load, validate=True): + """ + Create a load duration curve for a single generator. + + Inputs: + grouped_load: dataframe with columns [binned_load, num_hours] + + Outputs: + load_duration_df: dataframe with columns + [load_bin, num_hours, num_hours_at_or_below, + num_hours_above, energy_not_met_at_load_level, + energy_not_met_above_load_level, max_power_not_met] + + """ + + # Validate input parameters + if validate: + args_dict = {'grouped_load': grouped_load} + validate_all_parameters(args_dict) + + # Set index as binned_load and fill in missing bins + grouped_load = grouped_load.set_index('binned_load')[['num_hours']] + grouped_load = grouped_load.merge(pd.DataFrame( + index=range(0, grouped_load.index[-1]+1)), left_index=True, + right_index=True, how='right').fillna(0) + + # Calculate cumulative hours at or below each load bin + grouped_load['num_hours_at_or_below'] = grouped_load['num_hours'].cumsum() + + # Calculate cumulative hours above each load bin (hours not met) + grouped_load['num_hours_above'] = \ + grouped_load['num_hours_at_or_below'].max() \ + - grouped_load['num_hours_at_or_below'] + + # Calculate energy not met at each load bin + grouped_load['energy_not_met_at_load_level'] = grouped_load.index \ + * grouped_load['num_hours'] + grouped_load['energy_not_met_above_load_level'] = \ + grouped_load['energy_not_met_at_load_level'].sum() \ + - grouped_load['energy_not_met_at_load_level'].cumsum() \ + - grouped_load.index * grouped_load['num_hours_above'] + + # Calculate the maximum power not met at each load bin + grouped_load['max_power_not_met'] = -grouped_load.index + grouped_load.index[-1] + + # Divide by the load bin to get max % not met (compared to load bin) + grouped_load['max_percent_not_met'] = grouped_load['max_power_not_met'] \ + / grouped_load.index * 100 + + return grouped_load + + +if __name__ == "__main__": + # Used for testing + # Get solar and power profiles + # System level + import os + spg = SolarProfileGenerator(46.34, -119.28, 'US/Pacific', 0, 0, 0, 200, 14, + validate=False) + spg.get_power_profiles() + spg.get_night_duration(percent_at_night=0.2, validate=False) + + # Sample generator options + generator_options = pd.read_excel('data/MCOR Prices.xlsx', sheet_name='generator_costs', + index_col=0) + + # Create a sample system + batt = SimpleLiIonBattery(False, 2000, 6000, validate=False) + pv = PV(False, 4000, 0, 0, 0.360, 3, 2, validate=False, pv_tracking=False, + pv_racking='ground') + gen = Generator(True, 50, 1, {'1/4 Load (gal/hr)': 1.8, '1/2 Load (gal/hr)': 2.9, + '3/4 Load (gal/hr)': 3.8, 'Full Load (gal/hr)': 4.8}, + 5000, validate=False) + system = SimpleMicrogridSystem('pv_359.4_batt_0kW_0kWh') + system.add_component(batt, validate=False) + system.add_component(pv, validate=False) + system.add_component(gen, validate=False) + + # Create a simulation object + load_profile = pd.read_csv(os.path.join('data', 'sample_load_profile.csv'), + index_col=0)['Load'] + load_profile.index = pd.date_range(start='1/1/2017', end='1/1/2018', + freq='{}S'.format(int(3600)))[:-1] + load_profile = load_profile.iloc[4951:4951+336] + sim = PVBattGenSimulator('pv_359.4_batt_0kW_0kWh_profile_0', + spg.power_profiles[95], + spg.temp_profiles[95], load_profile, + spg.night_profiles[95], system, + {'longitude': -119.28, 'latitude': 46.34, + 'timezone': 'US/Pacific', 'altitude': 0}, 3600, + 'night_const_batt', validate=False) + + # Run the simulation + sim.scale_power_profile() + sim.calc_dispatch() + sim.size_single_generator(generator_options, validate=False) + + # Plot dispatch + sim.dispatch_df[['load', 'pv_power', 'delta_battery_power', 'load_not_met']].plot() diff --git a/microgrid_system.py b/microgrid_system.py new file mode 100644 index 0000000..3343417 --- /dev/null +++ b/microgrid_system.py @@ -0,0 +1,1356 @@ +# -*- coding: utf-8 -*- +""" + +Class structure for microgrid system and its components. + +File contents: + Classes: + Component + PV (inherits from Component) + Battery (inherits from Component) + SimpleLiIonBattery (inherits from Battery) + Generator (inherits from Component) + FuelTank (inherits from Component) + MicrogridSystem + SimpleMicrogridSystem (inherits from MicrogridSystem) + +""" +from copy import deepcopy +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import warnings + +from validation import validate_all_parameters, log_error + + +# Microgrid components +class Component: + """ + Component class: solar module, battery, generator, fuel_tank + + Parameters + ---------- + + category: type of equipment, choices are: + ['pv', 'battery', 'generator', 'fuel_tank'] + + existing: whether or not the component already exists on site + + Methods + ---------- + + get_category: returns the category + + calc_capital_cost(): calculate capital costs + + calc_om_cost(): calculate om costs + + + """ + category = None + existing = False + capital_cost = None + om_cost = None + + def __repr__(self): + pass + + def get_category(self): + return self.category + + def calc_capital_cost(self, *args): + pass + + def calc_om_cost(self, *args): + pass + + +class PV(Component): + """ + PV system + + Parameters + ---------- + + category: equipment type, set to 'pv' + + existing: whether or not the component already exists on site + + pv_capacity: total_capacity in kW + + tilt: panel tilt in degrees + + azimuth: panel azimuth in degrees + + module_area: individual module_area in in^2 + + module_capacity: individual module_capacity in kW + + spacing_buffer: buffer to account for panel spacing in area calculation + + pv_racking: mount type of panels + (options: [roof, ground, carport]) + + pv_tracking: type of tracking + (options: [fixed, single_axis]) + + advanced_inputs: panel advanced inputs (currently does nothing) + + Methods + ---------- + + calc_area: calculates the total area of the array in ft^2 + + get_capacity: returns the total array capacity in kW + + calc_capital_cost: calculate capital costs + + calc_om_cost: calculate om costs + + """ + + def __init__(self, existing, pv_capacity, tilt, azimuth, module_capacity, module_area, + spacing_buffer, pv_tracking, pv_racking, advanced_inputs={}, validate=True): + # Assign parameters + self.category = 'pv' + self.existing = existing + self.pv_capacity = pv_capacity # in kW + self.tilt = tilt + self.azimuth = azimuth + self.module_area = module_area # in in^2 + self.module_capacity = module_capacity # in kW + self.spacing_buffer = spacing_buffer + self.pv_tracking = pv_tracking + self.pv_racking = pv_racking + self.advanced_inputs = advanced_inputs + + if validate: + # List of initialized parameters to validate + args_dict = {'existing': existing, 'pv_capacity': pv_capacity, + 'tilt': tilt, 'azimuth': azimuth, + 'module_capacity': module_capacity, + 'module_area_in2': module_area, + 'spacing_buffer': spacing_buffer, + 'pv_tracking': pv_tracking, 'pv_racking': pv_racking} + + # Validate input parameters + validate_all_parameters(args_dict) + + def __repr__(self): + return 'PV: Capacity: {:.1f}kW'.format(self.pv_capacity) + + def calc_area(self): + """ Calculates the area of the array """ + + # Calculate total array area in ft^2 + return self.pv_capacity / self.module_capacity * \ + self.module_area / 144 * self.spacing_buffer + + def get_capacity(self): + return self.pv_capacity + + def calc_capital_cost(self, cost_df, existing_components): + """ Calculates cost of PV array """ + + # Adjust total pv_capacity to consider existing pv + if 'pv' in existing_components.keys(): + adjusted_pv_capacity = max( + self.pv_capacity - existing_components['pv'].pv_capacity, 0) + else: + adjusted_pv_capacity = self.pv_capacity + + # Set cost to largest size in case pv exceeds max size on cost doc + pv_cost_per_w = cost_df.loc[(self.pv_racking + ';' + self.pv_tracking)].iloc[-1] + for col in cost_df: + if adjusted_pv_capacity <= col: + pv_cost_per_w = cost_df.loc[(self.pv_racking + ';' + self.pv_tracking), col] + break + + # If utility-scale roof or carport-mount, print user warning + if self.pv_capacity > 5000 and self.pv_racking in ['roof', 'carport']: + print(f'WARNING: A pv capacity of {self.pv_capacity}kW does not make sense with ' + f'{self.pv_racking} racking. It is recommended that you select ' + f'ground-mounted racking.') + + return adjusted_pv_capacity * 1000 * pv_cost_per_w + + def calc_om_cost(self, cost_df, existing_components): + """ Calculates O&M costs of a PV array """ + + # Adjust total pv_capacity to consider existing pv + if 'pv' in existing_components.keys(): + adjusted_pv_capacity = max( + self.pv_capacity - existing_components['pv'].pv_capacity, 0) + else: + adjusted_pv_capacity = self.pv_capacity + + pv_om_cost_per_kw = cost_df['PV_{};{}'.format( + self.pv_racking, self.pv_tracking)].values[1] + return adjusted_pv_capacity * pv_om_cost_per_kw + + +class Battery(Component): + """ + General battery class + + Parameters + ---------- + + category: equipment type, set to 'battery' + + existing: whether or not the component already exists on site + + power: battery power in kW + + batt_capacity: battery capacity in kW + + initial_soc: initial state of charge + default = 1 + + one_way_battery_efficiency: one way battery efficiency + default = 0.9 + + one_way_inverter_efficiency: one way inverter efficiency + default = 0.95 + + soc_upper_limit: state of charge upper limit + default = 1 + + soc_lower_limit: state of charge lower limit + default = 0.1 + + Methods + ---------- + + reset_state: resets the state of the battery to initial conditions + + update_state: only defined in child classes + + get_state: returns current state of charge, voltage, number of cycles, and time since + last used + + get_capacity_power: returns the battery capacity and power + + calc_capital_cost: calculate capital costs + + calc_om_cost: calculate om costs + + Calculated Attributes + ---------- + + soc: battery state of charge, initially set as initial_soc + + cycles: number of used cycles, initially set at 0, currently unused + + voltage: battery voltage, currently unused + + time_since_last_used: amount of time elapsed since the battery was last charged or + discharged, currently unused + + """ + + def __init__(self, existing, power, batt_capacity, initial_soc=1, + one_way_battery_efficiency=0.9, + one_way_inverter_efficiency=0.95, soc_upper_limit=1, + soc_lower_limit=0.1, validate=True): + # Battery parameters + self.category = 'battery' + self.existing = existing + self.power = power # kW + self.batt_capacity = batt_capacity # kWh + self.initial_soc = initial_soc + self.one_way_battery_efficiency = one_way_battery_efficiency + self.one_way_inverter_efficiency = one_way_inverter_efficiency + self.soc_upper_limit = soc_upper_limit + self.soc_lower_limit = soc_lower_limit + + # Initialize battery state + self.soc = deepcopy(self.initial_soc) + self.cycles = 0 + self.voltage = 0 + self.time_since_last_used = 0 + + if validate: + # List of initialized parameters to validate + args_dict = { + 'existing': existing, 'power': power, + 'batt_capacity': batt_capacity, 'initial_soc': initial_soc, + 'one_way_battery_efficiency': one_way_battery_efficiency, + 'one_way_inverter_efficiency': one_way_inverter_efficiency, + 'soc_upper_limit': soc_upper_limit, + 'soc_lower_limit': soc_lower_limit + } + + # Validate input parameters + validate_all_parameters(args_dict) + + def __repr__(self): + return 'Battery: Capacity: {:.1f}kWh, Power: {:.1f}kW'.format( + self.batt_capacity, self.power) + + def reset_state(self): + """ Resets the state to initial conditions. This is useful since many simulations + share one system (and therefore battery) object. + """ + # Initialize battery state + self.soc = deepcopy(self.initial_soc) + self.cycles = 0 + self.voltage = 0 + self.time_since_last_used = 0 + + def update_state(self, *args): + pass + + def get_state(self): + return self.soc, self.voltage, self.cycles, self.time_since_last_used + + def get_capacity_power(self): + return self.batt_capacity, self.power + + def calc_capital_cost(self, cost_df, *args): + """ Calculate battery system capital costs """ + + # Parse battery and inverter capital costs from costs dataframe + batt_cost_per_wh = cost_df['Battery System'].values[1] + inverter_cost_per_w = cost_df['Inverter'].values[1] + + return self.batt_capacity * 1000 * batt_cost_per_wh \ + + self.power * 1000 * inverter_cost_per_w + + def calc_om_cost(self, cost_df, *args): + """ Calculate battery system O&M costs """ + + # Parse battery and inverter o&m costs from costs dataframe + batt_om_cost_per_kwh = cost_df['Battery'].values[1] + return self.batt_capacity * batt_om_cost_per_kwh + + +class SimpleLiIonBattery(Battery): + """ + Models a simple lithium ion battery, where the battery discharges at a constant rate every + night, based on the available capacity. + + Parameters + ---------- + + all parameters from parent class Battery + + Methods + ---------- + + all methods from parent class Battery + + update_state: charges or discharges the battery for a single time step based on net + load + + Calculated Attributes + ---------- + + charge_eff: battery charging efficiency + + discharge_eff: battery discharging efficiency + + """ + + def __init__(self, existing, power, batt_capacity, initial_soc=1, + one_way_battery_efficiency=0.9, + one_way_inverter_efficiency=0.95, soc_upper_limit=1, + soc_lower_limit=0.1, validate=True): + super().__init__(existing, power, batt_capacity, initial_soc, + one_way_battery_efficiency, + one_way_inverter_efficiency, soc_upper_limit, + soc_lower_limit, validate) + + # Set efficiencies + self.charge_eff = self.one_way_battery_efficiency \ + * self.one_way_inverter_efficiency + self.discharge_eff = self.one_way_battery_efficiency \ + * self.one_way_inverter_efficiency + + def update_state(self, net_load, duration, temperature, is_night, + night_duration, night_hours_left, + soc_at_initial_hour_of_night, dispatch_strategy): + """ + Charge or discharge the battery for a single time step. + + Parameters + ---------- + + net_load: Net load to be charged into battery (negative values) or discharged from + battery (positive values) (in kW) + + duration: duration of timestep in seconds + + temperature: outdoor air temperature in degrees Celcius, currently does nothing + + is_night: if it is currently nighttime, boolean + + night_duration: length of current night in number of timesteps, if is_night is + false, it is 0 + + night_hours_left: remaining hours in night + + soc_at_initial_hour_of_night: the initial state of charge during the first hour of + the night + + dispatch_strategy: determines the battery dispatch strategy. + Options include: + night_const_batt (constant discharge at night) + night_dynamic_batt (updates the discharge rate based on remaining available + capacity) + + """ + + # Initialize delta power + delta_power = 0 + + # Spare capacity available for charging + spare_capacity = (self.soc_upper_limit - self.soc) * self.batt_capacity + + # Available capacity for discharging + available_capacity = (self.soc - self.soc_lower_limit) \ + * self.batt_capacity + + # Determine if the battery is charged: if there's extra power and room in the battery + if net_load < 0 < spare_capacity: + + # Amount of energy applied at terminals to fill battery + external_energy = spare_capacity/self.charge_eff + + # Charging power is minimum of net load, battery power rating and available + # capacity divided by # hours per timestep + delta_power = -min([abs(net_load), self.power, external_energy/(duration/3600)]) + + # Update SOC + self.soc -= delta_power*duration/3600*self.charge_eff / self.batt_capacity + + # Determine if the battery is discharged + elif net_load > 0 and available_capacity > 0: + + # Amount of energy available to meet the load + external_energy = available_capacity*self.discharge_eff + + # Discharging power is minimum of net load, battery power rating and available + # capacity divided by # hours per timestep + delta_power = min([abs(net_load), self.power, external_energy/(duration/3600)]) + + # If nighttime, limit to maximum nighttime power required to discharge at a + # constant rate + if is_night: + # Calculate the maximum nighttime discharge power for the whole night + if dispatch_strategy == 'night_const_batt': + max_nighttime_power = \ + (soc_at_initial_hour_of_night - self.soc_lower_limit) \ + * self.batt_capacity * self.discharge_eff / night_duration + elif dispatch_strategy == 'night_dynamic_batt': + max_nighttime_power = \ + (self.soc - self.soc_lower_limit) \ + * self.batt_capacity * self.discharge_eff / night_hours_left + else: + message = 'Invalid battery dispatch strategy defined: ' \ + '{}.'.format(dispatch_strategy) + log_error(message) + raise Exception(message) + + delta_power = min([delta_power, max_nighttime_power]) + + # Update SOC + self.soc -= delta_power * duration / 3600 / self.discharge_eff \ + / self.batt_capacity + + # Check that the SOC is above or below limit, with a slight buffer to allow for + # rounding errors + if self.soc > self.soc_upper_limit+1E-5 or self.soc < self.soc_lower_limit-1E-5: + message = 'Battery SOC is above or below allowable limit.' + log_error(message) + raise Exception(message) + + # Return power used to charge or discharge battery + return delta_power + + +class Generator(Component): + """ + Generator class + + Parameters + ---------- + + category: equipment type, set to 'generator' + + existing: whether or not the component already exists on site + + rated_power: generator rated power in kW + + num_units: number of generator units + + fuel_curve_model: gallons/hr at 1/4, 1/2, 3/4, and full load. + Expects a Pandas series with indices of ['1/4 Load (gal/hr)', '1/2 Load (gal/hr)', + '3/4 Load (gal/hr)', 'Full Load (gal/hr)'] + + ideal_minimum_load: fractional load level below which a warning is issued. + Currently unused. + Default = 0.3 + + loading_level_to_add_unit: fractional load level above which another generator unit is + added. Currently unused. + Default = 0.9 + + loading_level_to_remove_unit: fractional load level below which a generator unit is + removed (if possible). Currently unused. + Default = 0.3 + + capital_cost: cost for both parts and labor in USD + + Methods + ---------- + + get_rated_power: returns the generator rated power in kW + + get_fuel_curve_model: returns the fuel curve model coefficients + + calculate_fuel_consumption: calculates dispatch and fuel consumed by generators + + calc_capital_cost: calculate capital costs + + calc_om_cost: calculate om costs + + """ + + def __init__(self, existing, rated_power, num_units, fuel_curve_model, capital_cost, + ideal_minimum_load=0.3, loading_level_to_add_unit=0.9, + loading_level_to_remove_unit=0.3, validate=True): + self.category = 'generator' + self.existing = existing + self.rated_power = rated_power # kW + self.num_units = num_units + self.fuel_curve_model = fuel_curve_model + self.capital_cost = capital_cost + self.ideal_minimum_load = ideal_minimum_load + self.loading_level_to_add_unit = loading_level_to_add_unit + self.loading_level_to_remove_unit = loading_level_to_remove_unit + + if validate: + # List of initialized parameters to validate + args_dict = { + 'existing': existing, 'rated_power': rated_power, + 'num_units': num_units, + 'fuel_curve_model': fuel_curve_model, + 'capital_cost': capital_cost, + 'ideal_minimum_load': ideal_minimum_load, + 'loading_level_to_add_unit': loading_level_to_add_unit, + 'loading_level_to_remove_unit': loading_level_to_remove_unit + } + + # Validate input parameters + validate_all_parameters(args_dict) + + def __repr__(self): + return 'Generator: Rated Power: {:.1f}kW, Number: {}'.format( + self.rated_power, self.num_units) + + def get_rated_power(self): + return self.rated_power + + def get_fuel_curve_model(self): + return self.get_fuel_curve_model + + def calculate_fuel_consumption(self, unmet_load, duration, validate=True): + """ + Calculates fuel consumed by generator, and also return number of hours at each load + bin which is used for the load duration calculation. + Note: the current implementation assumes that all generators are dispatched together + at the same loading level. This will have to be updated in the future. + + Inputs: + unmet_load: dataframe with one column: 'load_not_met' + duration: time step length in seconds + + Outputs: + load_duration_df: dataframe with columns [binned_load, num_hours] + total_fuel: total fuel consumed in L + + """ + + # Validate input parameters + if validate: + args_dict = {'unmet_load': unmet_load, 'duration': duration} + validate_all_parameters(args_dict) + + # Create load bins + unmet_load.columns = ['load_not_met'] + unmet_load['binned_load'] = unmet_load['load_not_met'].apply(round) + grouped_load = unmet_load.groupby('binned_load')['load_not_met'].\ + count().to_frame(name='num_timesteps').reset_index() + + # Calculate the number of hours at each load bin + grouped_load['num_hours'] = grouped_load['num_timesteps'] * duration \ + / 3600 + + # Determine fuel consumption function + with warnings.catch_warnings(): + warnings.simplefilter('ignore', np.RankWarning) + fuel_func = np.poly1d(np.polyfit([0, 0.25, 0.5, 0.75, 1], + [0, + self.fuel_curve_model['1/4 Load (gal/hr)'], + self.fuel_curve_model['1/2 Load (gal/hr)'], + self.fuel_curve_model['3/4 Load (gal/hr)'], + self.fuel_curve_model['Full Load (gal/hr)']], + 10)) + + # Calculate fuel consumption for each bin in Gallons + grouped_load['fuel_used'] = grouped_load['binned_load'].apply( + lambda x: fuel_func(x/(self.rated_power*self.num_units))) + + # Calculate total fuel consumption + total_fuel = (grouped_load['fuel_used'] + * grouped_load['num_hours']).sum() + + return grouped_load, total_fuel + + def calc_capital_cost(self, *args): + """ Calculate generator capital costs """ + + return self.capital_cost * self.num_units + + def calc_om_cost(self, cost_df, *args): + """ Calculate generator O&M costs """ + + # Parse generator o&m costs from costs dataframe + gen_om_per_kW_scalar = cost_df['Generator_scalar'].values[1] + gen_om_exp = cost_df['Generator_exp'].values[1] + return gen_om_per_kW_scalar * (self.rated_power * self.num_units)**gen_om_exp + + +class FuelTank(Component): + """ + Fuel Tank class + + Parameters + ---------- + + category: equipment type, set to 'fuel_tank' + + existing: whether or not the component already exists on site + + tank_size: size of fuel tank (gal) + + num_units: number of fuel tanks + + tank_cost: cost of an individual tank in USD + + Methods + ---------- + + calc_capital_cost: calculate capital costs + + calc_om_cost: calculate the o&m costs + + """ + + def __init__(self, existing, tank_size, num_units, tank_cost, + validate=True): + self.category = 'fuel_tank' + self.existing = existing + self.tank_size = tank_size + self.num_units = num_units + self.tank_cost = tank_cost + + if validate: + # List of initialized parameters to validate + args_dict = {'existing': existing, 'fuel_tank_size': tank_size, + 'num_units': num_units, 'fuel_tank_cost': tank_cost} + + # Validate input parameters + validate_all_parameters(args_dict) + + def __repr__(self): + return 'Fuel Tank: Size: {:.1f}Gal, Number: {}'.format( + self.tank_size, self.num_units) + + def calc_capital_cost(self, *args): + """ Calculate capital cost of fuel tank(s) """ + + return self.num_units * self.tank_cost + + def calc_om_cost(self, *args): + return 0 + + +# System class, contains components +class MicrogridSystem: + """ + Microgrid system containing multiple components. + + Parameters + ---------- + + name: unique system name + + components: collection of Component objects + + costs_usd: dictionary containing different costs in USD + + capital_cost_usd: total captial costs in USD + + annual_benefits_usd: total annual benefits from the system in USD + + pv_area_ft2: total pv array area in ft^2 + + fuel_used_gal: aggregated (mean, std, and worst-case) fuel used by system from + simulations in gallons + + simple_payback_yr: system payback based on capital costs and annual benefits in years + + pv_percent: aggregated (mean, std, and worst-case) percent of load met by pv across + all simulations + + batt_percent: aggregated (mean, std, and worst-case) percent of load met by batteries + across all simulations + + gen_percent: aggregated (mean, std, and worst-case) percent of load met by generators + across all simulations + + generator_power_kW: aggregated (mean, std, and worst-case) required generator size + across all simulations + + load_duration: Pandas dataframe containing load duration curve aggregated across all + simulations. Contains columns: + [load_bin, num_hours, num_hours_at_or_below] + + simulations: dictionary to hold simulator objects + + outputs: dictionary of outputs from all simulations + + Methods + ---------- + + add_component: adds a component to the system + + get_components: returns the system components + + get_pv_area: returns the area of the pv area in ft^2 + + size_fuel_tank: sizes fuel tanks required to meet the maximum generator fuel + consumption + + calc_costs: calculates the system costs in USD + + calc_annual_pv_benefits: calculates the annual system benefits in USD + + calc_net_metering_revenue: helper function for calc_annual_pv_benefits. Calculates + revenue for either the total PV system or an existing system. + + calc_payback: calculates system payback in years + + set_outputs: sets system outputs from aggregated simulations + + get_outputs: returns all attributes + + print_outputs: prints all attributes + + plot_load_duration: plots the load duration curve + + get_name: returns the system name + + add_simulation: add a simulation to the simulations dictionary + + print_simulation_list: prints names of simulator objects + + get_simulation: returns a specific simulator object given its name + + plot_dispatch: plot the dispatch graph for the selected system + + plot_generator_histograms: plots histograms of generator power and fuel consumption + across the simulations + + plot_histogram: plots a histogram for a field in the outputs dictionary, and returns + the axes object + + """ + + def __init__(self, name): + self.name = name + self.components = None + self.costs_usd = None + self.capital_cost_usd = 0 + self.om_cost_usd = 0 + self.annual_benefits_usd = 0 + self.demand_benefits_usd = 0 + self.pv_area_ft2 = 0 + self.fuel_used_gal = 0 + self.simple_payback_yr = np.nan + self.pv_percent = None + self.batt_percent = None + self.gen_percent = None + self.generator_power_kW = None + self.load_duration = None + self.simulations = {} + self.outputs = None + + def __repr__(self): + return_string = '' + for component in self.components: + return_string += str(component) + return_string += '\n' + return return_string + + def add_component(self, *args): + pass + + def get_components(self): + return self.components + + def get_pv_area(self): + pass + + def calc_costs(self, *args): + pass + + def calc_annual_pv_benefits(self, *args): + pass + + def calc_payback(self): + """ + Calculate simple payback, based on capital cost and annual + benefits. + """ + + self.simple_payback_yr = self.capital_cost_usd / \ + (self.annual_benefits_usd + + self.demand_benefits_usd - + self.om_cost_usd) + if self.simple_payback_yr < 0: + self.simple_payback_yr = np.nan + + def set_outputs(self, outputs, validate=True): + """ Add the simulation results to the system attributes """ + + # Validate input parameters + if validate: + args_dict = {'outputs': outputs} + validate_all_parameters(args_dict) + + for key, val in outputs.items(): + if key in self.__dict__.keys(): + self.__setattr__(key, val) + + def get_outputs(self, params=None): + """ Return simulation outputs specified by the params list """ + + out_dict = {} + if params is None: + for param in self.__dict__.keys(): + out_dict[param] = self.__getattribute__(param) + + else: + for param in params: + try: + out_dict[param] = self.__getattribute__(param) + except AttributeError: + out_dict[param] = None + + return out_dict + + def print_outputs(self): + """ Print the system results """ + for key, val in self.__dict__.items(): + if type(val) in [str, dict, int, float, np.float64]: + print(key, val) + + def plot_load_duration(self): + """ Plots the load duration curve """ + + try: + fig = plt.figure() + + # Plot hours not met + ax2 = fig.add_subplot(111) + self.load_duration[['hours_not_met_max', + 'hours_not_met_average']].plot(ax=ax2) + ax2.set_xlabel('Generator Power (kW)') + ax2.set_ylabel('Number of Hours Not Met') + ax2.legend(['Maximum of Scenarios', 'Average of Scenarios']) + + except NameError: + print('You must set the system outputs first.') + + def get_name(self): + return self.name + + def add_simulation(self, sim_name, sim, validate): + """ Add a simulation to the simulations dictionary. """ + + # Validate input parameters + if validate: + args_dict = {'sim': sim} + validate_all_parameters(args_dict) + + if sim_name not in self.simulations.keys(): + self.simulations[sim_name] = sim + else: + print('Could not add simulation, a simulation with that name already exists.') + + def print_simulation_list(self): + print(self.simulations.keys()) + + def get_simulation(self, sim_name): + """ + If sim name is 'max' or 'min' return simulation with max or min pv. + """ + + if sim_name in self.simulations.keys(): + return self.simulations[sim_name] + elif sim_name == 'max': + sim_pv = {key: val.dispatch_df['pv_power'].sum() for key, val + in self.simulations.items()} + max_pv = max(sim_pv, key=sim_pv.get) + return self.simulations[max_pv] + elif sim_name == 'min': + sim_pv = {key: val.dispatch_df['pv_power'].sum() for key, val + in self.simulations.items()} + min_pv = min(sim_pv, key=sim_pv.get) + return self.simulations[min_pv] + else: + message = 'The simulation name you entered does not exist.' + log_error(message) + print(message) + + def plot_dispatch(self, sim_name, ax=None): + """ + Plot the dispatch graph for the selected system. + sim_name can either be a simulation name, 'max' to plot dispatch for the simulation + with the maximum PV or 'min' to plot dispatch for the simulation with the minimum + PV + """ + + if sim_name in self.simulations.keys(): + sim = self.simulations[sim_name] + else: + # Calculate the total PV for each simulation + sim_pv = {key: val.dispatch_df['pv_power'].sum() for key, val + in self.simulations.items()} + max_pv = max(sim_pv, key=sim_pv.get) + min_pv = min(sim_pv, key=sim_pv.get) + + # Get the system with either the max or min PV + if sim_name == 'max': + sim = self.simulations[max_pv] + elif sim_name == 'min': + sim = self.simulations[min_pv] + else: + fig = plt.figure(figsize=[16, 10]) + ax1 = fig.add_subplot(121) + self.plot_dispatch('max', ax=ax1) + ax1.set_title('Maximum PV Scenario for {:.0f}kW PV, \n' + '{:.0f}kW/{:.0f}kWh Battery, {:.0f}kW Generator ' + 'System'.format( + self.components['pv'].pv_capacity, + self.components['battery'].power, + self.components['battery'].batt_capacity, + self.components['generator'].rated_power)) + ax1.legend(['Load', 'PV', 'Battery', 'Generator'], fontsize=12) + ax2 = fig.add_subplot(122) + self.plot_dispatch('min', ax=ax2) + ax2.set_title('Minimum PV Scenario for {:.0f}kW PV, \n' + '{:.0f}kW/{:.0f}kWh Battery, {:.0f}kW Generator ' + 'System'.format( + self.components['pv'].pv_capacity, + self.components['battery'].power, + self.components['battery'].batt_capacity, + self.components['generator'].rated_power)) + ax2.legend(['Load', 'PV', 'Battery', 'Generator'], fontsize=12) + ax2.set_ylim(ax1.get_ylim()) + return + + # Plot the dispatch graph + if ax is None: + fig = plt.figure() + ax = fig.add_subplot(111) + else: + fig = ax.get_figure() + sim.dispatch_df[['load', 'pv_power', 'delta_battery_power', + 'load_not_met']].plot(ax=ax) + ax.set_xlabel('Time') + ax.set_ylabel('Power (kW)') + + # Add battery charging/discharging labels if there's room + if ax.get_window_extent().transformed( + fig.dpi_scale_trans.inverted()).height > 3 \ + and self.components['battery'].power > 0: + ax.text(1.01, 0, '(charging)', color='red', rotation='vertical', + va='bottom', transform=ax.transAxes) + ax.text(1.01, 1, '(discharging)', color='red', rotation='vertical', + va='top', transform=ax.transAxes) + ax.text(1.01, 0.5, 'Battery Power (kW)', color='red', + rotation='vertical', va='center', transform=ax.transAxes) + + def plot_generator_histograms(self): + """ + Plots histograms of generator power and fuel consumption across the + simulations. + + """ + + fig = plt.figure(figsize=[8, 4]) + ax1 = fig.add_subplot(121) + ax1 = self.plot_histogram('generator_power_kW', ax=ax1) + ax1.set_xlabel('Generator Power (kW)') + ax1.set_ylabel('Number of Scenarios') + ax2 = fig.add_subplot(122) + ax2 = self.plot_histogram('fuel_used_gal', ax=ax2) + ax2.set_xlabel('Fuel Consumption (Gal)') + plt.tight_layout() + + def plot_histogram(self, outputs_field, ax=None): + """ + Plots a histogram for a field in the outputs dictionary, and returns the axes object. + Options for outputs_field include: + 'pv_percent', 'batt_percent', 'gen_percent', 'storage_recovery_percent', + 'fuel_used_gal', 'generator_power_kW' + + """ + + if outputs_field in self.outputs.keys(): + if ax is None: + fig = plt.figure() + ax = fig.add_subplot(111) + ax.hist(self.outputs[outputs_field]) + return ax + else: + print('ERROR: {} is not a valid outputs field.'.format(outputs_field)) + + +class SimpleMicrogridSystem(MicrogridSystem): + """ + Simple system where you can only have one type of each component. + + For generators there can be multiple if they have the same specs. + + Parameters + ---------- + + same as for parent class MicrogridSystem + + + Methods + ---------- + + same as for parent class MicrogridSystem + + """ + + def __init__(self, name): + super().__init__(name) + self.components = {} + + def __repr__(self): + return_string = '' + for key, val in self.components.items(): + return_string += str(val) + return_string += '\n' + return return_string + + def add_component(self, component, validate=True): + """ Add a component (pv, battery, generator) to the system """ + + # Validate input parameters + if validate: + args_dict = {'component': component} + validate_all_parameters(args_dict) + + self.components[component.category] = component + + def get_pv_area(self): + """ Return the area of the PV array """ + if 'pv' in self.components: + self.pv_area_ft2 = self.components['pv'].calc_area() + + def size_fuel_tank(self, fuel_tank_sizes, existing_components, max_fuel_used): + """ + Sizes fuel tanks required for the maximum generator fuel consumption. + """ + + # Discount required capacity by any existing tanks + if 'fuel_tank' in existing_components: + max_fuel_used = max_fuel_used - \ + existing_components['fuel_tank'].tank_size \ + * existing_components['fuel_tank'].num_units + + # If additional tanks are required, calculate the most economical tank size and number + if max_fuel_used > 0: + # Calculate the required number of tanks and total cost for each possible tanks + # size + sizes_mod = fuel_tank_sizes.copy(deep=True) + sizes_mod['num_tanks'] = sizes_mod.index.map( + lambda x: int(np.ceil(max_fuel_used/x))) + sizes_mod['total_cost'] = sizes_mod['Cost (USD)'] * sizes_mod['num_tanks'] + + # Get the least expensive size and number + best_tank_size = sizes_mod.sort_values(by='total_cost').iloc[0] + fuel_tank = FuelTank(False, float(best_tank_size.name), + int(best_tank_size.num_tanks), best_tank_size['Cost (USD)']) + else: + fuel_tank = FuelTank(False, 0, 0, 0) + + # Add the fuel tank to the system components + self.add_component(fuel_tank) + + def calc_costs(self, system_costs, existing_components={}, validate=True): + """ Calculate the capital and maintenance costs of the system """ + + # Validate input parameters + if validate: + args_dict = {'system_costs': system_costs, + 'existing_components': existing_components} + validate_all_parameters(args_dict) + + # Create a dictionary to hold costs for each component + self.costs_usd = {} + + # Calculate the costs for each component and add to the total + for component_type, component in self.components.items(): + self.costs_usd['{}_capital'.format(component_type)] = \ + component.calc_capital_cost( + system_costs['{}_costs'.format(component_type)], + existing_components) + self.costs_usd['{}_o&m'.format(component_type)] = \ + component.calc_om_cost(system_costs['om_costs'], + existing_components) + self.capital_cost_usd += self.costs_usd[ + '{}_capital'.format(component_type)] + self.om_cost_usd += self.costs_usd['{}_o&m'.format(component_type)] + + def calc_annual_pv_benefits(self, tmy_solar, annual_load_profile, duration, + electricity_rate, net_metering_rate, demand_rate, + batt_sizing_method, batt_eff, inv_eff, + net_metering_limits=None, existing_components={}, + validate=True): + """ + Calculate the annual financial benefit of the system. + + tmy_solar_power: pv power time series for a 1kW array based on TMY data + annual_load_profile: annual load time series + duration: duration of each timestep in the time series in seconds + net_metering_limits: any limit on the net-metering policy in the form of: + {type: ['capacity_cap' or 'percent_of_load'], value: [ or ]} + electricity_rate: electricity rate in $/kWh + net_metering_rate: rate for exported energy in $/kWh + demand_rate: rate for demand charges in $/kW. Can either be a single number or a list + of 12 numbers. + existing_components: dictionary of any existing microgrid components + + """ + + # Validate input parameters + if validate: + args_dict = {'tmy_solar': tmy_solar, + 'annual_load_profile': annual_load_profile, + 'duration': duration, + 'electricity_rate': electricity_rate, + 'net_metering_rate': net_metering_rate, + 'demand_rate_list': demand_rate, + 'existing_components': existing_components} + if net_metering_limits is not None: + args_dict['net_metering_limits'] = net_metering_limits + validate_all_parameters(args_dict) + + # If no net-metering rate is set, use electricity rate + if net_metering_rate is None: + net_metering_rate = electricity_rate + + # Calculate revenue from the whole PV system + self.annual_benefits_usd = self.calc_net_metering_revenue( + annual_load_profile, tmy_solar, self.components['pv'].pv_capacity, + net_metering_limits, electricity_rate, net_metering_rate, + batt_sizing_method, batt_eff, inv_eff) + + # Reduce revenue generating capacity if there is an existing PV system + if 'pv' in existing_components: + existing_capacity_benefits_usd = \ + self.calc_net_metering_revenue( + annual_load_profile, tmy_solar, + existing_components['pv'].pv_capacity, net_metering_limits, + electricity_rate, net_metering_rate, batt_sizing_method, + batt_eff, inv_eff) + self.annual_benefits_usd = max( + self.annual_benefits_usd - existing_capacity_benefits_usd, 0) + + # If a demand rate is set, calculate demand charges with and without the pv system + if demand_rate is not None: + # Check if existing PV, and if so, subtract generation from load + if 'pv' in existing_components: + net_load = annual_load_profile - tmy_solar.values * \ + existing_components['pv'].pv_capacity + else: + net_load = annual_load_profile + + # Calculate demand charge without new PV capacity + base_demand = self.calc_demand_savings(net_load, demand_rate) + + # Calculate demand charge with new PV capacity + net_load = annual_load_profile - tmy_solar.values * \ + self.components['pv'].pv_capacity + new_demand = self.calc_demand_savings(net_load, demand_rate) + + # Subtract the demand charges to get the savings + self.demand_benefits_usd = max(base_demand - new_demand, 0) + + @staticmethod + def calc_demand_savings(load_profile, demand_rate): + """ + Helper function for calc_annual_pv_benefits. Calculates demand charge savings from PV + generation. + """ + + # Find peak by month + peak_month_demand = load_profile.resample('M').max() + + # Multiply by demand charge rate + return (peak_month_demand * demand_rate).sum() + + @staticmethod + def calc_net_metering_revenue(load_profile, pv_power_profile, pv_capacity, + net_metering_limits, electricity_rate, net_metering_rate, + batt_sizing_method, batt_eff, inv_eff): + """ + Helper function for calc_annual_pv_benefits. Calculates revenue for either the total + PV system or an existing system. + + """ + + # Calculate hourly power not-imported due to PV and power exported + power_df = pd.DataFrame(np.transpose([load_profile.values, pv_power_profile.values]), + columns=['load', 'pv'], index=load_profile.index) + power_df['pv_total'] = power_df['pv'] * pv_capacity + power_df['power_not_imported'] = power_df.apply( + lambda x: min(x['load'], x['pv_total']), axis=1) + power_df['power_exported'] = power_df['pv_total'] - power_df['power_not_imported'] + + revenue = None + if net_metering_limits is None: + # There are no net-metering limits + revenue = \ + power_df['power_not_imported'].sum() * electricity_rate + \ + power_df['power_exported'].sum() * net_metering_rate + + elif net_metering_limits['type'] == 'capacity_cap': + # There is an instantaneous capacity cap on net-metering + revenue = \ + power_df['power_not_imported'].sum() * electricity_rate + \ + power_df.apply(lambda x: min(x['power_exported'], + net_metering_limits['value']), + axis=1).sum() * net_metering_rate + + elif net_metering_limits['type'] == 'percent_of_load': + # There is a percent of load cap on net-metering + revenue = \ + power_df['power_not_imported'].sum() * electricity_rate + \ + max(min(load_profile.sum() * net_metering_limits[ + 'value'] / 100 - power_df['power_not_imported'].sum(), + power_df['power_exported'].sum()), 0) * net_metering_rate + + elif net_metering_limits['type'] == 'no_nm_use_battery': + # There is no net-metering, but the battery should be used to capture and use + # excess PV generation. This excess less losses is included in differed utility + # costs + rte = (batt_eff * inv_eff)**2 + revenue = \ + (power_df['power_not_imported'].sum() + + power_df['power_exported'].sum() * rte) * electricity_rate + + # Check that battery sizing method is 'no_pv_export' + if batt_sizing_method != 'no_pv_export': + print('Warning: the "no_nm_use_battery" option should only be used with a ' + 'battery sized with the "no_pv_export" method.') + + return revenue + + def calculate_smaller_generator_metrics(self, perc, generator_options, validate=True): + """ + Calculates metrics for a generator sized at x% of the system generator size. + """ + + # Validate input parameters + if validate: + args_dict = {'perc': perc, 'generator_options': generator_options} + validate_all_parameters(args_dict) + + # Find the closest actual generator size to the x% size + gen_size = self.components['generator'].rated_power * perc / 100 + gen_size_actual = generator_options.iloc[ + np.abs(generator_options.reset_index()['Power (kW)'] - gen_size). + sort_values().index[0]] + gen = Generator(existing=False, rated_power=gen_size_actual.name, + num_units=self.components['generator'].num_units, + fuel_curve_model=gen_size_actual[ + ['1/4 Load (gal/hr)', '1/2 Load (gal/hr)', + '3/4 Load (gal/hr)', 'Full Load (gal/hr)']].to_dict(), + capital_cost=gen_size_actual['Cost (USD)'], + validate=False) + + # Calculate fuel consumed by new generator for each simulation + fuel_consumed = [] + for sim_num, sim in self.simulations.items(): + fuel_consumed += [gen.calculate_fuel_consumption( + sim.dispatch_df[['load_not_met']], sim.duration, + validate=False)[1]] + + # Aggregate across simulations + typical_fuel_gal = np.mean(fuel_consumed) + conservative_fuel_gal = np.max(fuel_consumed) + + # Get unmet load metrics for closest whole size + gen_load_duration = \ + self.load_duration.loc[int(gen.rated_power * gen.num_units)].\ + rename(index={name: '{}%_smaller_gen_{}'.format(perc, name) + for name in self.load_duration.columns}) + + # Return DataFrame with generator size, cost, average and conservative fuel, and load + # duration metrics + gen_load_duration.loc['{}%_smaller_gen_size'.format( + perc)] = gen.rated_power * gen.num_units + gen_load_duration.loc['{}%_smaller_gen_typical_fuel_gal'.format( + perc)] = typical_fuel_gal + gen_load_duration.loc['{}%_smaller_gen_conservative_fuel_gal'.format( + perc)] = conservative_fuel_gal + gen_load_duration.loc['{}%_smaller_gen_cost'.format( + perc)] = gen.capital_cost * gen.num_units + return gen_load_duration + + +if __name__ == "__main__": + + # For testing + # Load in costs + system_costs = pd.read_excel('data/MCOR Prices.xlsx', sheet_name=None, index_col=0) + + # Create a PV object + pv = PV(existing=False, pv_capacity=500, tilt=0, azimuth=-180, + module_capacity=0.360, module_area=3, spacing_buffer=2, + pv_tracking='fixed', pv_racking='ground') + + # Create a battery object + batt = SimpleLiIonBattery(existing=False, power=500, batt_capacity=2000, + initial_soc=1, one_way_battery_efficiency=0.9, + one_way_inverter_efficiency=0.95, + soc_upper_limit=1, soc_lower_limit=0.1) + + gen = Generator(existing=False, rated_power=500, num_units=1, + fuel_curve_model={'1/4 Load (gal/hr)': 11, '1/2 Load (gal/hr)': 18.5, + '3/4 Load (gal/hr)': 26.4, 'Full Load (gal/hr)': 35.7}, + capital_cost=191000, ideal_minimum_load=0.3, + loading_level_to_add_unit=0.9, + loading_level_to_remove_unit=0.3, validate=True) + + fuel_tank = FuelTank(existing=False, tank_size=1000, num_units=2, + tank_cost=2000) + + # Create a microgrid system and add components + system = SimpleMicrogridSystem('pv_500kW_batt_500kW_2000kWh') + system.add_component(pv, validate=False) + system.add_component(batt, validate=False) + system.add_component(gen, validate=False) + system.add_component(fuel_tank, validate=False) + system.calc_costs(system_costs, {}, validate=False) diff --git a/output/.gitkeep b/output/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..68f52c1 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,29 @@ +# +##### Standard python packages ##### +pandas~=1.4.0 +numpy>=1.18.5 +scipy>=1.3.1 +html5lib>=1.0.1 +urllib3>=1.25.9 +tabulate>=0.8.7 +matplotlib>=3.2.2 +xlsxwriter>=1.2.9 +seaborn>=0.10.1 +xlrd>=1.2.0 +requests>=2.26.0 +openpyxl>=3.0.9 +beautifulsoup4~=4.11.1 +geopy~=2.2.0 +pyyaml~=6.0 + +#pvlib +# conda install -c pvlib pvlib +# https://pvlib-python.readthedocs.io/en/stable/installation.html +# +##### git repos ##### +# pvlib +git+https://github.com/pvlib/pvlib-python.git +# +##### Other stuff ##### +# Install numba to speed up solar power calculation: +# http://numba.pydata.org/#installing diff --git a/solar_data/nrel/46.34_-119.28/46.34_-119.28_1998.csv b/solar_data/nrel/46.34_-119.28/46.34_-119.28_1998.csv new file mode 100644 index 0000000..5835608 --- /dev/null +++ b/solar_data/nrel/46.34_-119.28/46.34_-119.28_1998.csv @@ -0,0 +1,8761 @@ +Year,Month,Day,Hour,Minute,DHI,DNI,GHI,Clearsky DHI,Clearsky DNI,Clearsky GHI,Cloud Type,Solar Zenith Angle,Temperature,Unnamed: 14 +1998,1,1,0,0,0,0,0,0,0,0,6,156.67000000000002,4, +1998,1,1,1,0,0,0,0,0,0,0,6,153.77,4, +1998,1,1,2,0,0,0,0,0,0,0,6,146.51,4, +1998,1,1,3,0,0,0,0,0,0,0,6,137.17000000000002,4, +1998,1,1,4,0,0,0,0,0,0,0,6,127.02,4, +1998,1,1,5,0,0,0,0,0,0,0,6,116.68,4, +1998,1,1,6,0,0,0,0,0,0,0,6,106.53,4, +1998,1,1,7,0,0,0,0,0,0,0,6,96.89,5, +1998,1,1,8,0,6,0,6,15,171,20,6,88.07000000000001,5, +1998,1,1,9,0,38,0,38,44,516,130,6,80.47,6, +1998,1,1,10,0,54,0,54,59,665,236,7,74.51,7, +1998,1,1,11,0,60,0,60,63,744,310,7,70.65,8, +1998,1,1,12,0,71,0,71,62,777,336,7,69.29,9, +1998,1,1,13,0,61,0,61,57,768,313,8,70.57000000000001,10, +1998,1,1,14,0,106,186,156,50,716,243,7,74.34,11, +1998,1,1,15,0,63,72,76,38,588,137,7,80.24,10, +1998,1,1,16,0,13,0,13,14,267,24,7,87.8,9, 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+2013-02-25 00:00:00-08:00,0.0,0.0,4.0,-2.5 +2013-02-25 01:00:00-08:00,0.0,0.0,4.0,-3.5 +2013-02-25 02:00:00-08:00,0.0,0.0,4.0,-3.5 +2013-02-25 03:00:00-08:00,0.0,0.0,8.0,-3.5 +2013-02-25 04:00:00-08:00,0.0,0.0,8.0,-4.5 +2013-02-25 05:00:00-08:00,0.0,0.0,6.0,-3.5 +2013-02-25 06:00:00-08:00,0.0,0.0,6.0,-3.5 +2013-02-25 07:00:00-08:00,8.8,20.199999999999996,6.0,-3.5 diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 0000000..cd6cec1 --- /dev/null +++ b/tests/README.md @@ -0,0 +1,18 @@ +# MCOR Tests + +## Running the tests +Either run the test modules directly with an IDE or run in a terminal from the project root +directory: +> python -m unittest tests/unit_tests/test* +> +> python -m unittest tests/system_tests/*test.py + +## Contents +### system_tests +There are two types of system-level tests used here, integration tests and parameter tests. +Integration tests compare simulation results for a few test cases against pre-determined +ground truth data. Parameter tests involve changing the value of each parameter and ensuring +that the results change in the expected way. + +### unit_tests +Currently, unit tests only exist for the alternative_solar_profiles.py module. diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/system_tests/data/ground_truth/existing_pv_ground_truth.csv b/tests/system_tests/data/ground_truth/existing_pv_ground_truth.csv new file mode 100644 index 0000000..6cea54e --- /dev/null +++ b/tests/system_tests/data/ground_truth/existing_pv_ground_truth.csv @@ -0,0 +1,17 @@ +,pv_capacity,battery_capacity,battery_power,generator_power,fuel_tank_size_gal,capital_cost_usd,pv_capital,battery_capital,generator_capital,fuel_tank_capital,pv_o&m,battery_o&m,generator_o&m,pv_area_ft2,annual_benefits_usd,demand_benefits_usd,simple_payback_yr,pv_percent mean,batt_percent mean,gen_percent mean,generator_power_kW mean,generator_power_kW std,generator_power_kW most-conservative,fuel_used_gal mean,fuel_used_gal std,fuel_used_gal most-conservative +pv_0.0kW_batt_0.0kW_0.0kWh,0.0,0.0,0.0,250.0,1000.0,106375.02,0.0,0.0,95500.0,10875.02,0.0,0.0,4126.4902139427695,0.0,0.0,0.0,,0.0,0.0,100.0,201.5,30.907118921051183,250.0,451.1935793734028,43.892908926520946,527.7401381815896 +pv_300.0kW_batt_0.0kW_0.0kWh,300.0,0.0,0.0,250.0,500.0,104315.22,0.0,0.0,95500.0,8815.22,0.0,0.0,4126.4902139427695,29231.018518518522,0.0,0.0,,34.07478150008514,0.0,65.92521849991486,186.5,47.699580710945455,250.0,308.1247197603265,59.88995743682082,377.3368772402752 +pv_300.0kW_batt_174.7kW_699.0kWh,300.0,698.9881573543996,174.7470393385999,230.0,500.0,530921.6127564207,0.0,434246.39275642077,87860.0,8815.22,0.0,1684.5614592241031,3902.607510458141,29231.018518518522,0.0,0.0,,34.07478150008514,14.291283972558375,51.633934527356494,168.0,42.61455150532503,230.0,239.6138482729404,69.36385884975215,332.50722769207437 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mean,gen_percent mean,generator_power_kW mean,generator_power_kW std,generator_power_kW most-conservative,fuel_used_gal mean,fuel_used_gal std,fuel_used_gal most-conservative +pv_0.0kW_batt_0.0kW_0.0kWh,0.0,0.0,0.0,250.0,1000.0,106375.02,0.0,0.0,95500.0,10875.02,0.0,0.0,4126.4902139427695,0.0,0.0,0.0,,0.0,0.0,100.0,201.5,30.907118921051183,250.0,451.1935793734028,43.892908926520946,527.7401381815896 +pv_131.8kW_batt_0.0kW_0.0kWh,131.8075707486467,0.0,0.0,250.0,500.0,320479.63602778054,216164.41602778056,0.0,95500.0,8815.22,1977.1135612297005,0.0,4126.4902139427695,12842.89847144877,5547.887394727085,0.0,,18.409104623536017,0.0,81.59089537646398,190.0,42.190046219457976,250.0,378.4783204047822,45.05609487218922,446.969680907067 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mean,gen_percent mean,generator_power_kW mean,generator_power_kW std,generator_power_kW most-conservative,fuel_used_gal mean,fuel_used_gal std,fuel_used_gal most-conservative +pv_0.0kW_batt_0.0kW_0.0kWh,0.0,0.0,0.0,250.0,1000.0,106375.02,0.0,0.0,95500.0,10875.02,0.0,0.0,4126.4902139427695,0.0,0.0,0.0,,0.0,0.0,100.0,201.5,30.907118921051183,250.0,451.1935793734028,43.892908926520946,527.7401381815896 +pv_131.8kW_batt_0.0kW_0.0kWh,131.8075707486467,0.0,0.0,250.0,500.0,320479.63602778054,216164.41602778056,0.0,95500.0,8815.22,1977.1135612297005,0.0,4126.4902139427695,12842.89847144877,5547.887394727085,0.0,,18.409104623536017,0.0,81.59089537646398,190.0,42.190046219457976,250.0,378.4783204047822,45.05609487218922,446.969680907067 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mean,batt_percent mean,gen_percent mean,generator_power_kW mean,generator_power_kW std,generator_power_kW most-conservative,fuel_used_gal mean,fuel_used_gal std,fuel_used_gal most-conservative +pv_0.0kW_batt_0.0kW_0.0kWh,0.0,0.0,0.0,250.0,1000.0,106375.02,0.0,0.0,95500.0,10875.02,0.0,0.0,4126.4902139427695,0.0,0.0,0.0,,0.0,0.0,100.0,201.5,30.907118921051183,250.0,451.1935793734028,43.892908926520946,527.7401381815896 +pv_131.8kW_batt_80.5kW_488.0kWh,131.8075707486467,488.03,80.5,250.0,500.0,607023.7660277805,216164.41602778056,286544.13,95500.0,8815.22,1977.1135612297005,1176.1523,4126.4902139427695,12842.89847144877,5547.887394727085,0.0,,18.409104623536017,7.057584663005433,74.53331071345856,184.0,38.0,250.0,344.25175671925786,48.42779363900852,411.20720308285587 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mean,gen_percent mean,generator_power_kW mean,generator_power_kW std,generator_power_kW most-conservative,fuel_used_gal mean,fuel_used_gal std,fuel_used_gal most-conservative +pv_0.0kW_batt_0.0kW_0.0kWh,0.0,0.0,0.0,125.0,500.0,56565.22,0.0,0.0,47750.0,8815.22,0.0,0.0,2595.3250132735025,0.0,0.0,0.0,,0.0,0.0,100.0,107.5,16.00781059358212,125.0,244.9445413581334,24.718778230829162,283.35002361209433 +pv_131.8kW_batt_0.0kW_0.0kWh,131.8075707486467,0.0,0.0,125.0,250.0,271164.72602778056,216164.41602778056,0.0,47750.0,7250.31,1977.1135612297005,0.0,2595.3250132735025,12842.89847144877,5547.887394727085,0.0,277.9897011567903,31.855157433513874,0.0,68.14484256648612,97.5,28.74456470360962,125.0,178.24459251226847,28.275155430261307,230.8946373294792 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a/tests/system_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2018.csv b/tests/system_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2018.csv new file mode 100644 index 0000000..854799b --- /dev/null +++ b/tests/system_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2018.csv @@ -0,0 +1,8761 @@ +Year,Month,Day,Hour,Minute,DHI,DNI,GHI,Clearsky DHI,Clearsky DNI,Clearsky GHI,Cloud Type,Solar Zenith Angle,Temperature,Unnamed: 14 +2018,1,1,0,0,0,0,0,0,0,0,4,156.66,-4.1000000000000005, +2018,1,1,1,0,0,0,0,0,0,0,8,153.77,-4.4, +2018,1,1,2,0,0,0,0,0,0,0,4,146.51,-4.800000000000001, +2018,1,1,3,0,0,0,0,0,0,0,4,137.17000000000002,-5.1000000000000005, +2018,1,1,4,0,0,0,0,0,0,0,8,127.02,-5.4, +2018,1,1,5,0,0,0,0,0,0,0,8,116.68,-5.6000000000000005, +2018,1,1,6,0,0,0,0,0,0,0,0,106.53,-5.800000000000001, +2018,1,1,7,0,0,0,0,0,0,0,4,96.89,-5.9, +2018,1,1,8,0,18,210,26,18,210,26,4,87.77,-5.4, +2018,1,1,9,0,58,4,59,46,562,140,4,80.36,-4.2, 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a/tests/system_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2019.csv b/tests/system_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2019.csv new file mode 100644 index 0000000..b9ca0aa --- /dev/null +++ b/tests/system_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2019.csv @@ -0,0 +1,8761 @@ +Year,Month,Day,Hour,Minute,DHI,DNI,GHI,Clearsky DHI,Clearsky DNI,Clearsky GHI,Cloud Type,Solar Zenith Angle,Temperature,Unnamed: 14 +2019,1,1,0,0,0,0,0,0,0,0,0,156.69,-1.0, +2019,1,1,1,0,0,0,0,0,0,0,4,153.78,-1.4, +2019,1,1,2,0,0,0,0,0,0,0,7,146.51,-1.9, +2019,1,1,3,0,0,0,0,0,0,0,7,137.16,-1.9, +2019,1,1,4,0,0,0,0,0,0,0,7,127.01,-1.7000000000000002, +2019,1,1,5,0,0,0,0,0,0,0,4,116.66,-1.4, +2019,1,1,6,0,0,0,0,0,0,0,7,106.52,-1.1, +2019,1,1,7,0,0,0,0,0,0,0,6,96.87,-0.9, +2019,1,1,8,0,14,116,19,16,198,24,7,87.77,-0.3, +2019,1,1,9,0,55,244,96,41,571,137,4,80.36,0.4, +2019,1,1,10,0,87,346,180,52,725,246,7,74.44,1.7000000000000002, 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a/tests/system_tests/data/solar_data/solar_profiles/46.34_-119.28_3d_10t/46.34_-119.28_solar_trial_8.csv b/tests/system_tests/data/solar_data/solar_profiles/46.34_-119.28_3d_10t/46.34_-119.28_solar_trial_8.csv new file mode 100644 index 0000000..27d92da --- /dev/null +++ b/tests/system_tests/data/solar_data/solar_profiles/46.34_-119.28_3d_10t/46.34_-119.28_solar_trial_8.csv @@ -0,0 +1,73 @@ +,ghi,dni,cloud_state,temp +1999-10-15 23:00:00-07:00,0.0,0.0,4.0,6.5 +1999-10-16 00:00:00-07:00,0.0,0.0,4.0,6.5 +1999-10-16 01:00:00-07:00,0.0,0.0,8.0,5.5 +1999-10-16 02:00:00-07:00,0.0,0.0,7.0,5.5 +1999-10-16 03:00:00-07:00,0.0,0.0,0.0,4.5 +1999-10-16 04:00:00-07:00,0.0,0.0,0.0,4.5 +1999-10-16 05:00:00-07:00,0.0,0.0,0.0,4.5 +1999-10-16 06:00:00-07:00,0.0,0.0,1.0,4.5 +1999-10-16 07:00:00-07:00,0.0,0.0,1.0,4.5 +1999-10-16 08:00:00-07:00,85.0,482.0,1.0,5.5 +1999-10-16 09:00:00-07:00,249.0,726.0,0.0,7.5 +1999-10-16 10:00:00-07:00,401.0,836.0,0.0,9.5 +1999-10-16 11:00:00-07:00,517.0,891.0,0.0,12.5 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b/tests/system_tests/data/solar_data/solar_profiles/46.34_-119.28_3d_10t/46.34_-119.28_solar_trial_9.csv new file mode 100644 index 0000000..25468a6 --- /dev/null +++ b/tests/system_tests/data/solar_data/solar_profiles/46.34_-119.28_3d_10t/46.34_-119.28_solar_trial_9.csv @@ -0,0 +1,73 @@ +,ghi,dni,cloud_state,temp +2007-04-02 14:00:00-07:00,620.8000000000001,354.0,6.0,13.5 +2007-04-02 15:00:00-07:00,485.09999999999997,258.3,6.0,13.5 +2007-04-02 16:00:00-07:00,392.0,324.0,6.0,13.5 +2007-04-02 17:00:00-07:00,273.0,214.80000000000004,7.0,12.5 +2007-04-02 18:00:00-07:00,101.5,53.69999999999999,7.0,11.5 +2007-04-02 19:00:00-07:00,15.200000000000001,0.0,7.0,10.5 +2007-04-02 20:00:00-07:00,0.0,0.0,7.0,9.5 +2007-04-02 21:00:00-07:00,0.0,0.0,8.0,8.5 +2007-04-02 22:00:00-07:00,0.0,0.0,7.0,8.5 +2007-04-02 23:00:00-07:00,0.0,0.0,7.0,7.5 +2007-04-03 00:00:00-07:00,0.0,0.0,7.0,7.5 +2007-04-03 01:00:00-07:00,0.0,0.0,7.0,6.5 +2007-04-03 02:00:00-07:00,0.0,0.0,4.0,6.5 +2007-04-03 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13:00:00-07:00,697.5,477.0,2.0,19.5 diff --git a/tests/system_tests/integration_test.py b/tests/system_tests/integration_test.py new file mode 100644 index 0000000..ede203c --- /dev/null +++ b/tests/system_tests/integration_test.py @@ -0,0 +1,181 @@ +""" +Integration tests for MCOR. Confirms that the results are consistent for a set of fixed + inputs. +""" + +import os +import unittest +from unittest.mock import patch +import json +import pandas as pd + +import generate_solar_profile +import alternative_solar_profiles +from generate_solar_profile import SolarProfileGenerator +from microgrid_optimizer import GridSearchOptimizer +from config import DATA_DIR, SYS_TESTS_DIR +from microgrid_system import PV + +SYS_DATA_DIR = os.path.join(SYS_TESTS_DIR, 'data') +TEST_SOLAR_DATA_DIR = os.path.join(SYS_DATA_DIR, 'solar_data') + + +class TestSimulation(unittest.TestCase): + """ Integration tests to check that results are consistent across subsequent + simulations. + """ + + @classmethod + def setUp(cls): + test_name = 'system_test_2' + with open(os.path.join(SYS_TESTS_DIR, 'test_configs', + '{}.json'.format(test_name))) as f: + cls.tc = json.load(f) + + # Component costs and generator options + cls.system_costs = pd.read_excel( + os.path.join(DATA_DIR, 'MCOR Prices.xlsx'), sheet_name=None, + index_col=0) + + @patch.object(generate_solar_profile, 'SOLAR_DATA_DIR', TEST_SOLAR_DATA_DIR) + @patch.object(alternative_solar_profiles, 'SOLAR_DATA_DIR', TEST_SOLAR_DATA_DIR) + def run_main(self): + # Load (in kW) + self.annual_load_profile = pd.read_csv( + os.path.join(SYS_DATA_DIR, self.tc['load_profile']), index_col=0)['Load'] + + if self.tc['off_grid_load_profile']: + self.tc['off_grid_load_profile'] = pd.read_csv( + os.path.join(SYS_DATA_DIR, self.tc['off_grid_load_profile']), + index_col=0)['Load'] + + # Get solar and power profiles + spg = SolarProfileGenerator(self.tc['latitude'], self.tc['longitude'], + self.tc['timezone'], self.tc['altitude'], self.tc['tilt'], + self.tc['azimuth'], float(self.tc['num_trials']), + float(self.tc['length_trials']), + pv_racking=self.tc['pv_racking'], + pv_tracking=self.tc['pv_tracking'], + suppress_warnings=False, + multithreading=False) + + spg.get_power_profiles() + spg.get_night_duration(percent_at_night=self.tc['percent_at_night']) + tmy_solar = spg.tmy_power_profile + module_params = spg.get_pv_params() + + # Set up parameter dictionaries + location = {'longitude': self.tc['longitude'], 'latitude': self.tc['latitude'], + 'timezone': self.tc['timezone'], 'altitude': self.tc['altitude']} + pv_params = {'tilt': self.tc['tilt'], 'azimuth': self.tc['azimuth'], + 'module_capacity': module_params['capacity'], + 'module_area': module_params['area_in2'], + 'spacing_buffer': self.tc['spacing_buffer'], + 'advanced_inputs': {}, 'pv_racking': self.tc['pv_racking'], + 'pv_tracking': self.tc['pv_tracking']} + battery_params = {'battery_power_to_energy': + self.tc['battery_power_to_energy'], + 'initial_soc': self.tc['initial_soc'], + 'one_way_battery_efficiency': + self.tc['one_way_battery_efficiency'], + 'one_way_inverter_efficiency': + self.tc['one_way_inverter_efficiency'], + 'soc_upper_limit': self.tc['soc_upper_limit'], + 'soc_lower_limit': self.tc['soc_lower_limit']} + + # Create an optimizer object + optim = GridSearchOptimizer(spg.power_profiles, spg.temp_profiles, + spg.night_profiles, self. + annual_load_profile, + location, tmy_solar, pv_params, + battery_params, self.system_costs, + electricity_rate=self.tc['utility_rate'], + net_metering_rate=self.tc['net_metering_rate'], + demand_rate=self.tc['demand_rate'], + existing_components= + self.tc['existing_components'], + output_tmy=True, + validate=True, + net_metering_limits= + self.tc['net_metering_limits'], + off_grid_load_profile= + self.tc['off_grid_load_profile'], + batt_sizing_method=self.tc['batt_sizing_method'], + gen_power_percent=()) + + # Create a grid of systems + optim.define_grid(include_pv=self.tc['include_pv'], + include_batt=self.tc['include_batt']) + + # Get load profiles for the corresponding solar profile periods + optim.get_load_profiles() + + # Run all simulations + optim.run_sims_par() + optim.parse_results() + + return optim + + def test_longest_night_battery(self): + # Set battery sizing method to longest_night + self.tc['batt_sizing_method'] = 'longest_night' + + # Run simulation + optim = self.run_main() + + # Compare results to ground truth data + ground_truth_df = pd.read_csv(os.path.join(SYS_DATA_DIR, 'ground_truth', + 'longest_night_ground_truth.csv'), + header=0, index_col=0) + pd.testing.assert_frame_equal(ground_truth_df, optim.results_grid, check_exact=False) + + def test_no_pv_export_battery(self): + self.tc['batt_sizing_method'] = 'no_pv_export' + + # Run simulation + optim = self.run_main() + + # Compare results to ground truth data + ground_truth_df = pd.read_csv(os.path.join(SYS_DATA_DIR, 'ground_truth', + 'no_pv_export_ground_truth.csv'), + header=0, index_col=0) + pd.testing.assert_frame_equal(ground_truth_df, optim.results_grid, check_exact=False) + + def test_net_metering_limits(self): + self.tc['net_metering_limits'] = {'type': 'percent_of_load', 'value': 100} + + # Run simulation + optim = self.run_main() + + # Compare results to ground truth data + ground_truth_df = pd.read_csv(os.path.join(SYS_DATA_DIR, 'ground_truth', + 'nm_limit_ground_truth.csv'), + header=0, index_col=0) + pd.testing.assert_frame_equal(ground_truth_df, optim.results_grid, check_exact=False) + + def test_existing_equipment(self): + pv = PV(existing=True, pv_capacity=300, tilt=self.tc['tilt'], + azimuth=self.tc['azimuth'], module_capacity=0.360, module_area=3, + spacing_buffer=2, pv_tracking='fixed', pv_racking='ground') + self.tc['existing_components'] = {'pv': pv} + + # Run simulation + optim = self.run_main() + + # Compare results to ground truth data + ground_truth_df = pd.read_csv(os.path.join(SYS_DATA_DIR, 'ground_truth', + 'existing_pv_ground_truth.csv'), + header=0, index_col=0) + pd.testing.assert_frame_equal(ground_truth_df, optim.results_grid, check_exact=False) + + def test_off_grid_load_profile(self): + self.tc['off_grid_load_profile'] = 'sample_off_grid_load_profile.csv' + + # Run simulation + optim = self.run_main() + + # Compare results to ground truth data + ground_truth_df = pd.read_csv(os.path.join(SYS_DATA_DIR, 'ground_truth', + 'off_grid_load_ground_truth.csv'), + header=0, index_col=0) + pd.testing.assert_frame_equal(ground_truth_df, optim.results_grid, check_exact=False) diff --git a/tests/system_tests/parameter_test.py b/tests/system_tests/parameter_test.py new file mode 100644 index 0000000..2b71c50 --- /dev/null +++ b/tests/system_tests/parameter_test.py @@ -0,0 +1,573 @@ +# -*- coding: utf-8 -*- +""" +System level testing of input parameters + +Created on Tues March 24 01:01:00 2020 + +@author: kevin.atkinson@pnnl.gov +""" + + +import sys +import os +import copy +import pandas as pd +import numpy as np +import json +import unittest +from unittest.mock import patch +import generate_solar_profile +import alternative_solar_profiles +from generate_solar_profile import SolarProfileGenerator +from microgrid_optimizer import GridSearchOptimizer +from config import DATA_DIR, SYS_TESTS_DIR +from microgrid_system import PV + + +SYS_DATA_DIR = os.path.join(SYS_TESTS_DIR, 'data') +TEST_SOLAR_DATA_DIR = os.path.join(SYS_DATA_DIR, 'solar_data') + + +class TestParameters(unittest.TestCase): + """ Integration tests that check the effect of input parameter + changes. + """ + + @classmethod + def setUpClass(cls): + cls.setUp() + cls.original_optim = cls.run_main(cls) + + @classmethod + def setUp(self): + test_name = 'system_test_1' + with open(os.path.join(SYS_TESTS_DIR, 'test_configs', + '{}.json'.format(test_name))) as json_file: + tc = json.load(json_file) + system = tc['system_level'] + pv = tc['pv'] + battery = tc['battery'] + + # load in test configuration + self.latitude = system['latitude'] + self.longitude = system['longitude'] + self.timezone = system['timezone'] + self.altitude = system['altitude'] + self.tilt = pv['tilt'] + self.azimuth = pv['azimuth'] + self.solar_source = pv['solar_source'] + self.num_trials = float(system['num_trials']) + self.length_trials = float(system['length_trials']) + self.pv_racking = pv['pv_racking'] + self.pv_tracking = pv['pv_tracking'] + self.suppress_warnings = pv['suppress_warnings'] + self.spacing_buffer = pv['spacing_buffer'] + self.solar_data_start_year = pv['solar_data_start_year'] + self.solar_data_end_year = pv['solar_data_end_year'] + self.battery_power_to_energy = battery['battery_power_to_energy'] + self.one_way_battery_efficiency = battery['one_way_battery_efficiency'] + self.initial_soc = battery['initial_soc'] + self.one_way_inverter_efficiency = battery[ + 'one_way_inverter_efficiency'] + self.soc_upper_limit = battery['soc_upper_limit'] + self.soc_lower_limit = battery['soc_lower_limit'] + self.utility_rate = tc['utility_rate'] + self.net_metering_rate = tc['net_metering_rate'] + self.demand_rate = tc['demand_rate'] + self.existing_components = tc['existing_components'] + self.net_metering_limits = tc['net_metering_limits'] + self.batt_sizing_method = battery['batt_sizing_method'] + self.include_pv = tuple(tc['include_pv']) + self.include_batt = tuple(tc['include_batt']) + self.percent_at_night = battery['percent_at_night'] + self.constraints = tc['constraints'] + self.ranking_criteria = tc['ranking_criteria'] + self.load_profile = tc['load_profile'] + self.off_grid_load_profile = tc['off_grid_load_profile'] + + if sys.platform == 'darwin': + self.multithreading = True + else: + self.multithreading = False + + # Component costs and generator options + self.system_costs = pd.read_excel( + os.path.join(DATA_DIR, 'MCOR Prices.xlsx'), sheet_name=None, + index_col=0) + + @patch.object(generate_solar_profile, 'SOLAR_DATA_DIR', TEST_SOLAR_DATA_DIR) + @patch.object(alternative_solar_profiles, 'SOLAR_DATA_DIR', TEST_SOLAR_DATA_DIR) + def run_main(self, generate_solar_data=False, + generate_solar_profiles=False): + # Load (in kW) + self.annual_load_profile = pd.read_csv( + os.path.join(SYS_DATA_DIR, self.load_profile), index_col=0)['Load'] + + if self.off_grid_load_profile: + self.off_grid_load_profile = pd.read_csv( + os.path.join(SYS_DATA_DIR, self.off_grid_load_profile), + index_col=0)['Load'] + + # Get solar and power profiles + spg = SolarProfileGenerator(self.latitude, self.longitude, + self.timezone, self.altitude, self.tilt, + self.azimuth, float(self.num_trials), + float(self.length_trials), + pv_racking=self.pv_racking, + pv_tracking=self.pv_tracking, + suppress_warnings=False, + multithreading=self.multithreading, + solar_source=self.solar_source, + start_year=self.solar_data_start_year, + end_year=self.solar_data_end_year) + if generate_solar_data: + spg.get_solar_data() + + if generate_solar_profiles: + spg.get_solar_profiles() + spg.get_power_profiles() + spg.get_night_duration(percent_at_night=self.percent_at_night) + tmy_solar = spg.tmy_power_profile + module_params = spg.get_pv_params() + + # Set up parameter dictionaries + location = {'longitude': self.longitude, 'latitude': self.latitude, + 'timezone': self.timezone, 'altitude': self.altitude} + pv_params = {'tilt': self.tilt, 'azimuth': self.azimuth, + 'module_capacity': module_params['capacity'], + 'module_area': module_params['area_in2'], + 'spacing_buffer': self.spacing_buffer, + 'advanced_inputs': {}, 'pv_racking': self.pv_racking, + 'pv_tracking': self.pv_tracking} + battery_params = {'battery_power_to_energy': + self.battery_power_to_energy, + 'initial_soc': self.initial_soc, + 'one_way_battery_efficiency': + self.one_way_battery_efficiency, + 'one_way_inverter_efficiency': + self.one_way_inverter_efficiency, + 'soc_upper_limit': self.soc_upper_limit, + 'soc_lower_limit': self.soc_lower_limit} + + # Create an optimizer object + optim = GridSearchOptimizer(spg.power_profiles, spg.temp_profiles, + spg.night_profiles, self. + annual_load_profile, + location, tmy_solar, pv_params, + battery_params, self.system_costs, + electricity_rate=self.utility_rate, + net_metering_rate=self.net_metering_rate, + demand_rate=self.demand_rate, + existing_components= + self.existing_components, + output_tmy=True, + validate=True, + net_metering_limits= + self.net_metering_limits, + off_grid_load_profile= + self.off_grid_load_profile, + batt_sizing_method=self.batt_sizing_method, + gen_power_percent=()) + + # Create a grid of systems + optim.define_grid(include_pv=self.include_pv, + include_batt=self.include_batt) + + # Get load profiles for the corresponding solar profile periods + optim.get_load_profiles() + + # Run all simulations + optim.run_sims_par() + optim.parse_results() + + return optim + + def compare_series(self, series_name, how, optim=None, + generate_solar_data=False, + generate_solar_profiles=False): + """ + Compares two Pandas series + :param series_name: column name from optim results grid dataframe + :param how: comparison to make for all elements of the series as + compared with the original optim object series. Options: + ['equal', 'not_equal', 'larger', 'smaller'] + :param optim: new optim object, if none provided, the simulation is rerun + :param generate_solar_data: boolean to re-download nrel data + :param generate_solar_profiles: boolean to re-create solar profiles + :return: + """ + + # If no new optim object is provided, re-run the simulation + if optim is None: + optim = self.run_main(generate_solar_data, generate_solar_profiles) + + new_series = optim.results_grid.loc[:, series_name] + orig_series = self.original_optim.results_grid.loc[:, series_name] + + # Remove null or zero values in series' + new_series = new_series[new_series != 0].dropna() + orig_series = orig_series[orig_series != 0].dropna() + + # Determine appropriate check and run assert + if how == 'equal': + self.assertListEqual(list(new_series), list(orig_series)) + elif how == 'not_equal': + with self.assertRaises(AssertionError): + self.assertListEqual(list(new_series), list(orig_series)) + elif how == 'larger': + series_diff = new_series.values - orig_series.values + # Assert that all elements of new series are larger than old series + self.assertFalse(len([elem for elem in series_diff if elem <= 0])) + elif how == 'smaller': + series_diff = new_series.values - orig_series.values + # Assert that all elements of new series are smaller than old series + self.assertFalse(len([elem for elem in series_diff if elem >= 0])) + elif how == 'smaller_eq': + series_diff = new_series.values - orig_series.values + # Assert that all elements of new series are smaller than or equal to old series + self.assertFalse(len([elem for elem in series_diff if elem > 0])) + elif how == 'larger_eq': + series_diff = new_series.values - orig_series.values + # Assert that all elements of new series are larger than or equal to old series + self.assertFalse(len([elem for elem in series_diff if elem < 0])) + else: + raise AssertionError + + def internal_error_catch(self, generate_solar_data=False, + generate_solar_profiles=False): + with self.assertRaises(AssertionError): + _ = self.run_main(generate_solar_data, generate_solar_profiles) + + # *****System Parameter Unit Tests****** # + def test_add_existing_pv(self): + pv = PV(existing=True, + pv_capacity=100, + tilt=self.tilt, + azimuth=self.azimuth, + spacing_buffer=self.spacing_buffer, + pv_tracking=self.pv_tracking, + pv_racking=self.pv_racking, + module_capacity=0.360, + module_area=3) + + self.existing_components = {'pv': pv} + optim = self.run_main() + + # Check that original grid had 21 systems + self.assertEqual(len(self.original_optim.results_grid), 21) + + # check that an additional size was included in the results grid + self.assertEqual(len(optim.results_grid), 26) + + # get original capital costs for all systems + orig_cap_cost = self.original_optim.results_grid['capital_cost_usd'] + + # remove the existing pv systems to compare to original system costs + new = optim.results_grid[optim.results_grid['pv_capacity'] != 100] + + # get new capital costs for all systems + with_exist_cap_cost = new['capital_cost_usd'] + + # remove pv_0.0kW_batt_0.0kW_0.0kWh system; cap cost equal + orig_cap_cost = orig_cap_cost.drop(index='pv_0.0kW_batt_0.0kW_0.0kWh') + with_exist_cap_cost.drop(index='pv_0.0kW_batt_0.0kW_0.0kWh', inplace=True) + + # Check that the capital cost for all systems is smaller + self.assertTrue(np.all(with_exist_cap_cost < orig_cap_cost)) + + # Check that existing pv system is included in the results grid + self.assertTrue(any(['pv_100.0' in system for system in optim.results_grid.index])) + + def test_include_pv(self): + self.include_pv = (500, 400) + optim = self.run_main() + + # Check that original grid had 21 systems + self.assertEqual(len(self.original_optim.results_grid), 21) + + # Check that new grid has 5 additional entries for each included pv + self.assertEqual(len(optim.results_grid), 31) + + # Check that both pv system sizes were included in the results grid + self.assertTrue(any(['pv_400.0' in system for system in optim.results_grid.index])) + self.assertTrue(any(['pv_500.0' in system for system in optim.results_grid.index])) + + def test_include_battery(self): + self.include_batt = ((1000, 100), (1000, 500)) + optim = self.run_main() + + # Check that original grid had 21 systems + self.assertEqual(len(self.original_optim.results_grid), 21) + + # Check that new grid has 4 extra entries for each included battery + self.assertEqual(len(optim.results_grid), 29) + + # Check that both battery sizes were included in the results grid + self.assertTrue(any(['batt_100.0kW_1000.0kWh' in system + for system in optim.results_grid.index])) + self.assertTrue(any(['batt_500.0kW_1000.0kWh' in system + for system in optim.results_grid.index])) + + def test_net_metering_limit_capacity_cap(self): + """Test enforcing an instantaneous export power cap (in kW)""" + self.net_metering_limits = {'type': 'capacity_cap', 'value': 20} + self.compare_series('annual_benefits_usd', 'smaller') + + def test_net_metering_limit_percent(self): + self.net_metering_limits = {'type': 'percent_of_load', 'value': 85} + self.compare_series('annual_benefits_usd', 'smaller_eq') + + def test_net_metering_rate_zero(self): + self.net_metering_rate = 0 + self.compare_series('annual_benefits_usd', 'smaller') + + def test_net_metering_rate_low(self): + self.net_metering_rate = self.utility_rate * 0.5 + self.compare_series('annual_benefits_usd', 'smaller_eq') + + def test_generator_cost(self): + self.system_cost_changes_helper('column', 'generator_costs', 'Cost (USD)', 10000, + 'capital_cost_usd', 'smaller') + + def test_fuel_tank_cost(self): + self.system_cost_changes_helper('column', 'fuel_tank_costs', 'Cost (USD)', + float(5000), 'capital_cost_usd', 'smaller') + + def test_pv_ground_fixed_cost(self): + self.system_cost_changes_helper('row', 'pv_costs', 'ground;fixed', 5, 'pv_capital', + 'larger') + + def test_pv_ground_tracking_cost(self): + self.pv_tracking = 'single_axis' + self.system_cost_changes_helper('row', 'pv_costs', 'ground;single_axis', 5, + 'pv_capital', 'larger') + + def test_pv_roof_cost(self): + self.pv_racking = 'roof' + self.system_cost_changes_helper('row', 'pv_costs', 'roof;fixed', 5, 'pv_capital', + 'larger') + + def test_pv_carport_cost(self): + self.pv_racking = 'carport' + self.system_cost_changes_helper('row', 'pv_costs', 'carport;fixed', 5, 'pv_capital', + 'larger') + + def test_battery_cost(self): + self.system_cost_changes_helper('row', 'battery_costs', 'Cost', 0.6, + 'battery_capital', 'larger_eq') + + def test_generator_om_scalar_cost(self): + self.system_cost_changes_helper('cell', 'om_costs', 'Cost', 200, 'generator_o&m', + 'larger', 'Generator_scalar') + + def test_generator_om_exp_cost(self): + self.system_cost_changes_helper('cell', 'om_costs', 'Cost', 1, 'generator_o&m', + 'larger', 'Generator_exp') + + def test_battery_om_cost(self): + self.system_cost_changes_helper('cell', 'om_costs', 'Cost', 3, 'battery_o&m', + 'larger', 'Battery') + + def test_pv_om_fixed_cost(self): + self.system_cost_changes_helper('cell', 'om_costs', 'Cost', 20, 'pv_o&m', 'larger', + 'PV_ground;fixed') + + def test_pv_om_tracking_cost(self): + self.pv_tracking = 'single_axis' + self.system_cost_changes_helper('cell', 'om_costs', 'Cost', 20, 'pv_o&m', 'larger', + 'PV_ground;single_axis') + + def test_pv_om_roof_cost(self): + self.pv_racking = 'roof' + self.system_cost_changes_helper('cell', 'om_costs', 'Cost', 20, 'pv_o&m', 'larger', + 'PV_roof;fixed') + + def test_pv_om_carport_cost(self): + self.pv_racking = 'carport' + self.system_cost_changes_helper('cell', 'om_costs', 'Cost', 20, 'pv_o&m', 'larger', + 'PV_carport;fixed') + + def system_cost_changes_helper(self, field_type, sheet, label, value, cost_column, how, + column=None): + new_system_costs = self.system_costs.copy() + if field_type == 'row': + new_system_costs[sheet].loc[label] = value + elif field_type == 'column': + new_system_costs[sheet].loc[:, label] = value + elif field_type == 'cell': + new_system_costs[sheet].loc[label, column] = value + else: + raise AttributeError + self.system_costs = new_system_costs + self.compare_series(cost_column, how) + + def test_constraints(self): + # Set constraint + constraint_value = 50000 + parameter = 'pv_area_ft2' + constraints = [{'parameter': parameter, 'type': 'max', 'value': constraint_value}] + + # Verify that systems that violated the constraint were included in + # the original results + self.assertTrue(len(self.original_optim.results_grid[ + self.original_optim.results_grid[parameter] > constraint_value])) + + # Apply constraints + optim_copy = copy.deepcopy(self.original_optim) + optim_copy.filter_results(constraints) + + # Verify that those systems are not in the updated results + self.assertFalse(len(optim_copy.results_grid[ + optim_copy.results_grid[parameter] > constraint_value])) + + def test_off_grid_load_profile(self): + # Add off grid load profile and re-run simulation + self.off_grid_load_profile = 'sample_off_grid_load_profile.csv' + optim = self.run_main() + + # Verify that pv capacity has not changed, but that both battery and + # generator capacity have decreased + with self.subTest(): + self.compare_series('pv_capacity', 'equal', optim) + with self.subTest(): + self.compare_series('battery_capacity', 'smaller', optim) + with self.subTest(): + self.compare_series('generator_power', 'smaller', optim) + + def test_add_rank(self): + # Set ranking criteria + parameter = 'capital_cost_usd' + ranking_criteria = [{'parameter': parameter, + 'order_type': 'ascending'}] + + # Copy optim object and rank + optim_copy = copy.deepcopy(self.original_optim) + optim_copy.rank_results(ranking_criteria) + + # Verify that the same systems are included as before + self.assertListEqual(list(self.original_optim.results_grid[ + parameter].sort_values()), + list(optim_copy.results_grid[ + parameter].sort_values())) + + # Verify that the new values are sorted + self.assertListEqual(list(optim_copy.results_grid[parameter]), + list(optim_copy.results_grid[parameter].sort_values())) + + def test_demand_rate(self): + # Add demand rate, should decrease payback time + self.demand_rate = 10 + optim = self.run_main() + + # Check that payback time has decreased for each system + # Get systems with postive payback times from original results + orig_pb = self.original_optim.results_grid['simple_payback_yr'] + non_null_index = orig_pb.dropna().index + new_pb = optim.results_grid['simple_payback_yr'] + + series_diff = orig_pb.loc[non_null_index].values - new_pb.loc[non_null_index].values + + # Assert that all elements of new series are smaller than old series + self.assertFalse(len([elem for elem in series_diff if elem <= 0])) + + def test_utility_rate(self): + # Increase utility rate, should increase annual benefits + self.utility_rate = self.utility_rate + 0.5 + self.compare_series('annual_benefits_usd', 'larger') + + def test_battery_percent_at_night(self): + # Change battery percent at night (when battery starts discharging), + # should impact battery load percent + self.percent_at_night = 0.2 + self.compare_series('batt_percent mean', 'not_equal') + + def test_battery_parameters_sizing_method(self): + # Switch battery sizing method to no_pv_export, should only have one + # battery size per pv size + self.batt_sizing_method = 'no_pv_export' + optim = self.run_main() + + # Check that original grid had 21 systems + self.assertEqual(len(self.original_optim.results_grid), 21) + + # Check that new grid only has 5 + self.assertEqual(len(optim.results_grid), 5) + + def test_battery_soc_upper_limit(self): + # Decrease soc upper limit, should decrease battery load percent + self.initial_soc = 0.8 + self.soc_upper_limit = 0.8 + self.compare_series('batt_percent mean', 'smaller') + + def test_battery_soc_lower_limit(self): + # Increase soc lower limit, should decrease battery load percent + self.soc_lower_limit = 0.5 + self.compare_series('batt_percent mean', 'smaller') + + def test_battery_initial_soc(self): + # Decrease initial battery SOC, should decrease battery load percent + self.initial_soc = 0.2 + self.compare_series('batt_percent mean', 'smaller') + + def test_battery_efficiency(self): + # Decrease battery efficiency, should increase required battery sizes + self.one_way_battery_efficiency = 0.8 + self.compare_series('battery_capacity', 'larger') + + def test_inverter_efficiency(self): + # Decrease inverter efficiency, should increase required battery sizes + self.one_way_inverter_efficiency = 0.8 + self.compare_series('battery_capacity', 'larger') + + def test_battery_parameters_power_to_energy(self): + # Increase power to energy ratio, should increase battery power ratings + self.battery_power_to_energy = 1 + self.compare_series('battery_power', 'larger') + + def test_load_profiles(self): + # Use a different load profile, PV and battery sizes should be different + self.load_profile = 'midrise_mf_flagstaff.csv' + optim = self.run_main() + self.compare_series('pv_capacity', 'smaller', optim) + self.compare_series('battery_capacity', 'smaller', optim) + + def test_pv_tilt(self): + # Decrease tilt, should increase required capacity + self.tilt -= 10 + self.compare_series('pv_capacity', 'larger') + + def test_pv_azimuth(self): + # Change azimuth, should increase required capacity + self.azimuth -= 30 + self.compare_series('pv_capacity', 'larger') + + def test_pv_racking(self): + # Use carport racking, should increase cost + self.pv_racking = 'carport' + self.compare_series('pv_capital', 'larger') + + def test_pv_tracking(self): + # Use single-axis tracking, should decrease required pv capacity + self.pv_tracking = 'single_axis' + optim = self.run_main() + self.compare_series('pv_capacity', 'smaller', optim) + + def test_lat_long_changes(self): + # Check that for different latitude and longitude, pv and battery sizes are different + self.latitude = self.latitude + 5 + self.longitude = self.longitude - 3 + optim = self.run_main(generate_solar_data=True, generate_solar_profiles=True) + self.compare_series('pv_capacity', 'not_equal', optim) + self.compare_series('battery_capacity', 'not_equal', optim) + + def test_solar_source(self): + # Check that if himawari is selected as the data source, the simulation runs without + # error + self.latitude = -18.04 + self.longitude = 178.04 + self.timezone = 'Pacific/Fiji' + self.solar_source = 'himawari' + self.solar_data_start_year = 2016 + self.solar_data_end_year = 2020 + optim = self.run_main(generate_solar_data=True, generate_solar_profiles=True) + self.assertTrue(True) diff --git a/tests/system_tests/test_configs/system_test_1.json b/tests/system_tests/test_configs/system_test_1.json new file mode 100644 index 0000000..38a9616 --- /dev/null +++ b/tests/system_tests/test_configs/system_test_1.json @@ -0,0 +1,44 @@ +{ + "load_profile": "sample_load_profile.csv", + "off_grid_load_profile": null, + "system_level": { + "latitude": 46.34, + "longitude": -119.28, + "timezone": "US/Pacific", + "altitude": 0, + "num_trials": 10, + "length_trials": 3 + }, + "pv": { + "tilt": 21, + "azimuth": 180, + "pv_racking": "ground", + "pv_tracking": "fixed", + "spacing_buffer": 2, + "suppress_warnings": false, + "solar_data_source": "nsrdb", + "solar_data_start_year": 1998, + "solar_data_end_year": 2020, + "solar_source": "nsrdb" + }, + "battery": { + "battery_power_to_energy": 0.25, + "initial_soc": 1, + "one_way_battery_efficiency": 0.9, + "one_way_inverter_efficiency": 0.95, + "soc_upper_limit": 1, + "soc_lower_limit": 0.2, + "batt_sizing_method": "longest_night", + "percent_at_night": 0.1 + }, + "utility_rate": 0.03263, + "demand_rate": null, + "constraints": [], + "ranking_criteria": [{"parameter": "simple_payback_yr", + "order_type": "ascending"}], + "existing_components": {}, + "include_pv": [], + "include_batt": [], + "net_metering_limits": null, + "net_metering_rate": null +} \ No newline at end of file diff --git a/tests/system_tests/test_configs/system_test_2.json b/tests/system_tests/test_configs/system_test_2.json new file mode 100644 index 0000000..ebaedbf --- /dev/null +++ b/tests/system_tests/test_configs/system_test_2.json @@ -0,0 +1,37 @@ +{ + "load_profile": "sample_load_profile.csv", + "off_grid_load_profile": null, + "latitude": 46.34, + "longitude": -119.28, + "timezone": "US/Pacific", + "altitude": 0, + "num_trials": 10, + "length_trials": 3, + "tilt": 20, + "azimuth": 180, + "pv_racking": "ground", + "pv_tracking": "fixed", + "spacing_buffer": 2, + "suppress_warnings": false, + "solar_data_source": "nsrdb", + "solar_data_start_year": 1998, + "solar_data_end_year": 2020, + "battery_power_to_energy": 0.25, + "initial_soc": 1, + "one_way_battery_efficiency": 0.9, + "one_way_inverter_efficiency": 0.95, + "soc_upper_limit": 1, + "soc_lower_limit": 0.2, + "batt_sizing_method": "longest_night", + "percent_at_night": 0.1, + "utility_rate": 0.03263, + "demand_rate": null, + "constraints": [], + "ranking_criteria": [{"parameter": "simple_payback_yr", + "order_type": "ascending"}], + "existing_components": {}, + "include_pv": [], + "include_batt": [], + "net_metering_limits": null, + "net_metering_rate": null +} \ No newline at end of file diff --git a/tests/unit_tests/data/expected_outputs/exp_simple_prob_daily_grouped.json b/tests/unit_tests/data/expected_outputs/exp_simple_prob_daily_grouped.json new file mode 100644 index 0000000..0953980 --- /dev/null +++ b/tests/unit_tests/data/expected_outputs/exp_simple_prob_daily_grouped.json @@ -0,0 +1,3836 @@ +{ + "1": [ + { + "index_": 0, + "month": 1, + "ghi_state_to": 2.0, + "dni_state_to": 1.0, + "cloud_state_to": 4, + "count": 1, + "count_sum": 61, + "prob": 0.0163934426 + }, + { + "index_": 1, + "month": 1, + "ghi_state_to": 2.0, + "dni_state_to": 1.0, + "cloud_state_to": 6, + "count": 1, + "count_sum": 61, + 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100644 index 0000000..b133cf1 --- /dev/null +++ b/tests/unit_tests/data/expected_outputs/rand_hourly_state.csv @@ -0,0 +1,25 @@ +,ghi_state,dni_state,cloud_state,temp_state +2009-02-10 00:00:00,2.7182818285,2.7182818285,1,14.0 +2009-02-10 01:00:00,2.7182818285,2.7182818285,4,13.0 +2009-02-10 02:00:00,2.7182818285,2.7182818285,7,12.0 +2009-02-10 03:00:00,2.7182818285,2.7182818285,7,12.0 +2009-02-10 04:00:00,2.7182818285,2.7182818285,7,12.0 +2009-02-10 05:00:00,2.7182818285,2.7182818285,7,12.0 +2009-02-10 06:00:00,2.7182818285,2.7182818285,7,12.0 +2009-02-10 07:00:00,2.7182818285,2.7182818285,7,12.0 +2009-02-10 08:00:00,2.0,2.0,8,13.0 +2009-02-10 09:00:00,3.0,6.0,7,15.0 +2009-02-10 10:00:00,3.0,6.0,7,16.0 +2009-02-10 11:00:00,2.0,5.0,8,17.0 +2009-02-10 12:00:00,2.0,4.0,8,18.0 +2009-02-10 13:00:00,0.0,0.0,1,18.0 +2009-02-10 14:00:00,2.0,6.0,4,19.0 +2009-02-10 15:00:00,2.0,6.0,4,19.0 +2009-02-10 16:00:00,0.0,0.0,8,18.0 +2009-02-10 17:00:00,5.0,10.0,7,17.0 +2009-02-10 18:00:00,6.0,10.0,7,17.0 +2009-02-10 19:00:00,2.7182818285,2.7182818285,7,16.0 +2009-02-10 20:00:00,2.7182818285,2.7182818285,7,16.0 +2009-02-10 21:00:00,2.7182818285,2.7182818285,6,16.0 +2009-02-10 22:00:00,2.7182818285,2.7182818285,7,16.0 +2009-02-10 23:00:00,2.7182818285,2.7182818285,7,16.0 diff --git a/tests/unit_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_1998.csv b/tests/unit_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_1998.csv new file mode 100644 index 0000000..5835608 --- /dev/null +++ b/tests/unit_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_1998.csv @@ -0,0 +1,8761 @@ +Year,Month,Day,Hour,Minute,DHI,DNI,GHI,Clearsky DHI,Clearsky DNI,Clearsky GHI,Cloud Type,Solar Zenith Angle,Temperature,Unnamed: 14 +1998,1,1,0,0,0,0,0,0,0,0,6,156.67000000000002,4, +1998,1,1,1,0,0,0,0,0,0,0,6,153.77,4, +1998,1,1,2,0,0,0,0,0,0,0,6,146.51,4, +1998,1,1,3,0,0,0,0,0,0,0,6,137.17000000000002,4, +1998,1,1,4,0,0,0,0,0,0,0,6,127.02,4, 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a/tests/unit_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2018.csv b/tests/unit_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2018.csv new file mode 100644 index 0000000..854799b --- /dev/null +++ b/tests/unit_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2018.csv @@ -0,0 +1,8761 @@ +Year,Month,Day,Hour,Minute,DHI,DNI,GHI,Clearsky DHI,Clearsky DNI,Clearsky GHI,Cloud Type,Solar Zenith Angle,Temperature,Unnamed: 14 +2018,1,1,0,0,0,0,0,0,0,0,4,156.66,-4.1000000000000005, +2018,1,1,1,0,0,0,0,0,0,0,8,153.77,-4.4, +2018,1,1,2,0,0,0,0,0,0,0,4,146.51,-4.800000000000001, +2018,1,1,3,0,0,0,0,0,0,0,4,137.17000000000002,-5.1000000000000005, +2018,1,1,4,0,0,0,0,0,0,0,8,127.02,-5.4, +2018,1,1,5,0,0,0,0,0,0,0,8,116.68,-5.6000000000000005, +2018,1,1,6,0,0,0,0,0,0,0,0,106.53,-5.800000000000001, +2018,1,1,7,0,0,0,0,0,0,0,4,96.89,-5.9, +2018,1,1,8,0,18,210,26,18,210,26,4,87.77,-5.4, +2018,1,1,9,0,58,4,59,46,562,140,4,80.36,-4.2, 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a/tests/unit_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2019.csv b/tests/unit_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2019.csv new file mode 100644 index 0000000..b9ca0aa --- /dev/null +++ b/tests/unit_tests/data/solar_data/nrel/46.34_-119.28/46.34_-119.28_2019.csv @@ -0,0 +1,8761 @@ +Year,Month,Day,Hour,Minute,DHI,DNI,GHI,Clearsky DHI,Clearsky DNI,Clearsky GHI,Cloud Type,Solar Zenith Angle,Temperature,Unnamed: 14 +2019,1,1,0,0,0,0,0,0,0,0,0,156.69,-1.0, +2019,1,1,1,0,0,0,0,0,0,0,4,153.78,-1.4, +2019,1,1,2,0,0,0,0,0,0,0,7,146.51,-1.9, +2019,1,1,3,0,0,0,0,0,0,0,7,137.16,-1.9, +2019,1,1,4,0,0,0,0,0,0,0,7,127.01,-1.7000000000000002, +2019,1,1,5,0,0,0,0,0,0,0,4,116.66,-1.4, +2019,1,1,6,0,0,0,0,0,0,0,7,106.52,-1.1, +2019,1,1,7,0,0,0,0,0,0,0,6,96.87,-0.9, +2019,1,1,8,0,14,116,19,16,198,24,7,87.77,-0.3, +2019,1,1,9,0,55,244,96,41,571,137,4,80.36,0.4, +2019,1,1,10,0,87,346,180,52,725,246,7,74.44,1.7000000000000002, 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+import multiprocessing +import unittest +from unittest.mock import patch +from datetime import timedelta, datetime +import warnings + +import pandas as pd +import numpy as np + +import alternative_solar_profiles +from alternative_solar_profiles import AlternativeSolarProfiles +from validation import ParamValidationError +from config import UNIT_TESTS_DIR +from tests.utils.state_loader import save_state, load_state + +TEST_DATA_DIR = os.path.join(UNIT_TESTS_DIR, 'data') +EXPECTED_OUTPUTS = os.path.join(TEST_DATA_DIR, 'expected_outputs') +TEST_SOLAR_DATA_DIR = os.path.join(TEST_DATA_DIR, 'solar_data') + + +# noinspection PyUnresolvedReferences +def count_consecutive_zeros(df): + """ + Calculates count of consecutive zeros + + Adapted from https://stackoverflow.com/questions/27626542/ + """ + df['consec'] = df.groupby((df.temperature != + df.temperature.shift()).cumsum()).cumcount() + 1 + consecutive_zero = df[df['temperature'] == 0] + return consecutive_zero.consec + + +class TestAspInit(unittest.TestCase): + def test_init(self): + """Test default attributes are assigned correctly""" + self.asp = AlternativeSolarProfiles(latitude=46.34, + longitude=-119.28, + num_trials=10.00, + length_trials=3.1, + validate=True) + self.assertEqual(self.asp.latitude, 46.34) + self.assertEqual(self.asp.longitude, -119.28) + self.assertIsInstance(self.asp.num_trials, int) + self.assertEqual(self.asp.num_trials, 10) + + self.assertIsInstance(self.asp.length_trials, int) + self.assertEqual(self.asp.length_trials, 3) + self.assertEqual(self.asp.start_year, 1998) + self.assertEqual(self.asp.end_year, 2016) + self.assertEqual(self.asp.num_hourly_ghi_states, 11) + self.assertEqual(self.asp.num_hourly_dni_states, 11) + self.assertEqual(self.asp.num_daily_ghi_states, 11) + self.assertEqual(self.asp.num_daily_dni_states, 11) + self.assertEqual(self.asp.cloud_hours, (10, 17)) + self.assertEqual(self.asp.temp_bins, range(-30, 50, 3)) + self.assertEqual(self.asp.max_iter, 200) + self.assertTrue(self.asp.multithreading) + + self.assertIsNone(self.asp.nrel_data_df) + self.assertIsNone(self.asp.hourly_states) + self.assertIsNone(self.asp.daily_states) + self.assertEqual(self.asp.num_iters, []) + self.assertIsNone(self.asp.simple_prob_hourly) + self.assertIsNone(self.asp.state_prob_hourly) + self.assertIsNone(self.asp.simple_prob_daily) + self.assertIsNone(self.asp.state_prob_daily) + self.assertIsNone(self.asp.state_prob_hourly_grouped) + self.assertIsNone(self.asp.state_prob_daily_grouped) + self.assertIsNone(self.asp.simple_prob_hourly_grouped) + self.assertIsNone(self.asp.simple_prob_daily_grouped) + self.assertEqual(self.asp.solar_trials, []) + + def test_validate(self): + """Test for invalid parameter raising validation error""" + with self.assertRaises(ParamValidationError): + AlternativeSolarProfiles(latitude=46.34, + longitude=-119.28, + num_trials=10.00, + length_trials=3.1, + cloud_hours='failure') + + +class TestASP(unittest.TestCase): + def setUp(self) -> None: + """Runs before every test in test class""" + self.asp = AlternativeSolarProfiles(latitude=46.34, + longitude=-119.28, + num_trials=10, + length_trials=3) + + def tearDown(self) -> None: + """Runs after each test""" + # Ensure that everything is cleaned up after each test + self.asp = None + + @patch.object(alternative_solar_profiles, 'SOLAR_DATA_DIR', TEST_SOLAR_DATA_DIR) + def load_partial_nrel(self): + """Helper function to only load partial nrel data""" + self.asp.start_year = 2008 + self.asp.end_year = 2009 + self.asp.load_nrel_data() + self.assertFalse(self.asp.nrel_data_df.empty) + + def load_hourly_grouped_probabilities(self): + """Load in previously calculated hourly values""" + + simple_grouped_hourly = os.path.join(EXPECTED_OUTPUTS, + 'exp_simple_prob_hourly_grouped.json') + state_grouped_hourly = os.path.join(EXPECTED_OUTPUTS, + 'exp_state_prob_hourly_grouped.json') + self.asp.simple_prob_hourly_grouped = load_state(simple_grouped_hourly) + self.asp.state_prob_hourly_grouped = load_state(state_grouped_hourly) + + def load_daily_grouped_probabilities(self): + """Load in previously calculated daily values""" + + simple_grouped_daily = os.path.join(EXPECTED_OUTPUTS, + 'exp_simple_prob_daily_grouped.json') + state_grouped_daily = os.path.join(EXPECTED_OUTPUTS, + 'exp_state_prob_daily_grouped.json') + self.asp.simple_prob_daily_grouped = load_state(simple_grouped_daily) + self.asp.state_prob_daily_grouped = load_state(state_grouped_daily) + + @patch.object(alternative_solar_profiles, 'SOLAR_DATA_DIR', TEST_SOLAR_DATA_DIR) + def test_load_nrel_data(self): + # load only part of the data so test runs more quickly + self.asp.end_year = 2000 + self.asp.load_nrel_data() + + # test type requirements + self.assertTrue(self.asp.nrel_data_df['year'].dtype == np.int64) + self.assertTrue(self.asp.nrel_data_df['month'].dtype == np.int64) + self.assertTrue(self.asp.nrel_data_df['day'].dtype == np.int64) + self.assertTrue(self.asp.nrel_data_df['hour'].dtype == np.int64) + self.assertTrue(np.issubdtype(self.asp.nrel_data_df['datetime'].dtype, + np.datetime64)) + + # test all years loaded into df + expected_years = list(range(1998, self.asp.end_year + 1)) + loaded_years = self.asp.nrel_data_df['year'].unique() + self.assertTrue(np.array_equal(loaded_years, expected_years)) + + # test all final columns + expected_cols = ['year', 'month', 'day', + 'hour', 'dni', 'ghi', 'clear_sky_dni', + 'clear_sky_ghi', 'cloud_type', + 'solar_zenith_angle', 'temperature', 'datetime'] + self.assertTrue(np.array_equal(self.asp.nrel_data_df.columns, + expected_cols)) + + def test_bad_nrel_data_load(self): + """Test load failure for lat/lon that haven't been downloaded""" + self.asp.latitude = 180 + self.asp.latitude = 2 + with self.assertRaises(Exception): + self.asp.load_nrel_data() + + def test_clean_temperature(self): + # load only two years of data so test is faster + self.load_partial_nrel() + + # copy of original data + unclean = self.asp.nrel_data_df.copy() + + # assert that there were lengths of consecutive zeros >= 4 + consecutive_zeroes = count_consecutive_zeros(self.asp.nrel_data_df) + self.assertFalse(consecutive_zeroes.max() < 4) + + self.asp.clean_temperature() + + # test that all lengths of consecutive zeros now < 4 + consecutive_zeroes = count_consecutive_zeros(self.asp.nrel_data_df) + self.assertTrue(consecutive_zeroes.max() < 4) + + # for brevity + cleaned = self.asp.nrel_data_df + + # assert non-zero values were not altered + self.assertFalse(np.array_equal(unclean[unclean['temperature'] != 0], + cleaned[cleaned['temperature'] != 0])) + + # assert that there are now nan values + self.assertTrue(np.any(np.isnan(cleaned.temperature))) + + # test case where no values are equal to 0 + unclean.loc[unclean.temperature == 0, 'temperature'] = np.pi + self.asp.nrel_data_df = unclean.copy() + self.asp.clean_temperature() + + # test that clean temperature did nothing + self.assertTrue(np.array_equal(unclean, self.asp.nrel_data_df)) + + def test_bin_hourly_data(self): + # load only two years of data so test is faster + self.load_partial_nrel() + + # bin the data + self.asp.bin_hourly_data() + + expected = pd.read_csv(os.path.join(EXPECTED_OUTPUTS, 'expected_hourly_states.csv'), + index_col=None) + + # np.isclose used because math.exp(1) is rounded when saved as csv + self.assertTrue(np.all(np.isclose(expected, self.asp.hourly_states))) + + # for brevity + num_dni = self.asp.num_hourly_dni_states + num_ghi = self.asp.num_hourly_ghi_states + + # assert states are in the allowable ranges + # additional state is for night states (state = math.exp(1)) + expected_dni_states = np.array([math.exp(1)] + list(range(0, num_dni))) + dni_states = self.asp.hourly_states.dni_state.unique() + self.assertTrue(set(dni_states).issubset(set(expected_dni_states))) + + # additional state is for night states (state = math.exp(1)) + expected_ghi_states = np.array([math.exp(1)] + list(range(0, num_ghi))) + ghi_states = self.asp.hourly_states.ghi_state.unique() + self.assertTrue(set(ghi_states).issubset(set(expected_ghi_states))) + + allowed_temp_bins = set(range(len(self.asp.temp_bins))) + temp_bins = set(self.asp.hourly_states.temp_state.unique()) + self.assertTrue(temp_bins.issubset(allowed_temp_bins)) + + # assert the values are binned as expected + # set new state quantities and temp_bins + self.asp.num_hourly_dni_states = 3 + self.asp.num_hourly_ghi_states = 4 + self.asp.temp_bins = range(-30, 60, 10) + + # generate some fake data for test + small_test = pd.DataFrame({'clear_sky_ghi': [0, 0, 260, 570, 369], + 'ghi': [0, 0, 130, 570, 0], + 'clear_sky_dni': [0, 0, 480, 926, 293], + 'dni': [0, 0, 80, 926, 0], + 'temperature': [-5, 2, 18, 12, 25], + 'year': [2020] * 5, + 'month': [11] * 5, + 'day': [2] * 5, + 'hour': list(range(5)), + 'cloud_type': np.random.randint(10, size=5) + }) + + # assign fake data to nrel dataframe + self.asp.nrel_data_df = small_test + self.asp.bin_hourly_data() + + # expected results of binning + expected_dni = np.array([math.exp(1), math.exp(1), 2, 0, 2]) + expected_ghi = np.array([math.exp(1), math.exp(1), 2, 0, 3]) + expected_temp_bins = np.array([3, 4, 5, 5, 6]) + + # assert expected is equal to calculated + self.assertTrue(np.array_equal(self.asp.hourly_states.dni_state, expected_dni)) + self.assertTrue(np.array_equal(self.asp.hourly_states.ghi_state, expected_ghi)) + self.assertTrue(np.array_equal(self.asp.hourly_states.temp_state, expected_temp_bins)) + + def test_bin_daily_data(self): + self.load_partial_nrel() + self.asp.bin_daily_data() + + expected = pd.read_csv(os.path.join(EXPECTED_OUTPUTS, 'expected_daily_states.csv'), + index_col=None) + + # np.isclose used because math.exp(1) is rounded when saved as csv + self.assertTrue(np.all(np.isclose(expected, self.asp.daily_states))) + + # for brevity + num_ghi = self.asp.num_daily_ghi_states + num_dni = self.asp.num_daily_dni_states + + expected_dni_states = np.array(list(range(0, num_dni))) + 1 + dni_states = self.asp.daily_states.dni_state.unique() + self.assertTrue(set(dni_states).issubset(set(expected_dni_states))) + + expected_ghi_states = np.array(list(range(0, num_ghi))) + 1 + ghi_states = self.asp.daily_states.ghi_state.unique() + self.assertTrue(set(ghi_states).issubset(set(expected_ghi_states))) + self.assertTrue(self.asp.daily_states is not None) + + # create fake nrel df + dummy_clouds = np.hstack([np.zeros(24), np.zeros(10), np.ones(8), np.zeros(6)]) + dummy_ghi = np.hstack([np.zeros(10), np.ones(8) * 36, np.zeros(6), ] * 2) + dummy_clear_ghi = np.hstack([np.zeros(10), np.ones(8) * 36, np.zeros(6), np.zeros(10), + np.ones(8) * 72, np.zeros(6)]) + + small_test = pd.DataFrame({'clear_sky_ghi': dummy_clear_ghi, + 'ghi': dummy_ghi, + 'clear_sky_dni': dummy_clear_ghi, + 'dni': dummy_ghi, + 'year': [2020] * 48, + 'month': [11] * 48, + 'day': [2] * 24 + [3] * 24, + 'hour': list(range(24)) + list(range(24)), + 'cloud_type': dummy_clouds + }) + + # assign fake data to nrel dataframe + self.asp.nrel_data_df = small_test + self.asp.bin_daily_data() + + # assert expected is equal to calculated + self.assertTrue(self.asp.daily_states is not None) + self.assertTrue(np.array_equal(self.asp.daily_states.cloud_state, np.array([0, 1]))) + self.assertTrue(np.array_equal(self.asp.daily_states.dni_state, np.array([11, 6]))) + self.assertTrue(np.array_equal(self.asp.daily_states.ghi_state, np.array([11, 6]))) + + def test_create_hourly_state_transition_matrices(self): + # read previously calculated hourly bin data + states = pd.read_csv(os.path.join(EXPECTED_OUTPUTS, 'expected_hourly_states.csv'), + index_col=None).round(10) + + # replace rounded exp with actual value + states = states.replace(round(math.exp(1), 10), math.exp(1)) + self.asp.hourly_states = states + self.asp.create_hourly_state_transition_matrices() + + simple_file = os.path.join(EXPECTED_OUTPUTS, 'expected_simple_prob_hourly.csv') + state_file = os.path.join(EXPECTED_OUTPUTS, 'expected_state_prob_hourly.csv') + + expected_simple = pd.read_csv(simple_file, index_col=None).round(10) + simple_prob_hourly = self.asp.simple_prob_hourly.round(10) + self.assertTrue(expected_simple.equals(simple_prob_hourly)) + + expected_state = pd.read_csv(state_file, index_col=None).round(10) + state_prob_hourly = self.asp.state_prob_hourly.round(10) + self.assertTrue(expected_state.equals(state_prob_hourly)) + + def test_create_daily_state_transition_matrices(self): + # read previously calculated daily bin data + states = pd.read_csv(os.path.join(EXPECTED_OUTPUTS, 'expected_daily_states.csv'), + index_col=None) + self.asp.daily_states = states + self.asp.create_daily_state_transition_matrices() + + simple_file = os.path.join(EXPECTED_OUTPUTS, 'expected_simple_prob_daily.csv') + state_file = os.path.join(EXPECTED_OUTPUTS, 'expected_state_prob_daily.csv') + + expected_simple_daily = pd.read_csv(simple_file, index_col=None).round(10) + simple_prob_daily = self.asp.simple_prob_daily.round(10) + self.assertTrue(expected_simple_daily.equals(simple_prob_daily)) + + expected_state = pd.read_csv(state_file, index_col=None).round(10) + state_prob_daily = self.asp.state_prob_daily.round(10) + self.assertTrue(expected_state.equals(state_prob_daily)) + + def test_create_state_transition_matrices(self): + self.asp.start_year = 2008 + self.asp.end_year = 2009 + self.asp.create_state_transition_matrices() + + # test that all negative cloud_types have been removed + self.assertFalse(np.any(self.asp.nrel_data_df['cloud_type'] < 0)) + + # assert that values have been assigned. functions tested individually + self.assertIsNotNone(self.asp.daily_states) + self.assertIsNotNone(self.asp.hourly_states) + self.assertIsNotNone(self.asp.simple_prob_daily) + self.assertIsNotNone(self.asp.state_prob_daily) + self.assertIsNotNone(self.asp.simple_prob_hourly) + self.assertIsNotNone(self.asp.state_prob_hourly) + + def test_generate_trial_date_ranges(self): + self.load_partial_nrel() + test_ranges = self.asp.generate_trial_date_ranges() + + # assert there are as many date ranges as num_trials + self.assertEqual(len(test_ranges), self.asp.num_trials) + + # for each date range assert they have the appropriate number of + # datetime objects and that they are that many hours apart + range_length = self.asp.length_trials * 24 + for date_range in test_ranges: + self.assertEqual(len(date_range), range_length) + first_hour = date_range[0] + last_hour = date_range[-1] + self.assertEqual(first_hour + timedelta(hours=range_length - 1), + last_hour) + + # test date range creation with seeded ranges and ensure a leap year + # would be included + ranges_start = [datetime(year=2020, month=month, day=28) + for month in range(1, self.asp.num_trials + 1)] + seeded_ranges = self.asp.generate_trial_date_ranges(ranges_start) + + for date_range in seeded_ranges: + self.assertEqual(len(date_range), range_length) + + first_hour = date_range[0] + last_hour = date_range[-1] + + self.assertIn(first_hour, ranges_start) + + # test leap day has been removed + self.assertNotIn(datetime(year=2020, month=2, day=29), date_range) + + # test a day has been added to replace the leap day + if first_hour.month == 2: + expected_offset = range_length + 23 + self.assertEqual(first_hour + timedelta(hours=expected_offset), last_hour) + else: + expected_offset = range_length - 1 + self.assertEqual(first_hour + timedelta(hours=expected_offset), last_hour) + + def test_preprocess_states(self): + # get expected output files + simple_hourly_file = os.path.join(EXPECTED_OUTPUTS, 'expected_simple_prob_hourly.csv') + state_hourly_file = os.path.join(EXPECTED_OUTPUTS, 'expected_state_prob_hourly.csv') + simple_daily_file = os.path.join(EXPECTED_OUTPUTS, 'expected_simple_prob_daily.csv') + state_daily_file = os.path.join(EXPECTED_OUTPUTS, 'expected_state_prob_daily.csv') + + # load expected outputs from file + simple_hourly = pd.read_csv(simple_hourly_file, index_col=None).round(10) + state_hourly = pd.read_csv(state_hourly_file, index_col=None).round(10) + simple_daily = pd.read_csv(simple_daily_file, index_col=None).round(10) + state_daily = pd.read_csv(state_daily_file, index_col=None).round(10) + + # set binned hourly/daily probabilities to previously calculated values + self.asp.simple_prob_hourly = simple_hourly + self.asp.state_prob_hourly = state_hourly + self.asp.simple_prob_daily = simple_daily + self.asp.state_prob_daily = state_daily + self.asp.preprocess_states() + + self.assertIsInstance(self.asp.simple_prob_hourly_grouped, dict) + self.assertIsInstance(self.asp.state_prob_hourly_grouped, dict) + self.assertIsInstance(self.asp.simple_prob_daily_grouped, dict) + self.assertIsInstance(self.asp.state_prob_daily_grouped, dict) + + simple_grouped_hourly = os.path.join(EXPECTED_OUTPUTS, + 'exp_simple_prob_hourly_grouped.json') + state_grouped_hourly = os.path.join(EXPECTED_OUTPUTS, + 'exp_state_prob_hourly_grouped.json') + simple_grouped_daily = os.path.join(EXPECTED_OUTPUTS, + 'exp_simple_prob_daily_grouped.json') + state_grouped_daily = os.path.join(EXPECTED_OUTPUTS, + 'exp_state_prob_daily_grouped.json') + + test_iter = ((simple_grouped_hourly, + self.asp.simple_prob_hourly_grouped), + (state_grouped_hourly, + self.asp.state_prob_hourly_grouped), + (simple_grouped_daily, + self.asp.simple_prob_daily_grouped), + (state_grouped_daily, + self.asp.state_prob_daily_grouped), + ) + + for expected_out_file, actual_output in test_iter: + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=FutureWarning) + expected = load_state(expected_out_file) + for key in expected: + self.assertTrue(np.array_equiv(expected[key], actual_output[key])) + + def test_generate_random_state_daily(self): + # get state transition probabilities data + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=FutureWarning) + self.load_daily_grouped_probabilities() + + # create date_range to generate random states for + range_length = self.asp.length_trials * 24 + date_range = pd.date_range(start='2009-02-09 15:00:00', periods=range_length, + freq='H') + + # generate the random states + gen_1 = self.asp.generate_random_state_daily(date_range=date_range) + + # get the number of unique days present + num_days = len(np.unique(date_range.date)) + + # assert a random state has been generated for each date present + self.assertEqual(len(gen_1), num_days) + + # get list of states present in daily states + states = [state[1:] for state in self.asp.state_prob_daily_grouped.keys()] + + # assert that the generated state was present in the daily states + for ind, row in gen_1.iterrows(): + self.assertIn(tuple(row), states) + + # generate another set of random states + gen_2 = self.asp.generate_random_state_daily(date_range=date_range) + + # assert they are not equal + self.assertFalse(np.array_equiv(gen_1, gen_2)) + + def test_generate_random_state_hourly(self): + # load in data needed for function + self.load_partial_nrel() + + # get state transition probabilities data + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=FutureWarning) + self.load_hourly_grouped_probabilities() + + # create date_range to generate random states for + date = datetime(year=2009, month=2, day=10).date() + one_day = pd.date_range(start=date, periods=24, freq='H') + + clear_sky_data = self.asp.nrel_data_df.merge( + one_day.to_frame(), + left_on='datetime', + right_on=0, + how='inner').set_index('datetime') + one_day_clear_sky_data = clear_sky_data[clear_sky_data.index.date == date] + night_states = one_day_clear_sky_data['clear_sky_ghi'] == 0 + current_state = [None, None, None, None] + + # returns boring result because current_state is None + rand_state_1 = self.asp.generate_random_state_hourly(one_day, night_states, + current_state) + + # generate a second set of states using first result + rand_state_2 = self.asp.generate_random_state_hourly(one_day, night_states, + rand_state_1) + + # get list of states present in hourly states + states = [state[3:] for state in self.asp.state_prob_hourly_grouped.keys()] + + # assert that the generated state was present in the hourly states + for ind, row in rand_state_2.iterrows(): + self.assertIn(tuple(row), states) + + # generate a third set of states + rand_state_3 = self.asp.generate_random_state_hourly(one_day, night_states, + rand_state_2) + # assert they are not equal + self.assertFalse(np.array_equiv(rand_state_2, rand_state_3)) + + def test_aggregate_hourly_states(self): + # load in data needed for function + self.load_partial_nrel() + + # load in previously calculated hourly states df + hourly_state = pd.read_csv(os.path.join(EXPECTED_OUTPUTS, 'rand_hourly_state.csv'), + parse_dates=True, index_col=0) + + # get df of one day clear sky data + date = datetime(year=2009, month=2, day=10).date() + one_day = pd.date_range(start=date, periods=24, freq='H') + clear_sky_data = self.asp.nrel_data_df.merge( + one_day.to_frame(), + left_on='datetime', + right_on=0, + how='inner').set_index('datetime') + one_day_clear_sky_data = clear_sky_data[clear_sky_data.index.date == date] + + # aggregate values + aggregated = self.asp.aggregate_hourly_states(one_day_clear_sky_data, hourly_state) + expected = (9.0, 7.0, [8]) + self.assertEqual(aggregated, expected) + + def test_calc_solar_params_from_states(self): + # load in data needed for function + self.load_partial_nrel() + + # load in previously calculated hourly states df + hourly_state = pd.read_csv(os.path.join(EXPECTED_OUTPUTS, 'rand_hourly_state.csv'), + parse_dates=True, + index_col=0) + + # get df of one day clear sky data + date = datetime(year=2009, month=2, day=10).date() + one_day = pd.date_range(start=date, periods=24, freq='H') + clear_sky_data = self.asp.nrel_data_df.merge( + one_day.to_frame(), + left_on='datetime', + right_on=0, + how='inner').set_index('datetime') + one_day_clear_sky_data = clear_sky_data[clear_sky_data.index.date == date] + + # calculate parameters + states = self.asp.calc_solar_params_from_states(hourly_state, one_day_clear_sky_data) + + # read in previously calculated results + expected = pd.read_csv(os.path.join(EXPECTED_OUTPUTS, 'exp_solar_params_calc.csv'), + parse_dates=True, index_col=0) + + # assert they are equal - round(15) for csv rounding differences + self.assertTrue(expected.round(10).equals(states.round(10))) + + def test_compare_hourly_daily_states(self): + # load in data needed for function + self.load_partial_nrel() + + # get state transition probabilities data + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=FutureWarning) + self.load_hourly_grouped_probabilities() + + # load in previously calculated hourly states df + daily_state = pd.read_csv(os.path.join(EXPECTED_OUTPUTS, 'rand_daily_states.csv'), + parse_dates=True, index_col=0) + + # daily state index read in as a datetime object by default; needs + # to be a date object. + daily_state.index = daily_state.index.date + + # create date_range to generate random states for + range_length = self.asp.length_trials * 24 + date_range = pd.date_range(start='2009-02-09 15:00:00', periods=range_length, + freq='H') + + # get df of clear sky data + clear_sky_data = self.asp.nrel_data_df.merge( + date_range.to_frame(), + left_on='datetime', + right_on=0, + how='inner').set_index('datetime') + + gen_1 = self.asp.compare_hourly_daily_states(daily_state, + date_range, + clear_sky_data, + queue=None) + hourly_states, gen_clear_sky = gen_1 + + # assert that the clear sky data has been unchanged + self.assertTrue(clear_sky_data.equals(gen_clear_sky)) + + # assert that the first and last days were not iterated through + # (all hours not present) + self.assertEqual(self.asp.num_iters[0], 0) + self.assertEqual(self.asp.num_iters[-1], 0) + + # assert that the number of iterations are less than the max num + self.assertTrue(np.all(np.less_equal(self.asp.num_iters, self.asp.max_iter))) + + # test that hourly states have been generated for each hour in trial + self.assertEqual(len(hourly_states), range_length) + + # test concurrent version of function + queue = multiprocessing.Queue() + self.asp.compare_hourly_daily_states(daily_state, + date_range, + clear_sky_data, + queue=queue) + mp_hourly_states, mp_clear_sky = queue.get() + + # same tests as before but with concurrent version + self.assertTrue(clear_sky_data.equals(mp_clear_sky)) + self.assertEqual(len(mp_hourly_states), range_length) + + @patch.object(alternative_solar_profiles.AlternativeSolarProfiles, + 'preprocess_states') + def test_create_trial_data(self, fake_preprocess): + + # patch preprocess_states to save time + fake_preprocess.return_value = None + + # reduce number of trials to save time + self.asp.num_trials = 2 + + # load in data needed for function + self.load_partial_nrel() + + # get state transition probabilities data + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=FutureWarning) + self.load_hourly_grouped_probabilities() + self.load_daily_grouped_probabilities() + self.asp.create_trial_data() + + self.assertIsNotNone(self.asp.solar_trials) + self.assertEqual(len(self.asp.solar_trials), self.asp.num_trials) + + +if __name__ == '__main__': + asp = AlternativeSolarProfiles(latitude=46.34, + longitude=-119.28, + num_trials=10, + length_trials=3) \ No newline at end of file diff --git a/tests/utils/state_loader.py b/tests/utils/state_loader.py new file mode 100644 index 0000000..4e1d6a5 --- /dev/null +++ b/tests/utils/state_loader.py @@ -0,0 +1,161 @@ +# -*- coding: utf-8 -*- +"""" +Functions to save and load the outputs of a AlternativeSolarProfiles instance. + +Files are saved as human-readable json files. + +save_state and load_state functions handle the dictionaries which store the +grouped state probability transition dataframes. + +save_trials and load_trials functions handle the list of dataframes which store +the calculated trial solar data. + +save_date_ranges and load_date_ranges functions handle the list of datetime +indexes from asp.generate_trial_date_ranges. + +Pickled objects are no longer being used so as to avoid repeating issues with +Pandas altering pickled formats and needing to create a custom unpickler. +""" + +import json +import ast + +import pandas as pd + + +def save_state(dict_dfs, file_name): + """ + Function to save dict of pandas dataframes + + Adapted from here: https://stackoverflow.com/questions/ + 33061302/dictionary-of-pandas-dataframe-to-json""" + + # convert dataframes to dictionary + dict_jsons = {} + + for key in dict_dfs.keys(): + if type(key) == int: + stored_key = key + elif type(key) == tuple: + stored_key = key.__str__() + else: + raise TypeError('dictionary key must be tuple or int') + # add the index to the dataframe dict to be retrieved in load_state + # column renamed to avoid pandas loading in as a RangeIndex instead of an int64 index + df = dict_dfs[key].reset_index().rename(columns={'index': 'index_'}) + dict_jsons[stored_key] = df.to_dict(orient='records') + + # add entry for dtypes and column order + # this assumes the column order and types are equal for all dfs + first_key = list(dict_dfs.keys())[0] + column_order = dict_dfs[first_key].columns.tolist() + ['index_'] + dtypes = dict_dfs[first_key].dtypes.apply(lambda x: x.name).to_dict() + + if type(first_key) == int: + dtypes['index_'] = 'int64' + else: + dtypes['index_'] = 'object' + + dict_jsons['c_order'] = column_order + dict_jsons['dtypes'] = dtypes + + with open(file_name, 'w') as fp: + json.dump(dict_jsons, fp, indent=4) + + +def load_state(file_name): + with open(file_name, 'r') as fp: + dict_jsons = json.load(fp) + + column_order = dict_jsons['c_order'] + dtypes = dict_jsons['dtypes'] + + dict_dfs = {} + for key in dict_jsons: + # skip the column order column + if key == 'c_order' or key == 'dtypes': + continue + # convert json to dataframe + dataframe = pd.DataFrame(dict_jsons[key]).astype(dtypes) + # reorder columns to match original + dataframe = dataframe[column_order] + + # put into the dictionary + if key.isdigit(): + stored_key = int(key) + else: + stored_key = ast.literal_eval(key) + dict_dfs[stored_key] = dataframe.set_index('index_') + + return dict_dfs + + +def save_trials(list_dfs, file_name): + # convert dataframes to dictionary + dict_jsons = {} + + for ind, df in enumerate(list_dfs): + # add the index to the dataframe dict to be retrieved in load_state + df = df.reset_index().rename(columns={'index': 'index_'}) + df['index_'] = df['index_'].dt.strftime('%Y-%m-%d %H:%M:%S') + dict_jsons[ind] = df.to_dict(orient='records') + + # add entry for dtypes and column order + # this assumes the column order and types are equal for all dfs + first_df = list_dfs[0] + + column_order = first_df.columns.tolist() + ['index_'] + dtypes = first_df.dtypes.apply(lambda x: x.name).to_dict() + dtypes['index_'] = 'object' + + dict_jsons['c_order'] = column_order + dict_jsons['dtypes'] = dtypes + + with open(file_name, 'w') as fp: + json.dump(dict_jsons, fp, indent=4) + + +def load_trials(file_name): + with open(file_name, 'r') as fp: + dict_jsons = json.load(fp) + + column_order = dict_jsons['c_order'] + dtypes = dict_jsons['dtypes'] + + list_dfs = [] + for key in dict_jsons: + # skip the column order column + if key == 'c_order' or key == 'dtypes': + continue + # convert json to dataframe + dataframe = pd.DataFrame(dict_jsons[key]).astype(dtypes) + dataframe['index_'] = pd.to_datetime(dataframe['index_'], format='%Y-%m-%d %H:%M:%S') + # reorder columns to match original + dataframe = dataframe[column_order] + list_dfs.append(dataframe.set_index('index_')) + + return list_dfs + + +def save_date_ranges(list_ranges, file_name): + # convert indexes to dictionary + dict_jsons = {} + + for ind, date_range in enumerate(list_ranges): + dict_jsons[ind] = date_range.strftime('%Y-%m-%d %H:%M:%S').tolist() + + with open(file_name, 'w') as fp: + json.dump(dict_jsons, fp, indent=4) + + +def load_date_ranges(file_name): + with open(file_name, 'r') as fp: + dict_jsons = json.load(fp) + + list_date_range = [] + for key in dict_jsons: + # convert json to dataframe + datetime_index = pd.DatetimeIndex(dict_jsons[key]) + list_date_range.append(datetime_index) + + return list_date_range diff --git a/validation.py b/validation.py new file mode 100644 index 0000000..a102d50 --- /dev/null +++ b/validation.py @@ -0,0 +1,1182 @@ +# -*- coding: utf-8 -*- +""" +Includes classes and functions for error handling and parameter + validation + +Classes: +- Error: base error class +- ParamValidationError: class for parameter validation errors + +Functions: +- validate_parameter: performs parameter validation +- log_error: logs errors and warnings to file + +""" + +import os +import datetime as dt +from pydoc import locate +from copy import deepcopy +import pandas as pd +import numpy as np +import logging +from scipy.stats import norm +from scipy.signal import periodogram +from pvlib import pvsystem +import pytz +from config import DATA_DIR + +VALIDATION_TYPE_STRINGS = { + "is_none": "not be blank", + "data_type": "be of type", + "max_val": "be <=", + "min_val": "be >=", + "enums": "be one of", + "size": "have size" +} + +# Load validation constraints csv +CONSTRAINTS_DF = pd.read_csv(os.path.join(DATA_DIR, 'parameter_validation.csv'), + index_col=0, usecols=range(9), + skiprows=1, encoding="ISO-8859-1", + names=['parameter', 'data_type', 'min_val', + 'max_val', 'enums', 'size', 'custom_func', + 'custom_args', 'custom_message']) + +# Format columns +CONSTRAINTS_DF['data_type'] = CONSTRAINTS_DF['data_type'].apply(lambda x: x.split(',')) +CONSTRAINTS_DF['enums'] = CONSTRAINTS_DF['enums'].apply( + lambda x: x.split(',') if isinstance(x, str) else x) + +# Turn into dict +CONSTRAINTS_DICT = {param_key: {col_key: col_val + for col_key, col_val in param_val.items() + if not (isinstance(col_val, float) + and not np.isfinite(col_val))} + for param_key, param_val + in CONSTRAINTS_DF.to_dict(orient='index').items()} + + +def log_error(error_message): + """ + Logs error message to file + :param error_message: Error message + + """ + + logging.basicConfig(filename='MCOR_error_messages.log', + level=logging.ERROR, + format='\n%(asctime)s %(message)s', + datefmt='%m/%d/%Y %I:%M:%S %p') + logging.exception(error_message) + + +class Error(Exception): + """Base class for exceptions in this module.""" + + def __init__(self, *args, **kwargs): + pass + + +class ParamValidationError(Exception): + """ + Custom exception that is raised for parameter validation errors. Saves results to a + logger. + """ + + def __init__(self, param_name, violation_type, violation_value, + user_value, custom_message=None): + """ + Creates a message based on the input arguments and saves to a logger. + :param param_name: Name of the parameter + :param violation_type: Type of violation, options are: + ["is_none", "data_type", "max_val", "min_val", "enums", "size"] + :param violation_value: The value that the parameter is violating + :param custom_message: A custom message that can be specified instead of the common + format + """ + + # Create error message + if custom_message is not None: + message = custom_message + else: + message = "The parameter {} must {} {}. You entered {}".format( + param_name, VALIDATION_TYPE_STRINGS[violation_type], + violation_value, user_value) + + # Log error + log_error(message) + super(ParamValidationError, self).__init__(message) + + +def validate_all_parameters(args_dict): + """ Validate all parameters given a dict. + The format of args_dict is: + {"param_name": param} + """ + + # Validate each parameter + for param_name, param in args_dict.items(): + kwargs = deepcopy(CONSTRAINTS_DICT[param_name]) + if 'custom_func' in kwargs.keys(): + if 'custom_args' in kwargs.keys(): + kwargs['custom_args'] = {key: args_dict[key] for key + in kwargs['custom_args'].split(',')} + else: + kwargs['custom_args'] = {} + + validate_parameter(param_name, param, **kwargs) + + +def validate_parameter(param_name, param_value, data_type=None, max_val=None, + min_val=None, enums=None, size=None, custom_func=None, + custom_args=None, custom_message=None): + """ + Performs parameter validation based on optional inputs for constraints: + + :param param_name: the name of the parameter to be validated + :param param_value: the value of the parameter to be validated + :param data_type: required data type + :param max_val: required maximum value (inclusive) + :param min_val: required minimum value (inclusive) + :param enums: set of allowed values + :param size: allowable size + :param custom_func: a custom function specifying a validation test + :param custom_args: arguments associated with the custom function + :param custom_message: error message associated with the custom function + """ + + # First check that the parameter is not None + try: + assert param_value is not None + except AssertionError: + raise ParamValidationError(param_name, "is_none", "", param_value) + + # Perform try, except with assert statement on all specified args + if data_type is not None: + try: + assert isinstance(param_value, tuple([locate(dtype) for dtype in data_type])) + except AssertionError: + raise ParamValidationError(param_name, "data_type", data_type, param_value) + + if max_val is not None: + try: + assert param_value <= max_val + except AssertionError: + raise ParamValidationError(param_name, "max_val", max_val, param_value) + + if min_val is not None: + try: + assert param_value >= min_val + except AssertionError: + raise ParamValidationError(param_name, "min_val", min_val, param_value) + + if enums is not None: + try: + assert param_value in enums + except AssertionError: + raise ParamValidationError(param_name, "enums", enums, param_value) + + if size is not None: + try: + assert len(param_value) == size + except AssertionError: + raise ParamValidationError(param_name, "size", size, param_value) + + if custom_func is not None: + try: + assert VALIDATION_FUNCS[custom_func](param_value, **custom_args) + except AssertionError: + raise ParamValidationError(param_name, "", "", param_value, + custom_message=custom_message) + + +# Custom parameter validation functions +def check_path(path): + """ Check that the path exists. """ + try: + _ = os.listdir(path) + return True + except FileNotFoundError: + return False + + +def check_sitename(sitename, path, start_year, end_year): + """ Check that data for the corresponding sitename has been downloaded. + """ + dirs = os.listdir(os.path.join(path, 'nrel')) + if sitename not in dirs: + return False + files = os.listdir(os.path.join(path, 'nrel', sitename)) + for year in range(start_year, end_year+1): + if '{}_{}.csv'.format(sitename, year) not in files: + return False + return True + + +def check_solar_profile(solar_profile): + """ Check that solar_profile has the necessary columns and index type. + """ + + # Check that solar_profile has the necessary columns + if len({'ghi', 'dni'} - set(solar_profile.columns)): + if len({'dhi', 'dni'} - set(solar_profile.columns)): + return False + + # Check the type of the index + if not isinstance(solar_profile.index, pd.DatetimeIndex): + return False + + return True + + +def check_temp_profile(temp_profile): + """ Check that temp_profile has the necessary columns and index type. + """ + + # Check that solar_profile has the necessary columns + if 'temp_celcius' not in temp_profile.columns: + return False + + # Check the type of the index + if not isinstance(temp_profile.index, pd.DatetimeIndex): + return False + return True + + +def check_power_profile(power_profile): + """ Check that power_profile has the necessary index type. """ + + # Check the type of the index + return isinstance(power_profile.index, pd.DatetimeIndex) + + +def check_night_profile(night_profile): + """ Check that night_profile has the necessary columns and index type. + """ + + # Check that night_profile has the necessary columns + if len({'is_night', 'is_first_hour_of_night', 'night_duration'} - + set(night_profile.columns)): + return False + + # Check the type of the index + if not isinstance(night_profile.index, pd.DatetimeIndex): + return False + return True + + +def check_strings(strings): + """ Check that strings has the appropriate keys and that those fields have valid values. + """ + + if len({'mods_per_string', 'strings_per_inv'} - set(strings.keys())): + return False + + validate_parameter('mods_per_string', strings['mods_per_string'], + **CONSTRAINTS_DICT['mods_per_string']) + validate_parameter('strings_per_inv', strings['strings_per_inv'], + **CONSTRAINTS_DICT['strings_per_inv']) + + return True + + +def check_night_duration(night_duration, is_night): + """ Check that the night duration is > 0 if it is night. """ + + if is_night: + return night_duration > 0 + else: + return night_duration == 0 + + +def check_fuel_curve_model(fuel_curve_model): + """ Check that expected keys are present. + """ + return len({'1/4 Load (gal/hr)', '1/2 Load (gal/hr)', '3/4 Load (gal/hr)', + 'Full Load (gal/hr)'} - set(fuel_curve_model.keys())) == 0 + + +def check_outputs(outputs): + """ Several checks on the aggregated system output. """ + + # Check that required keys are included + if len({'pv_percent', 'batt_percent', 'gen_percent', + 'storage_recovery_percent', 'fuel_used_gal', + 'generator_power_kW'} - set(outputs.keys())) > 0: + return False + return True + + +def check_existing_components(existing_components): + """ Check that each element in existing components is a Component of the appropriate type. + """ + + # Check that keys are allowable + if len(set(existing_components.keys()) - {'pv', 'batt', 'gen', 'fuel_tank'}) > 0: + return False + + # Check the datatype for each elem + type_key = {'pv': 'microgrid_system.PV', + 'batt': 'microgrid_system.Battery', + 'gen': 'microgrid_system.Generator', + 'fuel_tank': 'microgrid_system.FuelTank'} + for key, val in existing_components.items(): + validate_parameter(key, val, data_type=[type_key[key]]) + return True + + +def check_net_metering_limits(net_metering_limits): + """ Check that net metering limits have the format: + {type: ['capacity_cap' or 'percent_of_load'], value: [ or ]} + """ + + # Check that it has the required keys + if 'type' not in net_metering_limits.keys(): + return False + + # Check the data type and range of the value parameter + if net_metering_limits['type'] == 'no_nm_use_battery': + return True + else: + if 'value' not in net_metering_limits.keys(): + return False + if net_metering_limits['type'] == 'capacity_cap': + validate_parameter('net_metering_capacity_cap', + net_metering_limits['value'], + data_type=['int', 'float'], + min_val=0) + return True + elif net_metering_limits['type'] == 'percent_of_load': + validate_parameter('net_metering_percent', + net_metering_limits['value'], + data_type=['int', 'float'], + min_val=0, + max_val=200) + return True + # If the type is not one of the above, raise an error + else: + return False + + +def check_location(location): + """ Check that location has the following keys and value datatypes: + {'longitude': float, 'latitude': float, 'timezone': string, + 'altitude': float} + """ + + if len({'longitude', 'latitude', 'timezone', 'altitude'} - set(location.keys())) > 0: + return False + validate_parameter('longitude', location['longitude'], + **CONSTRAINTS_DICT['longitude']) + validate_parameter('latitude', location['latitude'], + **CONSTRAINTS_DICT['latitude']) + validate_parameter('timezone', location['timezone'], custom_args={}, + **CONSTRAINTS_DICT['timezone']) + validate_parameter('altitude', location['altitude'], + **CONSTRAINTS_DICT['altitude']) + return True + + +def check_system_costs(system_costs): + """ Check that system costs has the following keys: + {'generator_costs', 'fuel_tank_costs', 'pv_costs', 'battery_costs', 'om_costs'} + and run checks on each element. + """ + + if len({'generator_costs', 'fuel_tank_costs', 'pv_costs', 'battery_costs', + 'om_costs'} - set(system_costs.keys())) > 0: + return False + + validate_parameter('generator_costs', system_costs['generator_costs'], + custom_args={}, **CONSTRAINTS_DICT['generator_costs']) + validate_parameter('fuel_tank_costs', system_costs['fuel_tank_costs'], + custom_args={}, **CONSTRAINTS_DICT['fuel_tank_costs']) + validate_parameter('pv_costs', system_costs['pv_costs'], + custom_args={}, **CONSTRAINTS_DICT['pv_costs']) + validate_parameter('battery_costs', system_costs['battery_costs'], + custom_args={}, **CONSTRAINTS_DICT['battery_costs']) + validate_parameter('om_costs', system_costs['om_costs'], + custom_args={}, **CONSTRAINTS_DICT['om_costs']) + return True + + +def check_generator_costs(generator_costs): + """ Check that generator costs has the required columns. """ + + return not len({'1/4 Load (gal/hr)', '1/2 Load (gal/hr)', '3/4 Load (gal/hr)', + 'Full Load (gal/hr)', 'Cost (USD)'} - + set(generator_costs.columns)) + + +def check_battery_costs(battery_costs): + """ Check that battery costs have the required columns and acceptable values. """ + + if len({'Battery System', 'Inverter'} - set(battery_costs.columns)) > 0: + return False + validate_parameter('batt_cost_per_Wh', + battery_costs['Battery System'].values[1], + **CONSTRAINTS_DICT['batt_cost_per_Wh']) + validate_parameter('inverter_cost_per_W', + battery_costs['Inverter'].values[1], + **CONSTRAINTS_DICT['inverter_cost_per_W']) + return True + + +def check_pv_costs(pv_costs): + """ Check that PV costs have the appropriate columns and acceptable values. """ + + # Check that column names are numbers + if not all([isinstance(col, (int, float)) for col in pv_costs.columns]): + return False + + # Check that price values are acceptable + for price in np.ravel(pv_costs.values): + validate_parameter('pv_cost_per_W', price, **CONSTRAINTS_DICT['pv_cost_per_W']) + return True + + +def check_fuel_tank_costs(fuel_tank_costs): + """ Check that fuel tank costs have the required columns and acceptable values. """ + + if len({'Cost (USD)'} - set(fuel_tank_costs.columns)): + return False + + for size in fuel_tank_costs.index: + validate_parameter('fuel_tank_size', size, **CONSTRAINTS_DICT['fuel_tank_size']) + + for cost in fuel_tank_costs['Cost (USD)']: + validate_parameter('fuel_tank_cost', cost, **CONSTRAINTS_DICT['fuel_tank_cost']) + return True + + +def check_om_costs(om_costs): + """ Check that om_costs have the required columns and acceptable values. """ + + if len({'Generator_scalar', 'Generator_exp', 'Battery', 'PV_ground;fixed', + 'PV_ground;single_axis', 'PV_roof;fixed', 'PV_carport;fixed'} - + set(om_costs.columns)): + return False + + # Check that the om costs have acceptable values + for cost in om_costs.loc['Cost'].values: + validate_parameter('om_cost', cost, **CONSTRAINTS_DICT['om_cost']) + return True + + +def check_unmet_load(unmet_load): + """ Check that unmet load has a DateTimeIndex. """ + + return isinstance(unmet_load.index, pd.DatetimeIndex) + + +def check_grouped_load(grouped_load): + """ Check that grouped load has the necessary columns. """ + + return not len({'num_hours', 'binned_load'} - set(grouped_load.columns)) + + +def check_power_profiles(power_profiles): + """ Check that each power profile conforms to standards. """ + + for profile in power_profiles: + validate_parameter('power_profile', profile, custom_args={}, + **CONSTRAINTS_DICT['power_profile']) + return True + + +def check_temp_profiles(temp_profiles): + """ Check that each temperature profile conforms to standards. """ + + for profile in temp_profiles: + validate_parameter('temp_profile', profile, custom_args={}, + **CONSTRAINTS_DICT['temp_profile']) + return True + + +def check_night_profiles(night_profiles): + """ Check that each temperature profile conforms to standards. """ + + for profile in night_profiles: + validate_parameter('night_profile', profile, custom_args={}, + **CONSTRAINTS_DICT['night_profile']) + return True + + +def check_pv_params(pv_params): + """ Check that pv_params has the required keys and value formats. """ + + if len({'tilt', 'azimuth', 'module_capacity', 'module_area', + 'advanced_inputs', 'spacing_buffer', 'pv_tracking', 'pv_racking'} - + set(pv_params.keys())) > 0: + return False + validate_parameter('tilt', pv_params['tilt'], **CONSTRAINTS_DICT['tilt']) + validate_parameter('azimuth', pv_params['azimuth'], + **CONSTRAINTS_DICT['azimuth']) + validate_parameter('module_capacity', pv_params['module_capacity'], + **CONSTRAINTS_DICT['module_capacity']) + validate_parameter('module_area_in2', pv_params['module_area'], + **CONSTRAINTS_DICT['module_area_in2']) + validate_parameter('spacing_buffer', pv_params['spacing_buffer'], + **CONSTRAINTS_DICT['spacing_buffer']) + validate_parameter('pv_tracking', pv_params['pv_tracking'], + **CONSTRAINTS_DICT['pv_tracking']) + validate_parameter('pv_racking', pv_params['pv_racking'], + **CONSTRAINTS_DICT['pv_racking']) + return True + + +def check_battery_params(battery_params): + """ Check that battery_params has the required keys and value formats. """ + + if len({'battery_power_to_energy', 'initial_soc', + 'one_way_battery_efficiency', 'one_way_inverter_efficiency', + 'soc_upper_limit', 'soc_lower_limit'} - + set(battery_params.keys())) > 0: + return False + validate_parameter('battery_power_to_energy', + battery_params['battery_power_to_energy'], + **CONSTRAINTS_DICT['battery_power_to_energy']) + validate_parameter('one_way_battery_efficiency', + battery_params['one_way_battery_efficiency'], + **CONSTRAINTS_DICT['one_way_battery_efficiency']) + validate_parameter('one_way_inverter_efficiency', + battery_params['one_way_inverter_efficiency'], + **CONSTRAINTS_DICT['one_way_inverter_efficiency']) + validate_parameter('soc_upper_limit', battery_params['soc_upper_limit'], + **CONSTRAINTS_DICT['soc_upper_limit']) + validate_parameter('soc_lower_limit', battery_params['soc_lower_limit'], + **CONSTRAINTS_DICT['soc_lower_limit']) + + initial_soc_params = CONSTRAINTS_DICT['initial_soc'] + initial_soc_params['custom_args'] = {'soc_upper_limit': battery_params['soc_upper_limit'], + 'soc_lower_limit': battery_params['soc_lower_limit']} + validate_parameter('initial_soc', battery_params['initial_soc'], **initial_soc_params) + return True + + +def check_initial_soc(initial_soc, soc_upper_limit, soc_lower_limit): + """ Check that the initial SOC is within the upper and lower soc limits. """ + + if initial_soc > soc_upper_limit or initial_soc < soc_lower_limit: + return False + return True + + +def check_gen_power_percent(gen_power_percent): + """ Check that each value in gen load percent is between 0 and 100. """ + + for val in gen_power_percent: + validate_parameter('gen_power_percent', val, data_type=['int', 'float'], min_val=0, + max_val=100) + return True + + +def check_filter_constraints(filter_constraints): + """ Check that filter constraints contains allowable elements. """ + + if len(filter_constraints): + for elem in filter_constraints: + if not isinstance(elem, dict): + return False + if len({'parameter', 'type', 'value'} - set(elem.keys())): + return False + if elem['parameter'] not in ['capital_cost_usd', 'pv_area_ft2', + 'annual_benefits_usd', + 'simple_payback_yr', 'pv_capacity', + 'fuel_used_gal mean', + 'fuel_used_gal most-conservative', + 'pv_percent mean', + 'gen_percent mean']: + return False + if elem['type'] not in ['max', 'min']: + return False + if not isinstance(elem['value'], (float, int)): + return False + return True + + +def check_ranking_criteria(ranking_criteria): + """ Check that filter constraints contains allowable elements. """ + + if len(ranking_criteria): + for elem in ranking_criteria: + if not isinstance(elem, dict): + return False + if len({'parameter', 'order_type'} - set(elem.keys())): + return False + if elem['parameter'] not in ['capital_cost_usd', + 'annual_benefits_usd', + 'simple_payback_yr', + 'fuel_used_gal mean', + 'fuel_used_gal most-conservative']: + return False + if elem['order_type'] not in ['ascending', 'descending']: + return False + return True + + +def check_include_pv(include_pv): + """ Check that each element of include_pv is valid. """ + + for elem in include_pv: + validate_parameter('pv_capacity', elem, data_type=('int', 'float'), min_val=0) + return True + + +def check_include_batt(include_batt): + """ Check that each element of include_batt is valid. """ + + for elem in include_batt: + validate_parameter('battery size', elem, data_type=('tuple',), size=2) + validate_parameter('battery capacity', elem[0], data_type=('int', 'float'), min_val=0) + validate_parameter('battery capacity', elem[1], data_type=('int', 'float'), min_val=0) + return True + + +# Custom high-level validation +def check_annual_load_profile(annual_load_profile, duration): + """ Checks that annual_load_profile has a datetime index, has values for every hour (or + corresponding duration) of the year, and has non-negative values. + """ + + # Check that the index can be converted to datetime + try: + converted_index = pd.to_datetime(annual_load_profile.index).map( + lambda x: x.replace(year=2017)) + except: + message = 'The annual load profile must have a datetime index, and not contain ' \ + 'leap days.' + log_error(message) + raise Exception(message) + + # Check that the index has all of the expected values + comp_index = pd.date_range(start='1/1/2017', end='1/1/2018', + freq='{}S'.format(int(duration)))[:-1] + if len(set(comp_index).symmetric_difference(set(converted_index))): + message = 'The annual load profile must begin on January 1 at ' \ + '00:00:00 and have no missing values.' + log_error(message) + return False + + # Check that values are > 0 + if len(annual_load_profile[annual_load_profile < 0]): + message = 'The annual load profile must not have any negative values.' + log_error(message) + return False + + return True + + +def check_off_grid_load_profile(off_grid_load_profile, duration): + """ Checks that off_grid_load_profile has a datetime index, has values for every hour (or + corresponding duration) of the year, and has non-negative values. + """ + + # Check that the index can be converted to datetime + try: + converted_index = pd.to_datetime(off_grid_load_profile.index).map( + lambda x: x.replace(year=2017)) + except: + message = 'The off-grid load profile must have a datetime index, ' \ + 'and not contain leap days.' + log_error(message) + raise Exception(message) + + # Check that the index has all of the expected values + comp_index = pd.date_range(start='1/1/2017', end='1/1/2018', + freq='{}S'.format(int(duration)))[:-1] + if len(set(comp_index).symmetric_difference(set(converted_index))): + message = 'The off-grid load profile must begin on January 1 at ' \ + '00:00:00 and have no missing values.' + log_error(message) + return False + + # Check that values are > 0 + if len(off_grid_load_profile[off_grid_load_profile < 0]): + message = 'The off-grid load profile must not have any negative values.' + log_error(message) + return False + + return True + + +def check_annual_production(annual_production): + """ Checks that annual_production has a datetime index, has values for every hour of the + year, and has non-negative values. + """ + + # Check that the index can be converted to datetime + try: + converted_index = pd.to_datetime(annual_production.index).map( + lambda x: x.replace(year=2017)) + except: + message = 'The annual production profile must have a datetime index,' \ + ' and not contain leap days.' + log_error(message) + raise Exception(message) + + # Check that the index has all of the expected values + comp_index = pd.date_range(start='1/1/2017', end='1/1/2018', freq='H')[:-1] + if len(set(comp_index).symmetric_difference(set(converted_index))): + message = 'The annual production profile must begin on January 1 at ' \ + '00:00:00 and have no missing values.' + log_error(message) + return False + + # Check that values are > 0 + if len(annual_production[annual_production < 0]): + message = 'The annual production profile must not have any negative values.' + log_error(message) + return False + + return True + + +def check_temperature(temperature): + """ Checks that temperature profile has a datetime index, has values for every hour of the + year, and has non-negative values. + """ + + # Check that the index can be converted to datetime + try: + converted_index = pd.to_datetime(temperature.index).map( + lambda x: x.replace(year=2017)) + except: + message = 'The annual temperature profile must have a datetime index,'\ + ' and not contain leap days.' + log_error(message) + raise Exception(message) + + # Check that the index has all of the expected values + comp_index = pd.date_range(start='1/1/2017', end='1/1/2018', freq='H')[:-1] + if len(set(comp_index).symmetric_difference(set(converted_index))): + message = 'The annual temperature profile must begin on January 1 at '\ + '00:00:00 and have no missing values.' + log_error(message) + return False + + # Check that values are > 0 + if len(temperature[temperature < 0]): + message = 'The annual production profile must not have any negative values.' + log_error(message) + return False + + return True + + +def check_load_profile(load_profile): + """ Checks that load profile has a datetime index and non-negative values. """ + + # Check that it has a datetime index + if not isinstance(load_profile.index, pd.core.indexes.datetimes.DatetimeIndex): + return False + + # Check that all values are positive + if len(load_profile[load_profile < 0]): + return False + + return True + + +def check_spg_advanced_inputs(advanced_inputs): + """Checks that each field in advanced inputs is valid. """ + + # Validate each of the fields in advanced_inputs + for key, val in advanced_inputs.items(): + # Check that the field has a validation function + try: + kwargs = CONSTRAINTS_DICT[key] + except KeyError: + print("{} does not have a validation function. Skipping " + "validation...".format(key)) + else: + # If the field has a custom function, include custom args + if 'custom_func' in kwargs.keys(): + if 'custom_args' in kwargs.keys() and len(kwargs['custom_args']): + # Note: the following line requires that if a custom validation function + # for a parameter in advanced_inputs has custom arguments then those + # arguments must also be included in the advanced_inputs dictionary. + kwargs['custom_args'] = \ + {key: advanced_inputs[key] for key in + kwargs['custom_args'].split(',')} + else: + kwargs['custom_args'] = {} + + validate_parameter(key, val, **kwargs) + return True + + +def check_module(module): + """ Checks that all of the required fields are included, and that they have valid values. + """ + + # Check that the required fields are included + if len({'database', 'model', 'capacity', 'area_in2'} - set(module.keys())): + return False + + # Run validation on each field + validate_parameter('module_database', module['database'], + **CONSTRAINTS_DICT['module_database']) + validate_parameter('module_capacity', module['capacity'], + **CONSTRAINTS_DICT['module_capacity']) + validate_parameter('module_area_in2', module['area_in2'], + **CONSTRAINTS_DICT['module_area_in2']) + + module_name_params = CONSTRAINTS_DICT['module_name'] + module_name_params['custom_args'] = {'module_database': module['database']} + validate_parameter('module_name', module['model'], **module_name_params) + return True + + +def check_module_name(module_name, module_database): + """ Checks that the module name can be found in the database. """ + + try: + module_list = pvsystem.retrieve_sam(module_database) + module = module_list[module_name] + except (ValueError, KeyError): + return False + else: + return True + + +def check_inverter(inverter): + """ Checks that all of the required fields are included, and that they have valid values. + """ + + # Check that the required fields are included + if len({'database', 'model'} - set(inverter.keys())): + return False + + # Run validation on each field + validate_parameter('inverter_database', inverter['database'], + **CONSTRAINTS_DICT['inverter_database']) + inverter_name_params = CONSTRAINTS_DICT['inverter_name'] + inverter_name_params['custom_args'] = {'inverter_database': + inverter['database']} + validate_parameter('inverter_name', inverter['model'], **inverter_name_params) + + return True + + +def check_inverter_name(inverter_name, inverter_database): + """ Checks that the inverter name can be found in the database. """ + + try: + inverter_list = pvsystem.retrieve_sam(inverter_database) + inverter = inverter_list[inverter_name] + except (ValueError, KeyError): + return False + else: + return True + + +def check_cld_hours(cloud_hours): + """ Checks that the elements of cloud hours are between 0 and 23. """ + + for elem in cloud_hours: + if elem < 0 or elem > 23: + return False + return True + + +def check_start_datetimes(start_datetimes, num_trials, start_year, end_year): + """Checks that start_datetimes is the same length as num_trials, each element is a + datetime object, and each element is within the bounds of the start and end years. + """ + + if not len(start_datetimes) == num_trials: + return False + for elem in start_datetimes: + if not isinstance(elem, dt.datetime): + return False + if elem.year < start_year or elem.year > end_year: + return False + return True + + +def check_timezone(timezone): + """Checks that the timezone is a valid pytz timezone. """ + + if timezone in pytz.all_timezones: + return True + return False + + +def check_demand_rate_list(demand_rate_list): + """ Checks that the demand rate is in the form of a number or a list with 12 elements and + each element has appropriate values. + """ + + # If it is a list, check that there are 12 elements + if isinstance(demand_rate_list, list): + if len(demand_rate_list) != 12: + return False + + # If it is not a list, add it to one to check elements + else: + demand_rate_list = [demand_rate_list] + + # Check all elements of demand rate list + for elem in demand_rate_list: + validate_parameter('demand_rate', elem, **CONSTRAINTS_DICT['demand_rate']) + return True + + +def check_solar_source(solar_source, start_year, end_year): + """ Checks that the years specified are covered in the specified source for the solar + data. + """ + + if (solar_source == 'himawari') and \ + (not {start_year, end_year}.issubset(set(range(2016, 2021)))): + message = 'Himawari dataset only covers years 2016-2020. ' \ + 'Please check the start/end years.' + log_error(message) + return False + + if (solar_source == 'nsrdb') and \ + (not {start_year, end_year}.issubset(set(range(1998, 2021)))): + message = "NREL's NSRDB dataset only covers years 1998-2020. "\ + 'Please check the start/end years.' + log_error(message) + return False + + return True + + +# Parameter warning functions +def annual_load_profile_warnings(annual_load_profile): + """ Checks to make sure the annual load profile looks realistic and outputs a warning if + not. + """ + + # Check for 0's in the load profile + zeros = annual_load_profile[annual_load_profile <= 0] + + # Look for hourly and daily outliers (for daily: sum, min, max) using Chauvenet's + # criterion + hourly_outliers = chauvenet_outliers(annual_load_profile) + daily_sum_outliers = chauvenet_outliers(annual_load_profile.resample('D').sum()) + daily_min_outliers = chauvenet_outliers(annual_load_profile.resample('D').min()) + daily_max_outliers = chauvenet_outliers(annual_load_profile.resample('D').max()) + + # Check frequencies of data + # Calculate load profile periodogram + freq, power = periodogram(annual_load_profile.values, fs=1/3600) + pg = pd.DataFrame(power, index=freq, columns=['power']) + + # Get the peak frequency + peak_freq = pg[pg['power'] == max(pg['power'])].index[0] + min_period = 1 / peak_freq / 3600 + + # Get the next highest frequency + pg_crop = pg[:peak_freq].iloc[5:-5] + if len(pg_crop): + second_freq = pg_crop[pg_crop['power'] == max(pg_crop['power'])].index[0] + second_period = 1 / second_freq / 3600 + else: + second_period = 0 + + # Is daytime load (workday) higher than nighttime load + daytime = annual_load_profile[(annual_load_profile.index.hour >= 9) & + (annual_load_profile.index.hour <= 17 + )].median() + nighttime = annual_load_profile[(annual_load_profile.index.hour <= 7) | + (annual_load_profile.index.hour >= 18 + )].median() + + # Compile any warnings + warning_message = '' + if len(zeros): + warning_message += 'There are zeros at the following times: {}. ' \ + ''.format(list(zeros.index)) + if len(hourly_outliers): + warning_message += 'There are suspicious values for the following ' \ + 'hours: {}. '.format(list(hourly_outliers.index)) + if len(daily_sum_outliers) or len(daily_max_outliers) or len(daily_min_outliers): + warning_message += 'There are suspicious values for the following ' \ + 'days: {}. '.format( + set(pd.concat([daily_sum_outliers, + daily_min_outliers, + daily_max_outliers]).index)) + if int(min_period) not in [23, 24, 25] or int(second_period) not in range(165, 172): + warning_message += 'The load profile does not have natural periods ' \ + 'at 24 hours and/or 1 week. ' + if daytime < nighttime: + warning_message += 'The average daytime (working hours) load is ' \ + 'less than the average nighttime load.' + + if len(warning_message): + warning_message = 'Warning: There are one or more potential issues ' \ + 'with the annual load profile: {}' \ + ''.format(warning_message) + print(warning_message) + log_error(warning_message) + + +def normalize_profile(annual_load_profile): + """ Normalizes a load profile by daily, weekly and seasonal trends. """ + + # Decompose profile into daily, weekly, seasonal trends + # Normalized monthly trend + load_monthly = annual_load_profile.resample('M').median() + load_monthly.index = load_monthly.index.month + load_season_norm = annual_load_profile.reset_index().apply( + lambda x: x[annual_load_profile.name] - load_monthly[x[ + annual_load_profile.index.name].month], axis=1) + load_season_norm.index = annual_load_profile.index + + # Normalize weekly trend + load_season_norm = load_season_norm.to_frame(name='load').reset_index() + load_season_norm['dow'] = \ + load_season_norm[annual_load_profile.index.name].apply(lambda x: x.dayofweek) + load_dow = load_season_norm.groupby('dow')['load'].median() + load_season_weekly_norm = annual_load_profile.reset_index().apply( + lambda x: x[annual_load_profile.name] - + load_monthly[x[annual_load_profile.index.name].month] - + load_dow[x[annual_load_profile.index.name].dayofweek], axis=1) + load_season_weekly_norm.index = annual_load_profile.index + + # Normalize hourly trend + load_season_weekly_norm = load_season_weekly_norm.to_frame(name='load').reset_index() + load_season_weekly_norm['hour'] = load_season_weekly_norm[ + annual_load_profile.index.name].apply(lambda x: x.hour) + load_hourly = load_season_weekly_norm.groupby('hour')['load'].median() + load_normalized = annual_load_profile.reset_index().apply( + lambda x: x[annual_load_profile.name] - + load_monthly[x[annual_load_profile.index.name].month] - + load_dow[x[annual_load_profile.index.name].dayofweek] - + load_hourly[x[annual_load_profile.index.name].hour], axis=1) + load_normalized.index = annual_load_profile.index + + return load_normalized + + +def chauvenet_outliers(data): + """ + Given a Pandas Series of time series data, returns any data points that are considered + outliers according to Chauvenet's criterion: + P(data point) < 1/(2n), where n is the length of the series + """ + + # Create a dataframe to hold the data + data_df = data.to_frame(name='data') + + # Calculate the mean and standard deviation of the distribution + avg = data_df['data'].mean() + std = data_df['data'].std() + + # For each data point, calculate the likelihood that that value is drawn + # from the underlying distribution + data_df['num_stds'] = data_df['data'].apply(lambda x: np.abs((x-avg)/std)) + data_df['prob'] = data_df['num_stds'].apply(lambda x: (1-norm.cdf(x))*2) + + # Determine if each point is an outlier + data_df['outlier'] = data_df['prob'].apply(lambda x: x < 1/(2*len(data_df))) + + return data_df[data_df['outlier']] + + +def strings_warnings(strings, module_name, inverter_name): + """ Checks to make sure strings configuration is realistic and consistent with inverter + parameters and outputs a warning if not. + """ + + # Retrieve info on the module and inverter + module_list = pvsystem.retrieve_sam(module_name['database']) + module = module_list[module_name['model']] + inverter_list = pvsystem.retrieve_sam(inverter_name['database']) + inverter = inverter_list[inverter_name['model']] + + # Check that the DC/AC ratio is within a reasonable range + string_warnings = '' + dc = module_name['capacity'] * strings['mods_per_string'] * 1000 \ + * strings['strings_per_inv'] + ac = inverter['Paco'] + if not 1 <= dc/ac <= 1.1: + string_warnings += 'The DC/AC ratio for your PV system is {:.2f}. We recommend a ' \ + 'value between 1 and 1.1.'.format(dc/ac) + + # Check string parameters against inverter voltage and current + system_voltage = module['V_oc_ref'] * strings['mods_per_string'] + system_current = module['I_sc_ref'] * strings['strings_per_inv'] + if system_voltage > inverter['Vdcmax']: + string_warnings += 'The system voltage ({:.2f}V) exceeds the ' \ + 'maximum inverter voltage ({:.2f}V). ' \ + 'Consider decreasing the number of panels ' \ + 'in series or choosing a different ' \ + 'inverter. '.format(system_voltage, inverter['Vdcmax']) + + if system_current > inverter['Idcmax']: + string_warnings += 'The system current ({:.1f}A) exceeds the ' \ + 'maximum inverter current ({:.1f}A). ' \ + 'Consider decreasing the number of strings ' \ + 'in parallel or choosing a different ' \ + 'inverter. '.format(system_current, inverter['Idcmax']) + + if len(string_warnings): + string_warnings = 'Warning: You have one or more issues with your ' \ + 'string sizing: {}'.format(string_warnings) + print(string_warnings) + log_error(string_warnings) + + +def location_warnings(latitude, longitude, timezone): + """ Checks to make sure the location and timezone are consistent. """ + + # Determines proper timezone based on location + + # Checks against actual timezone + + pass + + +VALIDATION_FUNCS = {'check_path': check_path, + 'check_sitename': check_sitename, + 'check_solar_profile': check_solar_profile, + 'check_temp_profile': check_temp_profile, + 'check_strings': check_strings, + 'check_night_duration': check_night_duration, + 'check_fuel_curve_model': check_fuel_curve_model, + 'check_outputs': check_outputs, + 'check_existing_components': check_existing_components, + 'check_annual_load_profile': check_annual_load_profile, + 'check_net_metering_limits': check_net_metering_limits, + 'check_location': check_location, + 'check_night_profile': check_night_profile, + 'check_generator_costs': check_generator_costs, + 'check_unmet_load': check_unmet_load, + 'check_grouped_load': check_grouped_load, + 'check_power_profile': check_power_profile, + 'check_power_profiles': check_power_profiles, + 'check_temp_profiles': check_temp_profiles, + 'check_night_profiles': check_night_profiles, + 'check_pv_params': check_pv_params, + 'check_battery_params': check_battery_params, + 'check_gen_power_percent': check_gen_power_percent, + 'check_filter_constraints': check_filter_constraints, + 'check_ranking_criteria': check_ranking_criteria, + 'check_include_pv': check_include_pv, + 'check_include_batt': check_include_batt, + 'check_load_profile': check_load_profile, + 'check_spg_advanced_inputs': check_spg_advanced_inputs, + 'check_module': check_module, + 'check_inverter': check_inverter, + 'check_module_name': check_module_name, + 'check_system_costs': check_system_costs, + 'check_battery_costs': check_battery_costs, + 'check_pv_costs': check_pv_costs, + 'check_fuel_tank_costs': check_fuel_tank_costs, + 'check_om_costs': check_om_costs, + 'check_inverter_name': check_inverter_name, + 'check_cld_hours': check_cld_hours, + 'check_start_datetimes': check_start_datetimes, + 'check_timezone': check_timezone, + 'check_annual_production': check_annual_production, + 'check_temperature': check_temperature, + 'check_off_grid_load_profile': check_off_grid_load_profile, + 'check_demand_rate_list': check_demand_rate_list, + 'check_initial_soc': check_initial_soc, + 'check_solar_source': check_solar_source}