07_simulate_request_with_buffer_byhour.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from icecream import ic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\n",
    "            {\n",
    "                \"$id\": \"2\",\n",
    "                \"date\": \"2024-01-01T23:00:00Z\",\n",
    "                \"price\": 23.92\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"3\",\n",
    "                \"date\": \"2024-01-02T00:00:00Z\",\n",
    "                \"price\": 17.7\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"4\",\n",
    "                \"date\": \"2024-01-02T01:00:00Z\",\n",
    "                \"price\": 12.39\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"5\",\n",
    "                \"date\": \"2024-01-02T02:00:00Z\",\n",
    "                \"price\": 7.35\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"6\",\n",
    "                \"date\": \"2024-01-02T03:00:00Z\",\n",
    "                \"price\": 4.19\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"7\",\n",
    "                \"date\": \"2024-01-02T04:00:00Z\",\n",
    "                \"price\": 4.75\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"8\",\n",
    "                \"date\": \"2024-01-02T05:00:00Z\",\n",
    "                \"price\": 31.59\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"9\",\n",
    "                \"date\": \"2024-01-02T06:00:00Z\",\n",
    "                \"price\": 49.96\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"10\",\n",
    "                \"date\": \"2024-01-02T07:00:00Z\",\n",
    "                \"price\": 58.46\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"11\",\n",
    "                \"date\": \"2024-01-02T08:00:00Z\",\n",
    "                \"price\": 59.05\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"12\",\n",
    "                \"date\": \"2024-01-02T09:00:00Z\",\n",
    "                \"price\": 68.65\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"13\",\n",
    "                \"date\": \"2024-01-02T10:00:00Z\",\n",
    "                \"price\": 74.18\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"14\",\n",
    "                \"date\": \"2024-01-02T11:00:00Z\",\n",
    "                \"price\": 65.02\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"15\",\n",
    "                \"date\": \"2024-01-02T12:00:00Z\",\n",
    "                \"price\": 64.71\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"16\",\n",
    "                \"date\": \"2024-01-02T13:00:00Z\",\n",
    "                \"price\": 66.74\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"17\",\n",
    "                \"date\": \"2024-01-02T14:00:00Z\",\n",
    "                \"price\": 64.43\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"18\",\n",
    "                \"date\": \"2024-01-02T15:00:00Z\",\n",
    "                \"price\": 65.04\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"19\",\n",
    "                \"date\": \"2024-01-02T16:00:00Z\",\n",
    "                \"price\": 73.85\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"20\",\n",
    "                \"date\": \"2024-01-02T17:00:00Z\",\n",
    "                \"price\": 66.62\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"21\",\n",
    "                \"date\": \"2024-01-02T18:00:00Z\",\n",
    "                \"price\": 64.36\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"22\",\n",
    "                \"date\": \"2024-01-02T19:00:00Z\",\n",
    "                \"price\": 60\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"23\",\n",
    "                \"date\": \"2024-01-02T20:00:00Z\",\n",
    "                \"price\": 49.5\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"24\",\n",
    "                \"date\": \"2024-01-02T21:00:00Z\",\n",
    "                \"price\": 44.19\n",
    "            },\n",
    "            {\n",
    "                \"$id\": \"25\",\n",
    "                \"date\": \"2024-01-02T22:00:00Z\",\n",
    "                \"price\": 26.83\n",
    "            }\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2024-01-01T23:00:00Z</td>\n",
       "      <td>23.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2024-01-02T00:00:00Z</td>\n",
       "      <td>17.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2024-01-02T01:00:00Z</td>\n",
       "      <td>12.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2024-01-02T02:00:00Z</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2024-01-02T03:00:00Z</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>2024-01-02T04:00:00Z</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>2024-01-02T05:00:00Z</td>\n",
       "      <td>31.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>2024-01-02T06:00:00Z</td>\n",
       "      <td>49.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>2024-01-02T07:00:00Z</td>\n",
       "      <td>58.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>2024-01-02T08:00:00Z</td>\n",
       "      <td>59.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>2024-01-02T09:00:00Z</td>\n",
       "      <td>68.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>2024-01-02T10:00:00Z</td>\n",
       "      <td>74.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>2024-01-02T11:00:00Z</td>\n",
       "      <td>65.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>2024-01-02T12:00:00Z</td>\n",
       "      <td>64.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>2024-01-02T13:00:00Z</td>\n",
       "      <td>66.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>2024-01-02T14:00:00Z</td>\n",
       "      <td>64.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>2024-01-02T15:00:00Z</td>\n",
       "      <td>65.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>2024-01-02T16:00:00Z</td>\n",
       "      <td>73.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20</td>\n",
       "      <td>2024-01-02T17:00:00Z</td>\n",
       "      <td>66.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21</td>\n",
       "      <td>2024-01-02T18:00:00Z</td>\n",
       "      <td>64.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>22</td>\n",
       "      <td>2024-01-02T19:00:00Z</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23</td>\n",
       "      <td>2024-01-02T20:00:00Z</td>\n",
       "      <td>49.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24</td>\n",
       "      <td>2024-01-02T21:00:00Z</td>\n",
       "      <td>44.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>2024-01-02T22:00:00Z</td>\n",
       "      <td>26.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   $id                  date  price\n",
       "0    2  2024-01-01T23:00:00Z  23.92\n",
       "1    3  2024-01-02T00:00:00Z  17.70\n",
       "2    4  2024-01-02T01:00:00Z  12.39\n",
       "3    5  2024-01-02T02:00:00Z   7.35\n",
       "4    6  2024-01-02T03:00:00Z   4.19\n",
       "5    7  2024-01-02T04:00:00Z   4.75\n",
       "6    8  2024-01-02T05:00:00Z  31.59\n",
       "7    9  2024-01-02T06:00:00Z  49.96\n",
       "8   10  2024-01-02T07:00:00Z  58.46\n",
       "9   11  2024-01-02T08:00:00Z  59.05\n",
       "10  12  2024-01-02T09:00:00Z  68.65\n",
       "11  13  2024-01-02T10:00:00Z  74.18\n",
       "12  14  2024-01-02T11:00:00Z  65.02\n",
       "13  15  2024-01-02T12:00:00Z  64.71\n",
       "14  16  2024-01-02T13:00:00Z  66.74\n",
       "15  17  2024-01-02T14:00:00Z  64.43\n",
       "16  18  2024-01-02T15:00:00Z  65.04\n",
       "17  19  2024-01-02T16:00:00Z  73.85\n",
       "18  20  2024-01-02T17:00:00Z  66.62\n",
       "19  21  2024-01-02T18:00:00Z  64.36\n",
       "20  22  2024-01-02T19:00:00Z  60.00\n",
       "21  23  2024-01-02T20:00:00Z  49.50\n",
       "22  24  2024-01-02T21:00:00Z  44.19\n",
       "23  25  2024-01-02T22:00:00Z  26.83"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ic| ev_charging_hours_count_comfort: 2.1818181818181817\n",
      "    ev_charging_hours_count_max: 2.727272727272727\n",
      "    total_hours_count: 24\n",
      "ic| ev_charging_hours_count_comfort / total_hours_count: 0.0909090909090909\n",
      "ic| percent_of_hours_needed_comfort: 0.0909090909090909\n",
      "    percent_of_hours_needed_max: 0.11363636363636363\n",
      "ic| percent_of_hours_needed_comfort: 0.13636363636363635\n",
      "    percent_of_hours_needed_max: 0.17045454545454544\n"
     ]
    }
   ],
   "source": [
    "price_column_name = \"price\"\n",
    "\n",
    "\n",
    "ev_comfort_charge_capacity_kwh = int(0.8 * 60)  # 80% of 60kWh\n",
    "ev_max_charge_capacity_kwh = 60  # 60kWh\n",
    "buffer = 0.5  # 50%\n",
    "\n",
    "\n",
    "# hardcoded for now\n",
    "ev_pmax = 22  # kW\n",
    "ev_charged_per_hour = ev_pmax  # kWh\n",
    "\n",
    "\n",
    "ev_charging_hours_count_comfort = ev_comfort_charge_capacity_kwh / ev_charged_per_hour\n",
    "ev_charging_hours_count_max = ev_max_charge_capacity_kwh / ev_charged_per_hour\n",
    "total_hours_count = len(df)\n",
    "ic(ev_charging_hours_count_comfort, ev_charging_hours_count_max, total_hours_count)\n",
    "\n",
    "ic(ev_charging_hours_count_comfort / total_hours_count)\n",
    "\n",
    "percent_of_hours_needed_comfort = ev_charging_hours_count_comfort / total_hours_count\n",
    "percent_of_hours_needed_max = ev_charging_hours_count_max / total_hours_count\n",
    "ic(percent_of_hours_needed_comfort, percent_of_hours_needed_max)\n",
    "\n",
    "percent_of_hours_needed_comfort = percent_of_hours_needed_comfort * (1 + buffer)\n",
    "percent_of_hours_needed_max = percent_of_hours_needed_max * (1 + buffer)\n",
    "ic(percent_of_hours_needed_comfort, percent_of_hours_needed_max)\n",
    "\n",
    "df['is_in_lowest_hours_comfort'] = df[price_column_name] <= df[price_column_name].quantile(percent_of_hours_needed_comfort)\n",
    "\n",
    "df['is_in_lowest_hours_max'] = df[price_column_name] <= df[price_column_name].quantile(percent_of_hours_needed_max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "highest_price_in_lowest_hours_comfort = df[df['is_in_lowest_hours_comfort']][price_column_name].max()\n",
    "\n",
    "highest_price_in_lowest_hours_max = df[df['is_in_lowest_hours_max']][price_column_name].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12.39, 12.39)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_price_in_lowest_hours_comfort, highest_price_in_lowest_hours_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assume your dataframe is df\n",
    "df['DateTime'] = pd.to_datetime(df['date'])  # Ensure DateTime is in the correct format\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "# plot all the prices\n",
    "plt.plot(df['date'], df[price_column_name], label=price_column_name)\n",
    "\n",
    "# overlay the points where 'is_in_lowest_quarters' is True\n",
    "plt.scatter(df[df['is_in_lowest_hours_comfort']]['date'], df[df['is_in_lowest_hours_comfort']][price_column_name], color='red', label='Lowest hours')\n",
    "\n",
    "plt.xlabel('DateTime')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel(price_column_name)\n",
    "plt.title('Negative imbalance price over time')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}