This repo hosts the practical implementation of the journal paper “Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models”, Energies, 16(4), 2023, specifically the C2RGAN model, trained using a publicly available dataset from Huggingface (a different, propietary dataset was used in the original paper)
Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.
This repo contains the code to train the C2RGAN model, although it doesn't explore the conditional training of the model nor the separation of dataset on the basis of Dynamic Time Warping.
Dataset: https://huggingface.co/datasets/vitaliy-sharandin/energy-consumption-hourly-spain