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.
@article{
title = {Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models},
type = {article},
year = {2023},
pages = {1636},
volume = {16},
month = {2},
day = {7},
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last_modified = {2024-09-06T20:30:27.404Z},
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abstract = {<p>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.</p>},
bibtype = {article},
author = {Dalal, Dhaval and Bilal, Muhammad and Shah, Hritik and Sifat, Anwarul Islam and Pal, Anamitra and Augustin, Philip},
doi = {10.3390/en16041636},
journal = {Energies},
number = {4}
}