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• W4319601029 abstract "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." @default.
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• W4319601029 date "2023-02-07" @default.
• W4319601029 modified "2023-09-25" @default.
• W4319601029 title "Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models" @default.
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• W4319601029 doi "https://doi.org/10.3390/en16041636" @default.
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