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Scenario Reduction Network Based on Wasserstein Distance with Regularization.docx (5.03 MB)
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Scenario Reduction Network Based on Wasserstein Distance with Regularization

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posted on 17.11.2021, 21:22 by Yingyun SunYingyun Sun, Xiaochong Dong, Sarmad Majeed Malik
Power systems with high penetration of renewable energy contain various uncertainties. Scenario-based optimization problems need a large number of discrete scenarios to obtain a reliable approximation for the probabilistic model. It is important to choose typical scenarios and ease the computational burden. This paper presents a scenario reduction network model based on Wasserstein distance. Entropy regularization is used to transform the scenario reduction problem into an unconstrained problem. Through an explicit neural network structure design, the output of the scenario reduction network corresponds to Sinkhorn distance function. The scenario reduction network can generate the typical scenario set through unsupervised learning training. An efficient algorithm is proposed for continuous/discrete scenario reduction. The superiority of the scenario reduction network model is verified through case studies. The numerical results highlight high accuracy and computational efficiency of the proposed model over state-of-the-art model making it an ideal candidate for large-scale scenario reduction problems

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Email Address of Submitting Author

sunyy@ncepu.edu.cn

ORCID of Submitting Author

0000-0002-7516-753X

Submitting Author's Institution

North China Electric Power University

Submitting Author's Country

China

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