Robust Bilevel Optimal Dispatch of Park Integrated Energy System Considering Renewable Energy Uncertainty

Puming Wang, Liqin Zheng, Tianyi Diao, Shengquan Huang and Xiaoqing Bai ()
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Puming Wang: Guangxi Key Laboratory of Power System Optimization and Energy Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Liqin Zheng: State Grid Xiamen Electric Power Supply Company, Xiamen 361004, China
Tianyi Diao: State Grid Liaocheng Power Supply Company, Liaocheng 252000, China
Shengquan Huang: Guangxi Key Laboratory of Power System Optimization and Energy Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Xiaoqing Bai: Guangxi Key Laboratory of Power System Optimization and Energy Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China

Energies, 2023, vol. 16, issue 21, 1-23

Abstract: This paper focuses on optimizing the park integrated energy system (PIES) operation, and a robust bilevel optimal dispatch is proposed. Firstly, the robust uncertainty set is constructed based on the K-means++ algorithm to solve the uncertainty of renewable energy sources output in PIES. Then, the bi-level dispatch model is proposed, with the operator as the leader and consumers as the follower. The upper model establishes an electricity-heat-gas integrated energy network, and the lower model considers the demand response of consumers. Optimizing the pricing strategies of energy sources to determine the output of each energy conversion equipment and the demand response plan. Moreover, analyzing the decision-making process of the robust bi-level model and the solution method is given. Finally, case studies show that the proposed dispatch model can increase operator profits and reduce consumers’ energy costs. The in-sample and out-of-sample simulations demonstrate that the proposed ellipsoid uncertainty set possesses high compactness, good robustness, and low conservatism.

Keywords: K-means++; robust bilevel optimization; multi-energy flow model; demand response (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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