 Two-stage optimal scheduling strategy for community integrated energy system based on uncertainty and integrated demand response modelShengcheng Wu and Aiping Pang Renewable Energy, 2025, vol. 251, issue C Abstract: This study presents an optimization management framework for community integrated energy systems. First, the output scenarios of renewable energy are generated by the Monte Carlo method, and the K-means method is used to reduce the scenarios. According to the spatio-temporal characteristics of electric vehicles, this study proposes a charging/discharging decision-making method based on the fuzzy theory. Moreover, an integrated demand response model based on the real-time price mechanism and a two-stage optimization scheduling strategy is formulated. The results show that, compared with two traditional scenarios, the proposed strategy reduces comprehensive operating costs on typical days by 8.81% and 3.22% in summer and 6.55% and 3.33% in winter, respectively, while ensuring overall satisfaction. This study also analyzes the impact of the number of electric vehicles and the weight of the objective function on the optimization scheduling results. Keywords: Community integrated energy system; Electric vehicles; Integrated demand response; Optimal scheduling strategy (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:251:y:2025:i:c:s0960148125010353 DOI: 10.1016/j.renene.2025.123373 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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