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Two-stage optimal scheduling strategy for community integrated energy system based on uncertainty and integrated demand response model

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  • Wu, Shengcheng
  • Pang, Aiping

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.

Suggested Citation

  • Wu, Shengcheng & Pang, Aiping, 2025. "Two-stage optimal scheduling strategy for community integrated energy system based on uncertainty and integrated demand response model," Renewable Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:renene:v:251:y:2025:i:c:s0960148125010353
    DOI: 10.1016/j.renene.2025.123373
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    1. Zhang, Yifan & He, Yaoyao, 2025. "Optimizing integrated energy systems: A multi-scenario scheduling approach with stepped carbon trading and two-stage hydrogen production under uncertainty," Applied Energy, Elsevier, vol. 401(PB).

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