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Integrated energy system planning considering renewable energy uncertainties based on multi-scenario confidence gap decision

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  • Peng, Chunhua
  • Fan, Guozhu
  • Xiong, Zhisheng
  • Zeng, Xinzhi
  • Sun, Huijuan
  • Xu, Xuesong

Abstract

The problem of renewable energy uncertainties in the capacity planning of integrated energy system (IES) is prominent. To handle the multiple uncertainties, multi-scenario clustering analysis and classified confidence intervals of Gaussian mixture model (GMM) are combined, along with the robustness idea of information gap decision theory (IGDT), so a novel multi-scenario confidence gap decision theory (MCGDT) is proposed. Considering the comprehensive optimization objectives of maximizing exergy efficiency and minimizing annualized total cost, a robust capacity planning model for IES based on MCGDT is constructed to promote the cascade utilization of multiple energy and the economic performance of IES. Moreover, a new cross entropy-radar scanning differential evolution (CE-RSDE) algorithm is designed to improve solution efficiency and avoid prematurity in the optimization process. The simulation results of a typical case suggest that the proposed method leads to better capacity planning of IES, with up to 7.55% reduction of the annualized total cost and 14.03% increase in the exergy efficiency, while keeping strong robustness under the environment of multiple uncertainties.

Suggested Citation

  • Peng, Chunhua & Fan, Guozhu & Xiong, Zhisheng & Zeng, Xinzhi & Sun, Huijuan & Xu, Xuesong, 2023. "Integrated energy system planning considering renewable energy uncertainties based on multi-scenario confidence gap decision," Renewable Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:renene:v:216:y:2023:i:c:s0960148123010145
    DOI: 10.1016/j.renene.2023.119100
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    8. Li, Jinhang & Chen, Heng & Li, Jingjia & Zhang, Yixi & Pan, Peiyuan & Bian, Jiayu & Yu, Zhiyong, 2025. "Bi-level optimization model of hydrogen-blended gas units and multi-type energy storage system considering low-carbon operation," Energy, Elsevier, vol. 314(C).
    9. Wu, Xiao & Yang, Lihua & Zheng, Bingle, 2024. "Joint capacity configuration and demand response optimization of integrated energy system considering economic and dynamic control performance," Energy, Elsevier, vol. 301(C).
    10. Fan, Guozhu & Peng, Chunhua & Wang, Xuekui & Wu, Peng & Yang, Yifan & Sun, Huijuan, 2024. "Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC," Renewable Energy, Elsevier, vol. 226(C).
    11. Xue, Kai & Wang, Jinshi & Zhang, Shuo & Ou, Kejie & Chen, Weixiong & Zhao, Quanbin & Hu, Guangtao & Sun, Zhiyong, 2024. "Design optimization of community energy systems based on dual uncertainties of meteorology and load for robustness improvement," Renewable Energy, Elsevier, vol. 232(C).
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