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Resilience-economy-environment equilibrium based configuration interaction approach towards distributed energy system in energy intensive industry parks

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  • Xu, Jiuping
  • Tian, Yalou
  • Wang, Fengjuan
  • Yang, Guocan
  • Zhao, Chuandang

Abstract

Because unexpected, high-impact, extreme events and disasters can seriously threaten power system safety and resilience, there has been an increased research focus on power system resilience. This study proposes a bi-level multi-objective model for industrial park distributed energy configuration optimization to deal with extreme events, which considers the interactions between the authority and the industrial park in a leader–follower decision process and seeks to trade-off between economic cost, environmental protection, and power system resilience. An interactive algorithm is designed and the ɛ-constraint method is used to convert the model into a bi-level single objective model to solve the proposed complex model. A case example from an industrial park in Wuxi, Jiangsu Province, China, is given to demonstrate the practicality and efficiency of the proposed optimization method. The joint analysis of the power supply and financial losses found that a distributed energy system with an optimal resilience configuration could guarantee 95.85% of demand for three consecutive hours after an extreme event, maintain more than 50% of demand at any time, and reduce power outage financial losses at any time to a minimum of 0.232 million CNY. Based on the analyses and discussion, this model was proven to provide a reasonable and practical strategy for a resilience-economy-environment trade-off in industrial park distributed energy systems.

Suggested Citation

  • Xu, Jiuping & Tian, Yalou & Wang, Fengjuan & Yang, Guocan & Zhao, Chuandang, 2024. "Resilience-economy-environment equilibrium based configuration interaction approach towards distributed energy system in energy intensive industry parks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:rensus:v:191:y:2024:i:c:s1364032123009978
    DOI: 10.1016/j.rser.2023.114139
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