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Research on optimization of power system load response strategy and cost–benefit analysis based on electricity price algorithm model

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Listed:
  • Quanfeng Geng
  • Kai Yang
  • Jing Li
  • Guanglei Feng
  • Wenchao Hao
  • Lingling Gao

Abstract

This article proposes an innovative framework that amalgamates deep reinforcement learning (DRL) with cost–benefit analysis (CBA). The enhanced actor–critic DRL algorithm simultaneously addresses short-term price fluctuations and long-term system benefits, facilitating optimization across multiple time scales. Furthermore, it establishes a dynamic, multidimensional CBA model that encompasses a comprehensive evaluation of economic, social, and technological benefits, employing a fuzzy comprehensive evaluation method for quantitative analysis. The integration of DRL and CBA forms a closed-loop system that continuously refines strategy optimization through real-time adjustments of the reward function and evaluation weights. Experimental results validate the efficacy of this approach.

Suggested Citation

  • Quanfeng Geng & Kai Yang & Jing Li & Guanglei Feng & Wenchao Hao & Lingling Gao, 2025. "Research on optimization of power system load response strategy and cost–benefit analysis based on electricity price algorithm model," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 543-572.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:543-72.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae262
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