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

Quanfeng Geng, Kai Yang, Jing Li, Guanglei Feng, Wenchao Hao and Lingling Gao

International Journal of Low-Carbon Technologies, 2025, vol. 20, 543-72

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.

Keywords: load response optimization; deep reinforcement learning; cost–benefit analysis (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctae262 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:543-72.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().