ORIGINAL RESEARCH article

Front. Phys.

Sec. Social Physics

Volume 13 - 2025 | doi: 10.3389/fphy.2025.1606657

This article is part of the Research TopicReal-World Applications of Game Theory and Optimization, Volume IIIView all 3 articles

Research on Collaborative Optimization of the Electric-Carbon Joint Market Based on Renewable Energy Subsidies

Provisionally accepted
Peng  XiaPeng Xia1Bo  YuanBo Yuan1Gang  LuGang Lu1Qiuli  ZhaoQiuli Zhao1Fuqiang  ZhangFuqiang Zhang1Suyang  ZhouSuyang Zhou2*
  • 1State Grid Energy Research Institute (SGCC), Beijing, China
  • 2Southeast University, Nanjing, China

The final, formatted version of the article will be published soon.

This paper addresses the current challenges in coordinating interactions between the power market and the carbon market, particularly the shortcomings arising from the insufficient consideration of renewable energy subsidies. To tackle these issues, we propose a collaborative optimization approach for an integrated electric-carbon market that incorporates a renewable subsidy mechanism. The aim is to foster deeper integration between the power and carbon markets, enhance the share of clean energy in the overall mix, and drive low-carbon transitions.A joint market clearing model is constructed that explicitly includes renewable subsidy costs in the objective function, thereby capturing the true cost-benefit dynamics of renewable projects.A case study based on a regional power system demonstrates the model's effectiveness and feasibility, with the Particle Swarm Optimization (PSO) algorithm successfully converging to a near-optimal solution. This research not only provides theoretical support for the real-world application of coordinated electric-carbon market operations but also offers significant practical value by incorporating renewable energy incentives into the market design.

Keywords: Collaborative optimization, Particle Swarm Optimization, Market clearing model, Renewable energy subsidy, Low-carbon transition

Received: 06 Apr 2025; Accepted: 30 Apr 2025.

Copyright: © 2025 Xia, Yuan, Lu, Zhao, Zhang and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Suyang Zhou, Southeast University, Nanjing, China

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