Multi-Agent Collaborative Optimization in New Power Systems Driven by Integration of Evolutionary Game and Federated Learning

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Multi-Agent Collaborative Optimization in New Power Systems Driven by Integration of Evolutionary Game and Federated Learning

Power Generation Technology Vol. 47, Issue 3, Pages: 449-483(2026)

Intelligent Learning-Empowered Power Generation in New Power Systems|Updated：2026-08-17

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- Multi-Agent Collaborative Optimization in New Power Systems Driven by Integration of Evolutionary Game and Federated Learning
- “Aiming at the problem of collaborative optimization among multiple market entities in the new power system, the EGT-FL fusion framework was constructed with the main line of 'problem driven theoretical support method construction scenario implementation', and its advantages in privacy protection and convergence efficiency were verified, providing a new paradigm for multi-agent collaborative decision-making.”
- Affiliations：
  1.School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, Guangdong Province, China
  2.School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, Guangdong Province, China
  3.Foshan Graduate School of Innovation, Northeastern University, Foshan 528300, Guangdong Province, China
  4.College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, China
- Author bio：
- Funds：
  National Natural Science Foundation of China(52171331;U24B6010);Natural Science Foundation of Guangdong Province(2023A1515011311);Innovation Team Project for Ordinary Universities in Guangdong Province(2024KCXTD031);Guangzhou Education Bureau University Research Project(2024312278)
- DOI：10.12096/j.2096-4528.pgt.260301
  CLC：
- Revised：2026-03-18，
- Accepted：
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