Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networks  

作　　者：Jian-Dong Yao Wen-Bin Hao Zhi-Gao Meng Bo Xie Jian-Hua Chen Jia-Qi Wei 

机构地区：[1]State Grid Sichuan Electric Power Company Chengdu Power Supply Company,Chengdu,610041,China

出　　处：《Journal of Electronic Science and Technology》2025年第1期35-59,共25页电子科技学刊(英文版)

基　　金：supported by the Science and Technology Project of State Grid Sichuan Electric Power Company Chengdu Power Supply Company under Grant No.521904240005.

摘　　要：This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.

关 键 词：Distributed energy management Dynamic pricing Multi-agent reinforcement learning Renewable energy integration Virtual power plants 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]