文献类型：期刊文献

英文题名：Suppressing active power fluctuations at PCC in grid-connection microgrids via multiple BESSs: A collaborative multi-agent reinforcement learning approach

作者：He, Wangli[1];Li, Chengyuan[1];Cai, Chenhao[1];Qing, Xiangyun[1];Du, Wenli[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2024

卷号：373

外文期刊名：APPLIED ENERGY

收录：;EI(收录号：20242916710050);WOS:【SCI-EXPANDED(收录号:WOS:001273936400001)】；

基金：This work is supported by Shanghai Pilot Program for Basic Research (22TQ1400100-3) , National Natural Science Foundation of China (62293501, 62373154) , Shanghai International Science & Technology Cooperation Program (21550712400) and Major Science and Technology Projects of Longmen Laboratory (NO. LMZDXM202206) .

语种：英文

外文关键词：Grid-connection microgrids; Multi-agent reinforcement learning; Point of common coupling; Battery energy storage systems

摘要：In recent years, with the increasing proportion of photovoltaic (PV) power generation in grid-connected microgrids, suppressing power fluctuations at the point of common coupling (PCC) has become a challenge. This paper proposes a collaborative power dispatch algorithm for battery energy storage systems (BESSs) based on multi-agent reinforcement learning (MARL), aiming to suppress the PCC power fluctuations caused by the uncertainty of PV power generation. First, a distributed multi-agent communication framework is developed, which defines the neighboring areas of agents based on the physical distances between BESSs to reduce the communication and computational cost of agents. Subsequently, a distributed multi-agent dueling double deep Q-network power dispatch algorithm based on the communication framework is proposed. In the proposed algorithm, a distributed Markov decision process is designed, enabling agents to share actions and rewards with neighboring agents locally to collaboratively learn optimal charging and discharging actions for suppress PCC power fluctuations. Finally, the scalability and effectiveness of the proposed algorithm in suppressing PCC power and voltage fluctuations and reducing operational cost are validated through simulation experiments based on the IEEE-33 bus and IEEE-141 bus systems. The simulation results demonstrate significant advantages of the proposed algorithm compared with other baseline MARL and traditional optimization methods.