中国电力2025，Vol.58Issue(4)：230-236,7.DOI:10.11930/j.issn.1004-9649.202409074
基于条件生成对抗网络与多智能体强化学习的配电网可靠性评估方法
A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning
摘要
Abstract
To enhance computational efficiency and accuracy in reliability assessment of distribution networks with large-scale distributed photovoltaic integration,a novel assessment method is proposed based on conditional generative adversarial network and multi-agent reinforcement learning.Firstly,the sequential state sequences of the system are generated using Sequential Monte Carlo simulation,and a conditional generative adversarial network(CGAN)is combined with multi-resolution meteorological factors to characterize the multivariate characteristics of source-load scenarios,including temporal dependency,volatility,randomness,and source-load correlation.Secondly,a multi-agent reinforcement learning(MARL)model is established,and a training algorithm integrating imitation learning and exploratory learning is proposed,enabling the agents to acquire optimal policies through interactive learning with an expert experience model.Finally,the simulationg is verified based on the IEEE RBTS BUS-2 system.Simulation results demonstrate that the proposed method outperforms traditional methods in terms of learning curve and stability,significantly improving both the accuracy and computational efficiency in distribution network reliability assessment,possessing superior practical values.关键词
配电网/可靠性评估/生成对抗网络Key words
distribution network/reliability assessment/generative adversarial network引用本文复制引用
徐慧慧,田云飞,赵宇洋,彭婧,石庆鑫,成锐..基于条件生成对抗网络与多智能体强化学习的配电网可靠性评估方法[J].中国电力,2025,58(4):230-236,7.基金项目
国家自然科学基金资助项目(52307094) （52307094）
国网甘肃省电力公司咨询项目(W24FZ2730050). This work is supported by National Natural Science Foundation of China(No.52307094)and Consulting Project of State Grid Gansu Electric Power Co.,Ltd.(No.W24FZ2730050). （W24FZ2730050）
