电力系统及其自动化学报2026，Vol.38Issue(4)：95-105,11.DOI:10.19635/j.cnki.csu-epsa.001776
基于安全图强化学习的柔性互联配电网分布式电源承载力动态评估
Dynamic DG Hosting Capacity Evaluation for Flexible Interconnected Distribution Network Based on Safe Graph Reinforcement Learning
摘要
Abstract
Aimed at the problem that it is difficult to achieve dynamic and accurate evaluation of distributed generation(DG)hosting capacity based on the mathematical optimization methods at present,a dynamic evaluation method for the hosting capacity of DG in flexible interconnected distribution network is proposed,which is based on safe graph rein-forcement learning(RL).First,considering the dynamic hosting capacity enhancement and network loss optimization of DG under the regulation of soft open points(SOPs),a dynamic DG hosting capacity evaluation model is constructed,which is applicable to three-phase unbalanced distribution network.Second,the dynamic DG hosting capacity evalua-tion model is transformed into a standard form of constrained Markov decision process,so as to achieve a balance be-tween the optimal decision-making and safe actions.Third,a soft actor-critic(SAC)algorithm with embedded graph convolutional neural network is proposed for offline training and online execution.The graph convolutional neural net-work is embedded into the SAC algorithm strategy network,so that the real-time and accurate evaluation for the dynam-ic DG hosting capacity in the distribution network is achieved.Finally,simulations are conducted with an IEEE 33 node network as an example to validate the effectiveness of the proposed model and method.关键词
动态承载力/约束马尔可夫决策过程/图卷积神经网络/柔性动作-评价算法Key words
dynamic hosing capacity/constrained Markov decision process/graph convolutional neural network/soft actor-critic(SAC)algorithm分类
信息技术与安全科学引用本文复制引用
郭祚刚,郇嘉嘉,白浩,刘嘉文,谈赢杰..基于安全图强化学习的柔性互联配电网分布式电源承载力动态评估[J].电力系统及其自动化学报,2026,38(4):95-105,11.基金项目
广东电网有限责任公司电网科技项目(GDKJXM20222440). （GDKJXM20222440）
