电力建设2024，Vol.45Issue(5)：80-93,14.DOI:10.12204/j.issn.1000-7229.2024.05.009
考虑区域间辅助奖励的配电网电压优化控制
Voltage Optimization Control of Distribution Networks Considering Inter-Regional Auxiliary Rewards
摘要
Abstract
A soft open point can effectively solve the voltage fluctuation problem caused by the large-scale integration of distributed photovoltaics into a power distribution network.However,this can lead to increased collaboration between regions.Currently,when using multi-agent deep reinforcement learning algorithms for voltage optimization,each agent uses only rewards within its own region for training,resulting in a lack of coordination among agents and difficulty in guaranteeing the optimality of the output strategies.To address this problem,a method for voltage optimization in distribution networks that considers inter-regional auxiliary rewards was proposed.First,a multi-agent deep reinforcement learning framework based on multiple timescales was established for voltage optimization.Second,for agents controlling the soft open points,the rewards within their respective regions were defined as primary rewards,whereas the rewards from neighboring regions are defined as auxiliary rewards.The beneficial effect of auxiliary rewards on training was analyzed using the dot product of the primary and auxiliary reward loss functions with respect to the network parameter gradients.An adaptive modification of the auxiliary reward participation factor is implemented using an evolutionary game approach.Finally,the proposed method is validated in an improved IEEE 33 node system,which demonstrates stable training processes and improves strategy optimization for the agents.关键词
多智能体深度强化学习/电压优化/辅助奖励/演化博弈/参与因子Key words
multi-agent deep reinforcement learning/voltage optimization/auxiliary rewards/evolutionary game/participation factor分类
动力与电气工程引用本文复制引用
周祥,李晓露,柳劲松,林顺富..考虑区域间辅助奖励的配电网电压优化控制[J].电力建设,2024,45(5):80-93,14.基金项目
This work is supported by National Natural Science Foundation of China(No.51977127). 国家自然科学基金项目(51977127) （No.51977127）
