考虑区域间辅助奖励的配电网电压优化控制OA北大核心CSTPCD

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.