Data-Driven Adaptive Reactive Power and Voltage Control Method for Distribution Networks with Energy Storage by Using DRL-SAC Algorithm

Abstract

In distribution network and microgrids, energy storage (ES) systems possess four-quadrant operational capabilities, making them inherently high-quality resources for reactive power (RP) regulation. However, existing research has primarily focused on optimizing the active power of ES to achieve economic objectives, while the potential for RP and voltage control has not been fully explored. Meanwhile, traditional RP optimization methods have inherent limitations in terms of real-time performance, addressing uncertainty, and handling nonlinear problems. To address these challenges, this paper proposes a data-driven adaptive RP and voltage control method for distribution network with ES using deep reinforcement learning (DRL)–Soft Actor–Critic (SAC) algorithm. First of all, this method models the grid’s RP and voltage control problem as a sequential decision-making process, with the core being the construction of a control agent that integrates grid operational states with a deep neural network. Through continuous interaction with the environment, this agent autonomously learns and dynamically adapts to the random fluctuations in photovoltaic (PV) output and load without relying on precise physical models. Secondly, this paper sets minimizing network losses, voltage deviations, and the operational costs of RP equipment in ES as comprehensive optimization objectives, translating them into a reward function within the DRL-SAC framework. Leveraging the powerful nonlinear mapping capabilities and extremely fast forward computation speed of deep neural networks, the strategy achieves a data-driven approximation of the optimal RP control strategy in complex grid environments. Finally, the superiority of the strategy is comprehensively verified on the modified IEEE 33-bus system under three typical operating conditions (daytime fluctuation, extreme weather, sudden load change). The results show that the voltage qualification rate is increased to 99.1% and the network loss is reduced by 33.7%, providing an engineering-feasible solution for ES systems to participate in distribution network RP and voltage regulation.