A Multi-Agent Deep Reinforcement Learning Based Voltage Regulation Using Coordinated PV Inverters
Di Cao, Weihao Hu, Junbo Zhao, Qi Huang, Zhe Chen, Frede Blaabjerg
Abstract
This paper proposes a multi-agent deep reinforcement learning-based approach for distribution system voltage regulation with high penetration of photovoltaics (PVs). The designed agents can learn the coordinated control strategies from historical data through the counter-training of local policy networks and centric critic networks. The learned strategies allow us to perform online coordinated control. Comparative results with other methods show the enhanced control capability of the proposed method under various conditions.
Topics & Concepts
Reinforcement learningVoltageComputer sciencePhotovoltaic systemVoltage regulationReinforcementEngineeringControl theory (sociology)Control engineeringElectrical engineeringControl (management)Artificial intelligenceStructural engineeringOptimal Power Flow DistributionMicrogrid Control and OptimizationSmart Grid Energy Management