Voltage Hierarchical Control Strategy of Active Distribution Network Based on Deep Reinforcement Learning

Abstract

ObjectivesThe randomness and volatility of distributed power generation poses significant challenges for the voltage control in active distribution network (AND). In this context, there is an urgent need for an efficient voltage control strategy to ensure the safe operation of ADN.MethodsBased on the deep reinforcement learning method, a voltage control strategy for double-layer regional distribution networks was proposed. First, based on the adjustment characteristics of voltage regulating equipment and the complexity of controllable elements, a regional coordinated control area and a local autonomous control area were designed for the radiating grid structure of the ADN, and the voltage control model of each area was constructed. Then, the model was solved by deep Q-Network (DQN) algorithm and deep deterministic policy gradient (DDPG) algorithm to achieve the purpose of tracking voltage changes in real time, and effectively solve the voltage control problem during the operation of the ADN. Finally, the method was verified by IEEE 33-bus simulation examples.ResultsThe DQN algorithm and the DDPG algorithm were used to solve the control variables in the coordinated control region and the local autonomous region respectively, realizing real-time decision-making of voltage regulation in the ADN system, and solving the problems of bidirectional flow of ADN power flow and complex and changeable voltage.ConclusionsThe proposed control strategy has obvious effect on controlling voltage deviation, and has strong accuracy and practicality.

Keywords

• active distribution network (adn)
• regional coordination control
• local autonomous control
• deep reinforcement learning
• voltage control strategy