Intelligent Voltage Control Method in Active Distribution Networks Based on Averaged Weighted Double Deep Q-network Algorithm

Abstract

High penetration of distributed renewable energy sources and electric vehicles (EVs) makes future active distribution network (ADN) highly variable. These characteristics put great challenges to traditional voltage control methods. Voltage control based on the deep Q-network (DQN) algorithm offers a potential solution to this problem because it possesses humanlevel control performance. However, the traditional DQN methods may produce overestimation of action reward values, resulting in degradation of obtained solutions. In this paper, an intelligent voltage control method based on averaged weighted double deep Q-network (AWDDQN) algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network (DDQN) algorithm. Using the proposed method, the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process (MDP) model which is solved by the AWDDQN algorithm. The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN. The proposed voltage control method is validated using the IEEE 33-bus and 123bus systems containing renewable energy sources and EVs， and compared with the DQN and DDQN algorithms based methods, and traditional mixed-integer nonlinear program based methods. The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones.

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“…The terminal state occurs when the generators hit the threshold or when the pre-set simulation time comes to an end. [70] AWDDQN Voltage control The set of states of node voltages and adjustable resources of the ADN at a given time.…”