A Reinforcement Learning-based Approach for Active Optimization of High Proportional Distributed Power Supply Access to Rural Distribution Networks

Abstract

In the process of actively implementing the rural revitalization strategy and the “double carbon” goal, distributed power sources are connected to the rural distribution network on a large scale, resulting in a gradual transition to a new type of active distribution network, which has a greater impact on the distribution network in terms of voltage, network loss, and protection, thus posing greater demands on the distribution network in terms of planning and control, power quality management, and operation. This significantly impacts the voltage, network loss, and protection of the distribution network, thus putting higher requirements on the planning and control, power quality management, operation, and maintenance of distribution networks. Since the traditional OPF algorithm has great defects in solving large-scale decentralized, time-series, and stochastic nonlinear problems, in order to realize real-time control of distribution networks under more complex network conditions, this paper proposes an optimization algorithm based on reinforcement learning and applies it to real-time control of active distribution networks, taking the optimal network loss as the objective function and considering network voltage, line load factor, and distributed power supply. As a de-modeling artificial intelligence method, reinforcement learning has great advantages in solving large problems with complex mathematical models. The method’s control effect and convergence time can be improved by introducing imitation learning and migration learning, expanding the knowledge matrix, and introducing risk assessment mechanisms. Based on MATLAB R2017a simulation platform for the IEEE 33-node improvement model, the simulation results show that the reinforcement learning method has a better control effect in solving the real-time control problem of active distribution network, significantly alleviates the voltage crossing limit, effectively reduces the network loss, and has good real-time characteristics.