Voltage control and optimal scheduling strategy of new distribution system based on improved genetic algorithm

Abstract

Traditional centralized voltage control methods have limitations in real-time and adaptability. In order to improve the operation economy and voltage quality of the system, this paper proposes a voltage cooperative control and optimal scheduling strategy combining improved genetic algorithm. In this method, a mixed integer nonlinear optimization model is built with the goal of minimizing network loss, minimizing voltage deviation and minimizing operation cost of regulating equipment, and an annealing selection mechanism is introduced to significantly enhance the global optimization ability and convergence speed of the algorithm. Simulations on IEEE 33 nodes, 123 nodes and real European low voltage network data sets show that the proposed strategy can reduce the average voltage deviation of the system by 18.7%-32.4%, the total operating cost by 12.5%-21.3%, and the average optimization time by about 40% compared with the basic genetic algorithm and particle swarm optimization algorithm. To sum up, the proposed strategy can effectively coordinate photovoltaic inverters, on-load voltage regulating transformers, capacitor banks and other types of equipment, realize fast and economical voltage optimal dispatching, and provide reliable decision support for the safe and economical operation of distribution network under high penetration distributed energy access.