Improved hybrid genetic algorithm for location and volume determination of distributed power supply

• Published in: 2021 China Automation Congress (CAC) pp. 2068 - 2073
• Main Authors: Na, Wu, Hu, Youhua, Fan, Shuxian, Song, Zhenren
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.10.2021
• Subjects: Costs; Distributed power supply; Genetic algorithm; Investment cost; Load side power supply cost; Network loss cost; Particle swarm optimization; Power supplies; Resource management; Sociology; Stability analysis; Statistics; Voltage
• ISSN: 2688-0938
• DOI: 10.1109/CAC53003.2021.9727373

In order to allocate the distributed power more reasonably, improve the quality of power supply and reduce the cost, a distribution network optimal allocation model is established in this paper. Considering the investment, network loss cost and load power supply cost, an improved hybrid genetic algorithm is proposed. Firstly, the particle swarm optimization algorithm was used to optimize the genetic algorithm, and the elite population was taken as the initial population of the genetic algorithm, and the parameters of the genetic algorithm were adjusted by using the improved three-tangent crossover method and the adaptive mutation to improve the stability of the algorithm. Through the improved algorithm, the appropriate location and capacity are obtained, and the network loss, voltage quality, the investment and operation of the distributed power supply and the total cost of the grid supplying power to the load side are obtained. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified by the simulation of the IEEE-33 node distribution system.