Integrated Intelligent Energy ›› 2024, Vol. 46 ›› Issue (9): 28-36.doi: 10.3969/j.issn.2097-0706.2024.09.004

• Source-Grid Coordination • Previous Articles     Next Articles

Research on capacity allocation for source-grid-load-storage systems based on improved PSO

WANG Xiaoyan1(), WU Shuquan2()   

  1. 1. Guodian Nanjing Automation Company Limited, Nanjing 210032, China
    2. Huadian Zhejiang Longyou Thermal Power Company Limited, Quzhou 324400,China
  • Received:2024-05-17 Revised:2024-06-12 Published:2024-09-25
  • Supported by:
    Zhejiang Provincial Natural Science Foundation(LY23E070002)

Abstract:

With the advancement of renewable energy technologies, source-network-load-storage systems have become an important solution for reliable and stable operation of power systems. Since a rational capacity configuration can reduce the investment, ensure the power supply capacity and improve the renewable energy utilization rate of a power system, it is a vital parameter for system economic benefits and performance improvement. Thus, an improved Particle Swarm Optimization (PSO) is proposed to obtain the capacity and power configuration scheme with the minimal investment, the lowest renewable energy abandon rate and stable power supply for a source-load-storage system through multi-objective optimization. And variable inertia weights can enhance the search capability and convergence speed of the algorithm. The proposed algorithm is applied to an islanded source-network-load-storage system and compared with other typical optimization algorithms. Simulation results demonstrate that the multi-objective optimization based on the improved PSO can choose a proper capacity configuration for the system with decent convergence. The multi-objective optimization based on the improved PSO not only effectively realizes the capacity configuration for source-grid-load-storage systems but also significantly improves the convergence speed and solution quality. It offers a novel optimization tool for power system planning and operation.

Key words: renewable energy, source-load-grid-storage system, capacity configuration, particle swarm optimization, variable inertia weight, optimization configuration

CLC Number: