Study on the Optimal Configuration of a Wind-Solar-Battery-Fuel Cell System Based on a Regional Power Supply

• Main Authors: Zekun Wang, Yan Jia, Chang Cai, Yinpeng Chen, Na Li, Miao Yang, Qing'an Li
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Multienergy flow integrated power supply system; optimum ratio; improved genetic algorithm; average annual cost; loss of power supply probability
• Online Access: https://ieeexplore.ieee.org/document/9374458/

The integration of energy storage facilities into existing structures will result in increased costs. Therefore, it is of great significance to optimize the configuration of integrated power systems with multienergy flows to reduce the cost of the comprehensive utilization of energy. This study established a wind-solar-battery-fuel cell integrated power supply system to optimize the grid-connected regional power supply. First, the load is given with a known daily energy demand. The optimization goal is to minimize the average annual cost and the loss of power supply probability. The cost model includes the constraints of transportation costs and the benefits of selling hydrogen and oxygen. Then, an improved genetic algorithm is developed to optimize the structure of the wind-solar-battery-fuel cell integrated power supply system. The basic idea of the improved genetic algorithm is to change the coordination mode of the crossover operator and the mutation operator according to the size of the initial population fitness. Finally, according to the calculation results of the improved genetic algorithm, the optimal configuration of the capacity of devices in the system is obtained, verifying the effectiveness of the improved genetic algorithm. The cost calculation result of the genetic algorithm is 18.7% higher than that of the improved genetic algorithm, and it completely converges at approximately 70 steps. The cost calculation result of the particle swarm optimization is 17.1% higher than that of the improved genetic algorithm, and it completely converges at approximately 75 steps. The cost calculation result of the nondominated sorting genetic algorithm is 9.6% higher than that of the improved genetic algorithm, and it completely converges at approximately 58 steps. The system established in this research can fully meet the power demands for a given area and effectively reduce the local curtailment of wind energy and solar energy.