Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective

• Published in: Energies (Basel) Vol. 17; no. 18; p. 4614
• Main Authors: Shi, Zheng, Yan, Lu, Hu, Yingying, Wang, Yao, Qin, Wenping, Liang, Yan, Zhao, Haibo, Jing, Yongming, Deng, Jiaojiao, Zhang, Zhi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Algorithms; Alternative energy sources; Bargaining; Demand side management; dual objective; Efficiency; Electricity; Electricity distribution; Energy consumption; Energy storage; Fuel cells; Game theory; Income distribution; microgrid; negotiation price; Optimization; optimize scheduling; Renewable resources; Scheduling; shared energy storage; Supply & demand; Turbines; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17184614

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%.