Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination

Hongbin Sun (), Hongyu Zou (), Jianfeng Jia, Qiuzhen Shen, Zhenyu Duan and Xi Tang
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Hongbin Sun: School of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, China
Hongyu Zou: School of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, China
Jianfeng Jia: School of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, China
Qiuzhen Shen: School of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, China
Zhenyu Duan: School of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, China
Xi Tang: School of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, China

Energies, 2024, vol. 17, issue 22, 1-29

Abstract: This paper addresses the critical challenge of scheduling optimization in regional integrated energy systems, characterized by the coupling of multiple physical energy streams (electricity, heat, and cooling) and the participation of various stakeholders. To tackle this, a novel multi-load and multi-type integrated demand response model is proposed, which fully accounts for the heterogeneous characteristics of energy demands in different campus environments. A leader–follower two-layer game equilibrium model is introduced, where the system operator acts as the leader, and campus load aggregators, energy storage plants, and wind farm operators serve as followers. The layer employs an enhanced particle swarm optimization (PSO) algorithm to iteratively adjust energy sales prices and response compensation unit prices, influencing the user response plan through the demand response model. In the lower layer, the charging and discharging schedules of energy storage plants, wind farm energy supply, and outputs of energy conversion devices are optimized to guide system operation. The novelty of this approach lies in the integration of a game-theoretic framework with advanced optimization techniques to balance the interests of all participants and enhance system coordination. A case study is conducted to evaluate the effectiveness of the proposed strategy, demonstrating significant economic benefits. The results show that the model encourages stakeholders to invest in energy infrastructure and actively participate in coordinated dispatch, leading to improved overall system efficiency and comprehensive revenue enhancement for the multi-agent energy system.

Keywords: multi-entity integrated energy system; two-tier optimization; improved particle swarm; integrated demand response; master-slave game (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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