Coordinated Energy Scheduling of a Distributed Multi-Microgrid System Based on Multi-Agent Decisions

Yuyan Sun, Zexiang Cai, Ziyi Zhang, Caishan Guo, Guolong Ma and Yongxia Han
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Yuyan Sun: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Zexiang Cai: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Ziyi Zhang: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Caishan Guo: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Guolong Ma: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Yongxia Han: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China

Energies, 2020, vol. 13, issue 16, 1-20

Abstract: Regarding the different ownerships and autonomy of microgrids (MGs) in the distributed multi-microgrid (MMG) system, this paper establishes a multi-stage energy scheduling model based on a multi-agent system (MAS). The proposed mechanism enables a microgrid agent (MGA), a central energy management agent (CEMA), and a coordination control agent (CCA) to cooperate efficiently during various stages including prescheduling, coordinated optimization, rescheduling and participation willingness analysis. Based on the limited information sharing between agents, energy scheduling models of agents and coordinated diagrams are constructed to demonstrate the different roles of agents and their interactions within the MMG system. Distributed schemes are introduced for MG internal operations considering demand response, while centralized schemes under the control of the CCA are proposed to coordinate MGAs. Participation willingness is defined to analyze the MGA’s satisfaction degree of the matchmaking. A hierarchical optimization algorithm is applied to solve the above nonlinear problem. The upper layer establishes a mixed-integer linear programming (MILP) model to optimize the internal operation problem of each MG, and the lower layer applies the particle swarm optimization (PSO) algorithm for coordination. The simulation with a three-MG system verifies the rationality and effectiveness of the proposed model and method.

Keywords: energy scheduling; multi-microgrid system; multi-agent system; demand response; information sharing (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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