An Orderly Power Utilization Scheme Based on an Intelligent Multi-Agent Apanage Management System

Ruijiu Jin, Dongsheng Zuo, Xiangfeng Zhang and Wengang Sun
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Ruijiu Jin: College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Dongsheng Zuo: College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Xiangfeng Zhang: College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Wengang Sun: College of Urban Construction and Safety Engineering, Shanghai Institute of Technology, Shanghai 200235, China

Energies, 2019, vol. 12, issue 23, 1-21

Abstract: Orderly power utilization (OPU) is an important measure to alleviate contradiction between supply and demand in a power system peak load period. As a load management system becomes smarter, it is necessary to fully explore the interactive ability among users and make schemes for OPU more applicable. Therefore, an intelligent multi-agent apanage management system that includes a mutual aid mechanism (MAM) is proposed. In the decision-making scheme, users’ participation patterns and the potential of peak shifting and willingness are considered, as well as the interests of both power consumers and power grid are comprehensively considered. For residential users, the charging time for their electric vehicles (EVs) is managed to consume the locally distributed power generation. To fully exploit user response potential, the algorithm for improved clustering by fast search and find of density peaks (I-CFSFDP), i.e., clusters the power load curve, is proposed. To conduct electrical mutual aid among users and adjust the schemes reasonably, a multi-objective optimization model (M2OM) is established based on the cluster load curves. The objectives include the OPU control cost, the user’s electricity cost, and the consumption of distributed photovoltaic (PV). Our results of a case study show that the above method is effective and economical for improving interactive ability among users. Agents can coordinate their apanage power resources optimally. Experiments and examples verify the practicability and effectiveness of the improved algorithm proposed in this study.

Keywords: demand-side response; orderly power utilization; improved clustering by fast search and find of density peaks (I-CFSFDP); multi-agent system; multi-objective optimization (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: 2019
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