Detecting Prosumer-Community Groups in Smart Grids From the Multiagent Perspective

• Published in: IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2019-08, Vol.49 (8), p.1652-1664
• Main Authors: Cao, Jie, Bu, Zhan, Wang, Yuyao, Yang, Huan, Jiang, Jiuchuan, Li, Hui-Jia
• Format: Article
• Language: English
• Subjects: Automatic generation control; Communities; Computer simulation; Dynamic game model (DGM); Economics; Energy; Energy consumption; energy density (ED); Energy management; Finance; Flux density; Games; Multi-agent systems; Multiagent systems; Multiple objective analysis; Optimization; partially visible multiagent system (PVMAS); prosumer-community group (PCG); Smart grid; smart grid (SG); Smart grids
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2019.2899366

One of the greatest advancements of the modern era is the evolution of smart grid (SG), which integrates information communication technologies with advanced power electronic technologies to cope with the global energy shortage. The users in SGs are often called the "prosumers," who not only consume energy but also generate the energy and share it with the utility grid or with other energy consumers. In order to promote sustainable prosumer management in SGs, one of the feasible strategies is to aggregate the prosumers from different locations, but with similar energy behaviors and cohesive interconnections, such groups of prosumers are also called the prosumer-community groups (PCGs). The contribution of this paper is threefold. First, we provide a generalized definition of individual prosumer's energy density, which can be used to detect the underlying leader prosumers in SGs. Second, we formulate the PCG detection (PCG-D) as a multiobjective optimization problem, and present a novel dynamic game model to find the locally Pareto-optimal PCG structure. Third, we propose a partially visible multiagent system (PVMAS), where the viewing angles of both prosumers and PCGs are mutually restricted. The significance of our PVMAS is that it can nicely lead itself to parallelization for PCG-D, due to the fact that the feature updating of each agent is independent of each other. We conduct a series of comprehensive experiments on the simulated SG datasets to validate the performance of PVMAS through comparing it with existing community detection approaches in the literature.