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Electricity Self-Sufficient Community Clustering for Energy Resilience

Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan
Department of Statistical Modeling, Institute of Statistical Mathematics, Tachikawa, Tokyo 190-8562, Japan
Center for Semiconductor Research and Development, Toshiba Corporation, Kawasaki, Kanagawa 212-8520, Japan
Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan
Center for Cloud Research and Development, National Institute of Informatics, Chiyoda, Tokyo 100-0003, Japan
Chief Strategy Officer, Preferred Networks, Inc., Chiyoda, Tokyo 100-0004, Japan
Author to whom correspondence should be addressed.
Academic Editor: G.J.M. (Gerard) Smit
Energies 2016, 9(7), 543;
Received: 23 April 2016 / Revised: 25 June 2016 / Accepted: 7 July 2016 / Published: 14 July 2016
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
PDF [5317 KB, uploaded 14 July 2016]


Local electricity generation and sharing has been given considerable attention recently for its disaster resilience and other reasons. However, the process of designing local sharing communities (or local grids) is still unclear. Thus, this study empirically compares algorithms for electricity sharing community clustering in terms of self-sufficiency, sharing cost, and stability. The comparison is performed for all 12 months of a typical year in Yokohama, Japan. The analysis results indicate that, while each individual algorithm has some advantages, an exhaustive algorithm provides clusters that are highly self-sufficient. The exhaustive algorithm further demonstrates that a clustering result optimized for one month is available across many months without losing self-sufficiency. In fact, the clusters achieve complete self-sufficiency for five months in spring and autumn, when electricity demands are lower. View Full-Text
Keywords: electricity sharing; community clustering; vehicle to community system; graph partitioning; simulated annealing electricity sharing; community clustering; vehicle to community system; graph partitioning; simulated annealing

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Yamagata, Y.; Murakami, D.; Minami, K.; Arizumi, N.; Kuroda, S.; Tanjo, T.; Maruyama, H. Electricity Self-Sufficient Community Clustering for Energy Resilience. Energies 2016, 9, 543.

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