Electricity Self-Sufficient Community Clustering for Energy Resilience

Yoshiki Yamagata, Daisuke Murakami, Kazuhiro Minami, Nana Arizumi, Sho Kuroda, Tomoya Tanjo and Hiroshi Maruyama
Additional contact information
Yoshiki Yamagata: Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan
Daisuke Murakami: Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan
Kazuhiro Minami: Department of Statistical Modeling, Institute of Statistical Mathematics, Tachikawa, Tokyo 190-8562, Japan
Nana Arizumi: Center for Semiconductor Research and Development, Toshiba Corporation, Kawasaki, Kanagawa 212-8520, Japan
Tomoya Tanjo: Center for Cloud Research and Development, National Institute of Informatics, Chiyoda, Tokyo 100-0003, Japan
Hiroshi Maruyama: Chief Strategy Officer, Preferred Networks, Inc., Chiyoda, Tokyo 100-0004, Japan

Energies, 2016, vol. 9, issue 7, 1-13

Abstract: 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.

Keywords: electricity sharing; community clustering; vehicle to community system; graph partitioning; simulated annealing (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/9/7/543/pdf (application/pdf)
https://www.mdpi.com/1996-1073/9/7/543/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:9:y:2016:i:7:p:543-:d:73923

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().