Electricity Self-Sufficient Community Clustering for Energy Resilience

• Main Authors: Yoshiki Yamagata, Daisuke Murakami, Kazuhiro Minami, Nana Arizumi, Sho Kuroda, Tomoya Tanjo, Hiroshi Maruyama
• Format: Article
• Language: English
• Published: MDPI AG 2016-07-01
• Series: Energies
• Subjects: electricity sharing; community clustering; vehicle to community system; graph partitioning; simulated annealing
• Online Access: http://www.mdpi.com/1996-1073/9/7/543

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.