A New and Fair Peer-to-Peer Energy Sharing Framework for Energy Buildings

• Published in: IEEE transactions on smart grid Vol. 11; no. 5; pp. 3817 - 3826
• Main Authors: Cui, Shichang, Wang, Yan-Wu, Shi, Yang, Xiao, Jiang-Wen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Buildings; Computer simulation; Cost reduction; distributed optimization; Energy buildings; Energy costs; Energy management; energy sharing; game equilibrium; Game theory; Games; peer-to-peer; Peer-to-peer computing; Renewable energy sources; Schedules; Temperature measurement
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2020.2986337

With the rapid development of energy buildings, advanced energy management is urgently demanded for a green society. In this paper, focusing on the coordinated energy management for a building community, we present a new and fair peer-to-peer energy sharing framework to realize an economic and sustainable building community. Specifically, in the building-centric peer-to-peer mode, buildings directly share their energy supplies/demands and offer the related payments within the community under the constraints of community energy and payment balance. We propose a non-cooperative energy sharing game for the selfish buildings, and we further show that a generalized Nash equilibrium of the game is independent of the energy sharing payments. Consequently, we firstly derive the energy sharing profiles by seeking the equilibrium. Since the buildings' energy sharing payments are mutually coupled and influenced, we propose a cost reduction ratio distribution model to determine the payments to ensure the fairness in the sense that buildings can get as large cost reductions and similar cost reduction ratios as possible. Simulation results show that all buildings can reduce their energy costs and have smoother and smaller net demand profiles on the main grid, thus making the proposed schemes and algorithms promising in real applications.