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An efficient and privacy‐preserving scheme for P2P energy exchange among smart microgrids
Author(s) -
Hong Yuan,
Goel Sanjay,
Liu Wen Ming
Publication year - 2015
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.3355
Subject(s) - microgrid , smart grid , computer science , distributed computing , information exchange , grid , distributed generation , resilience (materials science) , private information retrieval , demand response , energy storage , scheme (mathematics) , energy supply , energy (signal processing) , power (physics) , renewable energy , engineering , computer security , electricity , control (management) , telecommunications , electrical engineering , mathematical analysis , statistics , physics , geometry , mathematics , quantum mechanics , artificial intelligence , thermodynamics
Summary To date, increasing number of entities on the smart grid begin to establish their local energy generator for ensuring reliability and resilience of power supply. These ‘microgrids’ can either connect to the power grid or isolate themselves from the grid by consuming their locally generated or stored energy. In reality, some microgrids may have excessive energy while the others may have to request extra energy from the main grid. To better balance the demand and supply of the distributed smart microgrids, it is desired to develop peer‐to‐peer (P2P) energy exchange models that enable microgrids to interactively exchange their local energy instead of consuming energy delivered from the main grid. However, in this scenario, all the microgrids have to disclose their private information (e.g., demand load and energy storage amount) to each other in the exchange. To tackle these issues, in this paper, we first formulate several novel energy exchange optimization problems that minimize the global energy loss during the exchange in different scenarios, and then develop an efficient and privacy‐preserving scheme to solve the energy exchange optimization problems without private information disclosure. We also extend the privacy‐preserving scheme to a collusion‐resistant scheme in which all the microgrids cannot learn any additional information through colluding with each other. The performance of our proposed approaches is experimentally validated on real microgrid data. Copyright © 2015 John Wiley & Sons, Ltd.