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Energies 2016, 9(12), 1031; doi:10.3390/en9121031

A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
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Academic Editor: Paras Mandal
Received: 20 October 2016 / Revised: 21 November 2016 / Accepted: 29 November 2016 / Published: 7 December 2016
(This article belongs to the Special Issue Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow)
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Abstract

With the development of microgrids (MGs), interconnected operation of multiple MGs is becoming a promising strategy for the smart grid. In this paper, a privacy-preserving distributed optimal scheduling method is proposed for the interconnected microgrids (IMG) with a battery energy storage system (BESS) and renewable energy resources (RESs). The optimal scheduling problem is modeled to minimize the coalitional operation cost of the IMG, including the fuel cost of conventional distributed generators and the life loss cost of BESSs. By using the framework of the alternating direction method of multipliers (ADMM), a distributed optimal scheduling model and an iteration solution algorithm for the IMG is introduced; only the expected exchanging power (EEP) of each MG is required during the iterations. Furthermore, a privacy-preserving strategy for the sharing of the EEP among MGs is designed to work with the mechanism of the distributed algorithm. According to the security analysis, the EEP can be delivered in a cooperative and privacy-preserving way. A case study and numerical results are given in terms of the convergence of the algorithm, the comparison of the costs and the implementation efficiency. View Full-Text
Keywords: microgrid; distributed optimization; cybersecurity; optimal scheduling microgrid; distributed optimization; cybersecurity; optimal scheduling
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Liu, N.; Wang, C.; Cheng, M.; Wang, J. A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids. Energies 2016, 9, 1031.

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