期刊
IEEE TRANSACTIONS ON SMART GRID
卷 9, 期 4, 页码 2625-2637出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2016.2614768
关键词
Electric vehicles; buildings; energy management systems; Dantzig-Wolfe decomposition; distributed optimization; data privacy
资金
- National Science Foundation [1239408]
- University of Washington's Clean Energy Institute
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1239408] Funding Source: National Science Foundation
The ability to control commercial buildings and electric vehicles (EVs) is a promising source of demand flexibility. In some cases, buildings and EVs share common infrastructure (e.g., a transformer) or interact with each other to accomplish a goal (e.g., reduce local peak demand). In such cases, the building and EV demand scheduling problems are effectively a single demand scheduling problem. Ideally, it would be solved as a single optimization problem. However, doing so might not be possible due to a number of concerns (e.g., data privacy). This paper proposes the use of a mixed-integer adaptation of the Dantzig-Wolfe decomposition to solve the building-EV demand scheduling problem in a decentralized fashion. The effectiveness of the proposed methodology is demonstrated in three case studies, where the building and EV problems are coupled by either: 1) demand limits; 2) a peak demand charge; or 3) an itemized billing tariff. Results show that the optimal solution can be reached while sharing a minimal amount of information. Furthermore, we show that the proposed methodology is scalable.
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