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Sustainability 2016, 8(12), 1234;

Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network

School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Author to whom correspondence should be addressed.
Academic Editor: Shuhui Li
Received: 20 September 2016 / Revised: 18 November 2016 / Accepted: 23 November 2016 / Published: 28 November 2016
(This article belongs to the Special Issue Smart Grid)
Full-Text   |   PDF [3558 KB, uploaded 28 November 2016]   |  


Due to the energy savings and environmental protection they provide, plug-in electric vehicles (PEVs) are increasing in number quickly. Rapid development of PEVs brings new opportunities and challenges to the electricity distribution network’s dispatching. A high number of uncoordinated charging PEVs has significant negative impacts on the secure and economic operation of a distribution network. In this paper, a bi-level programming approach that coordinates PEVs’ charging with the network load and electricity price of the open market is presented. The major objective of the upper level model is to minimize the total network costs and the deviation of electric vehicle aggregators’ charging power and the equivalent power. The subsequent objective of the lower level model after the upper level decision is to minimize the dispatching deviation of the sum of PEVs’ charging power and their optimization charging power under the upper level model. An improved particle swarm optimization algorithm is used to solve the bi-level programming. Numerical studies using a modified IEEE 69-bus distribution test system including six electric vehicle aggregators verify the efficiency of the proposed model. View Full-Text
Keywords: electric vehicles; smart distribution network; bi-level programming; dispatch optimization; coordination electric vehicles; smart distribution network; bi-level programming; dispatch optimization; coordination

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Deng, C.; Liang, N.; Tan, J.; Wang, G. Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network. Sustainability 2016, 8, 1234.

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