期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 125, 期 -, 页码 195-220出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2019.02.003
关键词
Charging station; Vehicle-to-grid (V2G); Renewable energy; Constraint-generation algorithm; Progressive hedging
Electric vehicles (EV) have received considerable attention in recent years due to their low operating cost, potential for energy sustainability, and zero tailpipe emissions. This study presents a novel two stage stochastic programming model integrating long- and short-term decisions to design and manage EV charging stations with renewable energy generation capability. The model captures the non-linear load congestion effect that increases exponentially as the electricity consumed by plugged-in EVs approaches the capacity of the charging station and linearizes it. The study proposes a hybrid decomposition algorithm that utilizes a Sample Average Approximation and an enhanced Progressive Hedging algorithm (PHA) inside a Constraint Generation algorithmic framework to efficiently solve the proposed optimization model. A case study based on Washington, D.C. is presented to visualize and validate the modeling results. Computational experiments demonstrate the effectiveness of the proposed algorithm in solving the problem in a practical amount of time. Finding of the study include that incorporating the load congestion factor encourages the opening of large-sized charging stations, increases the number of stored batteries, and that higher congestion costs call for a decrease in the opening of new charging stations. (C) 2019 Elsevier Ltd. All rights reserved.
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