Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 14, Issue 1, Pages 321-331Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2017.2662711
Keywords
Charging station planning; electric vehicle (EV); multi-objective optimization; smart grid; vehicle-to-grid
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Funding
- China Southern Power Grid [WYKJ00000027]
- State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University, China) [2007DA10512716401]
- Early Career Research Scheme of the Faculty of Engineering and Information Technology, The University of Sydney, Australia
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The increasing prevalence of electric vehicles (EVs) calls for the effective planning of the charging infrastructure. In this study, a multi-objective, multistage collaborative planning model is proposed for the coupled EV charging station infrastructure and power distribution network. The planning model aims to minimize the investment and operation costs of the distribution system while maximize the annually captured traffic flow. The uncertainties of EV charging loads are modeled for three different types of charging stations. The FISK's stochastic traffic assignment model is utilized to model realistic traffic flows. And a new class of volume-delay functions, conical congestion functions, is employed to overcome the shortcomings of the conventional Bureau of Public Roads function. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm is applied to find the nondominated solutions of the proposed collaborative planning model. Finally, simulations based on a 54-node distribution system are conducted to validate the effectiveness of the proposed method.
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