GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles

Yue Zhang, Qi Zhang, Arash Farnoosh, Siyuan Chen () and Yan Li
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Yue Zhang: China University of Petroleum
Qi Zhang: China University of Petroleum
Arash Farnoosh: IFPEN - IFP Energies nouvelles
Siyuan Chen: China University of Petroleum
Yan Li: China University of Petroleum

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Abstract: The rapid development of electric vehicles can greatly alleviate the environmental problems and energy tension. However, the lack of public supporting facilities has become the biggest problem hinders its development. How to reasonably plan the placement of charging stations to meet the needs of electric vehicles has become an urgent situation in China. Different from private charging piles, charging station could help to break the limitation of short range. It also has a special dual attribute of public service and high investment. Therefore, a mathematically optimal model with two objective functions is developed to analyze the relationship between upfront investments and operating costs and service coverage of charging station system and it was solved by Particle Swarm Optimization. Besides, we take into account the conveniences of stations for charging vehicles and their influences on the loads of the power grid. Geographic Information System is used to overlay the traffic system diagram on power system diagram to find the alternative construction sites. In this study, a district in Beijing is analyzed using the proposed method and model. And the following suggestions are given: government should lead the construction of charging station; service ability needs to be enhanced; it is better to make more investment at earlier stage; constructions of charging stations can facilitate EV's development.

Keywords: Electric vehicle; Charging station; Multi-objective particle swarm optimization; GIS (search for similar items in EconPapers)
Date: 2019-02-15
New Economics Papers: this item is included in nep-ene, nep-reg and nep-tre
Note: View the original document on HAL open archive server: https://ifp.hal.science/hal-02009151
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Citations: View citations in EconPapers (14)

Published in Energy, 2019, 169, pp.844-853

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