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GIS-Based Multi-Objective Particle Swarm Optimization of Charging Station of Electric Vehicles – Taking a District in Beijing as an Example

Author

Listed:
  • Zhang Yue

    (China University of Petroleum)

  • Arash Farnoosh

    (IFPEN - IFP Energies nouvelles, IFP School)

  • Qi Zang

    (China University of Petroleum)

  • Siyuan Chen

    (China University of Petroleum)

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 construction of charging facilities to meet the needs of electric vehicles has become an urgent situation in China. Different from other charging facilities, charging station could help to break the limitation of driving distance. It also has a special dual attribute of public service and high investment. So, this paper establishes a model with two objective functions of minimizing construction cost and maximizing its coverage and Particle Swarm Optimization was used to solve it. Besides, we take into account the conveniences of stations to charging vehicles and their influences on the loads of the power grid and GIS is used to overlay the traffic system diagram on power system diagram to find the alternative construction points. In this study, a district in Beijing is analyzed using the method and model we proposed. Finally, a planning strategy of charging station for Chinese market is suggested

Suggested Citation

  • Zhang Yue & Arash Farnoosh & Qi Zang & Siyuan Chen, 2018. "GIS-Based Multi-Objective Particle Swarm Optimization of Charging Station of Electric Vehicles – Taking a District in Beijing as an Example," Working Papers hal-03187920, HAL.
    • Handle: RePEc:hal:wpaper:hal-03187920
    Note: View the original document on HAL open archive server: https://ifp.hal.science/hal-03187920
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    References listed on IDEAS

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    1. Micari, Salvatore & Polimeni, Antonio & Napoli, Giuseppe & Andaloro, Laura & Antonucci, Vincenzo, 2017. "Electric vehicle charging infrastructure planning in a road network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 98-108.
    2. Densing, Martin & Turton, Hal & Bäuml, Georg, 2012. "Conditions for the successful deployment of electric vehicles – A global energy system perspective," Energy, Elsevier, vol. 47(1), pages 137-149.
    3. Avril, S. & Arnaud, G. & Florentin, A. & Vinard, M., 2010. "Multi-objective optimization of batteries and hydrogen storage technologies for remote photovoltaic systems," Energy, Elsevier, vol. 35(12), pages 5300-5308.
    4. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    5. Awasthi, Abhishek & Venkitusamy, Karthikeyan & Padmanaban, Sanjeevikumar & Selvamuthukumaran, Rajasekar & Blaabjerg, Frede & Singh, Asheesh K., 2017. "Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm," Energy, Elsevier, vol. 133(C), pages 70-78.
    6. Delgarm, N. & Sajadi, B. & Kowsary, F. & Delgarm, S., 2016. "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)," Applied Energy, Elsevier, vol. 170(C), pages 293-303.
    7. Alegre, Susana & Míguez, Juan V. & Carpio, José, 2017. "Modelling of electric and parallel-hybrid electric vehicle using Matlab/Simulink environment and planning of charging stations through a geographic information system and genetic algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1020-1027.
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