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

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  • Zhang, Yue
  • Zhang, Qi
  • Farnoosh, Arash
  • Chen, Siyuan
  • Li, Yan

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.

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  • Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
  • Handle: RePEc:eee:energy:v:169:y:2019:i:c:p:844-853
    DOI: 10.1016/j.energy.2018.12.062
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    as
    1. Chung, Sung Hoon & Kwon, Changhyun, 2015. "Multi-period planning for electric car charging station locations: A case of Korean Expressways," European Journal of Operational Research, Elsevier, vol. 242(2), pages 677-687.
    2. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    3. Kuppusamy, Saravanan & Magazine, Michael J. & Rao, Uday, 2017. "Electric vehicle adoption decisions in a fleet environment," European Journal of Operational Research, Elsevier, vol. 262(1), pages 123-135.
    4. Cai Dai & Yuping Wang & Wei Yue, 2015. "A new orthogonal evolutionary algorithm based on decomposition for multi-objective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(10), pages 1686-1698, October.
    5. Kaddani, Sami & Vanderpooten, Daniel & Vanpeperstraete, Jean-Michel & Aissi, Hassene, 2017. "Weighted sum model with partial preference information: Application to multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 665-679.
    6. Ghorbani, Narges & Kasaeian, Alibakhsh & Toopshekan, Ashkan & Bahrami, Leyli & Maghami, Amin, 2018. "Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability," Energy, Elsevier, vol. 154(C), pages 581-591.
    7. Hao, Han & Liu, Zongwei & Zhao, Fuquan & Li, Weiqi & Hang, Wen, 2015. "Scenario analysis of energy consumption and greenhouse gas emissions from China's passenger vehicles," Energy, Elsevier, vol. 91(C), pages 151-159.
    8. 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.
    9. 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.
    10. Narimani, Hossein & Razavi, Seyed-Ehsan & Azizivahed, Ali & Naderi, Ehsan & Fathi, Mehdi & Ataei, Mohammad H. & Narimani, Mohammad Rasoul, 2018. "A multi-objective framework for multi-area economic emission dispatch," Energy, Elsevier, vol. 154(C), pages 126-142.
    11. Ioakimidis, Christos S. & Thomas, Dimitrios & Rycerski, Pawel & Genikomsakis, Konstantinos N., 2018. "Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot," Energy, Elsevier, vol. 148(C), pages 148-158.
    12. Dang, Duc-Cuong & Guibadj, Rym Nesrine & Moukrim, Aziz, 2013. "An effective PSO-inspired algorithm for the team orienteering problem," European Journal of Operational Research, Elsevier, vol. 229(2), pages 332-344.
    13. 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.
    14. 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.
    15. Zenginis, Ioannis & Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2016. "Analysis and quality of service evaluation of a fast charging station for electric vehicles," Energy, Elsevier, vol. 112(C), pages 669-678.
    16. 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.
    17. Davidov, Sreten & Pantoš, Miloš, 2017. "Planning of electric vehicle infrastructure based on charging reliability and quality of service," Energy, Elsevier, vol. 118(C), pages 1156-1167.
    18. Lochtefeld, Darrell F. & Ciarallo, Frank W., 2015. "Multi-objectivization Via Decomposition: An analysis of helper-objectives and complete decomposition," European Journal of Operational Research, Elsevier, vol. 243(2), pages 395-404.
    19. Goh, C.K. & Tan, K.C. & Liu, D.S. & Chiam, S.C., 2010. "A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design," European Journal of Operational Research, Elsevier, vol. 202(1), pages 42-54, April.
    20. Du, Jiuyu & Ouyang, Minggao & Chen, Jingfu, 2017. "Prospects for Chinese electric vehicle technologies in 2016–2020: Ambition and rationality," Energy, Elsevier, vol. 120(C), pages 584-596.
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