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Multiple group search optimization based on decomposition for multi-objective dispatch with electric vehicle and wind power uncertainties

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  • Zhang, Xian
  • Chan, Ka Wing
  • Wang, Huaizhi
  • Zhou, Bin
  • Wang, Guibin
  • Qiu, Jing

Abstract

While the number of plug-in electric vehicles (PEVs) increases rapidly, the application potential of PEVs should be accounted in electric power dispatch with several conflicting and competing objectives such as providing vehicle-to-grid (V2G) service or coordinating with wind power. To solve this highly constrained multi-objective optimization problem (MOOP), a multiple group search optimization based on decomposition (MGSO/D) is proposed considering the uncertainties of PEVs and wind power. Specifically, the decomposition approach effectively reduces the computational complexity, and the innovatively incorporated producer-scrounger model effectively improves the diversity and spanning of the Pareto-optimal front (PF). Meanwhile, the estimation error punishment is utilized to take into account of uncertainties. The performance of MGSO/D and the effectiveness of the uncertainty model are investigated on the IEEE 30-bus and 118-bus system with wind farms and PEV aggregators. Simulation results demonstrate the superiority of MGSO/D to solve this MOOP with practical uncertainties by comparing with well-established Pareto heuristic methods.

Suggested Citation

  • Zhang, Xian & Chan, Ka Wing & Wang, Huaizhi & Zhou, Bin & Wang, Guibin & Qiu, Jing, 2020. "Multiple group search optimization based on decomposition for multi-objective dispatch with electric vehicle and wind power uncertainties," Applied Energy, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:appene:v:262:y:2020:i:c:s0306261920300192
    DOI: 10.1016/j.apenergy.2020.114507
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    References listed on IDEAS

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    1. Andersson, S.-L. & Elofsson, A.K. & Galus, M.D. & Göransson, L. & Karlsson, S. & Johnsson, F. & Andersson, G., 2010. "Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany," Energy Policy, Elsevier, vol. 38(6), pages 2751-2762, June.
    2. de Jong, Pieter & Kiperstok, Asher & Sánchez, Antonio Santos & Dargaville, Roger & Torres, Ednildo Andrade, 2016. "Integrating large scale wind power into the electricity grid in the Northeast of Brazil," Energy, Elsevier, vol. 100(C), pages 401-415.
    3. Peng, Chao & Zou, Jianxiao & Lian, Lian & Li, Liying, 2017. "An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits," Applied Energy, Elsevier, vol. 190(C), pages 591-599.
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    Cited by:

    1. Lu, Xin & Qiu, Jing & Zhang, Cuo & Lei, Gang & Zhu, Jianguo, 2024. "Seizing unconventional arbitrage opportunities in virtual power plants: A profitable and flexible recruitment approach," Applied Energy, Elsevier, vol. 358(C).
    2. Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.

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