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Capacity Optimization of a Centralized Charging Station in Joint Operation with a Wind Farm

Author

Listed:
  • Zhe Jiang

    (Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
    Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250003, China)

  • Xueshan Han

    (Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
    Collaborative Innovation Center for Global Energy Interconnection (Shandong), Jinan 250061, China)

  • Zhimin Li

    (Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Mingqiang Wang

    (Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
    Collaborative Innovation Center for Global Energy Interconnection (Shandong), Jinan 250061, China)

  • Guodong Liu

    (Power and Energy Systems Group, Oak Ridge National Laboratory, One Bethel Valley Road, P.O. Box 2008 MS6007, Oak Ridge, TN 37831, USA)

  • Mengxia Wang

    (Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan 250061, China
    Collaborative Innovation Center for Global Energy Interconnection (Shandong), Jinan 250061, China)

  • Wenbo Li

    (Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250003, China)

  • Thomas B. Ollis

    (Power and Energy Systems Group, Oak Ridge National Laboratory, One Bethel Valley Road, P.O. Box 2008 MS6007, Oak Ridge, TN 37831, USA)

Abstract

In the context of large-scale wind power integration and rapid development of electric vehicles (EVs), a joint operation pattern was proposed to use a centralized charging station (CCS) to address high uncertainties incurred by wind power integration. This would directly remove the significant indeterminacy of wind power. Because the CCS is adjacent to a wind power gathering station, it could work jointly with wind farms to operate in power system as an independent enterprise. By combining the actual operational characteristics of the wind farm and the CCS, a multidimensional operating index evaluation system was created for the joint system. Based on the wind farm’s known capacity, a multi-target capacity planning model was established for CCS to maximize the probability of realizing diverse indices of the system based on dependent-chance goal programming. In this model, planning and dispatching are combined to improve the feasibility of results in the operational stage. This approach takes the effects of randomness into account for wind power and battery swapping. The model was solved through combining Monte Carlo simulation and a genetic algorithm (GA) based on segmented encoding. As shown in the simulation results, the proposed model could comprehensively include factors such as initial investment, wind power price, battery life, etc., to optimize CCS capacity and to ultimately improve the operating indices of the joint system.

Suggested Citation

  • Zhe Jiang & Xueshan Han & Zhimin Li & Mingqiang Wang & Guodong Liu & Mengxia Wang & Wenbo Li & Thomas B. Ollis, 2018. "Capacity Optimization of a Centralized Charging Station in Joint Operation with a Wind Farm," Energies, MDPI, vol. 11(5), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1164-:d:144926
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    References listed on IDEAS

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    1. Doucette, Reed T. & McCulloch, Malcolm D., 2011. "Modeling the CO2 emissions from battery electric vehicles given the power generation mixes of different countries," Energy Policy, Elsevier, vol. 39(2), pages 803-811, February.
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    3. Zhe Jiang & Xueshan Han & Zhimin Li & Wenbo Li & Mengxia Wang & Mingqiang Wang, 2016. "Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station," Energies, MDPI, vol. 9(11), pages 1-17, October.
    4. Chunghun Kim & Eduard Muljadi & Chung Choo Chung, 2017. "Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation," Energies, MDPI, vol. 11(1), pages 1-18, December.
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    Cited by:

    1. Adu-Gyamfi, Gibbson & Song, Huaming & Obuobi, Bright & Nketiah, Emmanuel & Wang, Hong & Cudjoe, Dan, 2022. "Who will adopt? Investigating the adoption intention for battery swap technology for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Adu-Gyamfi, Gibbson & Song, Huaming & Nketiah, Emmanuel & Obuobi, Bright & Adjei, Mavis & Cudjoe, Dan, 2022. "Determinants of adoption intention of battery swap technology for electric vehicles," Energy, Elsevier, vol. 251(C).

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