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An Improved, Negatively Correlated Search for Solving the Unit Commitment Problem’s Integration with Electric Vehicles

Author

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  • Qun Niu

    (Shanghai Key Laboratory of Power Station Automation Technology, School of Mechanical Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Kecheng Jiang

    (Shanghai Key Laboratory of Power Station Automation Technology, School of Mechanical Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Zhile Yang

    (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

With the rapid development of plug-in electric vehicles (PEVs), the charging of a number of PEVs has already brought huge impact and burden to the power grid, particularly at the medium and low voltage distribution networks. This presents a big challenge for further mass roll-out of electric vehicles. To assess the impact of charging of substantial number of electric vehicles on the grid, a model of 30000 PEVs integrated with unit commitment (UCEV) was investigated in this study. The unit commitment was a large-scale, mixed-integer, nonlinear, NP-Hard (non-deterministic polynomial) optimization problem, while the integration of PEVs further increased the complexity of the model. In this paper, a global best inspired negatively correlated search (GBNCS) method which extends the evolutionary logic of negatively correlated search is proposed to tackle the UCEV problem. In the proposed algorithm, a rounding transfer function in GBNCS, is deployed to convert real-valued variables into binary ones; further, the global best information is combined in the population to improve the efficiency of the algorithm. Numerical results confirmed that the proposed GBNCS can achieve good performance in both a basic IEEE 10 unit commitment problem and the UCEV problem. It was also shown that, among four charging modes, the off-peak charging mode and EPRI (Electric Power Research Institute) charging mode are more economical in PEV charging.

Suggested Citation

  • Qun Niu & Kecheng Jiang & Zhile Yang, 2019. "An Improved, Negatively Correlated Search for Solving the Unit Commitment Problem’s Integration with Electric Vehicles," Sustainability, MDPI, vol. 11(24), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:6945-:d:294696
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    References listed on IDEAS

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    1. Foley, Aoife & Tyther, Barry & Calnan, Patrick & Ó Gallachóir, Brian, 2013. "Impacts of Electric Vehicle charging under electricity market operations," Applied Energy, Elsevier, vol. 101(C), pages 93-102.
    2. Yang, Zhile & Li, Kang & Guo, Yuanjun & Feng, Shengzhong & Niu, Qun & Xue, Yusheng & Foley, Aoife, 2019. "A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles," Energy, Elsevier, vol. 170(C), pages 889-905.
    3. Fernandes, Camila & Frías, Pablo & Latorre, Jesús M., 2012. "Impact of vehicle-to-grid on power system operation costs: The Spanish case study," Applied Energy, Elsevier, vol. 96(C), pages 194-202.
    4. Fazel Mohammadi & Gholam-Abbas Nazri & Mehrdad Saif, 2019. "A Bidirectional Power Charging Control Strategy for Plug-in Hybrid Electric Vehicles," Sustainability, MDPI, vol. 11(16), pages 1-24, August.
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    1. Md. Mosaraf Hossain Khan & Amran Hossain & Aasim Ullah & Molla Shahadat Hossain Lipu & S. M. Shahnewaz Siddiquee & M. Shafiul Alam & Taskin Jamal & Hafiz Ahmed, 2021. "Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment," Sustainability, MDPI, vol. 13(19), pages 1-18, October.

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