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A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles

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  • Yang, Zhile
  • Li, Kang
  • Guo, Yuanjun
  • Feng, Shengzhong
  • Niu, Qun
  • Xue, Yusheng
  • Foley, Aoife

Abstract

Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:170:y:2019:i:c:p:889-905
    DOI: 10.1016/j.energy.2018.12.165
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    References listed on IDEAS

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    3. Wang, Weida & Chen, Yincong & Yang, Chao & Li, Ying & Xu, Bin & Xiang, Changle, 2022. "An enhanced hypotrochoid spiral optimization algorithm based intertwined optimal sizing and control strategy of a hybrid electric air-ground vehicle," Energy, Elsevier, vol. 257(C).
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    5. Zhao, Shihao & Li, Kang & Yang, Zhile & Xu, Xinzhi & Zhang, Ning, 2022. "A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies," Applied Energy, Elsevier, vol. 314(C).
    6. Sridharan, S. & Sivakumar, S. & Shanmugasundaram, N. & Swapna, S. & Vasan Prabhu, V., 2023. "A hybrid approach based energy management for building resilience against power outage by shared parking station for EVs," Renewable Energy, Elsevier, vol. 216(C).
    7. Shabazbegian, Vahid & Ameli, Hossein & Ameli, Mohammad Taghi & Strbac, Goran & Qadrdan, Meysam, 2021. "Co-optimization of resilient gas and electricity networks; a novel possibilistic chance-constrained programming approach," Applied Energy, Elsevier, vol. 284(C).
    8. Tian, Man-Wen & Talebizadehsardari, Pouyan, 2021. "Energy cost and efficiency analysis of building resilience against power outage by shared parking station for electric vehicles and demand response program," Energy, Elsevier, vol. 215(PB).
    9. Wu, Juai & Zhang, Mengying & Xu, Tianheng & Gu, Duan & Xie, Dongliang & Zhang, Tengfei & Hu, Honglin & Zhou, Ting, 2023. "A review of key technologies in relation to large-scale clusters of electric vehicles supporting a new power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    10. Zhu, Xiaodong & Zhao, Shihao & Yang, Zhile & Zhang, Ning & Xu, Xinzhi, 2022. "A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors," Energy, Elsevier, vol. 238(PC).
    11. M. A. El-Shorbagy & A. A. Mousa & M. A. Farag, 2019. "An intelligent computing technique based on a dynamic-size subpopulations for unit commitment problem," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 911-944, September.
    12. Marcelo Becerra-Rozas & José Lemus-Romani & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Gino Astorga & Carlos Castro & José García, 2022. "Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review," Mathematics, MDPI, vol. 11(1), pages 1-32, December.
    13. 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.
    14. Mehigan, L. & Al Kez, Dlzar & Collins, Seán & Foley, Aoife & Ó’Gallachóir, Brian & Deane, Paul, 2020. "Renewables in the European power system and the impact on system rotational inertia," Energy, Elsevier, vol. 203(C).
    15. Basu, Mousumi, 2023. "Fuel constrained commitment scheduling for combined heat and power dispatch incorporating electric vehicle parking lot," Energy, Elsevier, vol. 276(C).
    16. Georgios Semertzidis & Dimitrios Stamatakis & Vasilios Tsalavoutis & Athanasios I. Tolis, 2022. "Optimized electric vehicle charging integrated in the unit commitment problem," Operational Research, Springer, vol. 22(5), pages 5137-5204, November.

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