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A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies

Author

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  • Zhao, Shihao
  • Li, Kang
  • Yang, Zhile
  • Xu, Xinzhi
  • Zhang, Ning

Abstract

The significant penetration of renewable power generations (RGs) and the large-scale use of plug-in electric vehicles (PEVs) have brought tangible impacts in tackling the climate change challenge the mankind has been facing due to substantive green-house gas and pollutant emissions from fossil-fuel based thermal power generation plants. However, the uncertainty of RGs has also exerted significant challenges to the grid operation and control. Therefore, dynamic power system scheduling to accommodate the intermittent RGs and mass roll-out of PEVs has become extremely important. In this paper, a novel power system rescheduling strategy is proposed to tackle this problem. Considering the uncertainty of the wind energy, a set of indices according to different wind power application scenarios is proposed to initiate a rescheduling scheme for power generations. In addition, a social learning particle swarm optimization algorithm based on real-value and binary parallel is proposed to schedule the output of generator units and the charging and discharging of the PEV. The effectiveness of the proposed active rescheduling framework and solving algorithm has been verified by extensive experiments considering different number of generating units and scenarios, achieving up to over 5.3% cost reduction. The experimental results have also shown that through expropriate management of the charging and discharging of PEVs would be significantly alleviate the negative impact on the grid stability caused by the intermittent wind power generations.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s030626192200174x
    DOI: 10.1016/j.apenergy.2022.118715
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    1. Chen, Yuanyi & Hu, Simon & Zheng, Yanchong & Xie, Shiwei & Yang, Qiang & Wang, Yubin & Hu, Qinru, 2024. "Coordinated optimization of logistics scheduling and electricity dispatch for electric logistics vehicles considering uncertain electricity prices and renewable generation," Applied Energy, Elsevier, vol. 364(C).
    2. Zhao, Shihao & Li, Kang & Yin, Mingjia & Yu, James & Yang, Zhile & Li, Yihuan, 2024. "Transportable energy storage assisted post-disaster restoration of distribution networks with renewable generations," Energy, Elsevier, vol. 295(C).
    3. Zhang, Shulei & Jia, Runda & Pan, Hengxin & Cao, Yankai, 2023. "A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid," Applied Energy, Elsevier, vol. 348(C).
    4. Jin, Jingliang & Wen, Qinglan & Zhao, Liya & Zhou, Chaoyang & Guo, Xiaojun, 2023. "Measuring environmental performance of power dispatch influenced by low-carbon approaches," Renewable Energy, Elsevier, vol. 209(C), pages 325-339.
    5. Alya AlHammadi & Nasser Al-Saif & Ameena Saad Al-Sumaiti & Mousa Marzband & Tareefa Alsumaiti & Ehsan Heydarian-Forushani, 2022. "Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates," Energies, MDPI, vol. 15(18), pages 1-20, September.
    6. Ibrahim Alsaidan & Mohd Bilal & Muhannad Alaraj & Mohammad Rizwan & Fahad M. Almasoudi, 2023. "A Novel EA-Based Techno–Economic Analysis of Charging System for Electric Vehicles: A Case Study of Qassim Region, Saudi Arabia," Mathematics, MDPI, vol. 11(9), pages 1-31, April.

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