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Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO

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  • Zhang, Yachao
  • Le, Jian
  • Liao, Xiaobing
  • Zheng, Feng
  • Liu, Kaipei
  • An, Xueli

Abstract

Since the intermittency and volatility of wind power has restricted its penetration into power grid, coordination scheduling of flexible resources and wind energy becomes a promising technique for promoting wind power utilization. Hence, this paper integrates large-scale electric vehicles (EVs) with wind power generation to formulate multi-objective hydro-thermal-wind with EVs scheduling (MOHTWES) problem. And what's more, an improved multi-objective particle swarm optimization (IMOPSO) algorithm is proposed for solving the above problem with various constraints. By introducing a unique dual population evolution mechanism and a hierarchical elitism preserving strategy based on crowding entropy, IMOPSO can achieve excellent and well-distributed Pareto optimal solutions in objective space. Furthermore, a set of constraint handling strategies are utilized to guarantee that the solutions obtained are in feasible region. Finally, a daily scheduling problem of hydro-thermal system is used to verify the performance of IMOPSO, the numerical results of which shows the Pareto optimal solutions obtained by IMOPSO have greater advantages than the comparison algorithms. Furthermore, it can be concluded from the simulation results for MOHTWES problem that, smart scheduling of EVs integrated with wind energy can promote wind power utilization and reduce the generation cost and emission simultaneously.

Suggested Citation

  • Zhang, Yachao & Le, Jian & Liao, Xiaobing & Zheng, Feng & Liu, Kaipei & An, Xueli, 2018. "Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO," Renewable Energy, Elsevier, vol. 128(PA), pages 91-107.
    • Handle: RePEc:eee:renene:v:128:y:2018:i:pa:p:91-107
    DOI: 10.1016/j.renene.2018.05.067
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    References listed on IDEAS

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    1. He, Lifu & Yang, Jun & Yan, Jun & Tang, Yufei & He, Haibo, 2016. "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, Elsevier, vol. 168(C), pages 179-192.
    2. Dubey, Hari Mohan & Pandit, Manjaree & Panigrahi, B.K., 2016. "Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index," Renewable Energy, Elsevier, vol. 99(C), pages 18-34.
    3. Kang, Junjie & Yuan, Jiahai & Hu, Zhaoguang & Xu, Yan, 2012. "Review on wind power development and relevant policies in China during the 11th Five-Year-Plan period," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 1907-1915.
    4. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    5. Alham, M.H. & Elshahed, M. & Ibrahim, Doaa Khalil & Abo El Zahab, Essam El Din, 2016. "A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management," Renewable Energy, Elsevier, vol. 96(PA), pages 800-811.
    6. Zhang, Sufang & Li, Xingmei, 2012. "Large scale wind power integration in China: Analysis from a policy perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1110-1115.
    7. Wang, Wenxiao & Li, Chaoshun & Liao, Xiang & Qin, Hui, 2017. "Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm," Applied Energy, Elsevier, vol. 187(C), pages 612-626.
    8. Shojaabadi, Saeed & Abapour, Saeed & Abapour, Mehdi & Nahavandi, Ali, 2016. "Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties," Renewable Energy, Elsevier, vol. 99(C), pages 237-252.
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    Cited by:

    1. Li, Huanhuan & Zhang, Runfan & Mahmud, Md. Apel & Hredzak, Branislav, 2022. "A novel coordinated optimization strategy for high utilization of renewable energy sources and reduction of coal costs and emissions in hybrid hydro-thermal-wind power systems," Applied Energy, Elsevier, vol. 320(C).
    2. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Li, Gang & Liu, Lingjun, 2022. "Impacts of different wind and solar power penetrations on cascade hydroplants operation," Renewable Energy, Elsevier, vol. 182(C), pages 227-244.
    3. Ji, Bin & Zhang, Binqiao & Yu, Samson S. & Zhang, Dezhi & Yuan, Xiaohui, 2021. "An enhanced Borg algorithmic framework for solving the hydro-thermal-wind Co-scheduling problem," Energy, Elsevier, vol. 218(C).
    4. Qiao, Baihao & Liu, Jing, 2020. "Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm," Renewable Energy, Elsevier, vol. 154(C), pages 316-336.
    5. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system," Renewable Energy, Elsevier, vol. 147(P1), pages 1418-1431.
    6. Li, Chaoshun & Wang, Wenxiao & Chen, Deshu, 2019. "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, Elsevier, vol. 171(C), pages 241-255.
    7. Yang Yang & Chong Lian & Chao Ma & Yusheng Zhang, 2019. "Research on Energy Storage Optimization for Large-Scale PV Power Stations under Given Long-Distance Delivery Mode," Energies, MDPI, vol. 13(1), pages 1-20, December.
    8. Daneshvar, Mohammadreza & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Asadi, Somayeh, 2020. "Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment," Energy, Elsevier, vol. 193(C).
    9. Patwal, Rituraj Singh & Narang, Nitin, 2020. "Multi-objective generation scheduling of integrated energy system using fuzzy based surrogate worth trade-off approach," Renewable Energy, Elsevier, vol. 156(C), pages 864-882.

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