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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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    Cited by:

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    3. 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.
    4. 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.
    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. 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).
    8. 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.
    9. 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).

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