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An enhanced Borg algorithmic framework for solving the hydro-thermal-wind Co-scheduling problem

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  • Ji, Bin
  • Zhang, Binqiao
  • Yu, Samson S.
  • Zhang, Dezhi
  • Yuan, Xiaohui

Abstract

An enhanced Borg (EBorg) algorithm has been proposed to solve the dual-objective short-term hydro-thermal-wind co-scheduling (HTW-CS) problem, aiming at minimizing the cost and emissions associated with electric power generation while satisfying various hydraulic and electric constraints. The sophisticatedly designed evolution framework of the EBorg consists of i) ε-dominance-based archive, ii) Pareto-dominance and crowding-distance-based population updating mechanism and iii) auto-adaptive multi-operator recombination, which guarantees the convergence capability and diversity and can avoid blindness selection of recombination operators. Meanwhile, a randomness-priority-based repairing constraint handling technique (CHT) is developed, and the performances of another two popular existing CHTs incorporated in the proposed search framework are compared and discussed. The proposed approaches are tested on the widely used HTW-CS case studies, and the results show that the proposed EBorg can achieve a decrease of cost and emissions compared with the existing methods. Additionally, energy performance in the case studies has shown an average 50% decrease of emissions with wind power integration, while the total cost is largely dependent on the cost coefficients related to wind uncertainty. Varying wind power cost coefficients and water inflow levels will result in different wind power and hydropower integration.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:218:y:2021:i:c:s0360544220326190
    DOI: 10.1016/j.energy.2020.119512
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    1. 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.
    2. McLarty, Dustin & Panossian, Nadia & Jabbari, Faryar & Traverso, Alberto, 2019. "Dynamic economic dispatch using complementary quadratic programming," Energy, Elsevier, vol. 166(C), pages 755-764.
    3. Ji, Bin & Yuan, Xiaohui & Chen, Zhihuan & Tian, Hao, 2014. "Improved gravitational search algorithm for unit commitment considering uncertainty of wind power," Energy, Elsevier, vol. 67(C), pages 52-62.
    4. 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.
    5. Chen, J.J. & Zhuang, Y.B. & Li, Y.Z. & Wang, P. & Zhao, Y.L. & Zhang, C.S., 2017. "Risk-aware short term hydro-wind-thermal scheduling using a probability interval optimization model," Applied Energy, Elsevier, vol. 189(C), pages 534-554.
    6. Chen, Yue & Wei, Wei & Liu, Feng & Mei, Shengwei, 2016. "Distributionally robust hydro-thermal-wind economic dispatch," Applied Energy, Elsevier, vol. 173(C), pages 511-519.
    7. Li, Y.Z. & Li, K.C. & Wang, P. & Liu, Y. & Lin, X.N. & Gooi, H.B. & Li, G.F. & Cai, D.L. & Luo, Y., 2017. "Risk constrained economic dispatch with integration of wind power by multi-objective optimization approach," Energy, Elsevier, vol. 126(C), pages 810-820.
    8. Wang, K.Y. & Luo, X.J. & Wu, L. & Liu, X.C., 2013. "Optimal coordination of wind-hydro-thermal based on water complementing wind," Renewable Energy, Elsevier, vol. 60(C), pages 169-178.
    9. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2016. "Optimization modeling to support renewables integration in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 316-325.
    10. Chen, Fang & Zhou, Jianzhong & Wang, Chao & Li, Chunlong & Lu, Peng, 2017. "A modified gravitational search algorithm based on a non-dominated sorting genetic approach for hydro-thermal-wind economic emission dispatching," Energy, Elsevier, vol. 121(C), pages 276-291.
    11. Panda, Ambarish & Tripathy, M. & Barisal, A.K. & Prakash, T., 2017. "A modified bacteria foraging based optimal power flow framework for Hydro-Thermal-Wind generation system in the presence of STATCOM," Energy, Elsevier, vol. 124(C), pages 720-740.
    12. Ji, Bin & Yuan, Xiaohui & Yuan, Yanbin & Lei, Xiaohui & Fernando, Tyrone & Iu, Herbert H.C., 2019. "Exact and heuristic methods for optimizing lock-quay system in inland waterway," European Journal of Operational Research, Elsevier, vol. 277(2), pages 740-755.
    13. Nazari-Heris, Morteza & Babaei, Amir Fakhim & Mohammadi-Ivatloo, Behnam & Asadi, Somayeh, 2018. "Improved harmony search algorithm for the solution of non-linear non-convex short-term hydrothermal scheduling," Energy, Elsevier, vol. 151(C), pages 226-237.
    14. Carlos Segura & Carlos A. Coello Coello & Gara Miranda & Coromoto León, 2016. "Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization," Annals of Operations Research, Springer, vol. 240(1), pages 217-250, May.
    Full references (including those not matched with items on IDEAS)

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    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. Tang, Xiongmin & Li, Zhengshuo & Xu, Xuancong & Zeng, Zhijun & Jiang, Tianhong & Fang, Wenrui & Meng, Anbo, 2022. "Multi-objective economic emission dispatch based on an extended crisscross search optimization algorithm," Energy, Elsevier, vol. 244(PA).
    3. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).

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