IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v218y2021ics0360544220326190.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220326190
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119512?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. McLarty, Dustin & Panossian, Nadia & Jabbari, Faryar & Traverso, Alberto, 2019. "Dynamic economic dispatch using complementary quadratic programming," Energy, Elsevier, vol. 166(C), pages 755-764.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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. 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.
    12. 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.
    13. 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.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Rahmani, Shima & Amjady, Nima, 2017. "A new optimal power flow approach for wind energy integrated power systems," Energy, Elsevier, vol. 134(C), pages 349-359.
    3. 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.
    4. Chen, J.J. & Zhao, Y.L. & Peng, K. & Wu, P.Z., 2017. "Optimal trade-off planning for wind-solar power day-ahead scheduling under uncertainties," Energy, Elsevier, vol. 141(C), pages 1969-1981.
    5. 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.
    6. Li, M.S. & Lin, Z.J. & Ji, T.Y. & Wu, Q.H., 2018. "Risk constrained stochastic economic dispatch considering dependence of multiple wind farms using pair-copula," Applied Energy, Elsevier, vol. 226(C), pages 967-978.
    7. 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).
    8. de Queiroz, Anderson Rodrigo, 2016. "Stochastic hydro-thermal scheduling optimization: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 382-395.
    9. Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Short term scheduling of hydrothermal power systems with photovoltaic and pumped storage plants using quasi-oppositional turbulent water flow optimization," Renewable Energy, Elsevier, vol. 191(C), pages 459-492.
    10. Yang, Yuqi & Zhou, Jianzhong & Liu, Guangbiao & Mo, Li & Wang, Yongqiang & Jia, Benjun & He, Feifei, 2020. "Multi-plan formulation of hydropower generation considering uncertainty of wind power," Applied Energy, Elsevier, vol. 260(C).
    11. Ming, Bo & Liu, Pan & Guo, Shenglian & Cheng, Lei & Zhou, Yanlai & Gao, Shida & Li, He, 2018. "Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1341-1352.
    12. Fang, Xin & Hodge, Bri-Mathias & Du, Ershun & Zhang, Ning & Li, Fangxing, 2018. "Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach," Applied Energy, Elsevier, vol. 230(C), pages 531-539.
    13. Massrur, Hamid Reza & Niknam, Taher & Aghaei, Jamshid & Shafie-khah, Miadreza & Catalão, João P.S., 2018. "A stochastic mid-term scheduling for integrated wind-thermal systems using self-adaptive optimization approach: A comparative study," Energy, Elsevier, vol. 155(C), pages 552-564.
    14. Sharifzadeh, Mahdi & Lubiano-Walochik, Helena & Shah, Nilay, 2017. "Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 385-398.
    15. Jun Dong & Peiwen Yang & Shilin Nie, 2019. "Day-Ahead Scheduling Model of the Distributed Small Hydro-Wind-Energy Storage Power System Based on Two-Stage Stochastic Robust Optimization," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    16. Fang, Xin & Hodge, Bri-Mathias & Jiang, Huaiguang & Zhang, Yingchen, 2019. "Decentralized wind uncertainty management: Alternating direction method of multipliers based distributionally-robust chance constrained optimal power flow," Applied Energy, Elsevier, vol. 239(C), pages 938-947.
    17. Lin, Zhenjia & Chen, Haoyong & Wu, Qiuwei & Li, Weiwei & Li, Mengshi & Ji, Tianyao, 2020. "Mean-tracking model based stochastic economic dispatch for power systems with high penetration of wind power," Energy, Elsevier, vol. 193(C).
    18. Kumbuso Joshua Nyoni & Anesu Maronga & Paul Gerard Tuohy & Agabu Shane, 2021. "Hydro–Connected Floating PV Renewable Energy System and Onshore Wind Potential in Zambia," Energies, MDPI, vol. 14(17), pages 1-42, August.
    19. Yongqi Zhao & Jiajia Chen, 2021. "A Quantitative Risk-Averse Model for Optimal Management of Multi-Source Standalone Microgrid with Demand Response and Pumped Hydro Storage," Energies, MDPI, vol. 14(9), pages 1-17, May.
    20. Wang, Xuebin & Chang, Jianxia & Meng, Xuejiao & Wang, Yimin, 2018. "Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems," Applied Energy, Elsevier, vol. 229(C), pages 945-962.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:218:y:2021:i:c:s0360544220326190. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.