IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i9p4706-d541596.html
   My bibliography  Save this article

Lagrangian Relaxation Based on Improved Proximal Bundle Method for Short-Term Hydrothermal Scheduling

Author

Listed:
  • Zhiyu Yan

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Shengli Liao

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Chuntian Cheng

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Josué Medellín-Azuara

    (Department of Civil and Environmental Engineering, University of California, Merced, CA 95340, USA)

  • Benxi Liu

    (Institute of Hydropower System & Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

Abstract

Short-term hydrothermal scheduling (STHS) can improve water use efficiency, reduce carbon emissions, and increase economic benefits by optimizing the commitment and dispatch of hydro and thermal generating units together. However, limited by the large system scale and complex hydraulic and electrical constraints, STHS poses great challenges in modeling for operators. This paper presents an improved proximal bundle method (IPBM) within the framework of Lagrangian relaxation for STHS, which incorporates the expert system (ES) technique into the proximal bundle method (PBM). In IPBM, initial values of Lagrange multipliers are firstly determined using the linear combination of optimal solutions in the ES. Then, each time PBM declares a null step in the iterations, the solution space is inferred from the ES, and an orthogonal design is performed in the solution space to derive new updates of the Lagrange multipliers. A case study in a large-scale hydrothermal system in China is implemented to demonstrate the effectiveness of the proposed method. Results in different cases indicate that IPBM is superior to standard PBM in global search ability and computational efficiency, providing an alternative for STHS.

Suggested Citation

  • Zhiyu Yan & Shengli Liao & Chuntian Cheng & Josué Medellín-Azuara & Benxi Liu, 2021. "Lagrangian Relaxation Based on Improved Proximal Bundle Method for Short-Term Hydrothermal Scheduling," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4706-:d:541596
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/9/4706/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/9/4706/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kassahun Birhanu & Tena Alamirew & Megersa Olumana Dinka & Semu Ayalew & Dagnachew Aklog, 2014. "Optimizing Reservoir Operation Policy Using Chance Constraint Nonlinear Programming for Koga Irrigation Dam, Ethiopia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(14), pages 4957-4970, November.
    2. Tang, Chunming & Liu, Shuai & Jian, Jinbao & Ou, Xiaomei, 2020. "A multi-step doubly stabilized bundle method for nonsmooth convex optimization," Applied Mathematics and Computation, Elsevier, vol. 376(C).
    Full references (including those not matched with items on IDEAS)

    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. Muhammad Usman Rashid & Abid Latif & Muhammad Azmat, 2018. "Optimizing Irrigation Deficit of Multipurpose Cascade Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1675-1687, March.
    2. Qiao-feng Tan & Guo-hua Fang & Xin Wen & Xiao-hui Lei & Xu Wang & Chao Wang & Yi Ji, 2020. "Bayesian Stochastic Dynamic Programming for Hydropower Generation Operation Based on Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1589-1607, March.
    3. Mahdi Sedighkia & Asghar Abdoli, 2023. "Design of optimal environmental flow regime at downstream of multireservoir systems by a coupled SWAT-reservoir operation optimization method," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(1), pages 834-854, January.
    4. Gi Joo Kim & Young-Oh Kim, 2021. "How Does the Coupling of Real-World Policies with Optimization Models Expand the Practicality of Solutions in Reservoir Operation Problems?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3121-3137, August.
    5. Aida Tayebiyan & Thamer Ahmed Mohammed Ali & Abdul Halim Ghazali & M. A. Malek, 2016. "Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1203-1216, February.
    6. Gi Joo Kim & Seung Beom Seo & Young-Oh Kim, 2022. "Adaptive Reservoir Management by Reforming the Zone-based Hedging Rules against Multi-year Droughts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3575-3590, August.
    7. Khadim, Fahad Khan & Dokou, Zoi & Bagtzoglou, Amvrossios C. & Yang, Meijian & Lijalem, Girmachew Addisu & Anagnostou, Emmanouil, 2021. "A numerical framework to advance agricultural water management under hydrological stress conditions in a data scarce environment," Agricultural Water Management, Elsevier, vol. 254(C).

    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:gam:jsusta:v:13:y:2021:i:9:p:4706-:d:541596. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.