IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v309y2023i2p890-909.html
   My bibliography  Save this article

Robust multi-stage economic dispatch with renewable generation and storage

Author

Listed:
  • Yıldıran, Uğur

Abstract

In this paper, an economic dispatch problem for power grids involving renewable generation and storage units is studied. A multi-stage robust optimization algorithm for computing base dispatches and re-dispatches is proposed. Unlike the other approaches in the literature, the proposed method finds an exact solution to the multi-stage robust optimization problem considered when there are no storage devices or storage devices are ideal (i.e. charging/discharging inefficiencies are neglectable). In the presence of non-ideal storage devices, the algorithm can produce upper-bounding approximations. The devised approximation scheme has two distinguishing features. Firstly, the base dispatches and, especially, multi-stage re-dispatch policies computed are guaranteed to be free of simultaneous charges/discharges. Secondly, it produces lower bounds allowing one to assess the quality of solutions. Optimality and convergence properties of the proposed method are proven and it is compared with other alternatives in the literature through numerical experiments. Results show that significant economic benefits can be achieved compared to the other approaches and solutions could be obtained within acceptable time limits for large-scale problems.

Suggested Citation

  • Yıldıran, Uğur, 2023. "Robust multi-stage economic dispatch with renewable generation and storage," European Journal of Operational Research, Elsevier, vol. 309(2), pages 890-909.
  • Handle: RePEc:eee:ejores:v:309:y:2023:i:2:p:890-909
    DOI: 10.1016/j.ejor.2023.01.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221723000772
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2023.01.042?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. Liu, Shiyu & Ren, Yanzhe & Zhang, Zhenyu & Xiao, Yao & Bie, Zhaohong & Wang, Xifan, 2021. "Optimal bid-offer strategy for a virtual energy storage merchant: A stochastic bi-level model with all-scenario feasibility," Applied Energy, Elsevier, vol. 299(C).
    2. Angelos Georghiou & Angelos Tsoukalas & Wolfram Wiesemann, 2019. "Robust Dual Dynamic Programming," Operations Research, INFORMS, vol. 67(3), pages 813-830, May.
    3. Shapiro, Alexander & Tekaya, Wajdi & da Costa, Joari Paulo & Soares, Murilo Pereira, 2013. "Risk neutral and risk averse Stochastic Dual Dynamic Programming method," European Journal of Operational Research, Elsevier, vol. 224(2), pages 375-391.
    4. Li, Zhengshuo & Guo, Qinglai & Sun, Hongbin & Wang, Jianhui, 2015. "Storage-like devices in load leveling: Complementarity constraints and a new and exact relaxation method," Applied Energy, Elsevier, vol. 151(C), pages 13-22.
    5. Steeger, Gregory & Rebennack, Steffen, 2017. "Dynamic convexification within nested Benders decomposition using Lagrangian relaxation: An application to the strategic bidding problem," European Journal of Operational Research, Elsevier, vol. 257(2), pages 669-686.
    6. Anthony Papavasiliou & Yuting Mou & Léopold Cambier & Damien Scieur, 2018. "Application of stochastic dual dynamic programming to the real-time dispatch of storage under renewable supply uncertainty," LIDAM Reprints CORE 2943, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Shapiro, Alexander, 2011. "Analysis of stochastic dual dynamic programming method," European Journal of Operational Research, Elsevier, vol. 209(1), pages 63-72, February.
    8. Yanıkoğlu, İhsan & Gorissen, Bram L. & den Hertog, Dick, 2019. "A survey of adjustable robust optimization," European Journal of Operational Research, Elsevier, vol. 277(3), pages 799-813.
    9. Schulze, Tim & Grothey, Andreas & McKinnon, Ken, 2017. "A stabilised scenario decomposition algorithm applied to stochastic unit commitment problems," European Journal of Operational Research, Elsevier, vol. 261(1), pages 247-259.
    10. G. Cobos, Noemi & Arroyo, José M. & Alguacil, Natalia & Street, Alexandre, 2018. "Network-constrained unit commitment under significant wind penetration: A multistage robust approach with non-fixed recourse," Applied Energy, Elsevier, vol. 232(C), pages 489-503.
    11. Jiang, Ruiwei & Zhang, Muhong & Li, Guang & Guan, Yongpei, 2014. "Two-stage network constrained robust unit commitment problem," European Journal of Operational Research, Elsevier, vol. 234(3), pages 751-762.
    12. García-Cerezo, Álvaro & Baringo, Luis & García-Bertrand, Raquel, 2021. "Robust transmission network expansion planning considering non-convex operational constraints," Energy Economics, Elsevier, vol. 98(C).
    13. Löhndorf, Nils & Shapiro, Alexander, 2019. "Modeling time-dependent randomness in stochastic dual dynamic programming," European Journal of Operational Research, Elsevier, vol. 273(2), pages 650-661.
    14. Anthony Papavasiliou & Yuting Mou & Léopold Cambier & Damien Scieur, 2018. "Application of stochastic dual dynamic programming to the real-time dispatch of storage under renewable supply uncertainty," LIDAM Reprints CORE 3044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Álvaro Lorca & X. Andy Sun & Eugene Litvinov & Tongxin Zheng, 2016. "Multistage Adaptive Robust Optimization for the Unit Commitment Problem," Operations Research, INFORMS, vol. 64(1), pages 32-51, February.
    16. van Ackooij, Wim & De Boeck, Jérôme & Detienne, Boris & Pan, Stefania & Poss, Michael, 2018. "Optimizing power generation in the presence of micro-grids," European Journal of Operational Research, Elsevier, vol. 271(2), pages 450-461.
    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. Xiong, Houbo & Zhou, Yue & Guo, Chuangxin & Ding, Yi & Luo, Fengji, 2023. "Multi-stage risk-based assessment for wind energy accommodation capability: A robust and non-anticipative method," Applied Energy, Elsevier, vol. 350(C).
    2. Charles Gauvin & Erick Delage & Michel Gendreau, 2018. "A successive linear programming algorithm with non-linear time series for the reservoir management problem," Computational Management Science, Springer, vol. 15(1), pages 55-86, January.
    3. Street, Alexandre & Valladão, Davi & Lawson, André & Velloso, Alexandre, 2020. "Assessing the cost of the Hazard-Decision simplification in multistage stochastic hydrothermal scheduling," Applied Energy, Elsevier, vol. 280(C).
    4. Luyu Wang & Houbo Xiong & Yunhui Shi & Chuangxin Guo, 2023. "Rolling Horizon Robust Real-Time Economic Dispatch with Multi-Stage Dynamic Modeling," Mathematics, MDPI, vol. 11(11), pages 1-20, June.
    5. Park, Jangho & Bayraksan, Güzin, 2023. "A multistage distributionally robust optimization approach to water allocation under climate uncertainty," European Journal of Operational Research, Elsevier, vol. 306(2), pages 849-871.
    6. Löhndorf, Nils & Wozabal, David, 2021. "Gas storage valuation in incomplete markets," European Journal of Operational Research, Elsevier, vol. 288(1), pages 318-330.
    7. Xiong, Houbo & Yan, Mingyu & Guo, Chuangxin & Ding, Yi & Zhou, Yue, 2023. "DP based multi-stage ARO for coordinated scheduling of CSP and wind energy with tractable storage scheme: Tight formulation and solution technique," Applied Energy, Elsevier, vol. 333(C).
    8. D. Ávila & A. Papavasiliou & N. Löhndorf, 2022. "Parallel and distributed computing for stochastic dual dynamic programming," Computational Management Science, Springer, vol. 19(2), pages 199-226, June.
    9. Guigues, Vincent & Shapiro, Alexander & Cheng, Yi, 2023. "Duality and sensitivity analysis of multistage linear stochastic programs," European Journal of Operational Research, Elsevier, vol. 308(2), pages 752-767.
    10. Lorenzo Reus & Rodolfo Prado, 2022. "Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 47-69, June.
    11. Qiu, Haifeng & Sun, Qirun & Lu, Xi & Beng Gooi, Hoay & Zhang, Suhan, 2022. "Optimality-feasibility-aware multistage unit commitment considering nonanticipative realization of uncertainty," Applied Energy, Elsevier, vol. 327(C).
    12. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    13. Angelos Georghiou & Angelos Tsoukalas & Wolfram Wiesemann, 2020. "A Primal–Dual Lifting Scheme for Two-Stage Robust Optimization," Operations Research, INFORMS, vol. 68(2), pages 572-590, March.
    14. Thuener Silva & Davi Valladão & Tito Homem-de-Mello, 2021. "A data-driven approach for a class of stochastic dynamic optimization problems," Computational Optimization and Applications, Springer, vol. 80(3), pages 687-729, December.
    15. Baringo, Luis & Boffino, Luigi & Oggioni, Giorgia, 2020. "Robust expansion planning of a distribution system with electric vehicles, storage and renewable units," Applied Energy, Elsevier, vol. 265(C).
    16. Psarros, Georgios N. & Papathanassiou, Stavros A., 2023. "Generation scheduling in island systems with variable renewable energy sources: A literature review," Renewable Energy, Elsevier, vol. 205(C), pages 1105-1124.
    17. Davi Valladão & Thuener Silva & Marcus Poggi, 2019. "Time-consistent risk-constrained dynamic portfolio optimization with transactional costs and time-dependent returns," Annals of Operations Research, Springer, vol. 282(1), pages 379-405, November.
    18. Murwan Siddig & Yongjia Song, 2022. "Adaptive partition-based SDDP algorithms for multistage stochastic linear programming with fixed recourse," Computational Optimization and Applications, Springer, vol. 81(1), pages 201-250, January.
    19. Yin, S. & Wang, J. & Li, Z. & Fang, X., 2021. "State-of-the-art short-term electricity market operation with solar generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    20. Qi, Mingyao & Yang, Ying & Cheng, Chun, 2023. "Location and inventory pre-positioning problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(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:eee:ejores:v:309:y:2023:i:2:p:890-909. 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.elsevier.com/locate/eor .

    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.