IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v33y2021i1p336-351.html
   My bibliography  Save this article

Robust Optimization for Electricity Generation

Author

Listed:
  • Haoxiang Yang

    (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208;)

  • David P. Morton

    (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208;)

  • Chaithanya Bandi

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208;)

  • Krishnamurthy Dvijotham

    (Google Deepmind, London N1C 4AG, United Kingdom)

Abstract

We consider a robust optimization problem in an electric power system under uncertain demand and availability of renewable energy resources. Solving the deterministic alternating current (AC) optimal power flow (ACOPF) problem has been considered challenging since the 1960s due to its nonconvexity. Linear approximation of the AC power flow system sees pervasive use, but does not guarantee a physically feasible system configuration. In recent years, various convex relaxation schemes for the ACOPF problem have been investigated, and under some assumptions, a physically feasible solution can be recovered. Based on these convex relaxations, we construct a robust convex optimization problem with recourse to solve for optimal controllable injections (fossil fuel, nuclear, etc.) in electric power systems under uncertainty (renewable energy generation, demand fluctuation, etc.). We propose a cutting-plane method to solve this robust optimization problem, and we establish convergence and other desirable properties. Experimental results indicate that our robust convex relaxation of the ACOPF problem can provide a tight lower bound.

Suggested Citation

  • Haoxiang Yang & David P. Morton & Chaithanya Bandi & Krishnamurthy Dvijotham, 2021. "Robust Optimization for Electricity Generation," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 336-351, January.
    • Handle: RePEc:inm:orijoc:v:33:y:2021:i:1:p:336-351
    DOI: 10.1287/ijoc.2020.0956
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/ijoc.2020.0956
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ijoc.2020.0956?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
    ---><---

    References listed on IDEAS

    as
    1. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    2. Burak Kocuk & Santanu S. Dey & X. Andy Sun, 2016. "Strong SOCP Relaxations for the Optimal Power Flow Problem," Operations Research, INFORMS, vol. 64(6), pages 1177-1196, December.
    3. 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.
    4. Itai Gurvich & James Luedtke & Tolga Tezcan, 2010. "Staffing Call Centers with Uncertain Demand Forecasts: A Chance-Constrained Optimization Approach," Management Science, INFORMS, vol. 56(7), pages 1093-1115, July.
    5. 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.
    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. Haoxiang Yang & Harsha Nagarajan, 2022. "Optimal Power Flow in Distribution Networks Under N – 1 Disruptions: A Multistage Stochastic Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 690-709, March.
    2. Martina Kuchlbauer & Frauke Liers & Michael Stingl, 2022. "Adaptive Bundle Methods for Nonlinear Robust Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2106-2124, July.

    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. 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).
    2. Ruiwei Jiang & Siqian Shen & Yiling Zhang, 2017. "Integer Programming Approaches for Appointment Scheduling with Random No-Shows and Service Durations," Operations Research, INFORMS, vol. 65(6), pages 1638-1656, December.
    3. Guanglei Wang & Hassan Hijazi, 2018. "Mathematical programming methods for microgrid design and operations: a survey on deterministic and stochastic approaches," Computational Optimization and Applications, Springer, vol. 71(2), pages 553-608, November.
    4. Detienne, Boris & Lefebvre, Henri & Malaguti, Enrico & Monaci, Michele, 2024. "Adjustable robust optimization with objective uncertainty," European Journal of Operational Research, Elsevier, vol. 312(1), pages 373-384.
    5. Zhao, Ning & You, Fengqi, 2022. "Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    6. Heng Yang & Ziliang Jin & Jianhua Wang & Yong Zhao & Hejia Wang & Weihua Xiao, 2019. "Data-Driven Stochastic Scheduling for Energy Integrated Systems," Energies, MDPI, vol. 12(12), pages 1-21, June.
    7. Ruiz, C. & Conejo, A.J., 2015. "Robust transmission expansion planning," European Journal of Operational Research, Elsevier, vol. 242(2), pages 390-401.
    8. Kramer, Anja & Krebs, Vanessa & Schmidt, Martin, 2021. "Strictly and Γ-robust counterparts of electricity market models: Perfect competition and Nash–Cournot equilibria," Operations Research Perspectives, Elsevier, vol. 8(C).
    9. Grani A. Hanasusanto & Vladimir Roitch & Daniel Kuhn & Wolfram Wiesemann, 2017. "Ambiguous Joint Chance Constraints Under Mean and Dispersion Information," Operations Research, INFORMS, vol. 65(3), pages 751-767, June.
    10. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).
    11. Mattia, Sara & Rossi, Fabrizio & Servilio, Mara & Smriglio, Stefano, 2017. "Staffing and scheduling flexible call centers by two-stage robust optimization," Omega, Elsevier, vol. 72(C), pages 25-37.
    12. François Clautiaux & Boris Detienne & Henri Lefebvre, 2023. "A two-stage robust approach for minimizing the weighted number of tardy jobs with objective uncertainty," Journal of Scheduling, Springer, vol. 26(2), pages 169-191, April.
    13. Jianwen Ren & Yingqiang Xu & Shiyuan Wang, 2018. "A Distributed Robust Dispatch Approach for Interconnected Systems with a High Proportion of Wind Power Penetration," Energies, MDPI, vol. 11(4), pages 1-18, April.
    14. Li, Xingchen & Xu, Guangcheng & Wu, Jie & Xu, Chengzhen & Zhu, Qingyuan, 2024. "Evaluation of bank efficiency by considering the uncertainty of nonperforming loans," Omega, Elsevier, vol. 126(C).
    15. Wenqing Chen & Melvyn Sim & Jie Sun & Chung-Piaw Teo, 2010. "From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization," Operations Research, INFORMS, vol. 58(2), pages 470-485, April.
    16. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    17. Stefan Mišković, 2017. "A VNS-LP algorithm for the robust dynamic maximal covering location problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 1011-1033, October.
    18. Tolga Tezcan & Banafsheh Behzad, 2012. "Robust Design and Control of Call Centers with Flexible Interactive Voice Response Systems," Manufacturing & Service Operations Management, INFORMS, vol. 14(3), pages 386-401, July.
    19. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    20. Sarhadi, Hassan & Naoum-Sawaya, Joe & Verma, Manish, 2020. "A robust optimization approach to locating and stockpiling marine oil-spill response facilities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(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:inm:orijoc:v:33:y:2021:i:1:p:336-351. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.