IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v284y2021ics0306261920315099.html
   My bibliography  Save this article

Two novel locally ideal three-period unit commitment formulations in power systems

Author

Listed:
  • Yang, Linfeng
  • Li, Wei
  • Xu, Yan
  • Zhang, Cuo
  • Chen, Shifei

Abstract

The thermal unit commitment problem has historically been formulated as a mixed integer quadratic programming problem, which is difficult to solve efficiently, especially for large-scale systems. The tighter characteristic reduces the search space; therefore, as a natural consequence, it significantly reduces the computational burden. In the literature, many tightened formulations for a single unit with parts of constraints were reported without a clear derivation process. In this paper, a systematic approach is developed to create tight formulations. The idea is to use new variables in high-dimensional space to capture all the states of a single unit within three periods and then use these state variables systematically to derive three-period locally ideal expressions for a subset of the constraints in unit commitment. Meanwhile, the linear dependence of those new state variables is leveraged to keep the compactness of the obtained formulations. Based on this approach, we propose two tight models. The proposed models and other four state-of-the-art models are tested on 56 instances over a scheduling period of 24 h for systems ranging from 10 to 1080 generating units. The simulation results show that our proposed unit commitment formulations are tighter and more efficient (Increased by 13.6%) than other state-of-the-art models. After transforming our models into mixed integer linear programming formulations, our models are still tighter and more efficient (Increased by 67.3%) than other models.

Suggested Citation

  • Yang, Linfeng & Li, Wei & Xu, Yan & Zhang, Cuo & Chen, Shifei, 2021. "Two novel locally ideal three-period unit commitment formulations in power systems," Applied Energy, Elsevier, vol. 284(C).
  • Handle: RePEc:eee:appene:v:284:y:2021:i:c:s0306261920315099
    DOI: 10.1016/j.apenergy.2020.116081
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920315099
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2020.116081?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. Fu, Yiwei & Lu, Zongxiang & Hu, Wei & Wu, Shuang & Wang, Yiting & Dong, Ling & Zhang, Jietan, 2019. "Research on joint optimal dispatching method for hybrid power system considering system security," Applied Energy, Elsevier, vol. 238(C), pages 147-163.
    2. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    3. Yang, Linfeng & Zhang, Chen & Jian, Jinbao & Meng, Ke & Xu, Yan & Dong, Zhaoyang, 2017. "A novel projected two-binary-variable formulation for unit commitment in power systems," Applied Energy, Elsevier, vol. 187(C), pages 732-745.
    4. Jubril, A.M. & Olaniyan, O.A. & Komolafe, O.A. & Ogunbona, P.O., 2014. "Economic-emission dispatch problem: A semi-definite programming approach," Applied Energy, Elsevier, vol. 134(C), pages 446-455.
    5. Jin, Ming & Feng, Wei & Liu, Ping & Marnay, Chris & Spanos, Costas, 2017. "MOD-DR: Microgrid optimal dispatch with demand response," Applied Energy, Elsevier, vol. 187(C), pages 758-776.
    6. He, Liangce & Lu, Zhigang & Zhang, Jiangfeng & Geng, Lijun & Zhao, Hao & Li, Xueping, 2018. "Low-carbon economic dispatch for electricity and natural gas systems considering carbon capture systems and power-to-gas," Applied Energy, Elsevier, vol. 224(C), pages 357-370.
    7. Hermans, Mathias & Bruninx, Kenneth & Delarue, Erik, 2020. "Impact of generator start-up lead times on short-term scheduling with high shares of renewables," Applied Energy, Elsevier, vol. 268(C).
    8. Wang, Jianxiao & Zhong, Haiwang & Lai, Xiaowen & Xia, Qing & Shu, Chang & Kang, Chongqing, 2017. "Distributed real-time demand response based on Lagrangian multiplier optimal selection approach," Applied Energy, Elsevier, vol. 190(C), pages 949-959.
    9. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    10. Moradi, Saeed & Khanmohammadi, Sohrab & Hagh, Mehrdad Tarafdar & Mohammadi-ivatloo, Behnam, 2015. "A semi-analytical non-iterative primary approach based on priority list to solve unit commitment problem," Energy, Elsevier, vol. 88(C), pages 244-259.
    11. C. Gentile & G. Morales-España & A. Ramos, 2017. "A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 177-201, March.
    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. Biéron, M. & Le Dréau, J. & Haas, B., 2023. "Assessment of the marginal technologies reacting to demand response events: A French case-study," Energy, Elsevier, vol. 275(C).
    2. Haiyan Zheng & Liying Huang & Ran Quan, 2023. "Mixed-Integer Conic Formulation of Unit Commitment with Stochastic Wind Power," Mathematics, MDPI, vol. 11(2), pages 1-16, January.

    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. Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
    2. Gu, Haifei & Li, Yang & Yu, Jie & Wu, Chen & Song, Tianli & Xu, Jinzhou, 2020. "Bi-level optimal low-carbon economic dispatch for an industrial park with consideration of multi-energy price incentives," Applied Energy, Elsevier, vol. 262(C).
    3. Pavičević, Matija & Kavvadias, Konstantinos & Pukšec, Tomislav & Quoilin, Sylvain, 2019. "Comparison of different model formulations for modelling future power systems with high shares of renewables – The Dispa-SET Balkans model," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    4. Shin, Hansol & Kim, Tae Hyun & Kim, Hyoungtae & Lee, Sungwoo & Kim, Wook, 2019. "Environmental shutdown of coal-fired generators for greenhouse gas reduction: A case study of South Korea," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Zhang, Menghan & Yang, Zhifang & Lin, Wei & Yu, Juan & Dai, Wei & Du, Ershun, 2021. "Enhancing economics of power systems through fast unit commitment with high time resolution," Applied Energy, Elsevier, vol. 281(C).
    6. Ma, Yixiang & Yu, Lean & Zhang, Guoxing & Lu, Zhiming & Wu, Jiaqian, 2023. "Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling," Renewable Energy, Elsevier, vol. 219(P1).
    7. Venter, Philip van Zyl & Terblanche, Stephanus Esias & van Eldik, Martin, 2018. "Turbine investment optimisation for energy recovery plants by utilising historic steam flow profiles," Energy, Elsevier, vol. 155(C), pages 668-677.
    8. Xiang, Yue & Guo, Yongtao & Wu, Gang & Liu, Junyong & Sun, Wei & Lei, Yutian & Zeng, Pingliang, 2022. "Low-carbon economic planning of integrated electricity-gas energy systems," Energy, Elsevier, vol. 249(C).
    9. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    10. Xiang, Yue & Wu, Gang & Shen, Xiaodong & Ma, Yuhang & Gou, Jing & Xu, Weiting & Liu, Junyong, 2021. "Low-carbon economic dispatch of electricity-gas systems," Energy, Elsevier, vol. 226(C).
    11. Fambri, Gabriele & Diaz-Londono, Cesar & Mazza, Andrea & Badami, Marco & Sihvonen, Teemu & Weiss, Robert, 2022. "Techno-economic analysis of Power-to-Gas plants in a gas and electricity distribution network system with high renewable energy penetration," Applied Energy, Elsevier, vol. 312(C).
    12. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    13. Laws, Nicholas D. & Anderson, Kate & DiOrio, Nicholas A. & Li, Xiangkun & McLaren, Joyce, 2018. "Impacts of valuing resilience on cost-optimal PV and storage systems for commercial buildings," Renewable Energy, Elsevier, vol. 127(C), pages 896-909.
    14. Wei, Wei & Liu, Feng & Wang, Jianhui & Chen, Laijun & Mei, Shengwei & Yuan, Tiejiang, 2016. "Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants," Applied Energy, Elsevier, vol. 183(C), pages 674-684.
    15. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
    16. Giri, Binoy Krishna & Roy, Sankar Kumar, 2024. "Fuzzy-random robust flexible programming on sustainable closed-loop renewable energy supply chain," Applied Energy, Elsevier, vol. 363(C).
    17. Li, Wei & Lu, Can, 2019. "The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China," Applied Energy, Elsevier, vol. 235(C), pages 685-698.
    18. Norman Maswanganyi & Caston Sigauke & Edmore Ranganai, 2021. "Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data," Energies, MDPI, vol. 14(20), pages 1-21, October.
    19. 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.
    20. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.

    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:appene:v:284:y:2021:i:c:s0306261920315099. 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/wps/find/journaldescription.cws_home/405891/description#description .

    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.