IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i19p3646-d270240.html
   My bibliography  Save this article

A Mixed-Integer Convex Programming Algorithm for Security-Constrained Unit Commitment of Power System with 110-kV Network and Pumped-Storage Hydro Units

Author

Listed:
  • Shunjiang Lin

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Guansheng Fan

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Yuan Lu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Mingbo Liu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Yi Lu

    (The Power Dispatching Control Center of Shenzhen Power Supply Bureau, Shenzhen 518001, China)

  • Qifeng Li

    (Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA)

Abstract

The secure operation of 110-kV networks should be considered in the optimal generation dispatch of regional power grids in large central cities. However, since 110-kV lines do not satisfy the premise of R << X in the direct current power flow (DCPF) model, the DCPF, which is mostly applied in the security-constrained unit commitment (SCUC) problem of high-voltage power grids, is no longer suitable for describing the active power flow of regional power grids in large central cities. Hence, the quadratic active power flow (QAPF) model considering the resistance of lines is proposed to describe the network security constraints, and an SCUC model for power system with 110-kV network and pumped-storage hydro (PSH) units is established. The analytical expressions of the spinning reserve (SR) capacity of PSH units are given considering different operational modes, and the SR capacity of PSH units is included in the constraint of the SR capacity requirement of the system. The QAPF is a set of quadratic equality constraints, making the SCUC model a mixed-integer nonlinear non-convex programming (MINNP) model. To reduce the computational complexity of solving the model when applied in actual large-scale regional networks, the QAPF model is relaxed by its convex hull, and the SCUC model is transformed into a mixed-integer convex programming (MICP) model, which can be solved to obtain the global optimal solution efficiently and reliably by the mature commercial solver GUROBI (24.3.3, GAMS Development Corporation, Guangzhou, China). Test results on the IEEE-9 bus system, the PEGASE 89 bus system and the Shenzhen city power grid including the 110-kV network demonstrate that the relaxed QAPF model has good calculation accuracy and efficiency, and it is suitable for solving the SCUC problem in large-scale regional networks.

Suggested Citation

  • Shunjiang Lin & Guansheng Fan & Yuan Lu & Mingbo Liu & Yi Lu & Qifeng Li, 2019. "A Mixed-Integer Convex Programming Algorithm for Security-Constrained Unit Commitment of Power System with 110-kV Network and Pumped-Storage Hydro Units," Energies, MDPI, vol. 12(19), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3646-:d:270240
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/19/3646/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/19/3646/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aghaei, Jamshid & Nikoobakht, Ahmad & Siano, Pierluigi & Nayeripour, Majid & Heidari, Alireza & Mardaneh, Mohammad, 2016. "Exploring the reliability effects on the short term AC security-constrained unit commitment: A stochastic evaluation," Energy, Elsevier, vol. 114(C), pages 1016-1032.
    2. Guillermo Gutierrez-Alcaraz & Victor H. Hinojosa, 2018. "Using Generalized Generation Distribution Factors in a MILP Model to Solve the Transmission-Constrained Unit Commitment Problem," Energies, MDPI, vol. 11(9), pages 1-17, August.
    3. Bai, Yang & Zhong, Haiwang & Xia, Qing & Kang, Chongqing & Xie, Le, 2015. "A decomposition method for network-constrained unit commitment with AC power flow constraints," Energy, Elsevier, vol. 88(C), pages 595-603.
    4. Varkani, Ali Karimi & Daraeepour, Ali & Monsef, Hassan, 2011. "A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets," Applied Energy, Elsevier, vol. 88(12), pages 5002-5012.
    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. Xing Chen & Suhua Lou & Yanjie Liang & Yaowu Wu & Xianglu He, 2021. "Optimal Scheduling of a Regional Power System Aiming at Accommodating Clean Energy," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    2. Harun Or Rashid Howlader & Oludamilare Bode Adewuyi & Ying-Yi Hong & Paras Mandal & Ashraf Mohamed Hemeida & Tomonobu Senjyu, 2019. "Energy Storage System Analysis Review for Optimal Unit Commitment," Energies, MDPI, vol. 13(1), pages 1-21, December.

    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. Isuru, Mohasha & Hotz, Matthias & Gooi, H.B. & Utschick, Wolfgang, 2020. "Network-constrained thermal unit commitment fortexhybrid AC/DC transmission grids under wind power uncertainty," Applied Energy, Elsevier, vol. 258(C).
    2. Kim, Tae Hyun & Shin, Hansol & Kwag, Kyuhyeong & Kim, Wook, 2020. "A parallel multi-period optimal scheduling algorithm in microgrids with energy storage systems using decomposed inter-temporal constraints," Energy, Elsevier, vol. 202(C).
    3. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    4. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    5. Misaghian, M.S. & Saffari, M. & Kia, M. & Heidari, A. & Shafie-khah, M. & Catalão, J.P.S., 2018. "Tri-level optimization of industrial microgrids considering renewable energy sources, combined heat and power units, thermal and electrical storage systems," Energy, Elsevier, vol. 161(C), pages 396-411.
    6. Sousa, Tiago & Morais, Hugo & Soares, João & Vale, Zita, 2012. "Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints," Applied Energy, Elsevier, vol. 96(C), pages 183-193.
    7. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    8. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    9. Ma, Tao & Yang, Hongxing & Lu, Lin & Peng, Jinqing, 2014. "Technical feasibility study on a standalone hybrid solar-wind system with pumped hydro storage for a remote island in Hong Kong," Renewable Energy, Elsevier, vol. 69(C), pages 7-15.
    10. Ming, Zeng & Junjie, Feng & Song, Xue & Zhijie, Wang & Xiaoli, Zhu & Yuejin, Wang, 2013. "Development of China's pumped storage plant and related policy analysis," Energy Policy, Elsevier, vol. 61(C), pages 104-113.
    11. Min Xie & Yuxin Du & Peijun Cheng & Wei Wei & Mingbo Liu, 2019. "A Cross-Entropy-Based Hybrid Membrane Computing Method for Power System Unit Commitment Problems," Energies, MDPI, vol. 12(3), pages 1-18, February.
    12. Su, Chengguo & Cheng, Chuntian & Wang, Peilin & Shen, Jianjian & Wu, Xinyu, 2019. "Optimization model for long-distance integrated transmission of wind farms and pumped-storage hydropower plants," Applied Energy, Elsevier, vol. 242(C), pages 285-293.
    13. Yuan, Wenlin & Wang, Xinqi & Su, Chengguo & Cheng, Chuntian & Liu, Zhe & Wu, Zening, 2021. "Stochastic optimization model for the short-term joint operation of photovoltaic power and hydropower plants based on chance-constrained programming," Energy, Elsevier, vol. 222(C).
    14. Chen, Dongwen & Hu, Xiao & Li, Yong & Abbas, Zulkarnain & Wang, Ruzhu & Li, Dehong, 2023. "Nodal conservation principle of potential energy flow analysis for energy flow calculation in energy internet," Energy, Elsevier, vol. 263(PA).
    15. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Amin, Muhammad Yasir, 2020. "Solar and wind power generation systems with pumped hydro storage: Review and future perspectives," Renewable Energy, Elsevier, vol. 148(C), pages 176-192.
    16. Chang-Gi Min & Mun-Kyeom Kim, 2017. "Flexibility-Based Reserve Scheduling of Pumped Hydroelectric Energy Storage in Korea," Energies, MDPI, vol. 10(10), pages 1-13, September.
    17. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2017. "Securing highly penetrated wind energy systems using linearized transmission switching mechanism," Applied Energy, Elsevier, vol. 190(C), pages 1207-1220.
    18. Fotouhi Ghazvini, Mohammad Ali & Canizes, Bruno & Vale, Zita & Morais, Hugo, 2013. "Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market," Applied Energy, Elsevier, vol. 101(C), pages 667-677.
    19. Muche, Thomas, 2014. "Optimal operation and forecasting policy for pump storage plants in day-ahead markets," Applied Energy, Elsevier, vol. 113(C), pages 1089-1099.
    20. Domínguez, R. & Conejo, A.J. & Carrión, M., 2014. "Operation of a fully renewable electric energy system with CSP plants," Applied Energy, Elsevier, vol. 119(C), pages 417-430.

    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:jeners:v:12:y:2019:i:19:p:3646-:d:270240. 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.