IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v87y2010i5p1782-1792.html
   My bibliography  Save this article

Metamodel-assisted evolutionary algorithms for the unit commitment problem with probabilistic outages

Author

Listed:
  • Georgopoulou, Chariklia A.
  • Giannakoglou, Kyriakos C.

Abstract

An efficient method for solving power generating unit commitment (UC) problems with probabilistic unit outages is proposed. It is based on a two-level evolutionary algorithm (EA) minimizing the expected total operating cost (TOC) of a system of power generating units over a scheduling period, with known failure and repair rates of each unit. To compute the cost function value of each EA population member, namely a candidate UC schedule, a Monte Carlo simulation must be carried out. Some thousands of replicates are generated according to the units' outage and repair rates and the corresponding probabilities. Each replicate is represented by a series of randomly generated availability and unavailability periods of time for each unit and the UC schedule under consideration accordingly. The expected TOC is the average of the TOCs of all Monte Carlo replicates. Therefore, the CPU cost per Monte Carlo evaluation increases noticeably and so does the CPU cost of running the EA. To reduce it, the use of a metamodel-assisted EA (MAEA) with on-line trained surrogate evaluation models or metamodels (namely, radial-basis function networks) is proposed. A novelty of this method is that the metamodels are trained on a few "representative" unit outage scenarios selected among the Monte Carlo replicates generated once during the optimization and, then, used to predict the expected TOC. Based on this low cost, approximate pre-evaluation, only a few top individuals within each generation undergo Monte Carlo simulations. The proposed MAEA is demonstrated on test problems and shown to drastically reduce the CPU cost, compared to EAs which are exclusively based on Monte Carlo simulations.

Suggested Citation

  • Georgopoulou, Chariklia A. & Giannakoglou, Kyriakos C., 2010. "Metamodel-assisted evolutionary algorithms for the unit commitment problem with probabilistic outages," Applied Energy, Elsevier, vol. 87(5), pages 1782-1792, May.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:5:p:1782-1792
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(09)00441-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Georgopoulou, Chariklia A. & Giannakoglou, Kyriakos C., 2009. "Two-level, two-objective evolutionary algorithms for solving unit commitment problems," Applied Energy, Elsevier, vol. 86(7-8), pages 1229-1239, July.
    2. Niknam, Taher & Khodaei, Amin & Fallahi, Farhad, 2009. "A new decomposition approach for the thermal unit commitment problem," Applied Energy, Elsevier, vol. 86(9), pages 1667-1674, September.
    3. Matthias Nowak & Werner Römisch, 2000. "Stochastic Lagrangian Relaxation Applied to Power Scheduling in a Hydro-Thermal System under Uncertainty," Annals of Operations Research, Springer, vol. 100(1), pages 251-272, December.
    4. Aghaei, J. & Shayanfar, H.A. & Amjady, N., 2009. "Joint market clearing in a stochastic framework considering power system security," Applied Energy, Elsevier, vol. 86(9), pages 1675-1682, September.
    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. 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.
    2. Dimitroulas, Dionisios K. & Georgilakis, Pavlos S., 2011. "A new memetic algorithm approach for the price based unit commitment problem," Applied Energy, Elsevier, vol. 88(12), pages 4687-4699.
    3. Geyer, Philipp & Schlüter, Arno, 2014. "Automated metamodel generation for Design Space Exploration and decision-making – A novel method supporting performance-oriented building design and retrofitting," Applied Energy, Elsevier, vol. 119(C), pages 537-556.
    4. Vrionis, Constantinos & Tsalavoutis, Vasilios & Tolis, Athanasios, 2020. "A Generation Expansion Planning model for integrating high shares of renewable energy: A Meta-Model Assisted Evolutionary Algorithm approach," Applied Energy, Elsevier, vol. 259(C).
    5. 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.
    6. Kyung-bin Kwon & Hyeongon Park & Jae-Kun Lyu & Jong-Keun Park, 2016. "Cost Analysis Method for Estimating Dynamic Reserve Considering Uncertainties in Supply and Demand," Energies, MDPI, vol. 9(10), pages 1-16, October.
    7. Ali, E.S. & Elazim, S.M. Abd & Balobaid, A.S., 2023. "Implementation of coyote optimization algorithm for solving unit commitment problem in power systems," Energy, Elsevier, vol. 263(PA).
    8. Azizipanah-Abarghooee, Rasoul & Golestaneh, Faranak & Gooi, Hoay Beng & Lin, Jeremy & Bavafa, Farhad & Terzija, Vladimir, 2016. "Corrective economic dispatch and operational cycles for probabilistic unit commitment with demand response and high wind power," Applied Energy, Elsevier, vol. 182(C), pages 634-651.

    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. Fernández-Blanco, Ricardo & Arroyo, José M. & Alguacil, Natalia, 2014. "Consumer payment minimization under uniform pricing: A mixed-integer linear programming approach," Applied Energy, Elsevier, vol. 114(C), pages 676-686.
    2. Vasilios A. Tsalavoutis & Constantinos G. Vrionis & Athanasios I. Tolis, 2021. "Optimizing a unit commitment problem using an evolutionary algorithm and a plurality of priority lists," Operational Research, Springer, vol. 21(1), pages 1-54, March.
    3. Dimitroulas, Dionisios K. & Georgilakis, Pavlos S., 2011. "A new memetic algorithm approach for the price based unit commitment problem," Applied Energy, Elsevier, vol. 88(12), pages 4687-4699.
    4. Amjady, Nima & Keynia, Farshid, 2010. "A new spinning reserve requirement forecast method for deregulated electricity markets," Applied Energy, Elsevier, vol. 87(6), pages 1870-1879, June.
    5. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    6. Schulze, Tim & McKinnon, Ken, 2016. "The value of stochastic programming in day-ahead and intra-day generation unit commitment," Energy, Elsevier, vol. 101(C), pages 592-605.
    7. Alexander Franz & Julia Rieck & Jürgen Zimmermann, 2019. "Fix-and-optimize procedures for solving the long-term unit commitment problem with pumped storages," Annals of Operations Research, Springer, vol. 274(1), pages 241-265, March.
    8. 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.
    9. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    10. Goudarzi, Arman & Swanson, Andrew G. & Van Coller, John & Siano, Pierluigi, 2017. "Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators," Applied Energy, Elsevier, vol. 189(C), pages 667-696.
    11. Cao, M.F. & Huang, G.H. & Lin, Q.G., 2010. "Integer programming with random-boundary intervals for planning municipal power systems," Applied Energy, Elsevier, vol. 87(8), pages 2506-2516, August.
    12. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    13. Klein Haneveld, W.K. & Vlerk, M.H. van der, 2000. "Optimizing electricity distribution using two-stage integer recourse models," Research Report 00A26, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    14. Ke, Xinda & Wu, Di & Rice, Jennie & Kintner-Meyer, Michael & Lu, Ning, 2016. "Quantifying impacts of heat waves on power grid operation," Applied Energy, Elsevier, vol. 183(C), pages 504-512.
    15. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2014. "Short-term electricity planning with increase wind capacity," Energy, Elsevier, vol. 69(C), pages 12-22.
    16. Tabandeh, Abbas & Abdollahi, Amir & Rashidinejad, Masoud, 2016. "Reliability constrained congestion management with uncertain negawatt demand response firms considering repairable advanced metering infrastructures," Energy, Elsevier, vol. 104(C), pages 213-228.
    17. Fallahi, Farhad & Nick, Mostafa & Riahy, Gholam H. & Hosseinian, Seyed Hossein & Doroudi, Aref, 2014. "The value of energy storage in optimal non-firm wind capacity connection to power systems," Renewable Energy, Elsevier, vol. 64(C), pages 34-42.
    18. Vijay, Avinash & Fouquet, Nicolas & Staffell, Iain & Hawkes, Adam, 2017. "The value of electricity and reserve services in low carbon electricity systems," Applied Energy, Elsevier, vol. 201(C), pages 111-123.
    19. Zhu, Y. & Li, Y.P. & Huang, G.H., 2012. "Planning municipal-scale energy systems under functional interval uncertainties," Renewable Energy, Elsevier, vol. 39(1), pages 71-84.
    20. Kargarian, A. & Raoofat, M. & Mohammadi, M., 2011. "Reactive power market management considering voltage control area reserve and system security," Applied Energy, Elsevier, vol. 88(11), pages 3832-3840.

    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:87:y:2010:i:5:p:1782-1792. 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.