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Adaptive mixed-integer programming unit commitment strategy for determining the value of forecasting

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  • Delarue, Erik
  • D'haeseleer, William

Abstract

This paper presents the development of a method to determine the value of forecasting (for load, wind power, etc.) in electricity-generation. An adaptive unit commitment (UC) strategy has been developed for this aim. An electricity generator faces demand with a given uncertainty. Forecasts are made to meet this load at the lowest cost. The adaptive UC strategy can be described as follows. Each hour, the generating company constructs a new forecast for a fixed number of hours. We assume that the first forecasted hour is in fact predicted correctly. For these forecasted hours, an optimal UC schedule is determined (given the on/off states of power plants for the current hour). The solution for the first hour (i.e., the one that was predicted correctly) is retained, and a new forecast is made. A 15,000Â MW power system is used in a 168 hour (one-week) schedule. The UC problems presented in this work are solved through a Mixed-Integer Linear Programming (MILP) approach. In the first case, the effect of limited (correct) forecasting is investigated. Forecasts are made 100% correctly, but the UC scheme is built modularly and compared with the reference case, where the UC problem is solved for the one-week problem as a whole. Depending on the number of forecasted hours, solutions differ by up to 0.5% with the reference case. In a second case, when a certain error is imposed on the forecasts made (up to 5%), the deviations from the optimal solution become larger and amount in certain cases to almost 1%.

Suggested Citation

  • Delarue, Erik & D'haeseleer, William, 2008. "Adaptive mixed-integer programming unit commitment strategy for determining the value of forecasting," Applied Energy, Elsevier, vol. 85(4), pages 171-181, April.
    • Handle: RePEc:eee:appene:v:85:y:2008:i:4:p:171-181
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    Cited by:

    1. Kong, Haining & Qi, Ershi & Li, Hui & Li, Gang & Zhang, Xing, 2010. "An MILP model for optimization of byproduct gases in the integrated iron and steel plant," Applied Energy, Elsevier, vol. 87(7), pages 2156-2163, July.
    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. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
    4. Mazzola, Simone & Vergara, Claudio & Astolfi, Marco & Li, Vivian & Perez-Arriaga, Ignacio & Macchi, Ennio, 2017. "Assessing the value of forecast-based dispatch in the operation of off-grid rural microgrids," Renewable Energy, Elsevier, vol. 108(C), pages 116-125.
    5. De Jonghe, Cedric & Delarue, Erik & Belmans, Ronnie & D'haeseleer, William, 2011. "Determining optimal electricity technology mix with high level of wind power penetration," Applied Energy, Elsevier, vol. 88(6), pages 2231-2238, June.
    6. 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.
    7. 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.
    8. Esmaili, Masoud & Shayanfar, Heidar Ali & Amjady, Nima, 2010. "Congestion management enhancing transient stability of power systems," Applied Energy, Elsevier, vol. 87(3), pages 971-981, March.
    9. Wang, Jinwen & Guo, Min & Liu, Yong, 2018. "Hydropower unit commitment with nonlinearity decoupled from mixed integer nonlinear problem," Energy, Elsevier, vol. 150(C), pages 839-846.
    10. 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.
    11. Hosseini, Seyyed Ahmad & Amjady, Nima & Shafie-khah, Miadreza & Catalão, João P.S., 2016. "A new multi-objective solution approach to solve transmission congestion management problem of energy markets," Applied Energy, Elsevier, vol. 165(C), pages 462-471.
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. Kenneth Bruninx & Erik Delarue & William D'haeseleer, 2013. "Statistical description of the error on wind power forecasts via a Lévy α-stable distribution," RSCAS Working Papers 2013/50, European University Institute.
    17. Mazidi, Mohammadreza & Monsef, Hassan & Siano, Pierluigi, 2016. "Robust day-ahead scheduling of smart distribution networks considering demand response programs," Applied Energy, Elsevier, vol. 178(C), pages 929-942.
    18. Jan Abrell & Friedrich Kunz, 2015. "Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market," Networks and Spatial Economics, Springer, vol. 15(1), pages 117-147, March.
    19. 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.
    20. Mazzola, Simone & Astolfi, Marco & Macchi, Ennio, 2015. "A detailed model for the optimal management of a multigood microgrid," Applied Energy, Elsevier, vol. 154(C), pages 862-873.
    21. 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.
    22. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.

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