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DYNAMOP Applied to the Unit Commitment Problem

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Abstract

In this article, we propose to apply a hybrid method called DYNAMOP (DYNAmic programming using Metaheuristic for Optimization Problems) to solve the Unit Commitment Problem (UCP). DYNAMOP uses a representation based on a path in the graph of states of dynamic programming, which is adapted to the dynamic structure of the problem and facilitates the hybridization between evolutionary algorithms and dynamic programming. Experiments indicate that the proposed approach outperforms the best known approach in literature.

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References

  1. Jacquin, S., Jourdan, L., Talbi, E.: Dynamic programming based metaheuristic for energy planning problems. In: EvoStar (2014)

    Google Scholar 

  2. Bellman, R.E.: Dynamic Programming. Princeton University Press, Princeton, NJ (1957)

    MATH  Google Scholar 

  3. Jeong, Y.W., Park, J.B., Shin, J.R., Lee, K.Y.: A thermal unit commitment approach using an improved quantum evolutionary algorithm. Electr. Power Compon. Syst. 37, 770–786 (2009)

    Article  Google Scholar 

  4. Pang, C., Chen, H.: Optimal short-term thermal unit commitment. IEEE Trans. Power Apparatus Syst. 95, 1336–1346 (1976)

    Article  Google Scholar 

  5. Su, C.C., Hsu, Y.Y.: Fuzzy dynamic programming: an application to unit commitment. IEEE Trans. Power Syst. 6, 1231–1237 (1991)

    Article  Google Scholar 

  6. Kazarlis, S.A., Bakirtzis, A., Petridis, V.: A genetic algorithm solution to the unit commitment problem. IEEE Trans. Power Syst. 11, 83–92 (1996)

    Article  Google Scholar 

  7. Lau, T., Chung, C., Wong, K., Chung, T., Ho, S.: Quantum-inspired evolutionary algorithm approach for unit commitment. IEEE Trans. Power Syst. 24, 1503–1512 (2009)

    Article  Google Scholar 

  8. Jeong, Y.W., Park, J.B., Jang, S.H., Lee, K.Y.: A new quantum-inspired binary pso: application to unit commitment problems for power systems. IEEE Trans. Power Syst. 25, 1486–1495 (2010)

    Article  Google Scholar 

  9. Chen, P.H.: Two-level hierarchical approach to unit commitment using expert system and elite pso. IEEE Trans. Power Syst. 27, 780–789 (2012)

    Article  Google Scholar 

  10. Talbi, E.G.: Metaheuristics: from design to implementation, vol. 74. John Wiley & Sons, Hoboken, NJ (2009)

    Book  Google Scholar 

  11. Saramourtsis, A., Damousis, J., Bakirtzis, A., Dokopoulos, P.: Genetic algorithm solution to the economic dispatch problemapplication to the electrical power grid of crete island. In: Proceedings of the Workshop Machine Learning Applications to Power Systems (ACAI), pp. 308–317 (2001)

    Google Scholar 

  12. Heidari, M., Chow, V.T., Kokotović, P.V., Meredith, D.D.: Discrete differential dynamic programing approach to water resources systems optimization. Water Resour. Res. 7, 273–282 (1971)

    Article  Google Scholar 

  13. Sniedovich, M., Viß, S.: The corridor method: a dynamic programming inspired metaheuristic. Control Cybern. 35, 551–578 (2006)

    MATH  Google Scholar 

  14. López-Ibánez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. IRIDIA, Université Libre de Bruxelles, Belgium, Technical report TR/IRIDIA/2011-004 (2011)

    Google Scholar 

  15. Humeau, J., Liefooghe, A., Talbi, E.G., Verel, S.: Paradiseo-mo: From fitness landscape analysis to efficient local search algorithms. J. Heuristics 19, 881–915 (2013)

    Article  Google Scholar 

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Correspondence to Sophie Jacquin .

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Jacquin, S., Jourdan, L., Talbi, EG. (2015). DYNAMOP Applied to the Unit Commitment Problem. In: Dhaenens, C., Jourdan, L., Marmion, ME. (eds) Learning and Intelligent Optimization. LION 2015. Lecture Notes in Computer Science(), vol 8994. Springer, Cham. https://doi.org/10.1007/978-3-319-19084-6_20

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  • DOI: https://doi.org/10.1007/978-3-319-19084-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19083-9

  • Online ISBN: 978-3-319-19084-6

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