Abstract
In the single objective Unit Commitment Problem (UCP) the problem is usually separated in two sub-problems : the commitment problem which aims to fix the on/off scheduling of each unit and the dispatching problem which goal is to schedule the production of each turned on unit. The dispatching problem is a continuous convex problem that can easily be solved exactly. For the first sub-problem genetic algorithms (GA) are often applied and usually handle binary vectors representing the solutions of the commitment problem.Then the solutions are decoded in solving the dispatching problem with an exact method to obtain the precise production of each unit. In this paper a multi-objective version of the UCP taking the emission of gas into account is presented. In this multi objective UCP the dispatching problem remains easy to solve whereas considering it separatly remains interesting. A multi-objective GA handling binary vectors is applied. However for a binary representation there is a set of solutions of the dispatching problem that are pareto equivalent. Three decoding strategies are proposed and compared. The main contribution of this paper is the third decoding strategy which attaches an approximation of the Pareto front from the associated dispatching problem to each genotypic solution. It is shown that this decoding strategy leads to better results in comparison to the other ones.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA – A Platform and Programming Language Independent Interface for Search Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)
Catalao, J., Mariano, S., Mendes, V., Ferreira, L.: A practical approach for profit-based unit commitment with emission limitations. International journal of electrical power & energy systems 32(3), 218–224 (2010)
Damousis, I.G., Bakirtzis, A.G., Dokopoulos, P.S.: A solution to the unit-commitment problem using integer-coded genetic algorithm. IEEE Transactions on Power Systems 19(2), 1165–1172 (2004)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Ehrgott, M.: Multicriteria optimization, vol. 2. Springer (2005)
Guan, X., Zhai, Q., Papalexopoulos, A.: Optimization based methods for unit commitment: Lagrangian relaxation versus general mixed integer programming. In: Power Engineering Society General Meeting, vol. 2. IEEE (2003)
Jeong, Y.W., Park, J.B., Shin, J.R., Lee, K.Y.: A thermal unit commitment approach using an improved quantum evolutionary algorithm. Electric Power Components and Systems 37(7), 770–786 (2009)
Kazarlis, S.A., Bakirtzis, A., Petridis, V.: A genetic algorithm solution to the unit commitment problem. IEEE Transactions on Power Systems 11(1), 83–92 (1996)
Knowles, J., Thiele, L., Zitzler, E.: A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. TIK Report 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, February 2006
Lau, T., Chung, C., Wong, K., Chung, T., Ho, S.: Quantum-inspired evolutionary algorithm approach for unit commitment. IEEE Transactions on Power Systems 24(3), 1503–1512 (2009)
Liefooghe, A., Jourdan, L., Talbi, E.G.: A software framework based on a conceptual unified model for evolutionary multiobjective optimization: Paradiseo-moeo. European Journal of Operational Research 209(2), 104–112 (2011)
López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. Tech. rep, IRIDIA (2011)
de Moura Gomes Viana, A.M.M.: Metaheuristics for the Unit Commitment Problem The Constraint Oriented Neighbourhoods Search Strategy. Ph.D. thesis, Faculty of Engineering, University of Porto (2004)
Saramourtsis, A., Damousis, J., Bakirtzis, A., Dokopoulos, P.: Genetic algorithm solution to the economic dispatch problem–application to the electrical power grid of crete island. In: Proc. Workshop Machine Learning Applications to Power Systems (ACAI), pp. 308–317 (2001)
Srinivasan, D., Tettamanzi, A.G.: An evolutionary algorithm for evaluation of emission compliance options in view of the clean air act amendments. IEEE Transactions on Power Systems 12(1), 336–341 (1997)
Swarup, K., Yamashiro, S.: Unit commitment solution methodology using genetic algorithm. IEEE Transactions on Power Systems 17(1), 87–91 (2002)
Zhang, X.H., Zhao, J.Q., Chen, X.Y.: Multi-objective unit commitment fuzzy modeling and optimization for energy-saving and emission reduction. In: Proceedings of the CSEE 22, 71–76 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Jacquin, S., Mousin, L., Machado, I., Talbi, EG., Jourdan, L. (2015). A Comparison of Decoding Strategies for the 0/1 Multi-objective Unit Commitment Problem. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9018. Springer, Cham. https://doi.org/10.1007/978-3-319-15934-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-15934-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15933-1
Online ISBN: 978-3-319-15934-8
eBook Packages: Computer ScienceComputer Science (R0)