A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem
A Biased Random Key Genetic Algorithm (BRKGA) is proposed to find solutions for the unit commitment problem. In this problem, one wishes to schedule energy production on a given set of thermal generation units in order to meet energy demands at minimum cost, while satisfying a set of technological and spinning reserve constraints. In the BRKGA, solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval \(\left[0,1\right]\). The GA proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy. Tests have been performed on benchmark large-scale power systems of up to 100 units for a 24 hours period. The results obtained have shown the proposed methodology to be an effective and efficient tool for finding solutions to large-scale unit commitment problems. Furthermore, from the comparisons made it can be concluded that the results produced improve upon some of the best known solutions.
KeywordsUnit Commitment Genetic Algorithm Optimization Electrical Power Generation
Unable to display preview. Download preview PDF.
- 1.Abookazemi, K., Mustafa, M.W., Ahmad, H.: Structured Genetic Algorithm Technique for Unit Commitment Problem. International Journal of Recent Trends in Engineering 1(3), 135–139 (2009)Google Scholar
- 3.Bean, J.C.: Genetic Algorithms and Random Keys for Sequencing and Optimization. ORSA Journal on Computing 6(2) (1994)Google Scholar
- 9.Gonçalves, J.F., Resende, M.G.C.: Biased random-key genetic algorithms for combinatorial optimization. Journal of Heuristics (2010), Published online (August 27, 2010). DOI: 10.1007/s10732-010-9143-1Google Scholar
- 10.Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
- 11.Jenkins, L., Purushothama, G.K.: Simulated annealing with local search-a hybrid algorithm for unit commitment. IEEE Transactions on Power Systems 18(1), 1218–1225 (2003)Google Scholar
- 19.Rajan, C.C.A., Mohan, M.R., Manivannan, K.: Refined simulated annealing method for solving unit commitment problem. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN 2002, vol. 1, pp. 333–338. IEEE, Los Alamitos (2002)Google Scholar
- 20.Salam, S.: Unit commitment solution methods. Proceedings of World Academy of Science, Engineering and Technology 26, 600–605 (2007)Google Scholar
- 21.Senjyu, T., Yamashiro, H., Uezato, K., Funabashi, T.: A unit commitment problem by using genetic algorithm based on unit characteristic classification. IEEE Power Engineering Society Winter Meeting 1 (2002)Google Scholar
- 23.Sriyanyong, P., Song, Y.H.: Unit commitment using particle swarm optimization combined with Lagrange relaxation. In: Power Engineering Society General Meeting, pp. 2752–2759. IEEE, Los Alamitos (2005)Google Scholar