A hybrid biased random key genetic algorithm approach for the unit commitment problem

This work proposes a hybrid genetic algorithm to address the Unit Commitment (UC) problem. In the UC problem, the goal is to schedule a subset of a given group of electrical power generating units and also to determine their production output in order to meet energy demands at minimum cost. In addition, the solution must satisfy a set of technological and operational constraints. The algorithm developed is a Hybrid Biased Random Key Genetic Algorithm (Hybrid BRKGA). The biased random key technique was chosen due to its reported good performance in several combinatorial optimization problems. In the algorithm, solutions are encoded using random keys, which are represented as vectors of real numbers in the interval [0,1]. The proposed GA 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. The BRKGA is hybridized with local search in order to intensify the search close to good solutions. Tests have been performed on benchmark large-scale power systems with up to 100 units for a 24-hour period. The results obtained have demonstrated that the proposed methodology is an effective and efficient tool for finding solutions to largescale UC problems. Furthermore, from the comparisons made it is possible to concluded that the results achieved improve the solutions obtained by the state-of-the-art methodologies reported. DEMA, Instituto Superior de Engenharia do Porto, 4200-072 Porto, Portugal Tel.: +351 22 83 40 500 E-mail: [email protected] LIAAD-INESC-TEC, Faculdade de Economia, Universidade do Porto, 4200-464 Porto, Portugal Tel: +351 225 571 240 E-mail: [email protected] ISR-Porto, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal Tel: +351 22 508 1811 E-mail: [email protected] 2 Roque, Fontes, and Fontes