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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

roque, lac (Autor) Outra; Dalila B.M.M. Fontes (Autor) FEP; fontes, facc (Autor) FEUP

Revista

Título: Journal of Combinatorial Optimization

Vol. 28

Páginas: 140-166

ISSN: 1382-6905

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 21 Citações

Scopus - 25 Citações

Outras Informações

ID Authenticus: P-009-K57

DOI: 10.1007/s10878-014-9710-8

Abstract (EN): This work proposes a hybrid genetic algorithm (GA) 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 (HBRKGA). It uses random keys to encode the solutions and introduces bias both in the parent selection procedure and in the crossover strategy. To intensify the search close to good solutions, the GA is hybridized with local search. Tests have been performed on benchmark large-scale power systems. The computational results demonstrate that the HBRKGA is effective and efficient. In addition, it is also shown that it improves the solutions obtained by current state-of-the-art methodologies.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 27