Application of Evolutionary Multiobjective Algorithms for Solving the Problem of Energy Dispatch in Hydroelectric Power Plants

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Marcelino, CG (Autor) Outra; Leonel Carvalho (Autor) Outra; Almeida, PEM (Autor) Outra; Wanner, EF (Autor) Outra; Vladimiro Miranda (Autor) FEUP

Ata de Conferência Internacional

Título: EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PT II

Páginas: 403-417

8th International Conference on Evolutionary Multi-Criterion Optimization (EMO)

Guimaraes, PORTUGAL, MAR 29-APR 01, 2015

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00A-9H9

DOI: 10.1007/978-3-319-15892-1_27

Abstract (EN): The Brazilian population increase and the purchase power growth have resulted in a widespread use of electric home appliances. Consequently, the demand for electricity has been growing steadily in an average of 5% a year. In this country, electric demand is supplied predominantly by hydro power. Many of the power plants installed do not operate efficiently from water consumption point of view. Energy Dispatch is defined as the allocation of operational values to each turbine inside a power plant to meet some criteria defined by the power plant owner. In this context, an optimal scheduling criterion could be the provision of the greatest amount of electricity with the lowest possible water consumption, i.e. maximization of water use efficiency. Some power plant operators rely on "Normal Mode of Operation" (NMO) as Energy Dispatch criterion. This criterion consists in equally dividing power demand between available turbines regardless whether the allocation represents an efficient good operation point for each turbine. This work proposes a multiobjective approach to solve electric dispatch problem in which the objective functions considered are maximization of hydroelectric productivity function and minimization of the distance between NMO and "Optimized Control Mode" (OCM). Two well-known Multiobjective Evolutionary Algorithms are used to solve this problem. Practical results have shown water savings in the order of million m(3)/s. In addition, statistical inference has revealed that SPEA2 algorithm is more robust than NSGA-II algorithm to solve this problem.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15