A novel movable partitions approach with neural networks and evolutionary algorithms for solving the hydroelectric unit commitment problem

  • Authors:

  • Pedro de Lima Abrão;Elizabeth Fialho Wanner;Paulo Eduardo Maciel de Almeida

  • Affiliations:

  • Centro Federal de Educação Tecnológica de Minas Gerais - CEFET-MG, Belo Horizonte, Brazil;Centro Federal de Educação Tecnológica de Minas Gerais - CEFET-MG, Belo Horizonte, Brazil;Centro Federal de Educação Tecnológica de Minas Gerais - CEFET-MG, Belo Horizonte, Brazil

  • Venue:
  • Proceedings of the 15th annual conference on Genetic and evolutionary computation
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents a method based on Neural Networks and Evolutionary Algorithms to solve the Hydroelectric Unit Commitment Problem. A Neural Network is used to model the production function and a novel approach based on movable partitions is proposed, which makes it easier to model the desired power output equality constraint in the optimization modeling. Three evolutionary algorithms are tested in order to find optimized operation points: differential evolution DE/best/1/bin, a balanced version of DE and Particle Swarm Optimization algorithm (PSO). The results show that the proposed method is effective in terms of water consumption, reaching in some cases more than 1% of economy whether compared to the traditional commitment strategy.