• DocumentCode
    617847
  • Title

    A novel mathematical modeling approach to the electric dispatch problem: Case study using Differential Evolution algorithms

  • Author

    Marcelino, Carolina G. ; Wanner, Elizabeth F. ; Almeida, Paulo E. M.

  • Author_Institution
    Intell. Syst. Lab., Centro Fed. de Educ. Tecnol. de Minas Gerais /CEFET-MG, Belo Horizonte, Brazil
  • fYear
    2013
  • fDate
    20-23 June 2013
  • Firstpage
    400
  • Lastpage
    407
  • Abstract
    Nowadays, the population growth and economic development causes the need for electricity power to increase every year. An unit dispatch problem is defined as the attribution of operational values to each generation unit inside a power plant, given some criteria to be obeyed like the total power to be generated, operational bounds of these units etc. In this context, an optimal dispatch programming for hydroelectric units in energy plants provides a bigger production of electricity to be generated with a minimal water amount. This paper presents an optimization solution for hydroelectric generating system of a plant, using Differential Evolution algorithms. The novel mathematical model proposed and validation of the obtained algorithms will be performed with practical simulation experiments. Throughout the text, the equations and models for the system simulation will be fully described, and the experiments and results will be objectively analysed through statistical inference. Simulation results indicate savings of 6.5 million litres of water for each month of operation using the proposed solution.
  • Keywords
    evolutionary computation; hydroelectric power stations; inference mechanisms; optimisation; power engineering computing; power generation dispatch; statistical mechanics; differential evolution algorithms; economic development; electric dispatch problem; electricity power; energy plants; generation unit; hydroelectric generating system; hydroelectric units; mathematical modeling approach; operational values; optimal dispatch programming; optimization solution; population growth; statistical inference; water; Evolutionary computation; Mathematical model; Optimization; Production; Sociology; Statistics; Vectors; Differential Evolution Algorithms; Optimization; Simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2013 IEEE Congress on
  • Conference_Location
    Cancun
  • Print_ISBN
    978-1-4799-0453-2
  • Electronic_ISBN
    978-1-4799-0452-5
  • Type

    conf

  • DOI
    10.1109/CEC.2013.6557597
  • Filename
    6557597