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
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