Solution to Economic – Emission Load Dispatch by Cultural Algorithm Combined With Local Search: Case Study

Economic-emission load dispatch uses the fuel cost variables and gas emission in a minimized way to obtain an optimal operation in generation units in a power plant, guaranteeing the supply of demand. The first variable is definitive to ensure business continuity and the second to comply with enviro...

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Main Authors: Carlos Alberto Oliveira De Freitas, Roberto Celio Limao de Oliveira, Deam James Azevedo Da Silva, Jandecy Cabral Leite, Jorge De Almeida Brito Junior
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8506356/
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author Carlos Alberto Oliveira De Freitas
Roberto Celio Limao de Oliveira
Deam James Azevedo Da Silva
Jandecy Cabral Leite
Jorge De Almeida Brito Junior
author_facet Carlos Alberto Oliveira De Freitas
Roberto Celio Limao de Oliveira
Deam James Azevedo Da Silva
Jandecy Cabral Leite
Jorge De Almeida Brito Junior
author_sort Carlos Alberto Oliveira De Freitas
collection DOAJ
description Economic-emission load dispatch uses the fuel cost variables and gas emission in a minimized way to obtain an optimal operation in generation units in a power plant, guaranteeing the supply of demand. The first variable is definitive to ensure business continuity and the second to comply with environmental legislation and no degradation of the environment. This paper analyzes the use of a new computational optimization algorithm based on the cultural algorithm (CA), improved with local search techniques simulated annealing and Tabu search, using data from a real power plant with 10 generators and the system of the IEEE with 13 generating units. The application has two options of operation: the classic one, which operates with all generators seeking to minimize the cost and emission meeting the specified demand; and the controlled one, which turns off the generators that have the highest incremental fuel cost but guaranteeing the demand and reducing the emission of gases. Simulations were performed on the six possible options in this application. The results obtained were compared with each other and with the results of other techniques reported in the literature. The local search that improved the CA and the new way of updating topographic knowledge allowed the results to be better than those found by other metaheuristics that solved the same problem of the real plant and the IEEE system.
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spelling doaj.art-67406dfbaf6349a18cd7e3fc36804ebe2022-12-21T22:23:02ZengIEEEIEEE Access2169-35362018-01-016640236404010.1109/ACCESS.2018.28777708506356Solution to Economic – Emission Load Dispatch by Cultural Algorithm Combined With Local Search: Case StudyCarlos Alberto Oliveira De Freitas0https://orcid.org/0000-0002-8913-953XRoberto Celio Limao de Oliveira1Deam James Azevedo Da Silva2Jandecy Cabral Leite3Jorge De Almeida Brito Junior4Post-Graduate Program in Electrical Engineering, Federal University of Para, Belém, BrazilPost-Graduate Program in Electrical Engineering, Federal University of Para, Belém, BrazilInstitute of Engineering and Geosciences, Federal University of Western Para, Santarém, BrazilResearch Department, Institute of Technology and Education Galileo of the Amazon, Manaus, BrazilResearch Department, Institute of Technology and Education Galileo of the Amazon, Manaus, BrazilEconomic-emission load dispatch uses the fuel cost variables and gas emission in a minimized way to obtain an optimal operation in generation units in a power plant, guaranteeing the supply of demand. The first variable is definitive to ensure business continuity and the second to comply with environmental legislation and no degradation of the environment. This paper analyzes the use of a new computational optimization algorithm based on the cultural algorithm (CA), improved with local search techniques simulated annealing and Tabu search, using data from a real power plant with 10 generators and the system of the IEEE with 13 generating units. The application has two options of operation: the classic one, which operates with all generators seeking to minimize the cost and emission meeting the specified demand; and the controlled one, which turns off the generators that have the highest incremental fuel cost but guaranteeing the demand and reducing the emission of gases. Simulations were performed on the six possible options in this application. The results obtained were compared with each other and with the results of other techniques reported in the literature. The local search that improved the CA and the new way of updating topographic knowledge allowed the results to be better than those found by other metaheuristics that solved the same problem of the real plant and the IEEE system.https://ieeexplore.ieee.org/document/8506356/Economic load dispatchemissioncultural algorithmpower plantssimulated annealingTabu search
spellingShingle Carlos Alberto Oliveira De Freitas
Roberto Celio Limao de Oliveira
Deam James Azevedo Da Silva
Jandecy Cabral Leite
Jorge De Almeida Brito Junior
Solution to Economic – Emission Load Dispatch by Cultural Algorithm Combined With Local Search: Case Study
IEEE Access
Economic load dispatch
emission
cultural algorithm
power plants
simulated annealing
Tabu search
title Solution to Economic – Emission Load Dispatch by Cultural Algorithm Combined With Local Search: Case Study
title_full Solution to Economic – Emission Load Dispatch by Cultural Algorithm Combined With Local Search: Case Study
title_fullStr Solution to Economic – Emission Load Dispatch by Cultural Algorithm Combined With Local Search: Case Study
title_full_unstemmed Solution to Economic – Emission Load Dispatch by Cultural Algorithm Combined With Local Search: Case Study
title_short Solution to Economic – Emission Load Dispatch by Cultural Algorithm Combined With Local Search: Case Study
title_sort solution to economic x2013 emission load dispatch by cultural algorithm combined with local search case study
topic Economic load dispatch
emission
cultural algorithm
power plants
simulated annealing
Tabu search
url https://ieeexplore.ieee.org/document/8506356/
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