Reduction of Losses and Operating Costs in Distribution Networks Using a Genetic Algorithm and Mathematical Optimization

This study deals with the minimization of the operational and investment cost in the distribution and operation of the power flow considering the installation of fixed-step capacitor banks. This issue is represented by a nonlinear mixed-integer programming mathematical model which is solved by apply...

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Published inElectronics (Basel) Vol. 10; no. 4; p. 419
Main Authors Riaño, Fabio Edison, Cruz, Jonathan Felipe, Montoya, Oscar Danilo, Chamorro, Harold R., Alvarado-Barrios, Lazaro
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LanguageEnglish
Published Basel MDPI AG 2021
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Abstract This study deals with the minimization of the operational and investment cost in the distribution and operation of the power flow considering the installation of fixed-step capacitor banks. This issue is represented by a nonlinear mixed-integer programming mathematical model which is solved by applying the Chu and Beasley genetic algorithm (CBGA). While this algorithm is a classical method for resolving this type of optimization problem, the solutions found using this approach are better than those reported in the literature using metaheuristic techniques and the General Algebraic Modeling System (GAMS). In addition, the time required for the CBGA to get results was reduced to a few seconds to make it a more robust, efficient, and capable tool for distribution system analysis. Finally, the computational sources used in this study were developed in the MATLAB programming environment by implementing test feeders composed of 10, 33, and 69 nodes with radial and meshed configurations.
AbstractList This study deals with the minimization of the operational and investment cost in the distribution and operation of the power flow considering the installation of fixed-step capacitor banks. This issue is represented by a nonlinear mixed-integer programming mathematical model which is solved by applying the Chu and Beasley genetic algorithm (CBGA). While this algorithm is a classical method for resolving this type of optimization problem, the solutions found using this approach are better than those reported in the literature using metaheuristic techniques and the General Algebraic Modeling System (GAMS). In addition, the time required for the CBGA to get results was reduced to a few seconds to make it a more robust, efficient, and capable tool for distribution system analysis. Finally, the computational sources used in this study were developed in the MATLAB programming environment by implementing test feeders composed of 10, 33, and 69 nodes with radial and meshed configurations.
Author Montoya, Oscar Danilo
Riaño, Fabio Edison
Chamorro, Harold R.
Cruz, Jonathan Felipe
Alvarado-Barrios, Lazaro
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  surname: Riaño
  fullname: Riaño, Fabio Edison
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  givenname: Jonathan Felipe
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  givenname: Oscar Danilo
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  fullname: Montoya, Oscar Danilo
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  givenname: Harold R.
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  fullname: Chamorro, Harold R.
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  givenname: Lazaro
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  fullname: Alvarado-Barrios, Lazaro
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SubjectTerms Capacitor banks
Chu and Beasley genetic algorithm
combinatorial optimization
Cost control
Customer services
discrete codification
Electrical Engineering
Elektro- och systemteknik
Energy
Feeders
fixed-step capacitor banks
Genetic algorithms
Heuristic methods
Integer programming
Linear programming
Mathematical models
Mixed integer
Net present value
operative costs minimization
Optimeringslära och systemteori
Optimization
Optimization algorithms
Optimization and Systems Theory
Power flow
Power supply
Programming environments
Robustness (mathematics)
Systems analysis
Test systems
Title Reduction of Losses and Operating Costs in Distribution Networks Using a Genetic Algorithm and Mathematical Optimization
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