A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation

A hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem f...

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Main Authors: J. Avilés, J. C. Mayo-Maldonado, O. Micheloud
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2018/9135842
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spelling doaj-2359f0b4d6e14f3fbb87a385154187da2020-11-25T00:26:46ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/91358429135842A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed GenerationJ. Avilés0J. C. Mayo-Maldonado1O. Micheloud2School of Engineering and Sciences at Tecnologico de Monterrey, Monterrey, MexicoSchool of Engineering and Sciences at Tecnologico de Monterrey, Monterrey, MexicoSchool of Engineering and Sciences at Tecnologico de Monterrey, Monterrey, MexicoA hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem from two fronts: optimal network configuration and optimal placement of DG. The hybrid scheme is based on a particle swarm optimization technique (PSO) and a genetic algorithm (GA) improved with a heuristic mutation operator. The GA-PSO scheme permits finding the optimal network topology, the optimal number, and capacity of the generation units, as well as their best location. Furthermore, the algorithm must design the system under power quality requirements, network radiality, and geographical constraints. The approach uses GPS coordinates as input data and develops a network topology from scratch, driven by overall costs and power losses minimization. Finally, the proposed algorithm is described in detail and real applications are discussed, from which satisfactory results were obtained.http://dx.doi.org/10.1155/2018/9135842
collection DOAJ
language English
format Article
sources DOAJ
author J. Avilés
J. C. Mayo-Maldonado
O. Micheloud
spellingShingle J. Avilés
J. C. Mayo-Maldonado
O. Micheloud
A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation
Mathematical Problems in Engineering
author_facet J. Avilés
J. C. Mayo-Maldonado
O. Micheloud
author_sort J. Avilés
title A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation
title_short A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation
title_full A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation
title_fullStr A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation
title_full_unstemmed A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation
title_sort hybrid evolutionary approach to design off-grid electrification projects with distributed generation
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2018-01-01
description A hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem from two fronts: optimal network configuration and optimal placement of DG. The hybrid scheme is based on a particle swarm optimization technique (PSO) and a genetic algorithm (GA) improved with a heuristic mutation operator. The GA-PSO scheme permits finding the optimal network topology, the optimal number, and capacity of the generation units, as well as their best location. Furthermore, the algorithm must design the system under power quality requirements, network radiality, and geographical constraints. The approach uses GPS coordinates as input data and develops a network topology from scratch, driven by overall costs and power losses minimization. Finally, the proposed algorithm is described in detail and real applications are discussed, from which satisfactory results were obtained.
url http://dx.doi.org/10.1155/2018/9135842
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