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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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/9135842 |
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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 |
work_keys_str_mv |
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