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A Multi-Objective Optimization Approach for Solar Farm Site Selection: Case Study in Maputo, Mozambique

Eduardo Sicuaio, Tome LU ; Zhao, Pengxiang LU ; Pilesjö, Petter LU ; Shindyapin, Andrey and Mansourian, Ali LU orcid (2024) In Sustainability 16(17).
Abstract
Solar energy is an important source of clean energy to combat climate change issues that motivate the establishment of solar farms. Establishing solar farms has been considered a proper alternative for energy production in countries like Mozambique, which need reliable and clean sources of energy for sustainable development. However, selecting proper sites for creating solar farms is a function of various economic, environmental, and technical criteria, which are usually conflicting with each other. This makes solar farm site selection a complex spatial problem that requires adapting proper techniques to solve it. In this study, we proposed a multi-objective optimization (MOO) approach for site selection of solar farms in Mozambique, by... (More)
Solar energy is an important source of clean energy to combat climate change issues that motivate the establishment of solar farms. Establishing solar farms has been considered a proper alternative for energy production in countries like Mozambique, which need reliable and clean sources of energy for sustainable development. However, selecting proper sites for creating solar farms is a function of various economic, environmental, and technical criteria, which are usually conflicting with each other. This makes solar farm site selection a complex spatial problem that requires adapting proper techniques to solve it. In this study, we proposed a multi-objective optimization (MOO) approach for site selection of solar farms in Mozambique, by optimizing six objective functions using an improved NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm. The MOO model is demonstrated by implementing a case study in KaMavota district, Maputo city, Mozambique. The improved NSGA-II algorithm displays a better performance in comparison to standard NSGA-II. The study also demonstrated how decision-makers can select optimum solutions, based on their preferences, despite trade-offs existing between all objective functions, which support the decision-making. (Less)
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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Geospatial Artificial Intelligence (GeoAI), Multi-Objective Optimization (MOO), Solar Farm, site selection, NSGA-II optimization algorithm
in
Sustainability
volume
16
issue
17
article number
7333
publisher
MDPI AG
external identifiers
  • scopus:85204152966
ISSN
2071-1050
DOI
10.3390/su16177333
language
English
LU publication?
yes
id
05619b71-f558-4856-abc4-9a834ad0bf7d
date added to LUP
2024-09-09 13:11:28
date last changed
2025-04-04 13:56:12
@article{05619b71-f558-4856-abc4-9a834ad0bf7d,
  abstract     = {{Solar energy is an important source of clean energy to combat climate change issues that motivate the establishment of solar farms. Establishing solar farms has been considered a proper alternative for energy production in countries like Mozambique, which need reliable and clean sources of energy for sustainable development. However, selecting proper sites for creating solar farms is a function of various economic, environmental, and technical criteria, which are usually conflicting with each other. This makes solar farm site selection a complex spatial problem that requires adapting proper techniques to solve it. In this study, we proposed a multi-objective optimization (MOO) approach for site selection of solar farms in Mozambique, by optimizing six objective functions using an improved NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm. The MOO model is demonstrated by implementing a case study in KaMavota district, Maputo city, Mozambique. The improved NSGA-II algorithm displays a better performance in comparison to standard NSGA-II. The study also demonstrated how decision-makers can select optimum solutions, based on their preferences, despite trade-offs existing between all objective functions, which support the decision-making.}},
  author       = {{Eduardo Sicuaio, Tome and Zhao, Pengxiang and Pilesjö, Petter and Shindyapin, Andrey and Mansourian, Ali}},
  issn         = {{2071-1050}},
  keywords     = {{Geospatial Artificial Intelligence (GeoAI); Multi-Objective Optimization (MOO); Solar Farm; site selection; NSGA-II optimization algorithm}},
  language     = {{eng}},
  number       = {{17}},
  publisher    = {{MDPI AG}},
  series       = {{Sustainability}},
  title        = {{A Multi-Objective Optimization Approach for Solar Farm Site Selection: Case Study in Maputo, Mozambique}},
  url          = {{http://dx.doi.org/10.3390/su16177333}},
  doi          = {{10.3390/su16177333}},
  volume       = {{16}},
  year         = {{2024}},
}