A Multi-Objective Optimization Approach for Solar Farm Site Selection: Case Study in Maputo, Mozambique
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/05619b71-f558-4856-abc4-9a834ad0bf7d
- author
- Eduardo Sicuaio, Tome LU ; Zhao, Pengxiang LU ; Pilesjö, Petter LU ; Shindyapin, Andrey and Mansourian, Ali LU
- organization
- publishing date
- 2024-08
- 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
- 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
- 2024-09-11 12:42:51
@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}}, }