TY - JOUR
T1 - A Multi-Objective Optimization Approach for Solar Farm Site Selection: Case Study in Maputo, Mozambique
AU - Eduardo Sicuaio, Tome
AU - Zhao, Pengxiang
AU - Pilesjö, Petter
AU - Shindyapin, Andrey
AU - Mansourian, Ali
PY - 2024/8
Y1 - 2024/8
N2 - 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.
AB - 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.
KW - Geospatial Artificial Intelligence (GeoAI)
KW - Multi-Objective Optimization (MOO)
KW - Solar Farm
KW - site selection
KW - NSGA-II optimization algorithm
U2 - 10.3390/su16177333
DO - 10.3390/su16177333
M3 - Article
SN - 2071-1050
VL - 16
JO - Sustainability
JF - Sustainability
IS - 17
M1 - 7333
ER -