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

Tomé Sicuaio, Pengxiang Zhao, Petter Pilesjö, Andrey Shindyapin and Ali Mansourian ()
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Tomé Sicuaio: Department of Physical Geography and Ecosystem Science, Lund University, S-223 62 Lund, Sweden
Pengxiang Zhao: Department of Physical Geography and Ecosystem Science, Lund University, S-223 62 Lund, Sweden
Petter Pilesjö: Department of Physical Geography and Ecosystem Science, Lund University, S-223 62 Lund, Sweden
Andrey Shindyapin: Department of Mathematics and Informatics, Faculty of Science, Eduardo Mondlane University, Maputo 257, Mozambique
Ali Mansourian: Department of Physical Geography and Ecosystem Science, Lund University, S-223 62 Lund, Sweden

Sustainability, 2024, vol. 16, issue 17, 1-20

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.

Keywords: multi-objective optimization; solar farms; metaheuristics algorithms; site selection; hypervolume; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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