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

Author

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  • Tomé Sicuaio

    (Department of Physical Geography and Ecosystem Science, Lund University, S-223 62 Lund, Sweden
    Department of Mathematics and Informatics, Faculty of Science, Eduardo Mondlane University, Maputo 257, Mozambique)

  • 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)

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.

Suggested Citation

  • Tomé Sicuaio & Pengxiang Zhao & Petter Pilesjö & Andrey Shindyapin & Ali Mansourian, 2024. "A Multi-Objective Optimization Approach for Solar Farm Site Selection: Case Study in Maputo, Mozambique," Sustainability, MDPI, vol. 16(17), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7333-:d:1464317
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    References listed on IDEAS

    as
    1. Klamroth, Kathrin & Lacour, Renaud & Vanderpooten, Daniel, 2015. "On the representation of the search region in multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 245(3), pages 767-778.
    2. Breen, M. & Upton, J. & Murphy, M.D., 2020. "Photovoltaic systems on dairy farms: Financial and renewable multi-objective optimization (FARMOO) analysis," Applied Energy, Elsevier, vol. 278(C).
    3. Stiglitz, Joseph E, 1981. "Pareto Optimality and Competition," Journal of Finance, American Finance Association, vol. 36(2), pages 235-251, May.
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