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Microgrid Planning by Stochastic Multi-Objective Multi-Year Optimization with Capacity Expansion and Non-Linear Asset Degradation

Author

Listed:
  • Davide Fioriti

    (Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56122 Pisa, Italy
    These authors contributed equally to this work.)

  • Marina Petrelli

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy
    These authors contributed equally to this work.)

  • Alberto Berizzi

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

  • Davide Poli

    (Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, 56122 Pisa, Italy)

Abstract

Decentralized microgrids have been proven to enable socioeconomic growth in developing countries. However, they are long-lasting investments whose profitability is highly uncertain due to unstable local socioeconomic contexts, which may delay the breakeven point, if ever reachable. Over the long term, capacity expansion and non-linear degradation of components also arise. Moreover, policymakers and developers are increasingly focusing on environmental and social considerations, raising the complexity of project development. Accordingly, multi-year planning has been simplified by addressing single challenges independently. In this paper, we propose a comprehensive procedure to efficiently solve stochastic multi-year problems for off-grid microgrids in developing countries, including capacity expansion and the non-linear degradation of battery and renewable assets. The novel procedure combines the efficient A-AUGMECON2 methodology for multi-objective formulation, the iterative decomposition of the non-linearities of the battery, and the inclusion of a two-step capacity expansion. A case study developed for Soroti, Uganda shows that the proposed model is suitable for planning purposes, with savings even beyond 20%. The Pareto frontier highlights the trade-offs among the net present cost, total emissions, and land use, which can support policy and business decision-making under uncertainty. The methodology renders these complex modeling challenges solvable and is scalable to energy system applications.

Suggested Citation

  • Davide Fioriti & Marina Petrelli & Alberto Berizzi & Davide Poli, 2026. "Microgrid Planning by Stochastic Multi-Objective Multi-Year Optimization with Capacity Expansion and Non-Linear Asset Degradation," Sustainability, MDPI, vol. 18(8), pages 1-21, April.
  • Handle: RePEc:gam:jsusta:v:18:y:2026:i:8:p:3785-:d:1917936
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