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Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming

Author

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  • Alberto Dolara

    (Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy)

  • Francesco Grimaccia

    (Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy)

  • Giulia Magistrati

    (Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy)

  • Gabriele Marchegiani

    (Elvi Energy S.r.l., Piazza del Tricolore 4, 20129 Milan, Italy)

Abstract

This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis between an optimization model based on linear programming and a model based on mixed integer programming has been carried out. The general formulation of these models has been presented and applied to a real case study micro-grid installed in Somalia. The case study is an islanded micro-grid supplying the city of Garowe by means of a hybrid power plant, consisting of diesel generators, photovoltaic systems and batteries. In both models the optimization is based on load demand and renewable energy production forecast. The optimized control of the battery state of charge, of the spinning reserve and diesel generators allows harvesting as much renewable power as possible or to minimize the use of fossil fuels in energy production.

Suggested Citation

  • Alberto Dolara & Francesco Grimaccia & Giulia Magistrati & Gabriele Marchegiani, 2017. "Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming," Energies, MDPI, vol. 10(2), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:241-:d:90589
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    References listed on IDEAS

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    9. Emanuele Ogliari & Francesco Grimaccia & Sonia Leva & Marco Mussetta, 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems," Energies, MDPI, vol. 6(4), pages 1-12, April.
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    2. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
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    5. Khawaja Haider Ali & Marvin Sigalo & Saptarshi Das & Enrico Anderlini & Asif Ali Tahir & Mohammad Abusara, 2021. "Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation," Energies, MDPI, vol. 14(18), pages 1-18, September.
    6. He Huang & DaPeng Liang & Zhen Tong, 2018. "Integrated Energy Micro-Grid Planning Using Electricity, Heating and Cooling Demands," Energies, MDPI, vol. 11(10), pages 1-20, October.
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    8. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    9. Bartosz Wachnik & Zbigniew Chyba, 2021. "Key Growth Factors and Limitations of Photovoltaic Companies in Poland and the Phenomenon of Technology Entrepreneurship under Conditions of Information Asymmetry," Energies, MDPI, vol. 14(24), pages 1-16, December.
    10. Tatiana González Grandón & Fernando de Cuadra García & Ignacio Pérez-Arriaga, 2021. "A Market-Driven Management Model for Renewable-Powered Undergrid Mini-Grids," Energies, MDPI, vol. 14(23), pages 1-29, November.
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    12. Moretti, L. & Polimeni, S. & Meraldi, L. & Raboni, P. & Leva, S. & Manzolini, G., 2019. "Assessing the impact of a two-layer predictive dispatch algorithm on design and operation of off-grid hybrid microgrids," Renewable Energy, Elsevier, vol. 143(C), pages 1439-1453.

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