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Optimal Power Flow Management for a Solar PV-Powered Soldier-Level Pico-Grid

Author

Listed:
  • Tawanda Kunatsa

    (Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South Africa)

  • Herman C. Myburgh

    (Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South Africa)

  • Allan De Freitas

    (Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0028, South Africa)

Abstract

Users ought to decide how to operate and manage power systems in order to achieve various goals. As a result, many strategies have been developed to aid in this regard. Optimal power flow management is one such strategy that assists users in properly operating and managing the supply and demand of power in an optimal way under specified constraints. However, in-depth research on optimal power flow management is yet to be explored when it comes to the supply and demand of power for the bulk of standalone renewable energy systems such as solar photovoltaics, especially when it comes to specific applications such as powering military soldier-level portable electronic devices. This paper presents an optimal power flow management modelling and optimisation approach for solar-powered soldier-level portable electronic devices. The OPTI toolbox in MATLAB is used to solve the formulated nonlinear optimal power flow management problem using SCIP as the solver. A globally optimal solution was arrived at in a case study in which the objective function was to minimise the difference between the power supplied to the portable electronic device electronics and the respective portable electronic device power demands. This ensured that the demand for solar-powered soldier-level portable electronic devices is met at all times in spite of the prohibitive case scenarios’ circumstances under the given constraints. This resolute approach underscores the importance placed on satisfying the demand needs of the specific devices while navigating and addressing the limitations posed by the existing conditions or constraints. Soldiers and the solar photovoltaic user fraternity at large will benefit from this work as they will be guided on how to optimally manage their power systems’ supply and demand scenarios. The model developed herein is applicable to any demand profile and any number of portable electronic device and is adaptable to any geographical location receiving any amount of solar radiation.

Suggested Citation

  • Tawanda Kunatsa & Herman C. Myburgh & Allan De Freitas, 2024. "Optimal Power Flow Management for a Solar PV-Powered Soldier-Level Pico-Grid," Energies, MDPI, vol. 17(2), pages 1-23, January.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:459-:d:1321041
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    References listed on IDEAS

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    1. Reynolds, Jonathan & Ahmad, Muhammad Waseem & Rezgui, Yacine & Hippolyte, Jean-Laurent, 2019. "Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 699-713.
    2. van der Heijde, Bram & Vandermeulen, Annelies & Salenbien, Robbe & Helsen, Lieve, 2019. "Representative days selection for district energy system optimisation: a solar district heating system with seasonal storage," Applied Energy, Elsevier, vol. 248(C), pages 79-94.
    3. Rangel, N. & Li, H. & Aristidou, P., 2023. "An optimisation tool for minimising fuel consumption, costs and emissions from Diesel-PV-Battery hybrid microgrids," Applied Energy, Elsevier, vol. 335(C).
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