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Optimisation of geographically deployed PV parks for reduction of intermittency to enhance grid stability

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  • Hookoom, Tavish
  • Bangarigadu, Kaviraj
  • Ramgolam, Yatindra Kumar

Abstract

A rapid rise in the installed capacity of PV projects in the recent years coupled with the stochastic nature of PV production is posing a considerable grid stability challenge. This research aims to study the effect of geographically dispersing the capacity of PV plants on the combined intermittency of their power output and the total PV energy production in the context of Mauritius. High-resolution irradiance data is gathered from nine sites across the island and treated to meet data quality standards. The high-quality data is used to obtain the normalised PV power output. A three-stage optimisation process is implemented to find the best combination of PV sites and their respective capacities that would result in the lowest level of intermittency in the power generation. Initially, a pilot algorithm was developed in MATLAB to test the equal share distribution of PV power among the sites; then the GRG Solver Solution was used for further optimisation. Results show that the combined PV power output is smoothened, intermittency of the power is significantly reduced by more than 48%, and the overall energy production is increased by up to 8.7% as compared to sites with low level of insolation while having no effect on LCOE.

Suggested Citation

  • Hookoom, Tavish & Bangarigadu, Kaviraj & Ramgolam, Yatindra Kumar, 2022. "Optimisation of geographically deployed PV parks for reduction of intermittency to enhance grid stability," Renewable Energy, Elsevier, vol. 187(C), pages 1020-1036.
    • Handle: RePEc:eee:renene:v:187:y:2022:i:c:p:1020-1036
    DOI: 10.1016/j.renene.2022.02.007
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    References listed on IDEAS

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    1. Lappalainen, Kari & Valkealahti, Seppo, 2017. "Output power variation of different PV array configurations during irradiance transitions caused by moving clouds," Applied Energy, Elsevier, vol. 190(C), pages 902-910.
    2. Al-Ghussain, Loiy & Abubaker, Ahmad M. & Darwish Ahmad, Adnan, 2021. "Superposition of Renewable-Energy Supply from Multiple Sites Maximizes Demand-Matching: Towards 100% Renewable Grids in 2050," Applied Energy, Elsevier, vol. 284(C).
    3. Lappalainen, Kari & Wang, Guang C. & Kleissl, Jan, 2020. "Estimation of the largest expected photovoltaic power ramp rates," Applied Energy, Elsevier, vol. 278(C).
    4. Stuart Smith & Leon Lasdon, 1992. "Solving Large Sparse Nonlinear Programs Using GRG," INFORMS Journal on Computing, INFORMS, vol. 4(1), pages 2-15, February.
    5. Moradi, Isaac, 2009. "Quality control of global solar radiation using sunshine duration hours," Energy, Elsevier, vol. 34(1), pages 1-6.
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