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Use of weather forecast for increasing the self-consumption rate of home solar systems: An Italian case study

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  • Petrollese, Mario
  • Cau, Giorgio
  • Cocco, Daniele
Abstract
With the aim of increasing the self-consumption rate of grid-connected Photovoltaic (PV) home systems, two main options can be implemented: the inclusion of an energy storage system, in particular a battery bank, and the adoption of a Demand Side Management (DSM) strategy. However, both the reshaping of the load consumption curve with the displacement of deferrable loads and the optimal management of the battery bank require estimation of the daily PV generation profile. The assessment of the on-site energy production can be carried out based on weather forecast data. However, the latter are characterized by uncertainty, which may affect the achievable self-consumption rate.

Suggested Citation

  • Petrollese, Mario & Cau, Giorgio & Cocco, Daniele, 2018. "Use of weather forecast for increasing the self-consumption rate of home solar systems: An Italian case study," Applied Energy, Elsevier, vol. 212(C), pages 746-758.
    • Handle: RePEc:eee:appene:v:212:y:2018:i:c:p:746-758
    DOI: 10.1016/j.apenergy.2017.12.075
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    Cited by:

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    4. Ma, Tao & Zhang, Yijie & Gu, Wenbo & Xiao, Gang & Yang, Hongxing & Wang, Shuxiao, 2022. "Strategy comparison and techno-economic evaluation of a grid-connected photovoltaic-battery system," Renewable Energy, Elsevier, vol. 197(C), pages 1049-1060.
    5. Azuatalam, Donald & Paridari, Kaveh & Ma, Yiju & Förstl, Markus & Chapman, Archie C. & Verbič, Gregor, 2019. "Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 555-570.
    6. Amir Ali Safaei Pirooz & Mohammad J. Sanjari & Young-Jin Kim & Stuart Moore & Richard Turner & Wayne W. Weaver & Dipti Srinivasan & Josep M. Guerrero & Mohammad Shahidehpour, 2023. "Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation," Energies, MDPI, vol. 16(8), pages 1-16, April.
    7. Andreolli, Francesca & D’Alpaos, Chiara & Moretto, Michele, 2022. "Valuing investments in domestic PV-Battery Systems under uncertainty," Energy Economics, Elsevier, vol. 106(C).
    8. Luthander, Rasmus & Nilsson, Annica M. & Widén, Joakim & Åberg, Magnus, 2019. "Graphical analysis of photovoltaic generation and load matching in buildings: A novel way of studying self-consumption and self-sufficiency," Applied Energy, Elsevier, vol. 250(C), pages 748-759.
    9. Shayan, Mostafa Esmaeili & Najafi, Gholamhassan & Ghobadian, Barat & Gorjian, Shiva & Mamat, Rizalman & Ghazali, Mohd Fairusham, 2022. "Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm," Renewable Energy, Elsevier, vol. 201(P2), pages 179-189.

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