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The Effect of Increasing Aggregation Levels of Electrical Consumption Data on Renewable Energy Community (REC) Analyses

Author

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  • Marco Raugi

    (Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, L.go Lucio Lazzarino 1, 56122 Pisa, Italy
    UNESCO Chair on “Sustainable Energy Communities”, University of Pisa, L.go Lucio Lazzarino 1, 56122 Pisa, Italy)

  • Valentina Consolo

    (Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, L.go Lucio Lazzarino 1, 56122 Pisa, Italy
    These authors contributed equally to this work.)

  • Roberto Rugani

    (Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, L.go Lucio Lazzarino 1, 56122 Pisa, Italy
    These authors contributed equally to this work.)

Abstract

The growing number of renewable energy communities (RECs) exemplifies the potential of citizen-driven actions towards a more sustainable future. However, obtaining hourly measured consumption data from REC members remains challenging, hindering accurate feasibility studies for the development of communities. This study examines the impact of estimating hourly consumption from aggregated data on REC analysis results. A case study with real consumption data from diverse users, representative of a typical community in Tuscany, Italy, was analysed to investigate various influencing factors. Multiple scenarios were simulated: two open-source tools estimated energy production from the community’s PV plants, and two REC configurations were considered—one with consumers and prosumers and another with consumers and a producer (with the same total installed power). Additionally, three locations were evaluated to consider the importance of geographical positioning. The study revealed that the impact of consumption data aggregation is more significant in scenarios with low energy sharing, such as the scenario where prosumers were replaced with a producer. Geographical positioning showed no major discrepancies in energy and economic outcomes, implying that using estimated hourly consumption data from aggregated data has a relevant impact regardless of location. Furthermore, different weather files did not affect the impact of aggregated consumption data.

Suggested Citation

  • Marco Raugi & Valentina Consolo & Roberto Rugani, 2024. "The Effect of Increasing Aggregation Levels of Electrical Consumption Data on Renewable Energy Community (REC) Analyses," Energies, MDPI, vol. 17(18), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4647-:d:1480011
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    References listed on IDEAS

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    1. Conor Sweeney & Ricardo J. Bessa & Jethro Browell & Pierre Pinson, 2020. "The future of forecasting for renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(2), March.
    2. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
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