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Renewable Energy Community Pairing Methodology Using Statistical Learning Applied to Georeferenced Energy Profiles

Author

Listed:
  • Alexandre Lucas

    (INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal)

  • Salvador Carvalhosa

    (INESC Technology and Science (INESC TEC), 4200-465 Porto, Portugal)

Abstract

Renewable energy communities (REC) are bound to play a crucial role in the energy transition, as their role, activities, and legal forms become clearer, and their dissemination becomes larger. Even though their mass grid integration, is regarded with high expectations, their diffusion, however, has not been an easy task. Its legal form and success, entail responsibilities, prospects, trust, and synergies to be explored between its members, whose collective dynamics should aim for optimal operation. In this regard, the pairing methodology of potential participants ahead of asset dimensioning seems to have been overlooked. This article presents a methodology for pairing consumers, based on their georeferenced load consumptions. A case study in an area of Porto (Asprela) was used to test the methodology. QGIS is used as a geo-representation tool and its PlanHeat plugin for district characterization support. A supervised statistical learning approach is used to identify the feature importance of an overall district energy consumption profile. With the main variables identified, the methodology applies standard K-means and Dynamic Time Warping clustering, from which, users from different clusters should be paired to explore PV as the main generation asset. To validate the assumption that this complementarity of load diagrams could decrease the total surplus of a typical PV generation, 18 pairings were tested. Results show that, even though it is not true that all pairings from different clusters lead to lower surplus, on average, this seems to be the trend. From the sample analyzed a maximum of 36% and an average of 12% less PV surplus generation is observed.

Suggested Citation

  • Alexandre Lucas & Salvador Carvalhosa, 2022. "Renewable Energy Community Pairing Methodology Using Statistical Learning Applied to Georeferenced Energy Profiles," Energies, MDPI, vol. 15(13), pages 1-16, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4789-:d:851837
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    References listed on IDEAS

    as
    1. Giorgino, Toni, 2009. "Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i07).
    2. Saveria Olga Murielle Boulanger & Martina Massari & Danila Longo & Beatrice Turillazzi & Carlo Alberto Nucci, 2021. "Designing Collaborative Energy Communities: A European Overview," Energies, MDPI, vol. 14(24), pages 1-17, December.
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