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Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response

Author

Listed:
  • Ruben Barreto

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Calvin Gonçalves

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Luis Gomes

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Pedro Faria

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Zita Vale

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

Abstract

In the energy sector, prosumers are becoming relevant entities for energy management systems since they can share energy with their citizen energy community (CEC). Thus, this paper proposes a novel methodology based on demand response (DR) participation in a CEC context, where unsupervised learning algorithms such as convolutional neural networks and k -means are used. This novel methodology can analyze future events on the grid and balance the consumption and generation using end-user flexibility. The end-users’ invitations to the DR event were according to their ranking obtained through three metrics. These metrics were energy flexibility, participation ratio, and flexibility history of the end-users. During the DR event, a continuous balancing assessment is performed to allow the invitation of additional end-users. Real data from a CEC with 50 buildings were used, where the results demonstrated that the end-users’ participation in two DR events allows reduction of energy costs by EUR 1.31, balancing the CEC energy resources.

Suggested Citation

  • Ruben Barreto & Calvin Gonçalves & Luis Gomes & Pedro Faria & Zita Vale, 2022. "Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response," Energies, MDPI, vol. 15(7), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2380-:d:778587
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    References listed on IDEAS

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    Cited by:

    1. Bruno Canizes & João Costa & Diego Bairrão & Zita Vale, 2023. "Local Renewable Energy Communities: Classification and Sizing," Energies, MDPI, vol. 16(5), pages 1-26, March.

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