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A Three-Stage Model to Manage Energy Communities, Share Benefits and Provide Local Grid Services

Author

Listed:
  • Rogério Rocha

    (INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

  • Ricardo Silva

    (INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal)

  • João Mello

    (INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

  • Sérgio Faria

    (INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal)

  • Fábio Retorta

    (INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
    Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal)

  • Clara Gouveia

    (INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal)

  • José Villar

    (INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal)

Abstract

This paper proposes a three-stage model for managing energy communities for local energy sharing and providing grid flexibility services to tackle local distribution grid constraints. The first stage addresses the minimization of each prosumer’s individual energy bill by optimizing the schedules of their flexible resources. The second stage optimizes the energy bill of the whole energy community by sharing the prosumers’ energy surplus internally and re-dispatching their batteries, while guaranteeing that each prosumer’s new energy bill is always be equal to or less than the bill that results for this prosumer from stage one. This collective optimization is designed to ensure an additional collective benefit, without loss for any community member. The third stage, which can be performed by the distribution system operator (DSO), aims to solve the local grid constraints by re-dispatching the flexible resources and, if still necessary, by curtailing local generation or consumption. Stage three minimizes the impact on the schedule obtained at previous stages by minimizing the loss of profit or utility for all prosumers, which are furthermore financially compensated accordingly. This paper describes how the settlement should be performed, including the allocation coefficients to be sent to the DSO to determine the self-consumed and supplied energies of each peer. Finally, some case studies allow an assessment of the performance of the proposed methodology. Results show, among other things, the potential benefits of allowing the allocation coefficients to take negative values to increase the retail market competition; the importance of stage one or, alternatively, the need for a fair internal price to avoid unfair collective benefit sharing among the community members; or how stage three can effectively contribute to grid constraint solving, profiting first from the existing flexible resources.

Suggested Citation

  • Rogério Rocha & Ricardo Silva & João Mello & Sérgio Faria & Fábio Retorta & Clara Gouveia & José Villar, 2023. "A Three-Stage Model to Manage Energy Communities, Share Benefits and Provide Local Grid Services," Energies, MDPI, vol. 16(3), pages 1-24, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1143-:d:1042190
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    References listed on IDEAS

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    1. Cornélusse, Bertrand & Savelli, Iacopo & Paoletti, Simone & Giannitrapani, Antonio & Vicino, Antonio, 2019. "A community microgrid architecture with an internal local market," Applied Energy, Elsevier, vol. 242(C), pages 547-560.
    2. Long, Chao & Wu, Jianzhong & Zhou, Yue & Jenkins, Nick, 2018. "Peer-to-peer energy sharing through a two-stage aggregated battery control in a community Microgrid," Applied Energy, Elsevier, vol. 226(C), pages 261-276.
    3. Zhou, Yue & Wu, Jianzhong & Song, Guanyu & Long, Chao, 2020. "Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community," Applied Energy, Elsevier, vol. 278(C).
    4. Adamu Sani Yahaya & Nadeem Javaid & Fahad A. Alzahrani & Amjad Rehman & Ibrar Ullah & Affaf Shahid & Muhammad Shafiq, 2020. "Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism," Sustainability, MDPI, vol. 12(8), pages 1-28, April.
    5. Bertrand Corn'elusse & Iacopo Savelli & Simone Paoletti & Antonio Giannitrapani & Antonio Vicino, 2018. "A Community Microgrid Architecture with an Internal Local Market," Papers 1810.09803, arXiv.org, revised Feb 2019.
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