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Day Ahead Operation Cost Optimization for Energy Communities

Author

Listed:
  • Maria Fotopoulou

    (Department of Electrical and Electronics Engineering, University of West Attica, 12241 Athens, Greece
    Centre for Research and Technology Hellas, 15125 Athens, Greece)

  • George J. Tsekouras

    (Department of Electrical and Electronics Engineering, University of West Attica, 12241 Athens, Greece)

  • Andreas Vlachos

    (Regulatory Authority for Energy, Waste and Water, 15125 Athens, Greece)

  • Dimitrios Rakopoulos

    (Centre for Research and Technology Hellas, 15125 Athens, Greece)

  • Ioanna Myrto Chatzigeorgiou

    (School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Fotios D. Kanellos

    (School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece)

  • Vassiliki Kontargyri

    (Department of Electrical and Electronics Engineering, University of West Attica, 12241 Athens, Greece)

Abstract

Energy communities constitute the main collective form for energy consumers to participate in the current energy transition. The purpose of this research paper is to present a tool that assists energy communities to achieve fair and sustainable daily operation. In this context, the proposed algorithm (i) assesses the day-ahead operation cost (or profit) of energy communities, taking into consideration photovoltaic (PV) production, battery energy storage system (BESS), and flexible loads, as well as the potential profit from selling energy to the power system, under the net billing scheme, and (ii) compares the derived cost for each member with the cost for non-cooperative operation, as single prosumers. Taking the aforementioned costs or profits into consideration, the developed algorithm then proposes three cost-sharing options for the members, peer-to-peer (P2P), so that their participation in the community is more beneficial than individual operation. The algorithm is tested on a hypothetical energy community in Greece, highlighting the importance of the cooperation amongst the members of the community for their mutual benefit; for the simulated case of different PV shares, the cooperation can result in a 24.5% cost decrease, while having a BESS can reduce the cost by 25.0%.

Suggested Citation

  • Maria Fotopoulou & George J. Tsekouras & Andreas Vlachos & Dimitrios Rakopoulos & Ioanna Myrto Chatzigeorgiou & Fotios D. Kanellos & Vassiliki Kontargyri, 2025. "Day Ahead Operation Cost Optimization for Energy Communities," Energies, MDPI, vol. 18(5), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1101-:d:1598516
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    References listed on IDEAS

    as
    1. Barone, G. & Buonomano, A. & Forzano, C. & Palombo, A. & Russo, G., 2023. "The role of energy communities in electricity grid balancing: A flexible tool for smart grid power distribution optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    2. Correa-Florez, Carlos Adrian & Michiorri, Andrea & Kariniotakis, Georges, 2018. "Robust optimization for day-ahead market participation of smart-home aggregators," Applied Energy, Elsevier, vol. 229(C), pages 433-445.
    3. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    4. Tiago Fonseca & Luis Lino Ferreira & Jorge Landeck & Lurian Klein & Paulo Sousa & Fayaz Ahmed, 2022. "Flexible Loads Scheduling Algorithms for Renewable Energy Communities," Energies, MDPI, vol. 15(23), pages 1-24, November.
    5. Hanyu Yang & Zhihao Sun & Xun Dou & Linxi Li & Jiancheng Yu & Xianxu Huo & Chao Pang, 2024. "Optimal Scheduling and Compensation Pricing Method for Load Aggregators Based on Limited Peak Shaving Budget and Time Segment Value," Energies, MDPI, vol. 17(22), pages 1-21, November.
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