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Design and optimal energy management of community microgrids with flexible renewable energy sources

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  • Tomin, Nikita
  • Shakirov, Vladislav
  • Kozlov, Aleksander
  • Sidorov, Denis
  • Kurbatsky, Victor
  • Rehtanz, Christian
  • Lora, Electo E.S.

Abstract

Energy communities is a new, but already successful prosumer model of the local energy systems' construction. It is based on distributed energy sources and the electricity consumers’ flexibility who are the members of the community. In search of the most effective ways to interact within themselves and with the external energy system, local energy communities become platforms for exciting experiments in the field of new energy practices including local markets for flexibility, building cooperative microgrids, achieving energy autonomy, and many others. This work aims to present a unified approach to building and optimally managing the community microgrids with an internal market, given the social, environmental, and economic benefits of a particular location of such a community. A new modeling framework is introduced, based on bilevel programming and reinforcement learning, for structuring and solving the internal local market of a community microgrids, composed of entities that may exchange energy and services among themselves. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator (Community EMS). We strengthen the traditional bilevel problem statement by the local energy management system (Local EMS) introduction based on Monte-Carlo tree search algorithm. Our approach makes it possible to enable interaction of the local control systems for microgrids with the community microgrid operator as part bilevel programming problem solution. Numerical results obtained on the real test case of the microgrid community for the settlements located in the Transbaikal National Park (Russia), which include various renewable energy sources (wind, solar power, biomass gasifiers) and storage devices, show reduction of the LCOE index from 20% to 40% and improving the quality of electricity supply to the analyzed settlements.

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  • Tomin, Nikita & Shakirov, Vladislav & Kozlov, Aleksander & Sidorov, Denis & Kurbatsky, Victor & Rehtanz, Christian & Lora, Electo E.S., 2022. "Design and optimal energy management of community microgrids with flexible renewable energy sources," Renewable Energy, Elsevier, vol. 183(C), pages 903-921.
    • Handle: RePEc:eee:renene:v:183:y:2022:i:c:p:903-921
    DOI: 10.1016/j.renene.2021.11.024
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    Cited by:

    1. Daniela Proto, 2022. "Renewable Energy Communities as an Enabling Framework to Boost Flexibility and Promote the Energy Transition," Energies, MDPI, vol. 15(23), pages 1-4, November.
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    5. Pagnini, Luisa & Bracco, Stefano & Delfino, Federico & de-Simón-Martín, Miguel, 2024. "Levelized cost of electricity in renewable energy communities: Uncertainty propagation analysis," Applied Energy, Elsevier, vol. 366(C).
    6. Marcelino, C.G. & Leite, G.M.C. & Wanner, E.F. & Jiménez-Fernández, S. & Salcedo-Sanz, S., 2023. "Evaluating the use of a Net-Metering mechanism in microgrids to reduce power generation costs with a swarm-intelligent algorithm," Energy, Elsevier, vol. 266(C).
    7. Tomin, Nikita & Shakirov, Vladislav & Kurbatsky, Victor & Muzychuk, Roman & Popova, Ekaterina & Sidorov, Denis & Kozlov, Alexandr & Yang, Dechang, 2022. "A multi-criteria approach to designing and managing a renewable energy community," Renewable Energy, Elsevier, vol. 199(C), pages 1153-1175.
    8. Fathy, Ahmed, 2023. "Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles," Applied Energy, Elsevier, vol. 334(C).
    9. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Hannes Agabus, 2023. "Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review," Energies, MDPI, vol. 16(5), pages 1-52, February.
    10. Giovanni Brusco & Daniele Menniti & Anna Pinnarelli & Nicola Sorrentino & Pasquale Vizza, 2023. "Power Cloud Framework for Prosumer Aggregation to Unlock End-User Flexibility," Energies, MDPI, vol. 16(20), pages 1-17, October.
    11. Chang, Weiguang & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Two-stage coordinated operation framework for virtual power plant with aggregated multi-stakeholder microgrids in a deregulated electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 943-956.
    12. Bruck, Axel & Díaz Ruano, Santiago & Auer, Hans, 2022. "One piece of the puzzle towards 100 Positive Energy Districts (PEDs) across Europe by 2025: An open-source approach to unveil favourable locations of PV-based PEDs from a techno-economic perspective," Energy, Elsevier, vol. 254(PA).
    13. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
    14. Chang, Weiguang & Dong, Wei & Yang, Qiang, 2023. "Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market," Applied Energy, Elsevier, vol. 336(C).
    15. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    16. Simoiu, Mircea Stefan & Fagarasan, Ioana & Ploix, Stéphane & Calofir, Vasile, 2022. "Modeling the energy community members’ willingness to change their behaviour with multi-agent systems: A stochastic approach," Renewable Energy, Elsevier, vol. 194(C), pages 1233-1246.
    17. Javier Parra-Domínguez & Esteban Sánchez & Ángel Ordóñez, 2023. "The Prosumer: A Systematic Review of the New Paradigm in Energy and Sustainable Development," Sustainability, MDPI, vol. 15(13), pages 1-44, July.
    18. Jiming Chen & Ke Ning & Xingzhi Xin & Fuhao Shi & Qing Zhang & Chaolin Li, 2022. "Day-Ahead Optimal Scheduling of an Integrated Energy System Based on a Piecewise Self-Adaptive Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 15(3), pages 1-18, January.
    19. Sima, Catalina Alexandra & Popescu, Claudia Laurenta & Popescu, Mihai Octavian & Roscia, Mariacristina & Seritan, George & Panait, Cornel, 2022. "Techno-economic assessment of university energy communities with on/off microgrid," Renewable Energy, Elsevier, vol. 193(C), pages 538-553.
    20. Petrucci, Andrea & Ayevide, Follivi Kloutse & Buonomano, Annamaria & Athienitis, Andreas, 2023. "Development of energy aggregators for virtual communities: The energy efficiency-flexibility nexus for demand response," Renewable Energy, Elsevier, vol. 215(C).

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