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Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

Author

Listed:
  • Giacomo Talluri

    (Department of Information Engineering, University of Florence, Via di S. Marta 3, 50139 Firenze, Italy)

  • Gabriele Maria Lozito

    (Department of Information Engineering, University of Florence, Via di S. Marta 3, 50139 Firenze, Italy)

  • Francesco Grasso

    (Department of Information Engineering, University of Florence, Via di S. Marta 3, 50139 Firenze, Italy)

  • Carlos Iturrino Garcia

    (Department of Information Engineering, University of Florence, Via di S. Marta 3, 50139 Firenze, Italy)

  • Antonio Luchetta

    (Department of Information Engineering, University of Florence, Via di S. Marta 3, 50139 Firenze, Italy)

Abstract

In this work, a strategy for scheduling a battery energy storage system (BESS) in a renewable energy community (REC) is proposed. RECs have been defined at EU level by the 2018/2001 Directive; some Member States transposition into national legislation defined RECs as virtual microgrids since they still use the existing low voltage local feeder and share the same low-medium voltage transformer. This work analyzes a REC which assets include PV generators, BESS and non-controllable loads, operating under the Italian legislative framework. A methodology is defined to optimize REC economic revenues and minimize the operation costs during the year. The proposed BESS control strategy is composed by three different modules: (i) a machine learning-based forecast algorithm that provides a 1-day-ahead projection for microgrid loads and PV generation, using historical dataset and weather forecasts; (ii) a mixed integer linear programming (MILP) algorithm that optimizes the BESS scheduling for minimal REC operating costs, taking into account electricity price, variable feed-in tariffs for PV generators, BESS costs and maximization of the self-consumption; (iii) a decision tree algorithm that works at the intra-hour level, with 1 min timestep and with real load and PV generation measurements adjusting the BESS scheduling in real time. Validation of the proposed strategy is performed on data acquired from a real small-scale REC set up with an Italian energy provider. A 10% average revenue increase could be obtained for the prosumer alone when compared to the non-optimized BESS usage scenario; such revenue increase is obtained by reducing the BESS usage by around 30% when compared to the unmanaged baseline scenario.

Suggested Citation

  • Giacomo Talluri & Gabriele Maria Lozito & Francesco Grasso & Carlos Iturrino Garcia & Antonio Luchetta, 2021. "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, MDPI, vol. 14(24), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8480-:d:703324
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    References listed on IDEAS

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    Cited by:

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    2. Min Song & Yu Wang & Yong Long, 2022. "Investment and Production Strategies of Renewable Energy Power under the Quota and Green Power Certificate System," Energies, MDPI, vol. 15(11), pages 1-24, June.
    3. Benedetto-Giuseppe Risi & Francesco Riganti-Fulginei & Antonino Laudani & Michele Quercio, 2023. "Compensation Admittance Load Flow: A Computational Tool for the Sustainability of the Electrical Grid," Sustainability, MDPI, vol. 15(19), pages 1-24, October.
    4. Emely Cruz-De-Jesús & Jose L. Martínez-Ramos & Alejandro Marano-Marcolini, 2022. "Optimal Scheduling of Controllable Resources in Energy Communities: An Overview of the Optimization Approaches," Energies, MDPI, vol. 16(1), pages 1-15, December.
    5. Xuehan Zhang & Yongju Son & Sungyun Choi, 2022. "Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources," Energies, MDPI, vol. 15(6), pages 1-18, March.
    6. Mihai Sanduleac & Alexandru Sandulescu & Cristina Efremov & Constantin Ionescu & Ioan Catalin Damian & Alexandru Mandis, 2023. "Aspects of Design in Low Voltage Resilient Grids—Focus on Battery Sizing and U Level Control with P Regulation in Microgrids of Energy Communities," Energies, MDPI, vol. 16(4), pages 1-25, February.
    7. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.

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