IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i18p8017-d1477611.html
   My bibliography  Save this article

BESS Reserve Optimisation in Energy Communities

Author

Listed:
  • Wolfram Rozas-Rodriguez

    (ETS de Ingeniería Informática, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain)

  • Rafael Pastor-Vargas

    (ETS de Ingeniería Informática, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain)

  • Andrew D. Peacock

    (School of Energy, Geoscience, Infrastructure and Society (EGIS), Heriot-Watt University, Edinburg EH14 4AS, UK)

  • David Kane

    (Trilemma Consulting Limited, Glasgow ML4 3NR, UK)

  • José Carpio-Ibañez

    (ETS de Ingenieros Industriales, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain)

Abstract

This paper investigates optimising battery energy storage systems (BESSs) to enhance the business models of Local Energy Markets (LEMs). LEMs are decentralised energy ecosystems facilitating peer-to-peer energy trading among consumers, producers, and prosumers. By incentivising local energy exchange and balancing supply and demand, LEMs contribute to grid resilience and sustainability. This study proposes a novel approach to BESS optimisation, utilising advanced artificial intelligence techniques, such as multilayer perceptron neural networks and extreme gradient boosting regressors. These models accurately forecast energy consumption and optimise BESS reserve allocation within the LEM framework. The findings demonstrate the potential of these AI-driven strategies to improve the BESS reserve capacity setting. This optimal setting will target meeting Energy Community site owners’ needs and avoiding fines from the distribution system operator for not meeting contract conditions.

Suggested Citation

  • Wolfram Rozas-Rodriguez & Rafael Pastor-Vargas & Andrew D. Peacock & David Kane & José Carpio-Ibañez, 2024. "BESS Reserve Optimisation in Energy Communities," Sustainability, MDPI, vol. 16(18), pages 1-18, September.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:8017-:d:1477611
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/18/8017/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/18/8017/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wolfram Rozas & Rafael Pastor-Vargas & Angel Miguel García-Vico & José Carpio, 2023. "Consumption–Production Profile Categorization in Energy Communities," Energies, MDPI, vol. 16(19), pages 1-27, October.
    2. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Verdone, Alessio & Scardapane, Simone & Panella, Massimo, 2024. "Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production," Applied Energy, Elsevier, vol. 353(PB).
    2. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    3. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    4. Chengmin Wang & Guangji Li & Imran Ali & Hongchao Zhang & Han Tian & Jian Lu, 2022. "The Efficiency Prediction of the Laser Charging Based on GA-BP," Energies, MDPI, vol. 15(9), pages 1-12, April.
    5. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    6. Siripat Somchit & Palamy Thongbouasy & Chitchai Srithapon & Rongrit Chatthaworn, 2023. "Optimal Transmission Expansion Planning with Long-Term Solar Photovoltaic Generation Forecast," Energies, MDPI, vol. 16(4), pages 1-17, February.
    7. Margarete Afonso de Sousa Guilhon Araujo & Soraida Aguilar & Reinaldo Castro Souza & Fernando Luiz Cyrino Oliveira, 2024. "Global Horizontal Irradiance in Brazil: A Comparative Study of Reanalysis Datasets with Ground-Based Data," Energies, MDPI, vol. 17(20), pages 1-25, October.
    8. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    9. Krebs-Moberg, Miles & Pitz, Mandy & Dorsette, Tiara L. & Gheewala, Shabbir H., 2021. "Third generation of photovoltaic panels: A life cycle assessment," Renewable Energy, Elsevier, vol. 164(C), pages 556-565.
    10. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    11. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    12. Huxley, O.T. & Taylor, J. & Everard, A. & Briggs, J. & Tilley, K. & Harwood, J. & Buckley, A., 2022. "The uncertainties involved in measuring national solar photovoltaic electricity generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    13. Fjelkestam Frederiksen, Cornelia A. & Cai, Zuansi, 2022. "Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance," Applied Energy, Elsevier, vol. 306(PB).
    14. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.
    15. Anderson Mitterhofer Iung & Fernando Luiz Cyrino Oliveira & André Luís Marques Marcato, 2023. "A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence," Energies, MDPI, vol. 16(3), pages 1-24, January.
    16. Gabriela Badareu & Marius Dalian Doran & Mihai Alexandru Firu & Ionuț Marius Croitoru & Nicoleta Mihaela Doran, 2024. "Exploring the Role of Robots and Artificial Intelligence in Advancing Renewable Energy Consumption," Energies, MDPI, vol. 17(17), pages 1-17, September.
    17. Anh Tuan Phan & Thi Tuyet Hong Vu & Dinh Quang Nguyen & Eleonora Riva Sanseverino & Hang Thi-Thuy Le & Van Cong Bui, 2022. "Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network," Energies, MDPI, vol. 15(23), pages 1-16, December.
    18. Chai, Jiale & Huang, Pei & Sun, Yongjun, 2019. "Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions," Energy, Elsevier, vol. 185(C), pages 176-189.
    19. Unterberger, Viktor & Lichtenegger, Klaus & Kaisermayer, Valentin & Gölles, Markus & Horn, Martin, 2021. "An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems," Applied Energy, Elsevier, vol. 293(C).
    20. Ranjbaran, Parisa & Yousefi, Hossein & Gharehpetian, G.B. & Astaraei, Fatemeh Razi, 2019. "A review on floating photovoltaic (FPV) power generation units," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 332-347.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:8017-:d:1477611. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.