IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i23p10313-d1529034.html
   My bibliography  Save this article

A Computationally Efficient Rule-Based Scheduling Algorithm for Battery Energy Storage Systems

Author

Listed:
  • Lorenzo Becchi

    (Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

  • Elisa Belloni

    (Department of Engineering, Università di Perugia, Via G. Duranti n.93, 06125 Perugia, Italy)

  • Marco Bindi

    (Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

  • Matteo Intravaia

    (Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

  • Francesco Grasso

    (Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

  • Gabriele Maria Lozito

    (Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

  • Maria Cristina Piccirilli

    (Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy)

Abstract

This paper presents a rule-based control strategy for the Battery Management System (BMS) of a prosumer connected to a low-voltage distribution network. The main objective of this work is to propose a computationally efficient algorithm capable of managing energy flows between the distribution network and a prosumer equipped with a photovoltaic (PV) energy production system. The goal of the BMS is to maximize the prosumer’s economic revenue by optimizing the use, storage, sale, and purchase of PV energy based on electricity market information and daily production/consumption curves. To achieve this goal, the method proposed in this paper consists of developing a rule-based algorithm that manages the prosumer’s Battery Energy Storage System (BESS). The rule-based approach in this type of problem allows for the reduction of computational costs, which is of fundamental importance in contexts where many users will be coordinated simultaneously. This means that the BMS presented in this work could play a vital role in emerging Renewable Energy Communities (RECs). From a general point of view, the method requires an algorithm to process the load and generation profiles of the prosumer for the following three days, together with the hourly price curve. The output is a battery scheduling plan for the timeframe, which is updated every hour. In this paper, the algorithm is validated in terms of economic performance achieved and computational times on two experimental datasets with different scenarios characterized by real productions and loads of prosumers for over a year. The annual economic results are presented in this work, and the proposed rule-based approach is compared with a linear programming optimization algorithm. The comparison highlights similar performance in terms of economic revenue, but the rule-based approach guarantees 30 times lower processing time.

Suggested Citation

  • Lorenzo Becchi & Elisa Belloni & Marco Bindi & Matteo Intravaia & Francesco Grasso & Gabriele Maria Lozito & Maria Cristina Piccirilli, 2024. "A Computationally Efficient Rule-Based Scheduling Algorithm for Battery Energy Storage Systems," Sustainability, MDPI, vol. 16(23), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10313-:d:1529034
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/23/10313/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/23/10313/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mustika, Alyssa Diva & Rigo-Mariani, Rémy & Debusschere, Vincent & Pachurka, Amaury, 2022. "A two-stage management strategy for the optimal operation and billing in an energy community with collective self-consumption," Applied Energy, Elsevier, vol. 310(C).
    2. Aisha Blfgeh & Hanadi Alkhudhayr, 2024. "A Machine Learning-Based Sustainable Energy Management of Wind Farms Using Bayesian Recurrent Neural Network," Sustainability, MDPI, vol. 16(19), pages 1-21, September.
    3. Lazzari, Florencia & Mor, Gerard & Cipriano, Jordi & Solsona, Francesc & Chemisana, Daniel & Guericke, Daniela, 2023. "Optimizing planning and operation of renewable energy communities with genetic algorithms," Applied Energy, Elsevier, vol. 338(C).
    4. Ramiz Salama & Fadi Al-Turjman, 2023. "Sustainable Energy Production in Smart Cities," Sustainability, MDPI, vol. 15(22), pages 1-25, November.
    5. Marco Bindi & Maria Cristina Piccirilli & Antonio Luchetta & Francesco Grasso, 2023. "A Comprehensive Review of Fault Diagnosis and Prognosis Techniques in High Voltage and Medium Voltage Electrical Power Lines," Energies, MDPI, vol. 16(21), pages 1-37, October.
    6. Qingyuan Yan & Zhaoyi Wang & Ling Xing & Chenchen Zhu, 2024. "Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System," Sustainability, MDPI, vol. 16(19), pages 1-44, September.
    7. Nicoleta Mihaela Doran & Gabriela Badareu & Marius Dalian Doran & Maria Enescu & Anamaria Liliana Staicu & Mariana Niculescu, 2024. "Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries," Sustainability, MDPI, vol. 16(12), pages 1-17, June.
    8. Xiangyi Li & Qing Wang & Ying Tang, 2024. "The Impact of Artificial Intelligence Development on Urban Energy Efficiency—Based on the Perspective of Smart City Policy," Sustainability, MDPI, vol. 16(8), pages 1-22, April.
    9. Wang, Haichao & Abdollahi, Elnaz & Lahdelma, Risto & Jiao, Wenling & Zhou, Zhigang, 2015. "Modelling and optimization of the smart hybrid renewable energy for communities (SHREC)," Renewable Energy, Elsevier, vol. 84(C), pages 114-123.
    10. Diego Andreotti & Matteo Spiller & Andrea Scrocca & Filippo Bovera & Giuliano Rancilio, 2024. "Modeling and Analysis of BESS Operations in Electricity Markets: Prediction and Strategies for Day-Ahead and Continuous Intra-Day Markets," Sustainability, MDPI, vol. 16(18), pages 1-35, September.
    11. Justyna Patalas-Maliszewska & Małgorzata Szmołda & Hanna Łosyk, 2024. "Integrating Artificial Intelligence into the Supply Chain in Order to Enhance Sustainable Production—A Systematic Literature Review," Sustainability, MDPI, vol. 16(16), pages 1-17, August.
    12. Giovanni Brumana & Elisa Ghirardi & Giuseppe Franchini, 2024. "Comparison of Different Power Generation Mixes for High Penetration of Renewables," Sustainability, MDPI, vol. 16(19), pages 1-16, September.
    13. Henni, Sarah & Staudt, Philipp & Weinhardt, Christof, 2021. "A sharing economy for residential communities with PV-coupled battery storage: Benefits, pricing and participant matching," Applied Energy, Elsevier, vol. 301(C).
    14. Secchi, Mattia & Barchi, Grazia & Macii, David & Moser, David & Petri, Dario, 2021. "Multi-objective battery sizing optimisation for renewable energy communities with distribution-level constraints: A prosumer-driven perspective," Applied Energy, Elsevier, vol. 297(C).
    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. Pedro Gomes da Cruz Filho & Danielle Devequi Gomes Nunes & Hayna Malta Santos & Alex Álisson Bandeira Santos & Bruna Aparecida Souza Machado, 2023. "From Patents to Progress: Genetic Algorithms in Harmonic Distortion Monitoring Technology," Energies, MDPI, vol. 16(24), pages 1-21, December.
    2. D'Adamo, Idiano & Mammetti, Marco & Ottaviani, Dario & Ozturk, Ilhan, 2023. "Photovoltaic systems and sustainable communities: New social models for ecological transition. The impact of incentive policies in profitability analyses," Renewable Energy, Elsevier, vol. 202(C), pages 1291-1304.
    3. Li, Na & Okur, Özge, 2023. "Economic analysis of energy communities: Investment options and cost allocation," Applied Energy, Elsevier, vol. 336(C).
    4. Ingrid Hopley & Mehrdad Ghahramani & Asma Aziz, 2024. "Techno-Economic Factors Impacting the Intrinsic Value of Behind-the-Meter Distributed Storage," Sustainability, MDPI, vol. 16(23), pages 1-26, November.
    5. Sameti, Mohammad & Haghighat, Fariborz, 2018. "Integration of distributed energy storage into net-zero energy district systems: Optimum design and operation," Energy, Elsevier, vol. 153(C), pages 575-591.
    6. Ivo Araújo & Leonel J. R. Nunes & David Patíño Vilas & António Curado, 2024. "Integrating Renewable Energy Produced by a Library Building on a University Campus in a Scenario of Collective Self-Consumption," Energies, MDPI, vol. 17(14), pages 1-17, July.
    7. Damo, U.M. & Ferrari, M.L. & Turan, A. & Massardo, A.F., 2019. "Solid oxide fuel cell hybrid system: A detailed review of an environmentally clean and efficient source of energy," Energy, Elsevier, vol. 168(C), pages 235-246.
    8. Wu, Zhuochun & Kang, Jidong & Mosteiro-Romero, Martín & Bartolini, Andrea & Ng, Tsan Sheng & Su, Bin, 2024. "A distributionally robust optimization model for building-integrated photovoltaic system expansion planning under demand and irradiance uncertainties," Applied Energy, Elsevier, vol. 372(C).
    9. Zhao, Naixin & Gu, Wenbo & Zheng, Zipeng & Ma, Tao, 2023. "Multi-objective bi-level planning of the integrated energy system considering uncertain user loads and carbon emission during the equipment manufacturing process," Renewable Energy, Elsevier, vol. 216(C).
    10. Giovanni Gino Zanvettor & Marco Casini & Antonio Vicino, 2024. "Optimal Operation of Energy Storage Facilities in Incentive-Based Energy Communities," Energies, MDPI, vol. 17(11), pages 1-20, May.
    11. Chien‐Chiang Lee & Ying Yuan & Huwei Wen, 2022. "Can digital economy alleviate CO2 emissions in the transport sector? Evidence from provincial panel data in China," Natural Resources Forum, Blackwell Publishing, vol. 46(3), pages 289-310, August.
    12. Pu, Yuchen & Li, Qi & Zou, Xueli & Li, Ruirui & Li, Luoyi & Chen, Weirong & Liu, Hong, 2021. "Optimal sizing for an integrated energy system considering degradation and seasonal hydrogen storage," Applied Energy, Elsevier, vol. 302(C).
    13. Gabriele Volpato & Gianluca Carraro & Enrico Dal Cin & Sergio Rech, 2024. "On the Different Fair Allocations of Economic Benefits for Energy Communities," Energies, MDPI, vol. 17(19), pages 1-26, September.
    14. Kerscher, Selina & Koirala, Arpan & Arboleya, Pablo, 2024. "Grid-optimal energy community planning from a systems perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    15. Maria Antoniadou, 2024. "Integrating Lean Management and Circular Economy for Sustainable Dentistry," Sustainability, MDPI, vol. 16(22), pages 1-25, November.
    16. Sohani, Ali & Cornaro, Cristina & Shahverdian, Mohammad Hassan & Moser, David & Pierro, Marco & Olabi, Abdul Ghani & Karimi, Nader & Nižetić, Sandro & Li, Larry K.B. & Doranehgard, Mohammad Hossein, 2023. "Techno-economic evaluation of a hybrid photovoltaic system with hot/cold water storage for poly-generation in a residential building," Applied Energy, Elsevier, vol. 331(C).
    17. Giuseppe Pinto & Elnaz Abdollahi & Alfonso Capozzoli & Laura Savoldi & Risto Lahdelma, 2019. "Optimization and Multicriteria Evaluation of Carbon-neutral Technologies for District Heating," Energies, MDPI, vol. 12(9), pages 1-19, April.
    18. Kang, Hyuna & Jung, Seunghoon & Kim, Hakpyeong & Hong, Juwon & Jeoung, Jaewon & Hong, Taehoon, 2023. "Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    19. Alyssa Diva Mustika & Rémy Rigo-Mariani & Vincent Debusschere & Amaury Pachurka, 2022. "New Members Selection for the Expansion of Energy Communities," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    20. Scheubel, Christopher & Zipperle, Thomas & Tzscheutschler, Peter, 2017. "Modeling of industrial-scale hybrid renewable energy systems (HRES) – The profitability of decentralized supply for industry," Renewable Energy, Elsevier, vol. 108(C), pages 52-63.

    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:23:p:10313-:d:1529034. 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.