Battery Management System for Energy Communities Using a Cost- Sensitive Rule-Based algorithm

Marcos Trujillo-Trujillo(1), Juan A. Méndez-Pérez(1), Santiago Torres-Álvarez(1), Alberto Hamilton-Castro(1), Jose M. Gonzalez-Cava(1), Iván Castilla-Rodríguez(1), Benjamín González-Díaz(2) 1. Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna (ULL). La Laguna (Tenerife), Spain. 2. Departamento de Ingeniería Industrial, Universidad de La Laguna (ULL). La Laguna (Tenerife), Spain. 2026-02-15

Abstract

Energy Communities (ECs) drive decentralized energy production and consumption, fostering active citizen participation. Within this framework, battery systems play a crucial role in efficiency and optimization, requiring an effective Battery Management System (BMS) to optimize energy control. This paper presents a rule-based approach for effective battery energy management within an energy community. The proposed system integrates a set of predefined rules that determine battery operation by considering factors such as hourly energy prices, photovoltaic power generation, and user consumption patterns. This approach enables more efficient energy utilization, reduces dependence on the electrical grid, facilitates cost reduction, and ensures stability of the local energy network.

Key words: Energy Community, Battery ManagementSystem, Rule-based System.

References

[1] Ahmed, S., Ali, A., & D’Angola, A. (2024). A Review of Renewable Energy Communities: Concepts, Scope, Progress, Challenges, and Recommendations. In Sustainability (Switzerland) (Vol. 16, Issue 5). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/su16051749

[2] Kyriakopoulos, G. L. (2022). Energy Communities Overview: Managerial Policies, Economic Aspects, Technologies, and Models. In Journal of Risk and Financial Management (Vol. 15, Issue 11). MDPI. https://doi.org/10.3390/jrfm15110521

[3] Marín, L. G., Sumner, M., Muñoz-Carpintero, D., Köbrich, D., Pholboon, S., Sáez, D., & Núñez, A. (2019). Hierarchical energy management system for microgrid operation based on robust model predictive control. Energies, 12(23). https://doi.org/10.3390/en12234453

[4] Chen, H., Cong, T. N., Yang, W., Tan, C., Li, Y., & Ding, Y. (2009). Progress in electrical energy storage system: A critical review. In Progress in Natural Science (Vol. 19, Issue 3, pp. 291–312). Science Press. https://doi.org/10.1016/j.pnsc.2008.07.014

[5] Amir, M., Deshmukh, R. G., Khalid, H. M., Said, Z., Raza, A., Muyeen, S. M., Nizami, A. S., Elavarasan, R. M., Saidur, R., & Sopian, K. (2023). Energy storage technologies: An integrated survey of developments, global economical/environmental effects, optimal scheduling model, and sustainable adaption policies. In Journal of Energy Storage (Vol. 72). Elsevier Ltd. https://doi.org/10.1016/j.est.2023.108694

[6] Beaudin, M., & Zareipour, H. (2017). Home energy management systems: A review of modelling and complexity. Lecture Notes in Energy, 33, 753–793. https://doi.org/10.1007/978-3-319-26950-4_35

[7] Balasingam, B., Ahmed, M., & Pattipati, K. (2020). Battery management systems-challenges and some solutions. Energies, 13(11). https://doi.org/10.3390/en13112825

[8] N. Voigt, T. Wawer, and T. Albert. (2019). “Optimizing prosumers’ battery energy storage management using machine learning,” University of Applied Sciences Osnabrueck, Lingen.

[9] Elkazaz, M., Sumner, M., & Thomas, D. (2019). Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification. Energies, 12(14), 2712. https://doi.org/10.3390/en12142712