RT journal article
T1 Design and Raspberry Pi-Based Implementation of an Intelligent Energy Management System for a Hybrid AC/DC Microgrid with Renewable Energy, Battery, Ultracapacitor and Hydrogen System
A1 Carrasco González, David
A1 Sarrias Mena, Raúl
A1 Horrillo Quintero, Pablo
A1 Llorens Iborra, Francisco
A1 Fernández Ramírez, Luis Miguel
A2 Ingeniería Eléctrica
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 Energy management system
K1 Fuzzy-logic
K1 Hybrid AC/DC microgrid
K1 Raspberry Pi microcontroller
AB Hybrid AC/DC microgrids (HMGs) have garnered significant research attention due to their ability to integrate consumption, generation, and storage devices within both AC and DC microgrids (MGs). In this context, this article presents the design and implementation of a novel intelligent energy management system (EMS) for a grid-connected HMG with AC and DC MGs, using a Raspberry Pi microcontroller. The DC MG integrates an ultracapacitor, a wind turbine, a hydrogen system and DC loads. Meanwhile, the AC MG comprises a battery bank, three-phase loads and a photovoltaic (PV) generator. The control system features local controllers for each device and a dynamic fuzzy-logic-based EMS implemented on a Raspberry Pi microcontroller to regulate all devices within the HMG. The fuzzy-logic-based EMS is compared to a conventional EMS based on state machine and an EMS based on a multivariable optimization algorithm (implemented using MATLAB's fmincon function) under different operating conditions, including different levels of generation, consumption and storage. The results demonstratesuperior energy management and reduced grid dependency with the fuzzy-logic-based EMS. An experimental setup, comprising an OPAL-RT 4512 emulator and a Raspberry Pi microcontroller communicating via Modbus protocol, validates the findings. Both simulated and experimental results confirm the satisfactory performance of the HMG when controlled by the proposed intelligent EMS under various operating conditions.
PB IEEE - Institute of Electrical and Electronics Engineers INC
YR 2025
FD 2025
LK http://hdl.handle.net/10498/35887
UL http://hdl.handle.net/10498/35887
LA eng
NO This work was partially supported by Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, FEDER, UE (Grant PID2021-123633OB-C32 supported by MCIN/AEI/10.13039/501100011033/ FEDER, UE).
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026