RT journal article
T1 A Real-Time Combined Dynamic Control Framework for Multi-Energy Microgrids Coupling Hydrogen, Electricity, Heating and Cooling Systems
A1 Horrillo Quintero, Pablo
A1 De la Cruz-Loredo, Iván
A1 García Triviño, Pablo
A1 Ugalde-Loo, Carlos E.
A1 Fernández Ramírez, Luis Miguel
A2 Ingeniería Eléctrica
K1 Multi-energy microgrid
K1 hydrogen
K1 energy storage
K1 fuzzy logic
K1 model predictive control
K1 dynamic control
AB While the energy management and control techniques have been extensively studied in electricalmicrogrids, optimizing electrical networks alongside other energy vectors, such as hydrogen, heatingand cooling systems, remains a significant challenge. Effective real-time control management withinmulti-energy microgrids (MEMGs) is particularly challenging due to the intermittent andunpredictable nature of renewable energy sources and varying multi-energy demand. Existingresearch on MEMGs often lacks a holistic, real-time approach that simultaneously incorporatesmultiple intelligent techniques. Furthermore, the integration of co-generation systems, particularlythose involving hydrogen and gas technologies, presents additional challenges in optimizing MEMGoperations. This paper proposes a novel dynamic control strategy that directly addresses thesechallenges by integrating fuzzy logic (FL), model predictive control (MPC), and nonlinearoptimization in real time. The strategy is designed to enhance MEMG performance by seamlesslycoordinating multiple energy vectors, with a particular focus on the effective management ofhydrogen storage and electrical batteries within a hybrid energy storage system (HESS). Theobjective is to minimize operational costs, gas consumption, and grid dependence, while maximizingsystem flexibility. The strategy is applied to an 8-unit residential building in Cardiff, UK, equippedwith a photovoltaic plant, fuel cell, electrolyzer, hydrogen storage, battery, gas and electric boilers,chiller, and a combined heat-and-power unit. When compared to two alternative strategies—one thatdoes not consider optimal cost allocation and another using a states-based EMS—the proposedframework yields a substantial reduction in costs by 33.86% and 18.38%. Gas consumption is reducedby 7.41% and 3.15%, respectively, while the HESS state of energy increases significantly by 100.06%and 20.02%, respectively. Furthermore, real-time experimental validation corroborates thepracticality and efficacy of the proposed framework
YR 2025
FD 2025-02-04
LK http://hdl.handle.net/10498/35390
UL http://hdl.handle.net/10498/35390
LA eng
NO This work was partially supported by Ministerio de Ciencia e Innovación, Agencia Estatal deInvestigación, and Unión Europea “NextGenerationEU/PRTR” (Grant TED2021-129631B-C32supported by MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR)
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026