Horrillo-Quintero, Pablo, De La Cruz Loredo, Ivan, Garcia-Trivino, Pablo, Ugalde Loo, Carlos ![]() Item availability restricted. |
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Abstract
While energy management and control techniques have been extensively studied in electrical microgrids, optimizing the operation of electrical networks alongside hydrogen, heating and cooling systems, remains a significant challenge. Effective real-time control management within multi-energy microgrids (MEMGs) is particularly challenging due to the intermittent and unpredictable nature of renewable energy sources and varying multi-energy demand. Existing research on MEMGs often lacks a holistic, real-time approach that simultaneously incorporates multiple intelligent techniques. Furthermore, the integration of co-generation systems, particularly those involving hydrogen and gas technologies, presents additional challenges in optimizing MEMG operations. This paper proposes a novel dynamic control strategy that directly addresses these challenges by integrating fuzzy logic, model predictive control, and nonlinear optimization in real time. The strategy is designed to enhance MEMG performance by seamlessly coordinating the operation of multiple energy systems, with a particular focus on the effective management of hydrogen storage and electrical batteries within a hybrid energy storage system (HESS). The objective is to minimize operational costs, gas consumption, and grid dependence, while maximizing system flexibility. The strategy is applied to an 8-unit residential building in Cardiff, UK, equipped with a photovoltaic plant, fuel cell, electrolyzer, hydrogen storage, electrical battery, gas and electric boilers, chiller, and a combined heat and power unit. When compared to two alternative strategies—one that does not consider optimal cost allocation and another using a state-based EMS—the proposed framework yields a substantial reduction in costs by 33.86% and 18.38%. Gas consumption is reduced by 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 verification corroborates the practicality and efficacy of the proposed framework.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Publisher: | Elsevier |
ISSN: | 0360-3199 |
Date of First Compliant Deposit: | 3 February 2025 |
Date of Acceptance: | 1 February 2025 |
Last Modified: | 11 Feb 2025 16:00 |
URI: | https://orca.cardiff.ac.uk/id/eprint/175836 |
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