Francisco José Vivas

Este trabajo presenta una metodología que integra modelado, optimización y control para la mejora del servicio del suministro eléctrico en redes de distribución residenciales. A partir del modelo eléctrico de la red, se cuantifica el desequilibrio de fase y se formula un problema de optimización basado en teoría de juegos cooperativos, cuya función objetivo integra los intereses tanto de los consumidores flexibles como del operador del sistema eléctrico. El problema se resuelve en lazo cerrado, garantizando un reparto equitativo de los beneficios entre los participantes. Los resultados de simulación en una red representativa de 100 hogares demuestran que, con una tasa de participación flexible del 60%, la metodología mantiene el desequilibrio de fase por debajo del 10% en todo momento y logra una distribución equitativa de beneficios entre los participantes.

The growing penetration of renewable energy sources requires resilient microgrids capable of providing stable and continuous operation. Hybrid energy storage systems (HESS), which integrate hydrogen-based storage systems (HBSS), battery storage systems (BSS), and supercapacitor banks (SCB), are essential to ensuring the flexibility and robustness of these microgrids. Accurate modelling of these microgrids is crucial for analysis, controller design, and performance optimization, but the complexity of HESS poses a significant challenge: simplified linear models fail to capture the inherent nonlinear dynamics, while nonlinear approaches often require excessive computational effort for real-time control applications. To address this challenge, this study presents a novel state space model with linear variable parameters (LPV), which effectively balances accuracy in capturing the nonlinear dynamics of the microgrid and computational efficiency. The research focuses on a high-voltage DC bus microgrid architecture, in which the BSS and SCB are connected directly in parallel to provide passive DC bus stabilization, a configuration that improves system resilience but has received limited attention in the existing literature. The proposed LPV framework employs recursive linearisation around variable operating points, generating a time-varying linear representation that accurately captures the nonlinear behaviour of the system. By relying exclusively on directly measurable state variables, the model eliminates the need for observers, facilitating its practical implementation. The developed model has been compared with a reference model validated in the literature, and the results have been excellent, with average errors, MAE, RAE and RMSE values remaining below 1.2% for all critical variables, including state-of-charge, DC bus voltage, and hydrogen level. At the same time, the model maintains remarkable computational efficiency, completing a 24-h simulation in just 1.49 s, more than twice as fast as its benchmark counterpart. This optimal combination of precision and efficiency makes the developed LPV model particularly suitable for advanced model-based control strategies, including real-time energy management systems (EMS) that use model predictive control (MPC). The developed model represents a significant advance in microgrid modelling, as it provides a general control-oriented approach that enables the design and operation of more resilient, efficient, and scalable renewable energy microgrids.

Power converters are the basic elements of any power electronics system in many areas and applications. Among them, the push–pull converter topology is one of the most widespread due to its high efficiency, versatility, galvanic isolation, reduced number of switching devices and the possibility of implementing high conversion ratios with respect to non-isolated topologies. Optimal design and control requires very accurate models that consider all the non-idealities associated with the actual converter. However, this leads to the use of high-order models, which are impractical for the design of model-based controllers in real-time applications. To obtain a trade-off model that combines the criteria of simplicity and accuracy, it is appropriate to assess whether it is necessary to consider all non-idealities to accurately model the dynamic response of the converter. For this purpose, this paper proposes a methodology based on a sensitivity analysis that allows quantifying the impact of each non-ideality on the converter behaviour response as a function of the converter topology, power and frequency. As a result of the study, practical models that combine the trade-off between precision and simplicity are obtained. The behaviour of the simplified models for each topology was evaluated and validated by simulation against the most complete and accurate non-ideal model found in the literature. The results have been excellent, with an error rate of less than 5% in all cases.

Power converters are a basic element for the control and design of any power electronic system. Among the many available topologies, the push–pull converter is widely used due to its versatility, safety and efficiency. For its correct analysis, sizing, simulation and control, models that meet the characteristics of generality, accuracy and simplicity are required, especially if its control is to be optimized by means of some analytical technique. This requires models that consider the practical non-idealities intrinsic to the converter, as well as being intuitive and easy to handle analytically in a control loop. In general, the models reviewed in the scientific literature adopt simplifications in their definition that are detrimental to their accuracy. In response to the posed problem, this work presents a generalized, complete, accurate and versatile model of real (non-ideal) push–pull converters, ideal for the analysis, simulation, and control of power systems. Following the premise of general and complete converters, the proposed model includes all the practical non-idealities of the converter elements, and it is accurate because it faithfully reflects its dynamics. Furthermore, the model is versatile, as its state space formulation allows for its easy adaptability to the converter operating conditions (voltage, current and temperature) for each sampling time. Also, the model is excellent for use in model-based control techniques, as well as for making very accurate simulators. The behavior of the developed model has been contrasted with a real push–pull converter, as well as with reference models present in the scientific literature for both dynamic and steady-state response tests. The results show excellent performance in all the studied cases, with behavior faithful to the real converter and with relative errors that are much lower than those obtained for the reference models. It follows that the model behaves like a digital twin of a real push–pull converter.

Electric vehicles (EVs), in their pure and hybrid variants, have become the main alternative to ensure the decarbonization of the current vehicle fleet. Due to its excellent performance, EV technology is closely linked to lithium-ion battery (LIB) technology. A LIB is a complex dynamic system with extraordinary nonlinear behavior defined by electrical, thermal and electrochemical dynamics. To ensure the proper management of a LIB in such demanding applications as EVs, it is crucial to have an accurate mathematical model that can adequately predict its dynamic behavior. Furthermore, this model must be able to iteratively adapt its parameters to accommodate system disturbances during its operation as well as performance loss in terms of efficiency and nominal capacity during its life cycle. To this end, a methodology that employs the extended Kalman filter to iteratively improve a fuzzy model applied to a real LIB is presented in this paper. This algorithm allows to improve the classical Takagi–Sugeno fuzzy model (TSFM) with each new set of data obtained, adapting the model to the variations of the battery characteristics throughout its operating cycle. Data for modeling and subsequent validation were collected during experimental tests on a real LIB under EVs driving cycle conditions according to the “worldwide harmonised light vehicle test procedure” (WLTP) standard. The TSFM results allow the creation of an accurate nonlinear dynamic model of the LIB, even under fluctuating operating conditions, demonstrating its suitability for modeling and design of model-based control systems for LIBs used in EVs applications.

This paper aims to analyse two energy storage methods—batteries and hydrogen storage technologies—that in some cases are treated as complementary technologies, but in other ones they are considered opposed technologies. A detailed technical description of each technology will allow to understand the evolution of batteries and hydrogen storage technologies: batteries looking for higher energy capacity and lower maintenance, while hydrogen storage technologies pursuing better volumetric and gravimetric densities. Additionally, as energy storage systems, a mathematical model is required to know the state of charge of the system. For this purpose, a mathematical model is proposed for conventional batteries, for compressed hydrogen tanks, for liquid hydrogen storage and for metal hydride tanks, which makes it possible to integrate energy storage systems into management strategies that aim to solve the energy balance in plants based on hybrid energy storage systems. From the technical point of view, most batteries are easier to operate and do not require special operating conditions, while hydrogen storage methods are currently functioning at the two extremes (high temperatures for metal and complex hydrides and low temperatures for liquid hydrogen or physisorption). Additionally, the technical comparison made in this paper also includes research trends and future possibilities in an attempt to help plan future policies.