Energy Management Strategies in hydrogen Smart-Grids: A laboratory experience

As microgrids gain reputation, nations are making decisions towards a new energetic paradigm where the centralized model is being abandoned in favor of a more sophisticated, reliable, environmentally friendly and decentralized one. The implementation of such sophisticated systems drive to find out new control techniques that make the system “smart”, bringing the Smart-Grid concept. This paper studies the role of Energy Management Strategies (EMSs) in hydrogen microgrids, covering both theoretical and experimental sides. It first describes the commissioning of a new labscale microgrid system to analyze a set of different EMS performance in real-life. This is followed by a summary of the approach used towards obtaining dynamic models to study and refine the different controllers implemented within this work. Then the implementation and validation of the developed EMSs using the new labscale microgrid are discussed. Experimental results are shown comparing the response of simple strategies (hysteresis band) against complex on-line optimization techniques, such as the Model Predictive Control. The difference between both approaches is extensively discussed. Results evidence how different control techniques can greatly influence the plant performance and finally we provide a set of guidelines for designing and operating Smart Grids.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-

Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy

[Abstract] This work deals with the prediction of variables for a hydrogen energy storage system integrated into a microgrid. Due to the fact that this kind of system has a nonlinear behaviour, the use of traditional techniques is not accurate enough to generate good models of the system under study. Then, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the power, the hydrogen level and the hydrogen system degradation. In this research, a hybrid intelligent model was created and validated over a dataset from a lab-size migrogrid. The achieved results show a better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption/generation with a mean absolute error of 0.63% with the test dataset respect to the maximum power of the system.Ministerio de Economía, Industria y Competitividad; DPI2017-85540-

Modeling and Control of Electrolysis Based Hydrogen Production System

Hydrogen fuel is essential for mitigating climate change and solving energy sector challenges. There are challenges associated with producing hydrogen through electrolysis as a result of the lack of an electrolyzer model and the high costs of hydrogen production. Therefore, this thesis intends to (1) model the electrolyzer accurately and (2) develop an energy management system (EMS) to minimize the cost of hydrogen (CoH) production. Most literature on electrolyzers assumes a linear model and ignores nonlinear behavior. This may lead to inefficient hydrogen production. Therefore, the first part of this thesis studies the modeling problem of electrolyzer in terms of the parameter estimation. Moreover, most existing EMSs ignore electrolyzer efficiency variations. Therefore, in the second part of this thesis, an EMS is developed for minimizing CoH production by accounting for efficiency variation. Historical electricity prices have also been incorporated into EMS for seasonal storage of hydrogen

A review of energy management strategies for renewable hybrid energy systems with hydrogen backup

Hybrid systems are presented as a viable, safe and effective solution to minimize the associated problems of the dependence on renewable energies with the environmental resources. In this way different renewable systems such as photovoltaic, wind, hydrogen and so on, can work together to configure hybrid renewable systems. However, to make them work properly in a holistic way by creating synergies among them is not an easy task. Recently hydrogen technology has appeared as a promising technology to hybridize renewable energy systems, since it allows the generation (by electrolyzers) and storage of hydrogen when there is a surplus of energy in the system, and at a later time (e.g. when there are insufficient renewable resources available) using the stored hydrogen to generate electrical energy by fuel cells. The choice of a correct energy management strategy should guarantee an optimum performance of the whole hybrid renewable system; therefore, it is necessary to know the most important criteria in order to define a management strategy that ensures the best solution from a technical and economic point of view. This paper presents a critical review and analysis of different energy management strategies for hybrid renewable systems based on hydrogen backup. In the same way, a review is also presented of the most important technical and economic optimization criteria, as well as problems and solutions studied in the scientific literature

MPC-Bases energy mangement system for hybrid renewable energies

Los sistemas de suministro de energía están formados por un conjunto de subsistemas que pueden interconectarse a través de la disposición de actuadores. El proceso es un sistema dinámico híbrido multivariable que presenta varios modos de configuración necesarios para el funcionamiento diario. En esta tesis se propone un sistema de gestión de energía basado en teorías de control. La principal dificultad que presentan los sistemas de suministro está en su dinámica definida por un conjunto de ecuaciones diferenciales y expresiones lógicas, además del carácter variable de la energía producida por las fuentes renovables. Con el fin de satisfacer el suministro de energía, se considera el diseño de un controlador híbrido basado en las predicciones de energía estimadas a partir de modelos físicos y mediciones. El control predictivo (MPC) es elegido como la estrategia de control, ya que es capaz de manejar las variaciones en el suministro y demanda de energía.Departamento de Ingeniería de Sistemas y Automátic

Control systems of offshore hydrogen production by renewable energies

Esta tesis trata sobre un proyecto de diseño de un Sistema de Gestión de Energía (SGE) que utiliza Modelo de Control Predictivo (MPC) para equilibrar el consumo de energía renovable con electrolizadores productores de hidrógeno. La energía generada se equilibra regulando el punto de operación y las conexiones de los electrolizadores usando un MPC basado en un algoritmo de Programación Mixta-Entera Cuadrática. Este algoritmo MPC permite tener en cuenta previsiones de energía, mejorando así el equilibrio y reduciendo el número de encendidos de los equipos. Se han realizado diferentes casos de estudio en instalaciones compuestas por unidades de generación de energía eléctrica a partir de energía renovable. Se considera la técnica de ósmosis inversa como paso intermedio para la producción de agua que alimenta a los electrolizadores. La validación se realiza utilizando datos meteorológicos medidos en un lugar propuesto para el sistema, mostrando el funcionamiento adecuado del SGE desarrollado.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria

Supervisory model predictive control of building integrated renewable and low carbon energy systems

To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank

Modular Supply Network Optimization of Renewable Ammonia and Methanol Co-production

To reduce the use of fossil fuels and other carbonaceous fuels, renewable energy sources such as solar, wind, geothermal energy have been suggested to be promising alternative energy that guarantee sustainable and clean environment. However, the availability of renewable energy has been limited due to its dependence on weather and geographical location. This challenge is intended to be solved by the utilization of the renewable energy in the production of chemical energy carriers. Hydrogen has been proposed as a potential renewable energy carrier, however, its chemical instability and high liquefaction energy makes researchers seek for other alternative energy carriers. Ammonia and methanol can serve as promising alternative energy carriers due to their chemical stability at room temperature, low liquefaction energy, high energy value. The co-production of these high energy dense energy carriers offers economic and environmental advantages since their synthesis involve the direct utilization of CO2 and common unit operations. This problem report aims to review the optimization of the co-production of methanol and ammonia from renewable energy. Form this review, research challenges and opportunities are identified in the following areas: (i) optimization of methanol and ammonia co-production under renewable and demand uncertainty, (ii) impacts of the modular exponent on the feasibility of co-production of ammonia and methanol, and (iii) development of modern computational tools for systems-based analysis