Optimal generation scheduling for renewable microgrids using hydrogen storage systems

The topic of this thesis is the development of a tool for an optimal energy management strategy (EMS) of the generators and energy storage systems constituent microgrids, both grid-connected or isolated (stand-alone power system) powered by Renewable Energy Sources (RES). In particular, a novel control system is designed based on the resolution of the unit commitment problem. For each time step, the proposed control system compares the expected power produced by the renewable generators with the expected load demand and determines the scheduling of the different energy storage devices and generators for the next few hours, which minimizes the operating cost of the overall microgrid. To take into account for forecasting uncertainties, the generation of the different scenarios is carried out through a discretization of the probability distribution function of the forecasting errors for wind speed, solar radiation and load requests by a set of finite states. A set of various scenarios are therefore analyzed and compared by the control system to find the minimum operating costs. The proposed algorithm is firstly applied to a microgrid at LABH2FER (Sardegna Ricerche, Italy). Since the microgrid is under construction, the expected performance is evaluated through a simulation modeling, implemented in Matlab-Simulink. Furthermore, in order to highlight the benefits of including weather forecasts and operating costs in the EMS, a comparative analysis with a simpler EMS based on control states of storage devices is carried out. The results of the comparative study demonstrate that a reduction of almost 5-10% in the annual operating costs and energy losses is achieved thanks to the implementation of the proposed control system. Moreover, the proposed control strategy is implemented and tested to a microgrid present at the University of Seville. Experimental results demonstrate the feasibility and the actual functionality of the control system. Additional benefits are also observed, such as the reduction in power exchanged with the upstream grid thanks to a better management of the storage systems

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

Prospects of Integrated Photovoltaic-Fuel Cell Systems in a Hydrogen Economy: A Comprehensive Review

From MDPI via Jisc Publications RouterHistory: accepted 2021-10-08, pub-electronic 2021-10-19Publication status: PublishedFunder: Petroleum Technology Development Fund Nigeria; Grant(s): PTDF/ED/PHD/OC/1078/17Integrated photovoltaic-fuel cell (IPVFC) systems, amongst other integrated energy generation methodologies are renewable and clean energy technologies that have received diverse research and development attentions over the last few decades due to their potential applications in a hydrogen economy. This article systematically updates the state-of-the-art of IPVFC systems and provides critical insights into the research and development gaps needed to be filled/addressed to advance these systems towards full commercialization. Design methodologies, renewable energy-based microgrid and off-grid applications, energy management strategies, optimizations and the prospects as self-sustaining power sources were covered. IPVFC systems could play an important role in the upcoming hydrogen economy since they depend on solar hydrogen which has almost zero emissions during operation. Highlighted herein are the advances as well as the technical challenges to be surmounted to realize numerous potential applications of IPVFC systems in unmanned aerial vehicles, hybrid electric vehicles, agricultural applications, telecommunications, desalination, synthesis of ammonia, boats, buildings, and distributed microgrid applications

Stand-alone solar-pv hydrogen energy systems incorporating reverse osmosis

The world’s increasing energy demand means the rate at which fossil fuels are consumed has increased resulting in greater carbon dioxide emissions. For many small (marginalised) or coastal communities, access to potable water is limited alongside good availability of renewable energy sources (solar or wind). One solution is to utilise small-scale renewably powered stand-alone energy systems to help supply power for everyday utilities and to operate desalination systems serving potable water (drinking) needs reducing diesel generator dependence. In such systems, on-site water production is essential so as to service electrolysis for hydrogen generation for Proton Exchange Membrane (PEM) fuel cells. Whilst small Reverse Osmosis (RO) units may function as a (useful) dump load, it also directly impacts the power management of stand-alone energy systems and affects operational characteristics. However, renewable energy sources are intermittent in nature, thus power generation from renewables may not be adequate to satisfy load demands. Therefore, energy storage and an effective Power Management Strategy (PMS) are vital to ensure system reliability. This thesis utilises a combination of experiments and modelling to analyse the performance of renewably powered stand-alone energy systems consisting of photovoltaic panels, PEM electrolysers, PEM fuel cells, batteries, metal hydrides and Reverse Osmosis (RO) under various scenarios. Laboratory experiments have been done to resolve time-resolved characteristics for these system components and ascertain their impact on system performance. However, the main objective of the study is to ascertain the differences between applying (simplistic) predictive/optimisation techniques compared to intelligent tools in renewable energy systems. This is achieved through applying intelligent tools such as Neural Networks and Particle Swarm Optimisation for different aspects that govern system design and operation as well as solar irradiance prediction. Results indicate the importance of device level transients, temporal resolution of available solar irradiance and type of external load profile (static or time-varying) as system performance is affected differently. In this regard, minute resolved simulations are utilised to account for all component transients including predicting the key input to the system, namely available solar resource which can be affected by various climatic conditions such as rainfall. System behaviour is (generally) more accurately predicted utilising Neural Network solar irradiance prediction compared to the ASHRAE clear sky model when benchmarked against measured irradiance data. Allowing Particle Swarm Optimisation (PSO) to further adjust specific control set-points within the systems PMS results in improvements in system operational characteristics compared to using simplistic rule-based design methods. In such systems, increasing energy storage capacities generally allow for more renewable energy penetration yet only affect the operational characteristics up to a threshold capacity. Additionally, simultaneously optimising system size and PMS to satisfy a multi-objective function, consisting of total Net Present Cost and CO2 emissions, yielded lower costs and carbon emissions compared to HOMER, a widely adopted sizing software tool. Further development of this thesis will allow further improvements in the development of renewably powered energy systems providing clean, reliable, cost-effective energy. All simulations are performed on a desktop PC having an Intel i3 processor using either MATLAB/Simulink or HOMER