Optimal Control of Hybrid Systems and Renewable Energies

This book is a collection of papers covering various aspects of the optimal control of power and energy production from renewable resources (wind, PV, biomass, hydrogen, etc.). In particular, attention is focused both on the optimal control of new technologies and on their integration in buildings, microgrids, and energy markets. The examples presented in this book are among the most promising technologies for satisfying an increasing share of thermal and electrical demands with renewable sources: from solar cooling plants to offshore wind generation; hybrid plants, combining traditional and renewable sources, are also considered, as well as traditional and innovative storage systems. Innovative solutions for transportation systems are also explored for both railway infrastructures and advanced light rail vehicles. The optimization and control of new solutions for the power network are addressed in detail: specifically, special attention is paid to microgrids as new paradigms for distribution networks, but also in other applications (e.g., shipboards). Finally, optimization and simulation models within SCADA and energy management systems are considered. This book is intended for engineers, researchers, and practitioners that work in the field of energy, smart grid, renewable resources, and their optimization and control

Control Strategy Influence on the Efficiency of a Hybrid Photovoltaic-Battery-Fuel Cell System Distributed Generation System for Domestic Applications☆

AbstractThe full exploitation of locally available renewable resources together with the reduction of system installation and management costs are key issues of diffused Distributed Generation (DG). In the given context, hybrid systems are already at an advanced stage of development which typically integrate several sub-systems. In such hybrid systems, Renewable Energy Sources generation systems (e.g. photovoltaic panels) are coupled to energy storage devices (electric batteries) and with programmable generators (a diesel generator or, more recently, with a sub-system based on fuel cells) allowing stable operations under a wide range of conditions. In this paper a solution which uses hydrogen and fuel cells as a programmable source is presented and is studied by means of a mixed experimental and numerical: a Hardware-In-Loop test bench designed and realized at the Department lab, able to reproduce the behavior of a hybrid system for domestic applications. The system is controlled by means of a rule-based control strategy acting on the common DC-bus whose optimization has a significant influence both on system design and on its overall system energy performances. Results show that Rule-Based strategy have a great potential towards cost reduction and components lifetime increase, while energy efficiency mainly depends on correct system sizing

A wide input-voltage range quasi-Z source boost DC-DC converter with high voltage-gain for fuel cell vehicles

A quasi-Z-source Boost DC-DC converter which uses a switched-capacitor is proposed for fuel cell vehicles. The topology can obtain a high voltage gain with a wide input-voltage range, and requires only a low voltage stress across each of the components. The performance of the proposed converter is compared with other converters which use Z-source networks. A scaled-down 400V/400W prototype is developed to validate the proposed technology. The respective variation in the output voltage is avoided when the wide variation in the input voltage happens, due to the PI controller in the voltage loop, and a maximum efficiency of 95.13% is measured

An intelligent power management system for unmanned earial vehicle propulsion applications

Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the fuel cell system and the battery into the propulsion motor drive. The main objective of the power management system is to obtain the controlled fuel cell current profile as a performance variable. The relationship between the fuel cell current and the fuel cell air supplying system compressor power is investigated and a referenced model is developed to obtain the optimum compressor power as a function of the fuel cell current. An adaptive controller is introduced to optimize the fuel cell air supplying system performances based on the referenced model. The adaptive neuro-fuzzy inference system based controller dynamically adapts the actual compressor operating power into the optimum value defined in the reference model. The online learning and training capabilities of the adaptive controller identify the nonlinear variations of the fuel cell current and generate a control signal for the compressor motor voltage to optimize the fuel cell air supplying system performances. The hybrid electric power system and the power management system were developed in real time environment and practical tests were conducted to validate the simulation results

Emerging Power Electronics Technologies for Sustainable Energy Conversion

This Special Issue summarizes, in a single reference, timely emerging topics related to power electronics for sustainable energy conversion. Furthermore, at the same time, it provides the reader with valuable information related to open research opportunity niches

Emerging Power Electronics Technologies for Sustainable Energy Conversion

This Special Issue summarizes, in a single reference, timely emerging topics related to power electronics for sustainable energy conversion. Furthermore, at the same time, it provides the reader with valuable information related to open research opportunity niches

Fuel Cell Renewable Hybrid Power Systems

Climate change is becoming visible today, and so this book—through including innovative solutions and experimental research as well as state-of-the-art studies in challenging areas related to sustainable energy development based on hybrid energy systems that combine renewable energy systems with fuel cells—represents a useful resource for researchers in these fields. In this context, hydrogen fuel cell technology is one of the alternative solutions for the development of future clean energy systems. As this book presents the latest solutions, readers working in research areas related to the above are invited to read it

Real-Time Power Management of A Fuel Cell/Ultracapacitor Hybrid

This thesis presents the system architecture design, system integration methodology, and real-time control of a fuel cell/ultracapacitor hybrid power system. The main objective is for the hybrid system to respond to real-world fluctuations in power without negatively impacting fuel cell life. A Proton Exchange Membrane (PEM) Fuel Cell is an electrochemical device which converts the chemical energy of pure hydrogen into electricity through a chemical reaction with oxygen. The high conversion efficiency, zero harmful emissions, high power-to-weight ratio, scalability, and low temperature operation make PEM fuel cells very attractive for stationary and portable power applications. However, fuel cells are limited in responding to fast transients in power demand, moreover power fluctuations have negative impact on fuel cell durability. This motivates the use of a supplementary energy storage device to assist the fuel cell by buffering sharp transients in power demand. The high power density, long cycle life, and efficiency of ultracapacitors make them an ideal solution for such auxiliary energy storage in a hybrid fuel cell system. The power management strategy that determines the power split between the fuel cell and ultracapacitor is key to the power following capability, long-term performance, and life-time of the fuel cell. In this thesis, a rule-based and a model predictive control strategy are designed, implemented and evaluated for power management of a fuel cell/ultracapacitor hybrid. The high-level control objectives are to respond to rapid variations in load while minimizing damaging fluctuations in fuel cell current and maintaining ultracapacitor charge (or voltage) within allowable bounds. An experimental test stand was created to evaluate the performance of the controllers. The test stand connects the fuel cell and ultracapacitor to an electronic load through two dc/dc converters, which provide two degrees of freedom, enabling independent low-level control of the DC BUS voltage and the current split between the fuel cell and ultracapacitor. Experiments show that both rule-based and model predictive power management strategies can be tuned to meet both high and low-level control objectives for a given power demand profile. However, the capability to explicitly enforce the constraints in model predictive scheme and its predictive nature in meeting power demands enables a more systematic design and results in general in smoother performance

Robust control strategies for hybrid solid oxide fuel cell systems

Solid Oxide Fuel Cell (SOFC) systems are electrochemical energy conversion devices characterized by the use of solid oxide as the electrolyte. They operate at high temperatures (between 800± ¡ 1000±C). Mitigating fuel starvation and improving load-following capability of SOFCs are conflicting control objectives. In this thesis, this issue is addressed using a hybrid SOFC ultra-capacitor configuration. The fuel cell is controlled by incorporating a steady-state property of fuel utilization into an input-shaping framework. Two comprehensive control strategies are developed. The first is a Lyapunov-based nonlinear control and the second is a standard H-infinity robust control. Both strategies additionally control the state of charge (SOC) of the ultra-capacitor that provides transient power compensation. A hardware-in-the-loop test-stand is developed where the proposed control strategies are verified. An investigation to improve the hybrid fuel cell system by incorporating a lithium-ion battery as an additional power source is conducted. Combining both battery and ultra-capacitor with a fuel cell is potentially a winning combination especially for high power applications. A novel SOC estimation method for lithium-ion battery is investigated. Based on the combined ultra-capacitor battery hybrid system, a lyapunov-Based nonlinear control strategy is designed