Power Electronics and Energy Management for Battery Storage Systems

The deployment of distributed renewable generation and e-mobility systems is creating a demand for improved dynamic performance, flexibility, and resilience in electrical grids. Various energy storages, such as stationary and electric vehicle batteries, together with power electronic interfaces, will play a key role in addressing these requests thanks to their enhanced functionality, fast response times, and configuration flexibility. For the large-scale implementation of this technology, the associated enabling developments are becoming of paramount importance. These include energy management algorithms; optimal sizing and coordinated control strategies of different storage technologies, including e-mobility storage; power electronic converters for interfacing renewables and battery systems, which allow for advanced interactions with the grid; and increase in round-trip efficiencies by means of advanced materials, components, and algorithms. This Special Issue contains the developments that have been published b researchers in the areas of power electronics, energy management and battery storage. A range of potential solutions to the existing barriers is presented, aiming to make the most out of these emerging technologies

Power Electronics Applications in Renewable Energy Systems

The renewable generation system is currently experiencing rapid growth in various power grids. The stability and dynamic response issues of power grids are receiving attention due to the increase in power electronics-based renewable energy. The main focus of this Special Issue is to provide solutions for power system planning and operation. Power electronics-based devices can offer new ancillary services to several industrial sectors. In order to fully include the capability of power conversion systems in the network integration of renewable generators, several studies should be carried out, including detailed studies of switching circuits, and comprehensive operating strategies for numerous devices, consisting of large-scale renewable generation clusters

Articles indexats publicats per investigadors del Campus de Terrassa: 2015

Aquest informe recull els 284 treballs publicats per 218 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2015Postprint (published version

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice

Ferrite-based micro-inductors for power systems on chip : from material elaboration to inductor optimisation

Les composants passifs intégrés sont des éléments clés pour les futures alimentations sur puce, compactes et présentant des performances améliorées: haut rendement et forte densité de puissance. L'objectif de ce travail de thèse est d'étudier les matériaux et la technologie pour réaliser de bobines à base de ferrite, intégrées sur silicium, avec des faibles empreintes (<4 mm ²) et de faible épaisseur (<250 µm). Ces bobines, dédiées à la conversion de puissance (˜ 1 W) doivent présenter une forte inductance spécifique et un facteur de qualité élevé dans la gamme de fréquence visée (5-10 MHz). Des ferrites de NiZn ont été sélectionnées comme matériaux magnétiques pour le noyau des bobines en raison de leur forte résistivité et de leur perméabilité stable dans la gamme de fréquence visée. Deux techniques sont développées pour les noyaux de ferrite: la sérigraphie d'une poudre synthétisée au laboratoire et la découpe automatique de films de ferrite commerciaux, suivi dans chaque cas du frittage et le placement sur les conducteurs pour former une bobine rectangulaire. Des bobines tests ont été réalisées dans un premier temps afin que la caractérisation puisse être effectuée : les propriétés magnétiques du noyau de ferrite notamment les pertes volumiques dans le noyau sont ainsi extraites. L'équation de Steinmetz a permis de corréler les courbes de pertes mesurées avec des expressions analytiques en fonction de la fréquence et de l'induction. La deuxième phase de la thèse est l'optimisation de la conception de la micro-bobine à base de ferrite, en tenant compte des pertes attendues. L'algorithme générique est utilisé pour optimiser les dimensions de la bobine avec pour objectif ; la minimisation des pertes et l'obtention de la valeur d'inductance spécifique souhaitée, sous faible polarisation en courant. La méthode des éléments finis pour le magnétisme FEMM est utilisée pour modéliser le comportement électromagnétique du composant. La deuxième série de prototypes a été réalisée afin de valider la méthode d'optimisation. En perspective, les procédés de photolithographie de résine épaisse et le dépôt électrolytique sont en cours de développement pour réaliser les enroulements de cuivre épais autour des noyaux de ferrite optimisés et ainsi former le composant complet.On-chip inductors are key passive elements for future power supplies on chip (PwrSoC), which are expected to be compact and show enhanced performance: high efficiency and high power density. The objective of this thesis work is to study the material and technology to realize small size (<4 mm²) and low profile (< 250 µm) ferrite-based on-chip inductor. This component is dedicated to low power conversion (˜ 1 W) and should provide high inductance density and high quality factor at medium frequency range (5-10 MHz). Fully sintered NiZn ferrites are selected as soft magnetic materials for the inductor core because of their high resistivity and moderate permeability stable in the frequencies range of interest. Two techniques are developed for the ferrite cores: screen printing of in-house made ferrite powder and cutting of commercial ferrite films, followed in each case by sintering and pick-and place assembling to form the rectangular toroid inductor. Test inductors were realized first so that the characterization could be carried out to study the magnetic properties of the ferrite core and the volumetric core losses. The core losses were fit from the measured curve with Steinmetz equation to obtain analytical expressions of losses versus frequency and induction. The second phase of the thesis is the design optimization for the on-chip ferrite based inductor, taking into account the expected losses. Genetic algorithm is employed to optimize the inductor design with the objective function as minimum losses and satisfying the specification on the inductance values under weak current-bias condition. Finite element method for magnetics FEMM is used as a tool to calculate inductance and losses. The second run of prototypes was done to validate the optimization method. In perspective, processes of thick-photoresist photolithography and electroplating are being developed to realize the completed thick copper windings surrounding ferrite cores

Methods and tools for the optimization of modular electrical power distribution cabinets in aeronautical applications

In recent years, aircraft manufacturers have been making progress in the design of more efficient aircrafts to reduce the environmental footprint. To attain this target, aircrafts manufactures work on the replacement of the hydraulic and bleed systems for electrical systems leading to a “More Electrical Aircraft”. However, the expected mass gain is a challenge, as previous technologies have been developed and optimized for decades. The new electrical solutions need to be look into detail to be competitive with previous technologies. All degrees of freedom must be considered, that is, new technologies and architectures. In particular, an HVDC network that reduces the number of rectifier stages seems a promising solution. From the HVDC network, the different three phase AC loads will be supplied by a series of power generic inverters. As the power consumption of the different loads change during the flight mission, the same inverter is used to supply different loads. The connection between the inverters and the loads is managed by a matrix of contactors. The proposed solution also considers redundant configurations, thus increasing system robustness. The design of the innovative system is presented in this document. That is, determining the optimal trade-off between the number of power inverters and the nominal power of each generic inverter that will also impact the size of the matrix of contactors. However, to assess the combinatory problem, the mass of the different components as a function of the nominal power needs to be calculated. A design environment is therefore created to perform automatic and optimized design of power converters. The different components are described using a “direct modelling” approach and coded using “object-oriented” programming. The components are validated experimentally or by numerical simulations. The different models are coupled to an optimization environment and to a frequency solver allowing a fast calculation of the steady-state waveforms. The optimization environment performs the precise design of the different parts of the power inverter: heatsink, power module, DC filter and coupling inductor. The power inverter is designed for different values of nominal power and switching frequency. The optimization assesses as well the usage of different technologies. Finally, the results are used to determine the optimal trade-off between the number of inverters and the nominal power of each inverter using a heuristic algorithm

Control of transmission system power flows

Power flow (PF) control can increase the utilization of the transmission system and connect lower cost generation with load. While PF controllers have demonstrated the ability to realize dynamic PF control for more than 25 years, PF control has been sparsely implemented. This research re-examines PF control in light of the recent development of fractionally-rated PF controllers and the incremental power flow (IPF) control concept. IPF control is the transfer of an incremental quantity of power from a specified source bus to specified destination bus along a specified path without influencing power flows on circuits outside of the path. The objectives of the research are to develop power system operation and planning methods compatible with IPF control, test the technical viability of IPF control, develop transmission planning frameworks leveraging PF and IPF control, develop power system operation and planning tools compatible with PF control, and quantify the impacts of PF and IPF control on multi-decade transmission planning. The results suggest that planning and operation of the power system are feasible with PF controllers and may lead to cost savings. The proposed planning frameworks may incent transmission investment and be compatible with the existing transmission planning process. If the results of the planning tool demonstration scale to the national level, the annual savings in electricity expenditures would be $13 billion per year (2010$). The proposed incremental packetized energy concept may facilitate a reduction in the environmental impact of energy consumption and lead to additional cost savings.Ph.D

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)

Feature Papers in Eng

This Special Issue is a collection of high-quality reviews and original papers from editorial board members, guest editors, and leading researchers discussing new knowledge or new cutting-edge developments in the field of engineering