DC & Hybrid Micro-Grids

This book is a printed version of the papers published in the Special Issue “DC & Hybrid Microgrids” of Applied Sciences. This Special Issue, co-organized by the University of Pisa, Italy and Østfold University College in Norway, has collected nine papers and the editorial, from 28 submitted, with authors from Asia, North America and Europe. The published articles provide an overview of the most recent research advances in direct current (DC) and hybrid microgrids, exploiting the opportunities offered by the use of renewable energy sources, battery energy storage systems, power converters, innovative control and energy management strategies

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

Onduleur quasi-Z-source pour un système de traction de véhicules électriques à sources multiples : contrôle et gestion

Abstract: Power electronics play a fundamental role and help to achieve the new goals of the automobiles in terms of energy economy and environment. The power electronic converters are the key elements which interface their power sources to the drivetrain of the electric vehicle (EV). They contribute to obtaining high efficiency and performance in power systems. However, traditional inverters such as voltage-source, current-source inverters and conventional two-stage inverters present some conceptual limitations. Consequently, many research efforts have been focused on developing new power electronic converters suitable for EVs application. In order to develop and enhance the performance of commercial multiple sources EV, this dissertation aims to select and to control the impedance source inverter and to provide management approaches for multiple sources EV traction system. A concise review of the main existing topologies of impedance source inverters has been presented. That enables to select QZSI (quasi-Z-source inverter) topology as promising architectures with better performance and reliability. The comparative study between the bidirectional conventional two-stage inverter and QZSI for EV applications has been presented. Furthermore, comparative study between different powertrain topologies regarding batteries aging index factors for an off-road EV has been explored. These studies permit to prove that QZSI topology represents a good candidate to be used in multi-source EV system. For improving the performance of QZSI applied to EVs, optimized fractional order PI (FOPI) controllers for QZSI is designed with the ant colony optimization algorithm (ACO-NM) to obtain more suitable aging performance index values for the battery. Moreover, this thesis proposes a hybrid energy storage system (HESS) for EVs to allow an efficient energy use of the battery for a longer distance coverage. Optimized FOPI controller and the finite control set model predictive controller (FCS-MPC) for HESS using bidirectional QZSI is applied for the multi-source EV. The flux-weakening controller has been designed to provide a correct operation with the maximum available torque at any speed within current and voltage limits. Simulation investigations are performed to verify the topologies studied and the efficacity of the proposed controller structure with the bidirectional QZSI. Furthermore, Opal-RT-based real-time simulation has been implemented to validate the effectiveness of the proposed HESS control strategy. The results confirm the EV performance enhancement with the addition of supercapacitors using the proposed control configuration, allowing the efficient use of battery energy with the reduction of root-mean-square value, the mean value, and the standard deviation by 57%, 59%, and 27%, respectively, of battery current compared to the battery-only based inverter.L'électronique de puissance joue un rôle fondamental et contribue à atteindre les nouveaux objectifs de l'automobile en termes d'économie d'énergie et d'environnement. Les convertisseurs d’électroniques de puissance sont considérés comme les éléments clés qui interfacent leurs sources d'alimentation avec la chaîne de traction du véhicule électrique (VE). Ils contribuent à obtenir une efficacité et des performances élevées dans les systèmes électriques. Cependant, les onduleurs traditionnels tels que les onduleurs à source de tension, les onduleurs à source de courant et les onduleurs conventionnels à deux étages qui constituent les onduleurs les plus couramment utilisés, présentent certaines limitations conceptuelles. Par conséquent, de nombreux efforts de recherche se sont concentrés sur le développement de nouveaux convertisseurs d’électroniques de puissance adaptés à l'application aux véhicules électriques. Afin de développer et d'améliorer les performances des VEs à sources multiples commerciales, cette thèse vise à sélectionner, contrôler l'onduleur à source impédante et fournit une approche de gestion pour l'application du système de traction du VE à sources multiples. Une revue concise des principales topologies existantes d'onduleur à source impédante a été présentée. Cela a permis de sélectionner la topologie de l’onduleur quasi-Z-source (QZS) comme architectures prometteuses pouvant être utilisées dans les véhicules électriques, avec de meilleures performances et de fiabilité. L'étude comparative entre l'onduleur bidirectionnel conventionnel à deux étages et de celui à QZS pour les applications du VE a été présentée. En outre, une étude comparative entre différentes topologies de groupes motopropulseurs concernant les facteurs d'indice de vieillissement des batteries pour une application du VE hors route a été explorée. Ces études ont permis de prouver que la topologie de l’onduleur QZS représente une bonne topologie candidate à utiliser dans un système de VE à sources multiples. Pour améliorer les performances de l’onduleur QZS appliquées aux véhicules électriques, des contrôleurs PI d'ordre fractionnaire (PIOF) optimisés pour l’onduleur QZS sont conçus avec l'algorithme de colonies de fourmis afin d'obtenir des valeurs d'indice de performance de vieillissement plus appropriées pour la batterie. De plus, cette thèse propose un système de stockage d'énergie hybride (SSEH) pour le VE afin de permettre une utilisation efficace de l'énergie de la batterie pour une couverture de distance plus longue et une extension de son autonomie. L’optimisation du contrôleur PIOF et du contrôleur par modèle prédictif d'ensemble de contrôle fini (CMP-ECF) pour l’onduleur QZS bidirectionnel a été appliqué au VE à sources multiples avec des approches de gestion appuyées par des règles. Le contrôleur d'affaiblissement de flux magnétique du moteur a été conçu pour fournir un fonctionnement correct avec le couple maximal disponible à n'importe quelle vitesse dans les limites de courant et de tension. Des investigations et des simulations sont effectuées pour vérifier les différentes topologies étudiées et l'efficacité de la structure de contrôleur proposée avec l’onduleur QZS bidirectionnel. De plus, une simulation en temps réel basée sur Opal-RT a été mise en œuvre pour valider l'efficacité de la stratégie de contrôle SSEH proposée. Les résultats confirment l'amélioration des performances du VE avec l'ajout d'un supercondensateur utilisant la configuration du contrôle proposée, permettant une utilisation efficace de l'énergie de la batterie avec une réduction de la valeur moyenne quadratique, de la valeur moyenne et de l'écart type de 57%, 59% et 27%, respectivement, du courant de la batterie par rapport à l'onduleur connecté directement à la batterie

Towards Wind Energy-based Charging Stations: A Review of Optimization Methods

Due to the growing importance of renewable sources in sustainable energy systems, the strategic deployment of robust optimization techniques plays a crucial role in the design of Electric Vehicle Charging Stations (EVCSs). These stations need to smoothly incorporate renewable sources, ensuring optimal energy utilization. This study provides a comprehensive overview of the methodologies and approaches employed in the enhancement of wind energy based EVCSs. The aim is to discern the most efficacious techniques for optimizing charging stations. Researchers engage diverse strategies and methodologies in the realm of sizing and optimization, encompassing a spectrum of algorithmic implementations and software solutions. Evidently, each algorithm or software application bears distinctive merits and demerits. Singular reliance on a solitary algorithm or software for charging utility optimization is discerned to be potentially limiting. The investigation reveals that achieving better results in Electric Vehicle Charging Station (EVCS) optimization is facilitated by the collaborative use of multiple algorithms like GA, PSO, and ACO, among others, or software tools like Homer or RETScreen

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Application of Ant Colony Optimization for Co-Design of Hybrid Electric Vehicles

One key subject matter for effective use of Hybrid Electric Vehicles (HEVs) is searching for drivetrains which their component dimensions and control parameters are co-optimally designed for a desired performance. This makes the design challenge as a problem, which needs to be addressed in a holistic way meeting various constraints. Along this line, the strong coupling between components sizes of a drivetrain and parameters of its controllers turns the optimal sizing and control design of HEVs into a Bi-level optimization problem. In this chapter, an important application of continuous Ant Colony Optimization (ACOR) for integrated sizing and control design of HEVs is thoroughly discussed for minimizing the drivetrain cost, minimizing the fuel consumption and addressing the control objectives at the meantime. The outcome of this chapter provides useful information related to incorporation of soft-computing, modeling and simulation concepts into optimization-based design of HEVs from all respects for designers and automotive engineers. It brings opportunities to the readers for understanding the criteria, constraints, and objective functions required for the optimal design of HEVs. Via introducing a two-folded iterative framework, fuel consumption and component sizing minimizations are of the main goals to be simultaneously addressed in this chapter using ACOR

Evaluation and optimisation of traction system for hybrid railway vehicles

Over the past decade, energy and environmental sustainability in urban rail transport have become increasingly important. Hybrid transportation systems present a multifaceted challenge, encompassing aspects such as hydrogen production, refuelling station infrastructure, propulsion system topology, power source sizing, and control. The evaluation and optimisation of these aspects are critical for the adaptation and commercialisation of hybrid railway vehicles. While there has been significant progress in the development of hybrid railway vehicles, further improvements in propulsion system design are necessary. This thesis explores strategies to achieve this ambitious goal by substituting diesel trains with hybrid trains. However, limited research has assessed the operational performance of replacing diesel trains with hybrid trains on the same tracks. This thesis develops various optimisation techniques for evaluating and refining the hybrid traction system to address this gap. In this research's first phase, the author developed a novel Hybrid Train Simulator designed to analyse driving performance and energy flow among multiple power sources, such as internal combustion engines, electrification, fuel cells, and batteries. The simulator incorporates a novel Automatic Smart Switching Control technique, which scales power among multiple power sources based on the route gradient for hybrid trains. This smart switching approach enhances battery and fuel cell life and reduces maintenance costs by employing it as needed, thereby eliminating the forced charging and discharging of excessively high currents. Simulation results demonstrate a 6% reduction in energy consumption for hybrid trains equipped with smart switching compared to those without it. In the second phase of this research, the author presents a novel technique to solve the optimisation problem of hybrid railway vehicle traction systems by utilising evolutionary and numerical optimisation techniques. The optimisation method employs a nonlinear programming solver, interpreting the problem via a non-convex function combined with an efficient "Mayfly algorithm." The developed hybrid optimisation algorithm minimises traction energy while using limited power to prevent unnecessary load on power sources, ensuring their prolonged life. The algorithm takes into account linear and non-linear variables, such as velocity, acceleration, traction forces, distance, time, power, and energy, to address the hybrid railway vehicle optimisation problem, focusing on the energy-time trade-off. The optimised trajectories exhibit an average reduction of 16.85% in total energy consumption, illustrating the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well-balanced energy-time trade-off, prioritising energy efficiency without significantly impacting journey duration, a critical aspect of sustainable transportation systems. In the third phase of this thesis, the author introduced artificial neural network models to solve the optimisation problem for hybrid railway vehicles. Based on time and power-based architecture, two ANN models are presented, capable of predicting optimal hybrid train trajectories. These models tackle the challenge of analysing large datasets of hybrid railway vehicles. Both models demonstrate the potential for efficiently predicting hybrid train target parameters. The results indicate that both ANN models effectively predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade-forward neural network topology in the time-based architecture outperforms the feed-forward neural network topology in terms of mean squared error and maximum error in the power-based architecture. Specifically, the cascade-forward neural network topology within the time-based structure exhibits a slightly lower MSE and maximum error than its power-based counterpart. Moreover, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time-based architecture exhibits lower differences (0.18% and 0.85%) compared to the power-based architecture (0.46% and 0.92%)

Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints