On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Analysis of topologies of hybrid on-board energy storage systems for electric vehicles

The paper analyzes possible topologies of hybrid energy storage systems (HESSs), consisting of a battery and a supercapacitor and selects two promising topologies that use one and two DC-DC converters. It discusses the mathematical models that have been developed to describe dynamic processes in HESS and proposes a structure of the control system for HESS, the efficient performance of which is confirmed by computer simulation.У роботі проаналізовано можливі топології гібридної бортової системи живлення (ГБСЖ), яка складається з акумуляторної батареї та суперконденсаторної батареї, в результаті чого відібрано дві перспективні топології із застосуванням одного і двох DC-DC перетворювачів. Побудовано математичні моделі, що описують динамічні процеси в ГБСЖ та розроблено структуру системи керування ГБСЖ, ефективність роботи якої підтверджена комп’ютерним симулюванням

Smart electric vehicle charging strategy in direct current microgrid

This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for integrating network loads, EV charging/discharging and dispatchable generators (DGs) using droop control within DCMG. A novel two-stage optimization framework is deployed, which optimizes power flow in the network using droop control within DCMG and solves charging tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest path problem considering system losses and battery degradation from the distribution system operator (DSO) and electric vehicles aggregator (EVA) respectively. Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters. Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability distribution for those load profiles and further tests show the scheme is suitable for decentralized computing of its low burn-in request, fast convergent and good parallel acceleration performance. Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic distribution model into the optimization framework, which becomes the first stage of the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed where the previous deterministic model is deployed in the second stage which stage one and stage two are combined as a chance-constrained problem in stage three and solved as a random walk problem. Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary services. Meanwhile, both system loss and battery degradation from DSO and EVA can be minimized.Open Acces

PV Charging and Storage for Electric Vehicles

Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles

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