Electric Vehicle - Smart Grid Integration: Load Modeling, Scheduling, and Cyber Security

The modern world has witnessed the surge of electric vehicles (EVs) driven by government policy worldwide to reduce transportation’s dependence on fossil fuels. According to (Slowik, 2019), the global EV market has grown sharply with the annual light-duty EV sales surpassing 2 million in 2018, which is about a 70% increase from 2017. The increase in EV population implies the rise in energy demand, and that introduces new challenges to the electricity sector. EV charging load demand in high penetration scenarios, which is foreseen, may lead to stability and quality issues in power grids. Generation capacity and the electricity infrastructure upgrade may be required to address those issues; however, it increases generation costs significantly. The most common EV chargers installed today deliver around 7 kW of power, which is over four times that of an average household power consumption in the US. EV charging load often shows two peaks in a day, one in the morning when people plug in the EV at the workplace and the other in the evening when people get home from work. Without proper energy management for EV charging, the vast power demand due to a large number of plugged-in EVs can stress the electric grid, degrade the electric power quality, and impact the wholesale electricity market. Although an EV battery may store energy up to 80 kWh, which requires more than 10 hours to charge at 7kW from empty, we found that most EVs need only 12 kWh per charge or 1.7 hours at 7 kW to meet daily commute requirement while they stay in the parking garage for a more extended period. This implies that EVs can have considerable time-flexibility for charging, and it is not necessary to start chargingright after plugging in, which is likely to result in the charging power add-up. A proper EV charging schedule can well allocate the charging load to prevent power peaks. Therefore, EV charging scheduling can play a significant role in mitigating the adverse effects of vast EV charging demand without upgrading the power grid capacity.To optimize the EV charging schedule while satisfies EVs’ charging demand, each EV’s stay duration and energy need are essential parameters for the optimization. Those parameters are based on predictions to minimize human intervention. Nonetheless, the uncertainty of EV user behavior poses a challenge to the prediction accuracy. Therefore, this dissertation demonstrates an ensemble machine learning-based method to model and predict the EV loads accurately, thereby improving the performance of EV charging scheduling.On the other hand, this smart EV-grid integration, which requires massive communication, including collecting, transmitting, and distributing real-time data within the network, makes it more susceptible to cyber-physical threats. Potential breaches could not only affect grid operation but also reduce consumers’ willingness to adopting EVs over conventional fuel-powered vehicles. This dissertation also presents the vulnerability analysis and risk assessment for a smart EV charging system to develop the countermeasures to secure the network. Also, while it is inevitable that the security has flaws, this dissertation provides a novel anomaly detection approach based on the invariant correlations of different measurements within the EV charging network

A Smart Charging Assistant for Electric Vehicles Considering Battery Degradation, Power Grid and User Constraints

Der Anstieg intermittierender Stromerzeugung aus erneuerbaren Energiequellen erschwert zunehmend einen effizienten und zuverlässigen Betrieb der Versorgungsnetze. Gleichzeitig steigt die Zahl der Elektrofahrzeuge, die zum Aufladen erhebliche Mengen an elektrischer Energie benötigen, rapide an. Energie- und Mobilitätssektor sind somit unweigerlich miteinander verbunden, was zur Folge hat, dass zuverlässige Elektromobilität von einer robusten Stromversorgung abhängt. Darüber hinaus empfinden Fahrzeugnutzer ihre individuelle Mobilität als eingeschränkt, da Elektrofahrzeuge im Vergleich zu Fahrzeugen mit Verbrennungsmotor derzeit eine geringere Reichweite aufweisen und mehr Zeit zum Aufladen benötigen. In der vorliegenden Arbeit wird daher ein neuartiges Konzept sowie eine Softwareanwendung (Ladeassistent) vorgestellt, die den Nutzer beim Laden seines Elektrofahrzeuges unterstützt und dabei die Interessen aller beteiligten Akteure berücksichtigt. Dafür werden zunächst Gestaltungsmerkmale möglicher Softwarearchitekturen verglichen, um eine geeignete Struktur von Modulen und deren Verknüpfung zu definieren. Anschließend werden anhand realer Daten sowohl Energieverbrauchs- als auch Batteriemodelle entwickelt, verbessert und validiert, welche die Fahr- und Ladeeigenschaften von Elektrofahrzeugen abbilden. Die wichtigsten Beiträge dieser Arbeit resultieren aus der Entwicklung und Validierung der folgenden drei Kernkomponenten des Ladeassistenten. Als Erstes wird das individuelle Mobilitätsverhalten der Nutzer modelliert und anhand von aufgezeichneten und halbsynthetischen Fahrdaten von Elektrofahrzeugen ausgewertet. Insbesondere wird ein neuartiger, zweistufiger Clustering-Algorithmus entwickelt, um häufig besuchte Orte der Nutzer zu ermitteln. Anschließend werden Ensembles von Random-Forest-Modellen verwendet, um die nächsten Aufenthaltsorte und die dort typischen Parkzeiten vorherzusagen. Als Zweites wird gemischt-ganzzahlige stochastische Optimierung angewandt, um Ladestopps in einem zukünftigen Zeithorizont möglichst komfortabel und kostengünstig zu planen. Dabei wird ein graphenbasierter Algorithmus eingesetzt, um den Energiebedarf und die Eintrittswahrscheinlichkeit von Mobilitätsszenarien eines Elektrofahrzeugnutzers zu quantifizieren. Zur Validierung werden zwei alternative Ladestrategien definiert und mit dem vorgeschlagenen System verglichen. Als Drittes wird ein nichtlineares Optimierungsschema entwickelt, um vorhandene Zeit- und Energieflexibilität in Ladevorgängen von Elektrofahrzeugen zu nutzen. Die Integration eines detaillierten Batteriemodells ermöglicht eine genaue Quantifizierung der Kosteneinsparungen aufgrund einer geringeren Batteriealterung und dynamischer Stromtarife. Anhand von Daten aus realen Ladevorgängen von Elektrofahrzeugen können Einflüsse auf die Rentabilität von Vehicle-to-Grid-Anwendungen herausgearbeitet werden. Aus der Umsetzung des vorgestellten Ansatzes in einer realistischen Umgebung geht ein Architekturentwurf und ein Kommunikationskonzept für optimierungsbasierte intelligente Ladesysteme hervor. Dabei werden weitere Herausforderungen im Zusammenhang mit standardisierter Ladekommunikation, Eingriffen der Energieversorger und Nutzerakzeptanz aufgedeckt

Implementations of Wireless and Wired Intelligent Systems for Healthcare with Focus on Diabetes and Ultrasound Applications

The research and implementations presented in this thesis focuses mainly on healthcare applications utilizing the wireless and wired communication and “Micro-Electro-Mechanical Systems” (MEMS) technologies, and secondly on security aspects. Chapters four and five presents new work in intelligent diabetes remote monitoring front-end system and into the corresponding new ultrasound simulator training systems. The motivation from the University of Sheffield of Electronic and Electrical Engineering Department and Sheffield Children Hospital with the partial grant scholarship from “Engineering and Physical Sciences Research Council” (EPSRC) for involvement in one “Collaborations for Leadership in Applied Health Research and Care” (CLAHRC) projects, was to improve the existing WithCare+ system and also the development of multiple new front-end solutions for it. My motivation to create solutions which will improve the life of patients who suffer from chronic disease such as type-1 diabetes, and also to provide new methods in management of that illness by clinicians and possible resulting annual government money saving, drives me to the successful result. From the other side, the motivation from the department of Neonatal in Sheffield Royal Hallamshire Hospital and the University of Sheffield of Electronic and Electrical Engineering Department drives me to the creation of a new, very low cost ultrasound simulation training system, using new components such as MEMS sensors. The hardware design and embedded source code was created in order to provide a ready library, for use by other projects, where 3D space orientation is required through exploitation of MEMS sensors and intelligent fusion filter algorithm. The third contribution affects the cryptographic aspects. The new implementation of fast and very efficient portable C code algorithm for t-adic NAF Key generation in ECC cryptographic principle for utilization of it with Koblitz curves presented in Appendix I

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Low-Cost Inventions and Patents

Inventions have led to the technological advances of mankind. There are inventions of all kinds, some of which have lasted hundreds of years or even longer. Low-cost technologies are expected to be easy to build, have little or no energy consumption, and be easy to maintain and operate. The use of sustainable technologies is essential in order to move towards a greater global coverage of technology, and therefore to improve human quality of life. Low-cost products always respond to a specific need, even if no in-depth analysis of the situation or possible solutions has been carried out. It is a consensus in all industrialized countries that patents have a decisive influence on the organization of the economy, as they are a key element in promoting technological innovation. Patents must aim to promote the technological development of countries, starting from their industrial situations

Testing the performance and feasibility of Bluetooth communications in pervasive systems

Smart and mobile environments require seamless connections. However, due to the frequent process of ''discovery'' and disconnection of mobile devices while data interchange is happening, wireless connections are often interrupted. To minimize this drawback, a protocol that enables an easy and fast synchronization is crucial. Bearing this in mind, Bluetooth technology appears to be a suitable solution to carry on such connections due to the discovery and pairing capabilities it provides. Nonetheless, the time and energy spent when several devices are being discovered and used at the same time still needs to be managed properly. It is essential that this process of discovery takes as little time and energy as possible. In addition to this, it is believed that the performance of the communications is not constant when the transmission speeds and throughput increase, but this has not been proved formally. Therefore, the purpose of this project is twofold: Firstly, to design and build a framework-system capable of performing controlled Bluetooth device discovery, pairing and communications. Secondly, to analyze and test the scalability and performance of the \emph{classic} Bluetooth standard under different scenarios and with various sensors and devices using the framework developed. To achieve the first goal, a generic Bluetooth platform will be used to control the test conditions and to form a ubiquitous wireless system connected to an Android Smartphone. For the latter goal, various stress-tests will be carried on to measure the consumption rate of battery life as well as the quality of the communications between the devices involved

Vibration-based condition monitoring of wind turbine blades

Significant advances in wind turbine technology have increased the need for maintenance through condition monitoring. Indeed condition monitoring techniques exist and are deployed on wind turbines across Europe and America but are limited in scope. The sensors and monitoring devices used can be very expensive to deploy, further increasing costs within the wind industry. The work outlined in this thesis primarily investigates potential low-cost alternatives in the laboratory environment using vibration-based and modal testing techniques that could be used to monitor the condition of wind turbine blades. The main contributions of this thesis are: (1) the review of vibration-based condition monitoring for changing natural frequency identification; (2) the application of low-cost piezoelectric sounders with proof mass for sensing and measuring vibrations which provide information on structural health; (3) the application of low-cost miniature Micro-Electro-Mechanical Systems (MEMS) accelerometers for detecting and measuring defects in micro wind turbine blades in laboratory experiments; (4) development of an in-service calibration technique for arbitrarily positioned MEMS accelerometers on a medium-sized wind turbine blade. This allowed for easier aligning of coordinate systems and setting the accelerometer calibration values using samples taken over a period of time; (5) laboratory validation of low-cost modal analysis techniques on a medium-sized wind turbine blade; (6) mimicked ice-loading and laboratory measurement of vibration characteristics using MEMS accelerometers on a real wind turbine blade and (7) conceptualisation and systems design of a novel embedded monitoring system that can be installed at manufacture, is self-powered, has signal processing capability and can operate remotely. By applying the conclusions of this work, which demonstrates that low-cost consumer electronics specifically MEMS accelerometers can measure the vibration characteristics of wind turbine blades, the implementation and deployment of these devices can contribute towards reducing the rising costs of condition monitoring within the wind industry

Characterisation of the heating mechanisms in a capacitively coupled argon RF discharge

A capacitively coupled rf plasma is investigated in the context of the heating mechanisms that sustain it under various conditions. These mechanisms are critically dependent on gas pressure, applied rf potential, rf current and discharge gap. Pressure ranges of 10 to 300 mTorr and rf potentials from a few Volts to several hundred Volts are investigated. The argon plasma is generated in two capacitively coupled rf systems. Plasma parameters are measured using a Langmuir probe. A microwave interferometer is used to compare density measurements with that of the probe. A current voltage monitor is used to measure the voltage, current and phase for the purpose of relating the control parameters to the plasma parameters. The design and construction of a retarding field energy analyser is presented. Plasma potential measurements using the analyser are compared with that of the Langmuir probe. Use of the analyser in electron collection mode has revealed a higher electron temperature behind the rf sheath than that measured by the Langmuir probe in the plasma. Transitions in the heating modes are investigated via plasma parameter and Electron Energy Probability Function (EEPF) measurement in the centre of the discharge. Particular attention is paid to the a-y transition. The relationship between the current voltage characteristic and the plasma parameters is considered. Pressure effects on the transition are determined. A model of the diffusion process in the capacitive rf discharge is devised and the resulting density profiles checked against known analytic solutions of the diffusion equation. A procedure to extract the ionisation profiles from experimental density measurements is devised using this model and a reconstruction algorithm. The twodimensional evolution of density and ionisation profiles is presented at low and high discharge powers over the 10 mTorr to 300 mTorr range. The total ionisation rate is compared with the theoretical ionisation rate calculated using simple particle balance equations. Disagreement in the ionisation rates is attributed to the use of unrealistic models and the assumption of uniform electron energy in the discharge. Spatially resolved EEPF measurements are made along the discharge axis. Rate coefficients for ionisation are calculated by integrating the product of the measured EEPF and the cross section for ionisation. Spatially resolved ionisation rates obtained from the coefficients are in good agreement with the measured ionisation rate profiles generated using the diffusion model and reconstruction algorithm