The increasing penetration of electric vehicles (EVs) and other distributed energy resources (DERs) offers enhanced flexibility and resilience. During extreme conditions, grid-connected EVs and DERs can provide electricity service and restore critical loads when the utility system is unavailable. On the other hand, during normal operation, these proactive devices can provide ancillary services to alleviate voltage fluctuations and support frequency regulation. In comparison with other DERs, EVs are more flexible in providing ancillary services due to their mobile nature. However, the proliferation of EVs and DERs also introduces operational challenges to the distribution grid. For instance, EVs primarily fulfill their transportation needs. Uncoordinated charging of a large number of EVs can increase the burden on the distribution system. Due to the limited charging rate and battery size, it is generally impractical for a single EV to directly participate in the ancillary service market. A conventional distribution system is designed for unidirectional flow of electric energy. With the growing installation of DERs on the distribution system, the flow of electric energy is bi-directional and, therefore, there is a higher risk of protection miscoordination due to the fault currents resulting from DERs. With limited communication capability, these undetected protective device (PD) actuations can cause uncertainties and delay the service restoration process. This dissertation makes contributions to the coordination of EVs and DERs. It introduces four innovative models for EV coordination: 1) A transactive energy (TE) trading mechanism is proposed to coordinate EVs and aggregators. 2) Optimal tools are provided to assist EVs and aggregators in optimal decision making while participating in TE. 3) A charging station model is developed to allow EVs to provide ancillary service aligned with their mobile nature. 4) A utility function model is presented to capture the EV owners' behaviors for providing ancillary services and charging vehicles. Charging stations can estimate the electric energy demand and optimize ancillary service provision to meet their goals. Simulation cases validated that the proposed optimization tools can align EV owners' preferences in providing ancillary service to enhance distribution system operation flexibility. To enhance the resilience of distribution systems, two novel optimization strategies are presented: 1) An advanced outage management (AOM) is proposed to utilize smart meters and fault indicators (FIs) to identify the most credible outage scenario and fault locations. 2) An advanced feeder restoration (AFR) is developed to provide an optimal restoration strategy to enhance system resilience. The proposed optimization models have been validated with realistic simulation cases. / Doctor of Philosophy / As Electric Vehicles (EVs) and other Distributed Energy Resources (DERs) become more common, they are changing how our distribution systems work. For example, during power outages, grid-connected DERs and EVs can be deployed to sustain essential electricity services such as hospitals and communications. On the other hand, during a normal operating condition, they can help maintain the stability of our electricity systems. It is a technical challenge to integrate these new EV and DER devices into the existing power grid. For example, EVs are mainly designed for transportation. Their clustered charging patterns can significantly increase the electrical demand if they are not managed properly. Also, the limited battery capacity and charging speed make it difficult for a single vehicle to provide meaningful support to the grid operation. For the EV management side, this research is concerned with how to better integrate EVs and similar technologies into the power grid. Four key contributions of this dissertation are: 1) Developing a trading mechanism for EVs and aggregators of EVs to exchange energy and ancillary services efficiently; 2) Creating computational technologies to help these entities optimize their decisions while meeting their requirements; 3) Structuring charging station operations that cater to the preferences of EV owners while supporting grid operation; and 4) Modeling EV owners' decision-making to set optimal pricing and service strategies at charging stations. These mechanisms and strategies will allow EV owners to support the power grid while meeting their transportation needs. Moreover, the study addresses the issue of enhancement of the distribution system's capability to restore services under extreme conditions. It provides an advanced outage management method that utilizes remote monitoring and control technologies, including smart meters and fault indicators, to identify the location of electrical faults and reduce the outage areas. The advanced feeder restoration method determines an optimal strategy to restore the electricity service efficiently while keeping the distribution grid stable.

Significant advances have been made in the research and development of electric vehicles (EV’s). Along with the major challenge of energy storage, being also addressed is the efficient design of system energy transfer and consumption. This has had the effect of fundamentally changing perspectives across the mobility and transportation sector. Applied predominantly to road-going vehicles, the industrial context of non-road Electric Vehicles (nrEV’s) and specifically the use of manned electric forklift trucks integrated within the production related materials handling system has, to-date, received far less attention. The overarching aim of this research is to examine the impact and potential for the use of contactless occasional recharging of nrEV’s integrated within a manufacturing line, recognising the need to balance the (sometimes competing) demands of delivering sustainable production while exercising environmental responsibility. Meeting the objectives of this research resulted in the development of a location allocation model for electric charging station determination based on a fundamental understanding of the nature and quality of process inherent key performance indicators (KPI’s) as well as comprehensive process and energy monitoring while considering both Lean and Green Management perspectives. The integration of the generated knowledge and information into a generally valid simulation tool for occasional charging system implementation allows to more thoroughly investigate the impact from occasional charging to overall efficiency and sustainability to be realised. An investigation into relevant literature identified the need for specifically generated energy consumption data and confirmed the need for an energy optimisation model specific to the area of production related materials handling. Empirical data collected from repeated standardised materials handling operations within a selected production related materials handling environment resulted in the development of the Standard Energy Consumption Activity tool (SECA). Further work within this pilot study confirmed the tool as capable of generating reliable and valid data and confirmed the SECA tool as a generally applicable benchmark for energy consumption determination in material handling based on fractional process functions. Integrating this approach into a comprehensive process analysis and charging infrastructure optimisation resulted in the development of an Excel-based simulation model. The (Occasional Charging Station Location Model) OCSLM is based upon Maximal Covering Location Modelling and an endogenous covering distance definition in order to simulate process related potentials and optimal charging system implementation allocations, the target being to increase vehicles usable battery energy. A comprehensive case study based upon six individual and one combined data set confirmed the general and wider applicability of the OCSLM model while the application of the model provides a set of novel results. The application demonstrated a theoretical increase in usable battery energy of between 40% and 60% and within the same case study the impact of technology implementation identified that a reduction in battery and system cost of between 5% and 45% can be realised. However, the use of contactless power transfer resulted in an increase in CO2 emissions of up to 6.89% revealing a negative impact to overall ecology from the use of this energy transfer system. Depending on the availability of fast connecting, contact based energy transmission systems, the approach and results of OCSLM have shown to be directly applicable to contact based systems with resulting CO2 emissions decreasing by 0.94% at an energy transfer efficiency of 96%. Further novelty, of benefit to both academic and industry practice, was realised through the framework and information of the research with the provision of SECA as a process function-based and generally applicable energy consumption standard, OCSLM as a Maximal Covering Location Modell with a focus on occasional charging based on an endogenous covering distance and integrating detailed energy and process monitoring into electric charging station allocation, and the methodology for the application of this approach for fast connecting contactless and contact charging models and cases.

The motivation of this research is to carry out the control and operation of smart inverters and voltage source converters (VSC) for distributed energy resources (DERs) such as photovoltaic (PV), battery, and plug-in hybrid electric vehicles (PHEV). The main contribution of the research includes solving a couple of issues for smart grids by controlling and implementing multifunctions of VSC and smart inverter as well as improving the operational scheme of the microgrid. The work is mainly focused on controlling and operating of smart inverter since it promises a new technology for the future microgrid. Two major applications of the smart inverter will be investigated in this work based on the connection modes: microgrid at grid-tied mode and autonomous mode. \indent In grid-tied connection, the smart inverter and VSC are used to integrate DER such as Photovoltaic (PV) and battery to provide suitable power to the system by controlling the supplied real and reactive power. The role of a smart inverter at autonomous mode includes supplying a sufficient voltage and frequency, mitigate abnormal condition of the load as well as equally sharing the total load's power. However, the operational control of the microgrid still has a major issue on the operation of the microgrid. The dissertation is divided into two main sections which are: 1- Low-level control of a single smart Inverter. 2- High-level control of the microgrid. The first part investigates a comprehensive research for a smart inverter and VSC technology at the two major connections of the microgrid. This involves controlling and modeling single smart inverter and VSC to solve specific issues of microgrid as well as improve the operation of the system. The research provides developed features for smart inverter comparing with a conventional voltage sourced converter (VSC). The two main connections for a microgrid have been deeply investigated to analyze a better way to develop and improve the operational procedure of the microgrid as well as solve specific issues of connecting the microgrid to the system. A detailed procedure for controlling VSC and designing an optimal operation of the controller is also covered in the first part of the dissertation. This section provides an optimal operation for controlling motor drive and demonstrates issues when motor load exists at an autonomous microgrid. It also provides a solution for specific issues at operating a microgrid at autonomous mode as well as improving the structural design for the grid-tied microgrid. The solution for autonomous microgrid includes changing the operational state of the switching pattern of the smart inverter to solve the issue of a common mode voltage (CMV) that appears across the motor load. It also solves the issue of power supplying to large loads, such as induction motors. The last section of the low-level section involves an improvement of the performance and operation of the PV charging station for a plug-in hybrid electric vehicle (PHEV) at grid-tied mode. This section provides a novel structure and smart controller for PV charging station using three-phase hybrid boost converter topology. It also provides a form of applications of a multifunction smart inverter using PV charging station. The second part of the research is focusing on improving the performance of the microgrid by integrating several smart inverters to form a microgrid. It investigates the issue of connecting DER units with the microgrid at real applications. One of the common issues of the microgrid is the circulating current which is caused by poor reactive power sharing accuracy. When more than two DER units are connected in parallel, a microgrid is forming be generating required power for the load. When the microgrid is operated at autonomous mode, all DER units participate in generating voltage and frequency as well as share the load's power. This section provides a smart and novel controlling technique to solve the issue of unequal power sharing. The feature of the smart inverter is realized by the communication link between smart inverters and the main operator. The analysis and derivation of the problem are presented in this section. The dissertation has led to two accepted conference papers, one accepted transaction IEEE manuscript, and one submitted IET transaction manuscript. The future work aims to improve the current work by investigating the performance of the smart inverter at real applications.

The fast-developing industry of electric vehicles is growing, and so is the driver community, which puts pressure on the electric charging grid. The purpose of this thesis is to simplify for the drivers of electric cars to charge their cars during trips. The research questions investigated are” How do the technologies and algorithms behind navigation APIs differ from each other?” and “What information is provided by the charging station APIs and how do they collect data about new stations?”. Information for the thesis was collected by reading and analyzing both documentation and previous work, as well as by conducting experiments. The study was limited to purely electric vehicles. We created an application to conduct experiments on the API combination Mapbox and Open Charge Map, we call it ChargeX. We compare, TomTom, Tesla, Plugshare, Google Maps and ChargeX. The most common shortest-path algorithms are Dijkstra’s, A* and Bidirectional A*. They provide reasonable solutions to the shortest path problem. The algorithms can be improved by considering traffic flow, travel time and distance between origin and destination and apply it as weights on the edges. What has the largest impact on the final route is the choice of charging stations. The algorithm for picking charging stations can be optimized in several ways for example by considering real time availability information of the charging stations, prioritize highways, calculate the temperature and altitude impact on the battery or prioritize faster chargers such as superchargers for Tesla.

This thesis deals with increasing the safety of the external perimeter. The theoretical part describes the parameters of areas exhibiting an increased safety risk. For these spaces, relevant safety hazards and corresponding elimination methods are determined. Then, the term "drone" is analyzed in the context of its applicability. The practical section characterizes the process of building and configuring unmanned aircraft to guard the perimeter and the designing and fabrication of an automatic charging station for the aircraft. The final part of the thesis is a brief manual describing correct handling of the created system.

This thesis is focused on the opportunities of conceptual development of the local distribution system Prototypa, a.s. At first, there is descripted condition of the current substations and internal electricity distribution system. Practical part of the thesis is dedicated to the evaluation of the construction of new transformer station and projection proposals of connection of the high-power charging station and new electric power supply.

The main aim of this master's thesis is create golf ball picker with charging station. These two components create the system, which automates process of golf ball collection on driving range. The robot replaces human manual activity in this branch and it eliminates injury. On the other hand, robot respects functional, psychological, ecological and aesthetic demands. It demarcates standard principles of these days and new way how to collect balls on driving range.

<div>Electric vehicles (EVs) face a major issue before becoming the norm of society, that is, their lack of range when it comes to long trips. Fast charging stations are a good step forward to help make it simpler for EVs, but it is still not as convenient when compared to vehicles with an internal combustion engine (ICE). Plenty of infrastructure changes have been proposed in the literature attempting to tackle this issue, but they typically tend to be either an expensive solution or a difficult practical implementation.</div><div> </div><div> This dissertation presents two solutions to help increase the range of EVs: a novel wireless charging method and a multi-motor architecture for EVs. The first proposed solution involves the ability for EVs to charge while en route from another vehicle, which will be referred to from here on as vehicle-to-vehicle recharging (VVR). The aim of this system is to bring an innovative way for EVs to charge their battery without getting off route on a highway. The electric vehicle can request such a service from a designated charger vehicle on demand and receive electric power wirelessly while en route. The vehicles that provide energy (charger vehicles) through wireless power transfer (WPT) only need to be semi-autonomous in order to ``engage'' or ``disengage'' during a trip. Also, a novel method for wireless power transfer will be presented, where the emitter (TX) or receiver (RX) pads can change angles to improve the efficiency of power transmission. This type of WPT system would be suitable for the VVR system presented in this dissertation, along with other applications.</div><div> </div><div> The second solution presented here will be an architecture for EVs with three or more different electric motors to help prolong the state of charge (SOC) of the battery. The key here is to use motors with different high efficiency regions. The proposed control algorithm optimizes the use of the motors on-board to keep them running in their most efficient regions. With this architecture, the powertrain would see a combined efficiency map that incorporates the best operating points of the motors. Therefore, the proposed architecture will allow the EV to operate with a higher range for a given battery capacity.</div><div> </div><div> The state-of-the-art is divided into four subsections relevant to the proposed solutions and where most of the innovations to reduce the burden of charging EVs can be found: (1) infrastructure changes, (2) device level innovations, (3) autonomous vehicles, and (4) electric vehicle architectures. The infrastructure changes highlight some of the proposed systems that aim to help EVs become a convenient solution to the public. Device level innovations covers some of the literature on technology that addresses EVs in terms of WPT. The autonomous vehicle subsection covers the importance of such technology in terms of safety and reliability, that could be implemented on the VVR system. Finally, the EV architectures covers the current typologies used in EVs. Furthermore, modeling, analysis, and simulation is presented to validate the feasibility of the proposed VVR system, the WPT system, and the multi-motor architecture for EVs.</div>

This project investigates the viability of using reconditioned batteries, which have lost part of their original capacity while powering electric vehicles (EVs), to minimize the expenses of fast-charging infrastructures under the three charging scenarios where fast-charging mode is likely to be needed the most. The analysis is conducted for the Spanish scenario and considers the retail electricity tariff that best suits the requirements of a FCS. The economic analysis is performed on an annual basis and is tackled with an optimization algorithm, formulated as a mixed-integer linear programming problem and run on MATLAB. The expected lifetime of the ESS, being made of reused automotive cells, is estimated with a semi-empirical approach, using an iterative process and implemented in MATLAB. A sensitivity analysis is conducted on three input parameters that were identified to have a considerable impact on the system design and performance.   Overall, results show that with current figures energy storage integration in FCSs is viable as it effectively reduces the infrastructure expenses in all scenarios. Peak-shaving is identified as the main source of cost savings while demand shifting is not effective at all. The latter is further discussed in the sensitivity analysis and some considerations are elaborated. The most profitable scenario for storage integration is the case of a fast-charger located in a urban environment while, surprisingly, the lowest cost savings are obtained in the highway case. The sensitivity analysis illustrates the impact and effects that electricity prices and specific cost of both the power converter and the second-life batteries produce on the optimal system design. Moreover, charging demand profiles are deeply analyzed and their main implications highlighted.

Temporal and spatial distribution of incoming vehicular charging demand is a significant challenge for the future planning of power systems. In this thesis the vehicular loading is-sue is categorized into two classes of stationary and mobile; they are then addressed in two phases. The mobile vehicular load is investigated first; a location-based forecasting algorithm for the charging demand of plug-in electric vehicles at potential off-home charging stations is proposed and implemented for real-world case-studies. The result of this part of the re-search is essential to realize the scale of fortification required for a power grid to handle vehicular charging demand at public charging stations. In the second phase of the thesis, a novel decentralized control strategy for scheduling vehicular charging demand at residential distribution networks is developed. The per-formance of the proposed algorithm is then evaluated on a sample test feeder employing real-world driving data. The proposed charging scheduling algorithm will significantly postpone the necessity for upgrading the assets of the network while effectively fulfilling customers’ transportation requirements and preferences. / October 2014