A “smarter grid” is expected to be more flexible and more reliable than traditional electric power grids. Among technologies required for the “smarter grid” deployment, this dissertation presents a sustainable microgrid and a spatial and temporal model of plug-in electric vehicle charging demand for the “smarter grid”. First, this dissertation proposes the dynamic modeling technique and operational strategies for a sustainable microgrid primarily powered by wind and solar energy resources. Multiple-input dc-dc converters are used to interface the renewable energy sources to the main dc bus. The intended application for such a microgrid is an area in which there is interest in achieving a sustainable energy solution, such as a telecommunication site or a residential area. Wind energy variations and rapidly changing solar irradiance are considered in order to explore the effect of such environmental variations to the intended microgrid. The proposed microgrid can be operated in an islanded mode in which it can continue to generate power during natural disasters or grid outages, thus improving disaster resiliency of the “smarter grid”. In addition, this dissertation presents the spatial and temporal model of electric vehicle charging demand for a rapid charging station located near a highway exit. Most previous studies have assumed a fixed charging location and fixed charging time during the off-peak hours for anticipating electric vehicle charging demand. Some other studies have based on limited charging scenarios at typical locations instead of a mathematical model. Therefore, from a distribution system perspective, electric vehicle charging demand is still unidentified quantity which may vary by space and time. In this context, this study proposes a mathematical model of electric vehicle charging demand for a rapid charging station. The mathematical model is based on the fluid dynamic traffic model and the M/M/s queueing theory. Firstly, the arrival rate of discharged vehicles at a charging station is predicted by the fluid dynamic model. Then, charging demand is forecasted by the M/M/s queueing theory with the arrival rate of discharged vehicles. The first letter M of M/M/s indicates that discharged vehicles arrive at a charging station with the Poisson distribution. The second letter M denotes that the time to charge each EV is exponentially distributed, and the third letter s means that there are s identical charging pumps at a charging station. This mathematical model of charging demand may allow grid’s distribution planners to anticipate charging demand at a specific charging station. / text

Owing to advancements in technology, substantial investments within the automotive industry, and the formulation of supportive state policies, the future landscape of the transportation sector is poised to witness a shift from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs). While EVs have made inroads in the market, they still face significant hurdles in the form of range anxiety and prolonged charging durations, inhibiting their widespread adoption. To tackle these challenges, a comprehensive approach to smart transportation electrification is proposed, emphasizing the pivotal roles of infrastructure development, particularly in the allocation of charging stations, and strategic operational decisions, including charging and platoon scheduling. This dissertation is structured around four essential components. The initial stage entails grasping the intricacies of charging demand, recognized as the foundational step before embarking on any transportation electrification initiative. Subsequently, the allocation of charging stations is addressed, with a specific focus on ride-sourcing vehicles, distinct from private EVs due to issues such as relocation time, waiting time, and dynamic pricing that affects spatiotemporal value of time (VOT) costs. This approach, which considers VOT costs, is essential in avoiding biased results in the planning of charging infrastructure for electrified ride-sourcing services. The third chapter centers on the optimization of charging and platoon scheduling, particularly within the context of long-haul freight vehicles. The objective here is to harness the flexibility of charging schedules to facilitate vehicle platooning, thereby reducing the demand for charging, and, consequently, energy consumption. This chapter involves the development of a mixed-integer programming model and explores various techniques, such as hyperparameter tuning and hybrid meta-heuristic methods, to optimize the model for large-scale applications. Lastly, the fourth chapter takes on the challenge of addressing uncertainty in scheduling problems. This is achieved by formulating a two-stage stochastic model and applying it within a hypothetical numerical example, providing a framework for optimizing charging station (CS) planning while accounting for uncertain operational parameters.

With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing cities, data is being generated, collected and analyzed to solve urban problems in the areas of transportation, epidemiology, emergency management, economics, and sustainability etc. The work in this area basically involves analyzing one or more types of data to identify and characterize their impact on other urban phenomena like traffic speed and ride-sharing, spread of diseases, emergency evacuation, share market and electricity demand etc. In this work, we perform spatio-temporal analysis of various urban datasets collected from different urban application areas. We start with presenting a framework for predicting traffic demand around a location of interest and explain how it can be used to analyze other urban activities. We use a similar method to characterize and analyze spatio-temporal criminal activity in an urban city. At the end, we analyze the impact of nearby traffic volume on the electric vehicle charging demand at a charging station. / Master of Science

Recent years have seen a growing tendency that a large number of generators are connected to the electricity distribution networks, including renewables such as solar photovoltaics, wind turbines and biomass-fired power plants. Meanwhile, on the demand side, there are also some new types of electric loads being connected at increasing rates, with the most important of them being the electric vehicles (EVs). Uncertainties both from generation and consumption of electricity mentioned above are thereby being introduced, making the management of the system more challenging. With the proportion of electric vehicle ownership rapidly increasing, uncontrolled charging of large populations may bring about power system issues such as increased peak demand and voltage variations, while at the same time the cost of electricity generation, as well as the resulting Greenhouse Gases (GHG) emissions, will also rise. The work reported in this PhD Thesis aims to provide solutions to the three significant challenges related to EV integration, namely voltage regulation, generation cost minimisation and GHG emissions reduction. A novel, high-resolution, bottom-up probabilistic EV charging demand model was developed, that uses data from the UK Time Use Survey and the National Travel Survey to synthesise realistic EV charging time series based on user activity patterns. Coupled with manufacturers' data for representative EV models, the developed probabilistic model converts single user activity profiles into electrical demand, which can then be aggregated to simulate larger numbers at a neighbourhood, city or regional level. The EV charging demand model has been integrated into a domestic electrical demand model previously developed by researchers in our group at the University of Edinburgh. The integrated model is used to show how demand management can be used to assist voltage regulation in the distribution system. The node voltage sensitivity method is used to optimise the planning of EV charging based on the influence that every EV charger has on the network depending on their point of connection. The model and the charging strategy were tested on a realistic "highly urban" low voltage network and the results obtained show that voltage fluctuation due to the high percentage of EV ownership (and charging) can be significantly and maintained within the statutory range during a full 24-hour cycle of operation. The developed model is also used to assess the generation cost as well as the environmental impact, in terms of GHG emissions, as a result of EV charging, and an optimisation algorithm has been developed that in combination with domestic demand management, minimises the incurred costs and GHG emissions. The obtained results indicate that although the increased population of EVs in distribution networks will stress the system and have adverse economic and environmental effects, these may be minimised with careful off-line planning.

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