This thesis focuses on the concept, design, and simulation of the Reconfigurable Multi-Source Inverter for EV applications and its effectiveness when combined with a HESS. The current trends in the automotive market, including different vehicle types, and the adoption of electrified vehicles by the public are discussed. The benefits and logistics of different vehicle architectures are analyzed and compared. Hybrid vehicles will be essential in helping transition society from conventional internal combustion engine vehicles to purely electric vehicles. The individual components of these electrified vehicles are reviewed, and common topologies are discussed with the benefits of each system compared. The batteries required for these electric vehicles are costly and require many individual cells in order to operate efficiently. Many hybrids vehicles make use of expensive power electronics, such as DC/DC converters to help boost the operating voltage of the battery pack without adding additional cells. A Reconfigurable Multi-Source Inverter in introduced and its switching structure is explained in depth. Its’ ability to make use of multiple DC sources to create four different voltage levels is outlined and possible modulation techniques are presented. This thesis aims to introduce a novel Reconfigurable Multi-Source Inverter using a Space Vector Pulse Width Modulation (SVPWM) scheme and is further investigated through simulations and with plans for experimental validation on an R-L load. / Thesis / Master of Applied Science (MASc) / One of the main factors affecting the cost of electrified vehicles is the expense of building a high voltage battery pack. Motor’s used in electric vehicle applications typically operate at higher voltages and therefore require large battery pack or costly power electronics to step the voltage of the pack up to a suitable operating level. A Reconfigurable Multi-Source Inverter uses a combination of two sources to create different voltage levels. This novel inverter can be used to maximize the voltage of smaller packs to help reduce the overall cost of vehicle electrification.

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According to U.S. Department of Energy, offshore wind energy has the potential to generate 7,200 TWh of energy annually, which is nearly twice the current annual energy consumption in the United States. With technical advances in the offshore wind industry, particularly in the floating platforms, windfarms are pushing further into the ocean. This creates new engineering challenges for transmission of energy from offshore site to onshore. One possible solution is to convert the energy produced into chemical energy of ammonia, which was investigated by Dr. Eric Morgan. In his doctoral dissertation, he assessed the technical requirements and economics of a 300 tons/day capacity ammonia plant powered by offshore wind. However, in his dissertation, one of the assumptions was connection to the grid which provided auxiliary power to keep the ammonia plant operational and produce at rated capacity. It also allowed selling of excess power to the grid in the scenario of excess power production by wind farm during high winds. This thesis explores the technical and economical feasibility of a similar system, except that the ammonia plant will be on a plantship and there is no connection to the grid. This creates a challenge as the ammonia synthesis plant must operate between 65-100% loads. Thus, the concept of multiple mini-ammonia plants is used to address the scenario of wind energy production at less than rated power. This will allow operation of one or more mini-ammonia plant (corresponding to the available energy from offshore wind). In the event of wind speed lower than the cutoff wind speed for the turbine, the ammonia plant will use the produced ammonia as fuel, with the help of a gas turbine running on either Brayton cycle or combined cycle, to keep the plant idling. It will maintain the reaction conditions of the synthesis chamber and will not produce any ammonia. This is an important step as it takes days to reach the reaction conditions to start ammonia production again after shutting down due to unavailability of energy at low winds. Thus, at any windspeed, a mini-ammonia plant would either idle or operate between 65-100% load. This model will be used to simulate the total energy consumption, total energy captured by the wind farm, and the total ammonia produced. This will further help in assessing the final cost of producing, transporting, and consuming ammonia as fuel and thereby provide a better understanding of the feasibility of implementing this technology. According to U.S. Department of Energy, offshore wind energy has the potential to generate 7,200 TWh of energy annually, which is nearly twice the current annual energy consumption in the United States. With technical advances in the offshore wind industry, particularly in the floating platforms, windfarms are pushing further into the ocean. This creates new engineering challenges for transmission of energy from offshore site to onshore. One possible solution is to convert the energy produced into chemical energy of ammonia, which was investigated by Dr. Eric Morgan. In his doctoral dissertation, he assessed the technical requirements and economics of a 300 tons/day capacity ammonia plant powered by offshore wind. However, in his dissertation, one of the assumptions was connection to the grid which provided auxiliary power to keep the ammonia plant operational and produce at rated capacity. It also allowed selling of excess power to the grid in the scenario of excess power production by wind farm during high winds.\\ \par This thesis explores the technical and economical feasibility of a similar system, except that the ammonia plant will be on a plantship and there is no connection to the grid. This creates a challenge as the ammonia synthesis plant must operate between 65-100\% loads. Thus, the concept of multiple mini-ammonia plants is used to address the scenario of wind energy production at less than rated power. This will allow operation of one or more mini-ammonia plant (corresponding to the available energy from offshore wind). In the event of wind speed lower than the cutoff wind speed for the turbine, the ammonia plant will use the produced ammonia as fuel, with the help of a gas turbine running on either Brayton cycle or combined cycle, to keep the plant idling. It will maintain the reaction conditions of the synthesis chamber and will not produce any ammonia. This is an important step as it takes days to reach the reaction conditions to start ammonia production again after shutting down due to unavailability of energy at low winds. Thus, at any windspeed, a mini-ammonia plant would either idle or operate between 65-100\% load. This model will be used to simulate the total energy consumption, total energy captured by the wind farm, and the total ammonia produced. This will further help in assessing the final cost of producing, transporting, and consuming ammonia as fuel and thereby provide a better understanding of the feasibility of implementing this technology.

According to the International Energy Agency, the global Electric Vehicle (EV) sales are experiencing approximately 24% annual growth and the total market could reach 4 million in 2020 and 21.5 million by 2030. However, the mass production of lithium-ion batteries (LIBs) to power EV creates concerns over environmental impacts and the long-term sustainability of critical elements for producing the major battery components. Although much investment has been made, it is still imperative to develop an effective LIB production and recycling process. This dissertation demonstrates a green and sustainable paradigm for LIBs where the batteries are manufactured and direct recycled to form a closed loop. The water-based cathode electrode delivers comparable cycle life and rate performance to the ones from the conventional organic solvent-based process. The direct recycling process has the advantages to regenerate the cathode material from electrode instead of decomposing into elements. Utilization of a water-soluble binder enables separating the cathode compound from spent electrodes using water, which is then successfully regenerated to deliver comparable electrochemical performance to the pristine one. When scaled up, the degraded cathode material can be directly regenerated by an optimized relithiation thermal synthesis (RTS) method to resynthesize the homogeneous cathode powder of high quality. The key factors and sintering procedures are studied to ensure the performance of the product. The pilot scale test successfully scales up to Kg-level with recycled output materials delivering good electrochemical performance. To automate the direct recycling process and improve the efficiency, machine learning and sensors are utilized in a novel battery disassembly platform. It can classify different batteries based on their types and sizes. The processing temperature is instantly monitored using thermal imager, and the prediction model is trained to give the prediction for measures taken by a closed loop control system. Furthermore, the image recognition is employed for quality control after the cutting process and the defect can be mitigated to ensure effective dismantling of End-of-life (EOL) batteries. The integration of machine learning techniques makes the elaborate dismantling process safer and more efficient. / Doctor of Philosophy / According to the International Energy Agency, the global Electric Vehicle (EV) sales are experiencing approximately 24% annual growth and the total market could reach 4 million in 2020 and 21.5 million by 2030. However, the mass production of lithium-ion batteries (LIBs) to power EV creates concerns over environmental impacts and the long-term sustainability of critical elements for producing the major battery components. In this work, a green and sustainable manufacturing and recycling paradigm for LIBs is ushered and scaled up to pilot-scale test. Compared with the electrodes produced by conventional organic solvent-based process, the water-based electrodes can deliver comparable battery performance, meanwhile reduce the cost as well as the pollution to environment. The spent batteries are successfully regenerated to form the closed loop system with minimal external toxic solvent used. At pilot-scale, Kg-level battery material can be directly regenerated to deliver high-quality cathode powder. It provides the guidance of design parameters for large-scale battery recycling in industry. To automate the direct recycling process and improve the efficiency, machine learning and sensors are utilized in a novel battery disassembly platform. The integration of machine learning techniques makes the elaborate dismantling process safer and more efficient.

In this thesis case studies have been made to study the impact ofSMES system on power system transmission with a decoupled Optimal Power Flow program. Linear Programming (LP) and Quadratic Programming (QP) storage scheduling methods including transmission security have been developed, LP method is suitable for single storage system and QP method is capable of solving multiple storage scheduling problem. Results on single storage system is compared with Load Shaving method, which indicate that three methods have approximately the same storage schedule and daily fuel cost. Results on two storage system with QP method suggest that the coordination between storage units can help to transfer more power from base units and reduce total fuel cost in peak hours. / Master of Science

In the modern power grid, the increasing penetration of intermittent energy sources like solar and wind into the comes with unsought challenges. With increasing smart grid directcurrent (DC) deployments in distribution feeders, microgrids, smart buildings, and highvoltage transmission, there is a need for better understanding the landscape of power flow (PF) solutions as well as for efficient PF solvers with performance guarantees. This thesis puts forth three approaches with complementary strengths towards coping with the PF task, consisting of solving a system on non-linear equations, in DC power systems. We consider a possibly meshed network hosting ZIP loads and constant-voltage/power generators. Uncertainty is another inevitable side-effect of a modern power grid with vast deployments of renewable generation. Since energy storage systems (ESS) can be employed to mitigate the effect of uncertainties, their energy and power ratings along with their charging control strategies become of vital importance for renewable energy producers. This thesis also deals with the task of sizing ESS under a model predictive control (MPC) operation for a single ESS used to smoothen out a random energy signal. To account for correlations in the energy signal and enable charging adjustments in response to real-time fluctuations, we adopt a linear charging policy, designed by minimizing the initial ESS investment plus the average operational cost. Since charging decisions become random, the energy and power limits are posed as chance constraints. The chance constraints are enforced in a distributionally robust fashion. The proposed scheme is contrasted to a charging policy under Gaussian uncertainties and a deterministic formulation. / M.S. / Power systems are undergoing major changes as more renewable energy resources are being deployed across their networks. Two of the major changes are the increase in direct-current (DC) generation and loads and making up for the uncertainty introduced by these resources. In this thesis, we have tackled these two important aspects; a DC power flow (PF) solver and an energy storage system (ESS) sizing under uncertainty. The three DC PF solvers proposed in this thesis exhibit complementary values and can handle a wide range of loads and generation types. We have also proposed a distributionally robust ESS sizing under model predictive control framework, capable of handling worst-case uncertainties.

High renewable energy penetration is a goal for many countries to increase energy security and reduce carbon emissions from conventional power plants. Wind energy is one of leading sources among different renewable resources. However, high wind energy penetration in the system brings new challenges to the electric power system due to its variable and stochastic nature, and non-correlation between wind and load profiles. The term non-correlation is used in this research refers to the fact that wind or any other renewable generation, which is nature driven, does not follow the load like conventional power plants. Wind spill is a challenge to utilities with high wind energy penetration levels. This occurs from situations mentioned above and the fact that wind generation sometimes exceeds the servable load minus must-run generation. In these cases there is no option but to curtail non-usable wind generation. This dissertation presents grid-scale energy storage and demand response options as an optimization problem to minimize spilled wind energy. Even after managing this spilled wind energy, there is still a challenge in a system with high wind energy penetration coming from wind power forecast error. Wind power forecast error is handled by having more back-up energy and spilling the non-usable wind power. This research offers a way to use the grid-scale energy storage units to mitigate impacts of wind power forecast error by. A signal processing method is proposed to decompose the fluctuating wind power forecast error signal, based on the fact that each energy storage or conventional unit is more efficient to operate within specific cycling regimes. Finally, an algorithm is proposed schedule energy storage for mitigating both impacts. / Ph. D.

A project located at the VPI & SU Swine Center was used to investigate the utilization of a waste treatment lagoon as an energy source to heat swine housing. The investigation test procedure consisted of two main components: (1) the design and testing of a heating system using a solar assisted heat pump and (2) the development of a computer model to simulate lagoon response to energy gains and losses. The energy lost by the lagoon was considered to occur by conduction, convection, evaporation and radiation. Under natural weather conditions, the losses due to these factors become gains when the lagoon temperature is less than the temperature of the surrounding earth and air. Energy gain by the lagoon under most operating conditions was due to solar insolation. By using the information generated by the computer model and data collected during the testing of the system, it was concluded that there is low level energy available in the waste treatment lagoon. Also, the removal of the low level energy for heating swine housing has little effect upon the thermal stability of the lagoon. / Master of Science

The purpose of this thesis is the development and analysis of applications for ionic polymers as energy harvesting devices. The specific need is a self-contained energy harvester to supply renewable power harvested from ambient vibrations to a wireless sensor. Ionic polymers were investigated as mechanical to electrical energy transducers. An ionic polymer device was designed to harvest energy from vibrations and supply power for a wireless structural health monitoring sensor.The ionic polymer energy harvester is tested to ascertain whether the idea is feasible. Transfer functions are constructed for both the open-circuit voltage and the closed-circuit current. The impedance of the device is also quantified. Using the voltage transfer function and the current transfer function it is possible to calculate the power being produced by the device.Power generation is not the only energy harvesting application of ionic polymers, energy storage is another possibility. The ionic polymer device is tested to characterize its charge and discharge capabilities. It is charged with both DC and AC currents. An energy storage comparison is performed between the ionic polymers and capacitors. While the polymers performed well, the electrolytic capacitors are able to store more energy. However, the ionic polymers show potential as capacitors and have the possibility of improved performance as energy storage devices. Current is measured across resistive loads and the supplied power is calculated. Although the power is small, the ionic polymers are able to discharge energy across a load proving that they are capable of supplying power. / Master of Science

Increasing dynamics in power systems on account of renewable integration, electric vehicle penetration and rising demands have resulted in the exploration of energy storage for potential solutions. Recent technology- and industry-driven developments have led to a drastic decrease in costs of these storages, further advocating their usage. This thesis compiles the author's research on optimal integration of energy storage. Unpredictability is modelled using random variables favouring the need of stochastic optimization algorithms such as Lyapunov optimization and stochastic approximation. Moreover, consumer interactions in a competitive environment implore the need of topics from game theory. The concept of Nash equilibrium is introduced and methods to identify such equilibrium points are laid down. Utilizing these notions, two research contributions are made. Firstly, a strategy for controlling heterogeneous energy storage units operating at different timescales is put forth. They strategy is consequently employed optimally for arbitrage in an electricity market consisting of day-ahead and real-time pricing. Secondly, energy storages owned by consumers connected to different nodes of a power distribution grid are coordinated in a competitive market. A generalized Nash equilibrium problem is formulated for their participation in arbitrage and energy balancing, which is then solved using a novel emph{weighted} Lyapunov approach. In both cases, we design real-time algorithms with provable suboptimality guarantees in terms of the original centralized and equilibrium problems. The algorithms are tested on realistic scenarios comprising of actual data from electricity markets corroborating the analytical findings. / Master of Science / Modern power system, which is responsible for generation and transport of electricity, is witnessing a lot of changes such as the increased adoption of wind and solar energy, promotion of electric vehicles, and ever increasing consumer demands. Amidst such developments, energy storage devices like batteries are being propagated as a necessary addition to the power system for its safe operation. This has been further supported by the decrease in prices of these devices over time. An effective assimilation of energy storage however, requires extensive mathematical studies on account of unpredictable renewable generation and consumer demands.This thesis focuses upon the preceding concern. To this note, two novel research contributions are made. In the first, an individual consumer is modeled who wishes to reduce his/her energy costs by simultaneously employing energy storages belonging to different technologies. In the latter, a more challenging multi-consumer interaction is reviewed where multiple end consumers wish to reduce costs while competing against each other over limited resources. In either of the cases, efficient algorithms are designed that are shown to produce desirable results over real-life data and have mathematically provable performance guarantees.