The burgeoning adoption of electric vehicles (EVs) necessitates a comprehensive exploration of the charging infrastructure, delving into both the optimization of EV charger converters and the pivotal role of EV chargers in the power grid. This dissertation comprises six technical chapters, with a focused exploration of converters in Chapters 2 to 4 and an in-depth analysis of the role of EVs in power grids in Chapters 5 to 7.Chapters 2 to 4 showcase advancements in EV charger converters. Chapter 2 introduces a novel active harmonic reduction technique, mitigating the dominant third-order harmonic in the power factor corrector circuit’s input current. This innovation not only enhances grid power quality but also marks a critical step toward efficient and sustainable EV charging. In Chapter 3, a new gate signal modulation method in the dc-dc dual active converter minimizes conduction and switching losses, optimizing the charging process. Chapter 4 extends the converter optimization paradigm with a DC link voltage optimization method, enhancing the efficiency of the entire EV charger across ac-dc and dc-dc stages over the battery charging cycle. Chapters 5 to 7 transition seamlessly to the role of EV charging systems in the power grid. Chapter 5 explores the optimal utilization of bidirectional EVs for grid frequency support during critical events such as loss of generation and frequency drops. This chapter highlights the potential for EVs not merely as energy consumers but as dynamic contributors to grid stability. Chapter 6 presents a dynamic EV charging pricing strategy to distribute the EVs between charging stations (CSs) uniformly and thereby increase the revenue of the charging station operator (CSO) and enhance the charging satisfaction of EV users. Finally, in Chapter 7, a two-stage stochastic programming approach is developed for electric energy procurement in EV charging stations equipped with battery energy storage and photovoltaic generation. This innovative approach provides a roadmap for sustainable energy procurement, emphasizing the synergy between EV charging stations and renewable energy sources. In conclusion, this dissertation provides a holistic and pioneering exploration of EV charging systems, from converter optimization to grid integration. The research contributes significantly to the advancement of EV charging technology, offering solutions to enhance efficiency, power quality, and grid stability. The findings not only address current challenges in electric mobility but also lay a foundation for a sustainable and resilient energy future.

In recent years, there has been an increasing need for effective voltage controls in power systems due to the growing complexity and dynamic nature of practical power grid operations. Deep reinforcement learning (DRL) techniques now have been widely explored and applied to various electric power operation analyses under different control structures. With massive data available from phasor measurement units (PMU), it is possible to explore the application of DRL to ensure that electricity is delivered reliably.For steady-state power system voltage regulation and control, this study proposed a novel deep reinforcement learning (DRL) based method to provide voltage control that can quickly remedy voltage violations under different operating conditions. Multiple types of devices, adjustable voltage ratio (AVR) and switched shunts, are considered as controlled devices. A modified deep deterministic policy gradient (DDPG) algorithm is applied to accommodate both the continuous and discrete control action spaces of different devices. A case study conducted on the WECC 240-Bus system validates the effectiveness of the proposed method. System dynamic stability and performance after serious disturbances using DRL are further discussed in this study. A real-time voltage control method is proposed based on DRL, which continuously regulates the excitation system in response to system disturbances. Dynamic performance is considered by incorporating historical voltage data, voltage rate of change, voltage deviation, and regulation amount. A versatile transmission-level power system dynamic training and simulation platform is developed by integrating the simulation software PSS/E and a user-written DRL agent code developed in Python. The platform developed facilitates the training and testing of various power system algorithms and power grids in dynamic simulations with all the modeling capabilities available within PSS/E. The efficacy of the proposed method is evaluated based on the developed platform. To enhance the controller's resilience in addressing communication failures, a dynamic voltage control method employing the Multi-agent DDPG algorithm is proposed. The algorithm follows the principle of centralized training and decentralized execution. Each agent has independent actor neural networks and critic neural networks. Simulation outcomes underscore the method’s efficacy, showcasing its capability in providing voltage support and handling communication failures among agents.

There has been a significant growth in the distributed energy resources (DERs) connected to the distribution networks in recent years. For a distribution system with a high penetration of DERs, a detailed modeling and representation of the distribution network is needed to accurately assess its steady-state and dynamic behavior. In this dissertation, a field-validated model for a real sub-transmission and distribution network is developed, including one of the feeders modeled with the secondary network and loads and solar PV units at their household/user location. A procedure is developed combining data from various sources such as the utility database, geoinformation data, and field measurements to create an accurate network model. Applying a single line to ground fault to the detailed distribution feeder model, a high voltage swell, with potentially detrimental impacts on connected equipment, is shown in one of the non-faulted phases of the feeder. The reason for this voltage swell is analyzed in detail. It is found that with appropriate control the solar PV units on the feeder can reduce the severity of the voltage swell, but not entirely eliminate it. For integrated studies of the transmission-distribution (T&D) network, a T&D co-simulation framework is developed, which is capable of power flow as well as dynamic simulations, and supports unbalanced modeling and disturbances in the distribution as well as the sub-transmission system. The power flow co-simulation framework is developed as a module that can be run on a cloud-based platform. Using the developed framework, the T&D system response is studied for balanced and unbalanced faults on the distribution and sub-transmission system. For some faults the resultant loss of generation can potentially lead to the entire feeder tripping due to high unbalance at the substation. However, it is found that advanced inverter controls may improve the response of the distribution feeders to the faults. The dissertation also highlights the importance of modeling the secondary network for both steady-state and dynamic studies. Lastly, a preliminary investigation is conducted to include different dynamic elements such as grid-forming inverters in a T&D network simulation.

This report studies the optimal mechanisms for the vertically integrated utility to dispatch and incentivize the third-party demand response (DR) providers in its territory. A framework is proposed, with three-layer coupled Stackelberg and simultaneous games, to study the interactions and competitions among the pro t-seeking process of the utility, the third-party DR providers, and the individual end users (EUs) in the DR programs. Two coupled single-leader-multiple-followers Stackelberg games with a three-layer structure are proposed to capture the interactions among the utility (modeled in the upper layer), the third-party DR providers (modeled in the middle layer), and the EUs in each DR program (modeled in the lower layer). The competitions among the EUs in each DR program is captured through a non-cooperative simultaneous game. An inconvenience cost function is proposed to model the DR provision willingness and capacity of different EUs. The Stackelberg game between the middle-layer DR provider and the lower-layer EUs is solved by converting the original bi-level programming to a single level programming. This converted single level programming is embedded in an iterative algorithm toward solving the entire coupled games framework. Case studies are performed on IEEE 34-bus and IEEE69-bus test systems to illustrate the application of the proposed framework.

The high R/X ratio of typical distribution systems makes the system voltage vulnerable to active power injection from the distributed energy resources (DERs). Moreover, the intermittent and uncertain nature of the DER generation brings new challenges to voltage management. As guided by the previous IEEE standard 1547-2003, most of the existing photovoltaic (PV) systems in the real distribution networks are equipped with conventional inverters, which only allow the PV systems to operate at unity power factor to generate active power. To utilize the voltage control capability of the existing PV systems following the guideline of the revised IEEE standard 1547-2018, this dissertation proposes a two-stage stochastic optimization strategy aimed at optimally placing the PV smart inverters with Volt-VAr capability among the existing PV systems for distribution systems with high PV penetration to mitigate voltage violations. PV smart inverters are fast-response devices compared to conventional voltage control devices in the distribution system. Historically, distribution system planning and operation studies are mainly based on quasi-static simulation, which ignores system dynamic transitions between static solutions. However, as high-penetration PV systems are present in the distribution system, the fast transients of the PV smart inverters cannot be ignored. A detailed dynamic model of the PV smart inverter with Volt-VAr control capability is developed as a dynamic link library (DLL) in OpenDSS to validate the system voltage stability with autonomous control of the optimally placed PV smart inverters. Static and dynamic verification is conducted on an actual 12.47 kV, 9 km-long Arizona utility feeder that serves residential customers. To achieve fast simulation and accommodate more complex PV models with desired accuracy and efficiency, an integrative dynamic simulation framework for OpenDSS with adaptive step size control is proposed. Based on the original fixed-step size simulation framework in OpenDSS, the proposed framework adds a function in the OpenDSS main program to adjust its step size to meet the minimum step size requirement from all the PV inverters in the system. Simulations are conducted using both the original and the proposed framework to validate the proposed simulation framework.

Power system robustness against high impact low probability events is becoming a major concern. About 90% of US power outages reported in the last three decades are due to Hurricanes and tropical storms. Various works of literature are focused on modelling the resilience framework against hurricanes. To depict distinct phases of a system response during these disturbances, an aggregated trapezoid model is derived from the conventional trapezoid model and proposed in this work. The model is analytically investigated for transmission system performance, based on which resiliency metrics are developed for the same.A probabilistic-based Monte Carlo Simulations (MCS) approach has been proposed in this work to incorporate the stochastic nature of the power system and hurricane uncertainty. Furthermore, the system's resilience to hurricanes is evaluated on the modified reliability test system (RTS), which is provided in this work, by performing steady-state and dynamic security assessment incorporating protection modelling and corrective action schemes using the Siemens Power System Simulator for Engineering (PSS®E) software. Based on the results of steady-state (both deterministic and stochastic approach) and dynamic (both deterministic and stochastic approach) analysis, resilience metrics are quantified. Finally, this work highlights the interdependency of operational and infrastructure resilience as they cannot be considered discrete characteristics of the system. The objective of this work is to incorporate dynamic analysis and stochasticity in the resilience evaluation for a wind penetrated power system.

The fast growth of the power system industry and the increase in the usage of computerized management systems introduces more complexities to power systems operations. Although these computerized management systems help system operators manage power systems reliably and efficiently, they introduce the threat of cyber-attacks. In this regard, this dissertation focuses on the load-redistribution (LR) attacks, which cause overflows in power systems. Previous researchers have shown the possibility of launching undetectable LR attacks against power systems, even when protection schemes exist. This fact pushes researchers to develop detection mechanisms. In this thesis, real-time detection mechanisms are developed based on the fundamental knowledge of power systems, operation research, and machine learning. First, power systems domain insight is used to identify an underlying exploitable structure for the core problem of LR attacks. Secondly, a greedy algorithm’s ability to solve the identified structure to optimality is proved, which helps operators quickly find the best attack vector and the most sensitive buses for each target transmission asset. Then, two quantitative security indices are proposed and leveraged to develop a measurement threat analysis (MTA) tool. Finally, a machine learning-based classifier is used to enhance the MTA tool’s functionality in flagging tiny LR attacks and distinguishing them from measurement/forecasting errors. On the other hand, after acknowledging that an adversarial LR attack interferes with the system, establishing a corrective action is imperative to mitigate or remove the potential consequences of the attack. This dissertation proposes two corrective actions; the first one is developed based on the worst-case attack scenario, considering the information provided by the MTA tool. After The MTA tool flags an LR attack in the system, it should determine the primary target and other affected transmission assets, using which the operator can estimate the actual loads in the post-attack stage. This estimation is essential since the corresponding security constraints in the first corrective action model are modeled based on these loads. The second one is a robust optimization that considers various load scenarios. The functionality of this robust model does not depend on the information provided by the MTA tool and is more reliable.

This research identifies several barriers to large scale implementation of solar photovoltaics into the modern US electricity system, along with solutions to help mitigate these challenges. The need for new technologies and utility rate plans are identified as two of these key barriers. In place of expensive, developing technologies this research explores the use of thermal energy storage (TES), a widely used, inexpensive, mature technology as a potential solution for a portion of this problem. A real-life example from Arizona State University (ASU) is used to illustrate the potential of TES. In addition, shortcomings of modern electricity rate plans are identified using both cost and system characteristics of residential solar and battery systems. This rate and system modeling also gives insight into the value that solar can provide to residential customers in a variety of settings.

The stable and efficient operation of the transmission network is fundamental to the power system’s ability to deliver electricity reliably and cheaply. As average temperatures continue to rise, the ability of the transmission network to meet demand is diminished. Higher temperatures lead to congestion by reducing thermal limits of lines while simultaneously reducing generation potential. Furthermore, they contribute to the growing frequency and ferocity of devasting weather events. Due to prohibitive costs and limited real estate for building new lines, it is necessary to consider flexible investment options (e.g., transmission switching, capacity expansion, etc.) to improve the functionality and efficiency of the grid. Increased flexibility, however, requires many discrete choices, rendering fully accurate models intractable. This dissertation derives several classes of structural valid inequalities and employs them to accelerate the solution process for each of the proposed expansion planning problems. The valid inequalities leverage the variability of the cumulative capacity-reactance products of parallel simple paths in networks with flexible topology, such as those found in transmission expansion planning problems. Ongoing changes to the climate and weather will have vastly differing impacts a regional and local scale, yet these effects are difficult to predict. This dissertation models the long-term and short-term uncertainty of rising temperatures and severe weather events on transmission network components in both stochastic and robust mixed-integer linear programming frameworks. It develops a novel test case constructed from publicly available data on the Arizona transmission network. The models and test case are used to test the impacts of climate and weather on regional expansion decisions.

Adhering to an ever-increasing demand for innovation in the field of onboard electric vehicle (EV) charging, several technical aspects pertaining to the design and performance enhancement of integrated multi-port charger topologies are discussed in this study. This study also elucidates various research challenges pertaining to each module of the topology and elucidates technically validated solutions for each.Firstly, targeting the input side totempole power factor corrector (TPFC) circuit, a novel digital filter based Active Mitigation Scheme (AMS) is proposed to curb the third harmonic component, along with a novel discretized sampling-based robust control scheme. Experimental verification of these techniques yields an enhanced Total Harmonic Distortion (THD) of 1.68%, enhanced efficiency of 98.1% and resultant power factor of 0.998 (lag). Further, focusing on the bidirectional CLLC based DC/DC converter topology, a general harmonic approximation (GHA) based secondary side turnoff current minimization technique is discussed. Numerous fabrication and design-based constraints and correlations for parametric modelling of high frequency planar transformer (HFPT) are explained with analytical and 3D Finite Element Analysis (FEA) findings. Further, characterization of the plant transfer function of all-inclusive CLLC model is described along with hybrid Sliding Mode Control (SMC) based control scheme. The steady state experimental results at 1kW rated load show a peak efficiency of 98.49%, while the quantification of dynamic response portray a settling time reduction of 46.4% and an over/undershoot reduction of 33%. Further, comprehensive modeling of triple active bridge (TAB) DC/DC converter topology is presented with special focus on the control scheme and decoupling capabilities to independently regulate the output bridges. With an objective to reduce the overall losses and to add a dimension of controllability, a three-loop control scheme is proposed with power flow optimization. Inculcating the benefits of multiport and resonant topologies, a comprehensive multi-variable loss optimization study of a Triple Active C^3 L^3 (TAC^3L^3) converter is discussed. The performance of eight different hybrid modulation schemes is compared with respect to the developed global loss minimization objective function. Experimental validations for various loading conditions are presented for a wide-gain bidirectional operation (400V/500-600V/24-28V), portraying a peak converter efficiency of 97.42%.