<p dir="ltr">The safe and reliable delivery of electricity is critical for the functioning of our modern society. However, high-impact, low-probability (HILP) catastrophic events (such as extreme weather caused by climate change, or cyber-physical attacks) pose an ever-growing threat to the power grid. At the same time, modern advancements in computational capabilities, communication infrastructure, and measurement technologies provide opportunities for new operation and control strategies that enhance the resilience of electric power systems to such HILP events. In this work, optimization-based operation and control approaches are proposed to improve resilience in two power systems applications. First, a real-time linearized-trajectory model-predictive controller (LTMPC) is developed for ensuring voltage, frequency, and transient (rotor angle) stability in systems engineered to operate as microgrids. Such microgrids are capable of seamlessly transitioning from grid-connected operation to an islanded mode and thus, enhance system resilience. The proposed LTMPC enables rapid deployment of such systems by reducing engineering costs and development time while maintaining stable operation. On the other hand, some power systems, such as distribution feeders, are not designed to operate as standalone microgrids. For these cases, a method is proposed for forming ad-hoc microgrids from intact sections of the damaged feeder in the aftermath of a HILP event. A feeder operating center-on-a-laptop (FOCAL) is introduced that coordinates the control of possibly hundreds of inverter-interfaced distributed energy resources (e.g., rooftop solar, battery storage) to improve system resilience. Theoretical analysis as well as numerical case studies and simulations of the proposed strategies are presented for both applications.</p>

<p dir="ltr"><b><i>Background</i></b><b>:</b> The increase in EV deployment is presenting numerous energy challenges to the utility distribution infrastructure. The energy demands created by EV charging sessions and the growing call to develop a network of DCFC charging facilities increases operational risk to the utilities in the ability to provide safe and reliable electricity to all customers.</p><p dir="ltr"><b><i>Purpose:</i></b> The purpose of this study is to identify the extent of impact to the utility distribution grid from an increasing EV (electric vehicle) adoption.</p><p dir="ltr"><b><i>Setting</i></b><b>: </b>In total, there were 3,020 rows of distribution circuit feeder data collected from the PG&E DIDF and National Grid NY System Reporting Tool between 2022 – 2023. Additionally, 48 documents, engineering reports, rate filings, articles, research studies, and utility whitepapers were examined.</p><p dir="ltr"><b><i>Research Design:</i></b> Impact analysis using a mixed methodology.</p><p dir="ltr"><b><i>Data Collection and Analysis:</i></b> A single research question was used to formulate an impact analysis to the utility distribution infrastructure under a mixed methodology. A quantitative analysis to determine circuit burden based on historical feeder capacity data and conduct hypothetical impact testing based on a set of ten variables. A qualitative analysis was administered to support these results and further design recommendations for the utility system under a logic model.</p><p dir="ltr"><b><i>Findings:</i></b> The PG&E and Utility National Grid EV and Circuit Impact Analysis demonstrated high susceptibility to overburden under a moderate number of level 2 EV chargers and significantly more when the loading impact was the result of DCFC facilities. The additional exploratory research yielded a consistent theme of mitigation strategies applicable to all electric utilities.</p><p><br></p><p dir="ltr"><b><i>Conclusions</i></b><i>:</i> Portions of the electric distribution infrastructure, operated by hundreds of utilities across the United States must be analyzed, upgraded, and adequately managed under systematic programs which promote facility upgrades, energy management, technology integration, such as AMI. Further, the execution of regulatory strategies for smart policy development and investment into hosting capacity tools are critical to reducing EV impact to the utility.</p><p dir="ltr"><b><i>Keywords</i></b><i>: </i>EV, electric utility, EV grid impacts, EV grid analysis, EV managed charging, EV AMI infrastructure.</p>

<p dir="ltr">Distributed energy resources (DERs), such as rooftop solar generation and energy storage systems, are becoming more prevalent in distribution systems. DERs are connected to the distribution system via power electronic converters, introducing faster dynamics in the system. Understanding the system dynamics under a high penetration of inverter-based DERs is critical for power system researchers and practitioners, driving the development of modeling techniques and simulation software. Aiming to reduce computational complexity, existing tools and techniques often employ various approximations. Meanwhile, modern advancements in computational hardware capabilities provide opportunities to include the faster time-scale dynamics. To address this, the primary objective of this thesis is to develop an open-source Python simulation package, Dynamic Simulation using Sparse Tableau Approach in Python, DynaSTPy (pronounced “dynasty”), capable of capturing the dynamics of all components in a distribution feeder. The distribution feeder is modeled as a system of Differential-Algebraic Equations (DAEs). Further, each component in the feeder is modeled based on the Sparse Tableau Approach (STA), which involves the representation of component model equations using sparse matrices, facilitating a systematic procedure to model the components and construct the system DAEs. In sinusoidal steady state, the DAEs can be represented in phasor form, extending the approach to perform power flow analysis of distribution feeders.</p>

<p dir="ltr">In recent years, a surge in data-driven computation has significantly impacted security analysis in cyber-physical systems (CPSs), especially in decentralized environments. This transformation can be attributed to the remarkable computational power offered by high-performance computers (HPCs), coupled with advancements in distributed computing techniques and sophisticated learning algorithms like deep learning and reinforcement learning. Within this context, wireless communication systems and decentralized computing systems emerge as highly suitable environments for leveraging data-driven computation in security analysis. Our research endeavors have focused on exploring the vast potential of various deep learning algorithms within the CPS domains. We have not only delved into the intricacies of existing algorithms but also designed novel approaches tailored to the specific requirements of CPSs. A pivotal aspect of our work was the development of a comprehensive decentralized computing platform prototype, which served as the foundation for simulating complex networking scenarios typical of CPS environments. Within this framework, we harnessed deep learning techniques such as restricted Boltzmann machine (RBM) and deep convolutional neural network (DCNN) to address critical security concerns such as the detection of Quality of Service (QoS) degradation and Denial of Service (DoS) attacks in smart grids. Our experimental results showcased the superior performance of deep learning-based approaches compared to traditional pattern-based methods. Additionally, we devised a decentralized computing system that encompassed a novel decentralized learning algorithm, blockchain-based learning automation, distributed storage for data and models, and cryptography mechanisms to bolster the security and privacy of both data and models. Notably, our prototype demonstrated excellent efficacy, achieving a fine balance between model inference performance and confidentiality. Furthermore, we delved into the integration of domain knowledge from CPSs into our deep learning models. This integration shed light on the vulnerability of these models to dedicated adversarial attacks. Through these multifaceted endeavors, we aim to fortify the security posture of CPSs while unlocking the full potential of data-driven computation in safeguarding critical infrastructures.</p>

<p>U disertaciji je obrađen problem proračuna nesimetričnih tokova<br />snaga neuravnoteženih prenosnih i aktivnih distributivnih mreža<br />velikih dimenzija, naročito onih sa energetskim resursima<br />zasnovanim na uređajima energetske elektronike. Pri tome je dat dokaz<br />da tradicionalna klasifikacija čvorova nije dovoljna da se precizno<br />modeluju i rešavaju nesimetrični tokovi snaga navedenih mreža.<br />Zatim je predložena nova klasifikacija čvorova sa odgovarajućim<br />metodima tokova snaga. Time je omogućena vrlo precizna formulacija<br />i proračun modela nesimetričnih tokova snaga navedenih mreža. Ta<br />preciznost metoda tokova snaga je rezultat toga što su novom<br />klasifikacijom čvorova obuhvaćene sve praktično primenjene<br />upravljačke strategije tradicionalnih naizmeničnih mašina, a<br />naročito energetskih resursa koji su zasnovani na energetskoj<br />elektronici.</p> / <p>This thesis deals with power flow calculations of unbalanced large scale<br />transmission networks and active distributive networks, especially ones<br />with electronically interfaced resources. The proof that the traditional bus<br />classification is not sufficient for precise modeling and calculation of power<br />flow of these networks is given first. Then, a new bus classification and<br />corresponding very precise power flow model and calculation of<br />aforementioned networks are proposed. This precision of power flow<br />calculation is the result of encompassing of all control strategies of modern<br />energy resources by the new bus classification.</p>

Small signal oscillation has been always a major concern in the operation of power systems. In a generator, the electromechanical coupling between the rotor and the rest of the system causes it to behave in a manner similar to a spring mass damper system. Following any disturbance, such as sudden change in loads, actuations in the output of turbine and faults etc. it exhibits an oscillatory behaviour around the equilibrium state. The use of fast acting high gain AVRs and evolution of large interconnected power systems with transfer of bulk power across weak transmission links have further aggravated the problem of these low frequency oscillations. Small oscillations in the range of about 0.1Hz to 3.5Hz can persist for long periods, limiting the power transfer capability of the transmission lines. These oscillations can be reduced by incorporating auxiliary controllers on generator excitation system. Power System Stabilizers (PSSs) were developed to produce additional damping by modulating the generator excitation voltage. Designing effective PSS for all operating conditions especially in large interconnected power systems still remains a difficult and challenging task. More and more power electronic based controllers have been and will be used in power systems. Many of these controllers such as Static Var Compensators (SVCs), Static Synchronous Compensators (STATCOMs) and Unified Power Flow Controllers (UPFCs) etc., are incorporated in power transmission networks to improve its operational capability. In addition, some of the energy storage systems such as Battery Energy Storage systems (BESS), Super conducting Magnetic Energy Storage System (SMES) as well large non-conventional energy sources are also increasingly being integrated with the power grid. With large integration of these devices, there is a significant impact on system stability, more importantly on small signal oscillatory instability of the power system. This thesis primarily focuses on impact of such devices on small signal oscillatory stability of the power systems. More specifically in this thesis small signal stability analysis of a Single Machine Infinite Bus (SMIB) system with a grid connected wind powered Permanent Magnet Synchronous Generator (PMSG) has been presented. A SMIB system has been purposely chosen so that general conclusions can be obtained on the behaviour of the embedded STATCOM/Energy Source (ES) system on system stability. With a better understanding of the impact of such a system it would be probably possible to analyze more complicated multimachine power system and their impact on system stability. Small signal model of the complete system which comprises the generator, transmission network, inter connecting STATCOM, the wind power generator and all associated controllers has been developed. The performances of the system following a small disturbance at various operating conditions have been analyzed. To obtain quantitative estimates of the damping and synchronizing torques generated in the system, expressions for damping and synchronizing torque clients have been developed. With these analyses, the relative impact of the STATCOM and STATCOM with ES on system performance have been assessed. It is shown that with active and reactive power modulation capabilities effective and efficient control of small signal oscillations in power systems can be achieved.

<p dir="ltr">The urgent global shift towards decarbonization necessitates the development of robust frameworks to navigate the complex technological, financial, and regulatory challenges emerging in the clean energy transition. Furthermore, the increased adoption of renewable energy sources (RES) is correlated to the exponential growth in weather data research over the last few years. This circular relationship, where big data drives renewable growth, which feeds back the data pipeline, serves as the primary focus of this study: the development of computational tools across diverse spatial and temporal scales for the optimal design and operation of renewable energy-based systems. Two scales are considered, differentiated by their primary objectives and techniques used. </p><p dir="ltr"> In the first one, the integration of probabilistic forecasts into the operations of RES microgrids (MGs) is studied in detail. It is revealed that longer scheduling horizons can reduce dispatch costs but at the expense of forecast accuracy due to increased prediction accuracy decay (PAD). To address this, a novel method that determines how to split the time horizon into timeblocks to minimize dispatch costs and maximize forecast accuracy is proposed. This forms the basis of an optimal rolling horizon strategy (ORoHS) which schedules distributed energy resources over varying prediction/execution horizons. Results offer Pareto-optimal fronts, showing the trade-offs between cost and accuracy at varying confidence levels. Solar power proved more cost-effective than wind power due to lower variability, despite wind’s higher energy output. The ORoHS strategy outperformed common scheduling methods. In the case study, it achieved a cost of \$4.68 compared to \$9.89 (greedy policy) and \$9.37 (two-hour RoHS). The second study proposes the Caribbean Energy Corridor (CEC) project, a novel, ambitious initiative that aims to achieve total grid connectivity between the Caribbean islands. The analysis makes use of thorough data procedures and optimization methods for the resource assessment and design tasks needed to build such an infrastructure. Renewable energy potentials are quantified under different temporal and spatial coverages to maximize usage. Prioritizing offshore wind development, the CEC’s could significantly surpass anticipated growth in energy demand, with an estimated installed capacity of 34 GW of clean energy upon completion. The corridor is modeled as an HVDC grid with 32 nodes and 31 links. Underwater transmission is optimized with a Submarine-Cable-Dynamic-Programming (SCDP) algorithm that determines the best routes across the bathymetry of the region. It is found that the levelized cost of electricity remains on the low end at \$0.11/kWh, despite high initial capital investments. Projected savings reach \$ 100 billion when compared with ”business-as-usual” scenarios and the current social cost of carbon. Furthermore, this infrastructure has the potential to create around 50,000 jobs in construction, policy, and research within the coming decades, while simultaneously establishing a robust and sustainable energy-water nexus in the region. Finally, the broader implications of these works are explored, highlighting their potential to address global challenges such as energy accessibility, prosperity in conflict zones, and sharing these discoveries with the upcoming generations.</p>