abstract: Accurate forecasting of electricity prices has been a key factor for bidding strategies in the electricity markets. The increase in renewable generation due to large scale PV and wind deployment in California has led to an increase in day-ahead and real-time price volatility. This has also led to prices going negative due to the supply-demand imbalance caused by excess renewable generation during instances of low demand. This research focuses on applying machine learning models to analyze the impact of renewable generation on the hourly locational marginal prices (LMPs) for California Independent System Operator (CAISO). Historical data involving the load, renewable generation from solar and wind, fuel prices, aggregated generation outages is extracted and collected together in a dataset and used as features to train different machine learning models. Tree- based machine learning models such as Extra Trees, Gradient Boost, Extreme Gradient Boost (XGBoost) as well as models based on neural networks such as Long short term memory networks (LSTMs) are implemented for price forecasting. The focus is to capture the best relation between the features and the target LMP variable and determine the weight of every feature in determining the price. The impact of renewable generation on LMP forecasting is determined for several different days in 2018. It is seen that the prices are impacted significantly by solar and wind generation and it ranks second in terms of impact after the electric load. The results of this research propose a method to evaluate the impact of several parameters on the day-ahead price forecast and would be useful for the grid operators to evaluate the parameters that could significantly impact the day-ahead price prediction and which parameters with low impact could be ignored to avoid an error in the forecast. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

This dissertation studies two important models in the field of the distributed generation technologies to provide resiliency to the electric power distribution system. In the first part of the dissertation, we study the impact of assessing a Combined Cooling Heating Power system (CCHP) on the optimization and management of an on-site energy system under stochastic settings. These mathematical models propose a scalable stochastic decision model for large-scale microgrid operation formulated as a two-stage stochastic linear programming model. The model is solved enhanced algorithm strategies for Benders decomposition are introduced to find an optimal solution for larger instances efficiently. Some observations are made with different capacities of the power grid, dynamic pricing mechanisms with various levels of uncertainty, and sizes of power generation units. In the second part of the dissertation, we study a mathematical model that designs a Microgrid (MG) that integrates conventional fuel based generating (FBG) units, renewable sources of energy, distributed energy storage (DES) units, and electricity demand response. Curtailment of renewable resources generation during the MG operation affects the long-term revenues expected and increases the greenhouses emission. Considering the variability of renewable resources, researchers should pay more attention to scalable stochastic models for MG for multiple nodes. This study bridges the research gap by developing a scalable chance-constrained two-stage stochastic program to ensure that a significant portion of the renewable resource power output at each operating hour will be utilized. Finally, some managerial insights are drawn into the operation performance of the Combined Cooling Heating Power and a Microgrid.

Large scale outages have occurred worldwide in recent decades with some impacting 15-25% of a nation’s population. The complexity of blackouts has been extensively studied but many questions remain. As there are no perfect solutions to prevent blackouts, usually caused by a complex sequence of cascading events, a number of different measures need to be undertaken to minimize impact of future disturbances. Increase in deployment of phasor measurement units (PMUs) across the grid has given power industry an unprecedented technology to study dynamic behavior of the system in real time. Integration of large scale synchronized measurements with SCADA system requires a careful roadmap and methodology. When properly engineered, tested, and implemented, information extracted from synchrophasor data streams provides realtime observability for transmission system. Synchrophasor data can provide operators with quick insight into precursors of blackout (e.g., angular divergence) which are unavailable in traditional SCADA systems. Current visualization tools and SE functions, supported by SCADA, provide some basic monitoring. Inaccuracies in measurements and system models, absence of redundancy in the measured parameters or breaker statuses in most cases, and lack of synchronization and time resolution in SCADA data result in limited functionality and precision for a typical EMS required in today’s operating environment of tighter margins that require more frequent and more precise data. Addition of synchrophasor data, typically having several orders of magnitude higher temporal resolution, (i.e., 60 to 120 measurements per second as opposed to one measurement every 4 to 8 seconds), can help detect higher speed phenomena and system oscillations. Also, time synchronization to one micro-second allows for accurate comparison of phase angles across the grid and identification of major disturbances and islanding. This dissertation proposes a more comprehensive, holistic set of criteria for optimizing PMU placement with consideration for diverse factors that can influence PMU siting decision-making process and incorporates several practical implementation aspects. An innovative approach to interoperability testing is presented and solutions are offered to address the challenges. The proposed methodology is tested to prove the concept and address real-life implementation challenges, such as interoperability among the PMUs located across a large area.

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Natural disasters have become more severe and frequent than previous assessments with global warming. The increasing risk of natural disasters presents different groups of populations with diverse vulnerabilities, particularly those underrepresented social groups which need specific support before, during, and after extreme disasters. Hence, it is highly desired to examine vulnerability quantitatively and qualitatively across different social groups in risk to natural disasters. This dissertation study aims to investigate the measure of social vulnerability to two types of climate change-related natural disasters: sea-level-rise floodings and wildfires. In the study of sea-level-rise floodings, high-risk flooding areas are first identified for a coastal city. Then, we measure social vulnerability index (SVI) using a new SVI metric to identify vulnerable social groups which should be paid more attention for coastal flooding disaster mitigation. Compared to existing SVI methods, the new SVI leverages principal component analysis and analytic hierarchy process to achieve a better social vulnerability analysis. In the study of wildfires, we focus on the understanding of minority vulnerabilities and their disparities to wildfires over time and space. Minority vulnerabilities are analyzed with spatial clustering methods including Local Moran’s I and Getis-Ord Gi*. The vulnerability disparity is measured based on a reference point from which the quantity separates a minority group on a particular place. Both location quotient and location amplitude index are used to quantitively measure the vulnerability disparity among different minorities. Lastly, in addition to the “direct” impact of disasters on vulnerable population, this dissertation study also conducts vulnerability analysis to failed infrastructure (e.g., power systems) due to disasters, i.e., the “indirect” impact of disasters on different social groups. Recently, scheduled power outages known as Public Safety Power Shutoff (PSPS) are becoming increasingly common to mitigate threats of wildfires to power systems. However, current PSPS decision making processes do not consider the unequal distribution of various social groups, particularly those who are more vulnerable to the power outage. This study investigates the measure of social vulnerability in high-risk fire areas to PSPS, which will help decision makers to better determine the efficiency of a PSPS event for wildfire mitigation.

Amidst the backdrop of a for years lasting electricity crisis in South Africa, the study aims to examine the impact of prolonged urban energy infrastructure disruptions on residents on the household level. This investigation will aid a deeper understanding of the interdependencies between infrastructural services, households, and their socio-economic situations in urban landscapes. The study contributes to the literature of urban infrastructural disruptions and explores individual coping mechanisms and resilience strategies of the affected people.  For that, the research poses the following research question: How do urban dwellers react to, are limited by and cope with long-lasting and frequent urban electricity outages in the domestic realm in Cape Town. Drawing upon the theoretical frameworks of assemblage thinking, social practice theory and the concept of resilience, the thesis takes up a relational approach that emphasizes the complexity of the correlation between electricity networks and the life of urban residents. The researcher conducted seven qualitative in-depth interviews with residents of Cape Town regarding their individual experience with load shedding. The findings have shown that residents actively counteract power outages by assembling alternative energy appliances and the adjustment of routines. It was also visible that the respective socio-economic living situations affected the resilience of residents. Specifically, participants from less affluent backgrounds showed lower resilience in maintaining their usual daily social practices. The paper advocates for the importance of including especially the most vulnerable groups in counter initiatives in a way that would reduce the impact of power outages.

This research has developed an assessment model and framework to measure cascading economic impacts in terms of gross domestic product (GDP) loss due to severe weather-induced power outages. The major objectives of this research were to (1) identify physical correlations between different industries within an economic system, (2) define deterministic relationships through the values of interconnectedness and interdependency between 71 industries, (3) complete probabilistic estimation of economic impacts using historical economic data spanning from 1997 to 2016, and (4) develop an assessment model that can be used in the future to measure economic loss in terms of gross domestic product (GDP) across 71 industries.

With the constant requirement for improving the levels of quality and reliability in the power distribution systems, especially with regard to the quality of service (duration and frequency of outages), it has intensified the need for a better planning and operation of the electrical grids in all voltage levels. One of the main challenges in the distribution systems is the balance between customer satisfaction, regulation (legislation) of power sector and the return on the capital invested by the utilities. Often this search for resource optimization ends up being hampered by process problems, lack of tools, few experts, among other issues, in most cases favoring a poor planning of electrical system reliability. In many cases the planning criteria used by the distribution companies are related only to the equipment load, voltage level control and new customer access, so it seldom has the reliability among the elements. Even when reliability is part of these criteria it's usually based just on the assessment of historical outages. Due to this deficiency, the subject of this dissertation is the use of a method for forecasting reliability in the electrical planning stage, i.e., it will be proposed actions such as installation of new switches, use of different network topologies, reduction of failure rates and recovery times, and finally calculated the impact of these actions on the reliability KPIs. The methodology for forecasting the indicators is based on logical-structural matrix, which use information such as failure rates and mean time to restore the power to different network segments. It will also be presented a case study proposing planning actions in a real distribution grid with subsequent evaluation of the impact on reliability KPIs of an electrical utility. / Com a constante exigência de elevação dos patamares de qualidade e confiabilidade dos sistemas de distribuição de energia elétrica, especialmente no que diz respeito à qualidade do serviço (duração e frequência de interrupções), temse intensificado a necessidade de aprimoramento do planejamento e operação dos sistemas elétricos de energia. Um dos principais desafios nos sistemas de distribuição de energia elétrica está relacionado a encontrar o equilíbrio entre a satisfação dos consumidores, a regulação que rege o setor elétrico e a remuneração do capital investido pelas empresas de distribuição. Muitas vezes esta busca pela otimização dos recursos acaba prejudicada por deficiência de processos, falta de ferramentas, mão de obra de especialistas entre outros, favorecendo na maioria dos casos um planejamento deficiente sob os aspectos de confiabilidade dos sistemas elétricos de distribuição. Em muitos casos os critérios de planejamento adotados pelas distribuidoras estão relacionados apenas ao carregamento de equipamentos, níveis de tensão e atendimento de novas cargas, poucas vezes tem-se dentre os critérios de planejamento itens relacionados à confiabilidade dos sistemas e quando estes fazem parte das etapas de planejamento geralmente são baseados somente em avaliações de base de dados históricas de interrupções. Devido a esta deficiência o tema desta dissertação é a utilização de um método para previsão de confiabilidade na fase de planejamento de sistemas elétricos de distribuição, ou seja, serão propostas ações de planejamento como instalação de equipamentos de seccionamento, diferentes topologias de rede, redução de taxas de falha e tempos de restabelecimento e por fim o calculo do impacto destas ações nos indicadores de confiabilidade. A metodologia para a previsão de indicadores é estabelecida com base em matrizes lógico-estruturais, as quais utilizam informações como taxa de falhas e tempos médios de restabelecimento para diferentes seguimentos de rede. Será apresentado também um estudo de caso propondo ações de planejamento em um sistema real de distribuição com posterior avaliação do impacto nos indicadores de confiabilidade de uma empresa distribuidora de energia elétrica.

Master of Science / Department of Electrical and Computer Engineering / Anil Pahwa / Unpredictable power outages due to environmental factors such as lighting, wind, trees, and animals, have always been a concern for utilities because they are often unavoidable. This research aims to study squirrel-related outages by modeling past real-life outage data and provide the optimal result which would assist utilities in increasing electric system reliability. This research is a novel approach to benchmark system performance in order to identify areas and durations with higher than expected outages. The model is illustrated with seven years (2005-2011) of animal-related outage data and 14 years of weather data (1998-2011) for four cities in Kansas, used as training data to predict future outages. The past data indicates that the number of outages on any day varies with the seasons and weather conditions on that day. The prediction is based on a Bayesian Model using conditional probability table, which is calculated based on training data. Since future weather conditions are unknown and random, Monte Carlo Simulation is used with the past 14 years of weather data to create different yearly scenarios. These scenarios are then used with the models to predict expected outages. Multiple runs of Monte Carlo analysis provide a probability distribution of expected outages. Further work discusses about cost-to-benefit analysis of implementation of outage mitigation methods. The analysis is performed by considering different combinations of outage reduction and mitigation levels. In this research, eight cases of outage reduction and nine cases of mitigation levels are defined. The probability of benefit is calculated by a statistical approach for every combination. Several optimal strategies are constructed using the probability values and outage history. The outcomes are compared with each other to propose the most beneficial outage mitigation strategy. This research will immensely assist utilities in reducing the outages due to squirrels more effectively with higher benefits and therefore improve reliability of the electricity supply to consumers.