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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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.

Neste estudo, a rede elétrica é utilizada como instrumento de análise para a integração territorial restrita da cidade de Tefé ao meio técnico-científico-informacional. A ocorrência de interrupções e racionamentos no fornecimento de energia elétrica na cidade, localizada no interior do estado do Amazonas, proporciona prejuízos econômicos, sociais e culturais significativos à população, dificultando seu pleno desenvolvimento. Dessa forma, o conhecimento dos principais eventos da evolução histórica da rede elétrica no território nacional e das políticas de planejamento governamental que corroboraram para o surgimento deste problema é analisado com o propósito de entender as dinâmicas socioespaciais que não facilitam a inserção dessa fração do território ao meio geográfico contemporâneo. Os projetos de integração na fronteira amazônica produziram um crescimento sem planejamento dos objetos técnicos, que, estruturados às pressas, proporcionaram obras imediatistas, realizadas em curto prazo -- o que Milton Santos (1994) chama, para os países subdesenvolvidos, de -saltar etapas?. Isso propiciou à população consequências negativas vinculadas às estruturas sociotécnicas amazônicas, que não atendem integralmente às funções que deveriam realizar, como é o caso da rede elétrica do Amazonas. A realização de uma análise de eventos, projetos e propostas que permitem sugestionar uma mitigação ou solução para esse obstáculo ao exercício do direito à cidadania do tefeense é direcionada nesta pesquisa. / In this study, the grid is used as an analytical tool for the restricted territorial integration in the city of Tefé to the technical and scientific-informational mean. The occurrence of interruptions and shortages in the electricity supply in the city, located within the State of Amazonas, provides significant economic, social and cultural losses to the population, hindering their full development. Thus, the knowledge of the main events of the historical evolution for the electric grid in the country and the government planning policies which confirmed to the emergence of this problem is analyzed in order to understand the socio-spatial dynamics that do not facilitate the insertion of this fraction in the contemporary geographical territory. Integration projects in the Amazon frontier produced an unplanned growth of technical objects, which were structured hastily, providing immediately works made in the short term, which according to Milton Santos (1994) is named by -leap-frog? in developing countries. With that, the population is linked to negative consequences related to the Amazonians socio-technical structures that do not match the functions they should undertake such as Amazon electricity grid. Performing an analysis of the events, projects and proposals allow us to suggest a mitigation or a solution to this obstacle that is related to the exercise of the tefeense citizenship right.

Provisioning Internet services remains an area of concern for Internet service providers. Despite investments to improve resources and technology, the understanding of sociotechnical factors that influence the service-provisioning life cycle remains limited. The purpose of this case study was to evaluate the influence of sociotechnical factors associated with telecom service provisioning and to explore the critical success and failure factors, specifically in the telecommunication industry of Kuwait. Guided by sociotechnical systems theory, this qualitative exploratory case study approach examined a purposeful sample of 19 participants comprising of managers, engineers, and technicians who had the knowledge and experience of the service-provisioning life cycle. Semistructured interviews, project logs, and a self-created follow-up questionnaire were the primary sources of data. Thematic analysis techniques assisted in coding the data and developing themes, which resulted in a set of critical success and failure factors that influence the service-provisioning life cycle. Cross-functional communication, risk management practices, infrastructure availability, and employee skill development were among the emergent factors that influenced the service implementation. Internet service providers may use the results from this study to improve the service-provisioning life cycle. Successful implementations will promote an environment of positive social change that will increase employee motivation, productivity, and employee morale.