This dissertation introduces a new planning and operational tool to integrate photovoltaic (PV) systems into the utility's generation mix. It is recognized that much of the existing research concentrated on the central PV system, its operations, and long-term planning with PV system and concluded that technical problems in PV_ operation. will _power was subtracted from the utility load with the expectation that conventional generation would meet the load. This approach is valid for small penetration levels and for PV facilities connected near the load centers. Second, PV system was studied on a case-by-case basis. This made the interactions between the PV systems and conventional power systems not well known to the operator in the dispatch center on one hand, and to the PV system manufacturer, on the other hand. In addition, several constraints such as thermal generation ramping capabilities, energy costs, tie-line interchange, spinning reserve requirements, hydro availability and generating capacity, and pumped-storage scheduling are not adequately represented in this process. These are real problems and their solutions are sought in this dissertation. Finally, the value of PV systems does not lie only in serving load, but also in reducing problems associated with emissions. It is felt that a comprehensive methodology that would take into account the PV system characteristics and the forth mentioned constraints, as well as more global penetration is developed. The proposed methodology is designed to handle load dynamics and PV fluctuations, so as to minimize operational problems. The objective of this study is to determine the economic and operational impacts when large photovoltaic systems are incorporated into the electric utility generation mix. The proposed methodology handles combustion turbines, hydro and pumped-storage hydro power systems. Performance analysis shows that hydro availability, generation mix and characteristics, PV power output dynamics and performance, time of the year, and energy costs influence the economic and operational impacts of large-scale PV generation. Results show that while hydro dispatching increases acceptable PV penetration levels, generation mix and energy costs influence the breakeven capital cost. According to this study, for a 10 percent PV penetration level (1200 MW) and high energy costs, the breakeven capital cost is $968/kW and $1200/kW for Richmond (Virginia) and Raleigh (North Carolina), respectively. This corresponds to an energy cost of 3.20 and 3.00 ¢/kWh for Richmond and Raleigh. / Ph. D.

The objective of the proposed research is to quantify the limits of overstressed and aging circuit breakers in terms of probability of failure and to provide guidelines to determine network reconfigurations, generator commitment, and economic dispatch strategies that account for these limits. The proposed temporary power system operating strategies address circuit breaker adequacy issues and allow overstressed breakers to be operated longer and more reliably until they are replaced with adequate equipment. The expansion of electric networks with new power sources (nuclear plants, distributed generation) results in increased short-circuit or fault currents levels. As fault currents increase, they will eventually exceed circuit breaker ratings. Circuit breakers exposed to fault currents in excess of their ratings are said to be overstressed, underrated, or inadequate. Insufficient ratings expose overstressed breakers to increased failure probabilities. Extensive common-mode outages caused by circuit breaker failures reduce the reliability of power systems. To durably avoid outages and system unreliability, overstressed breakers must eventually be replaced. Large-scale replacements of overstressed breakers cannot be completed in a short time because of budgetary limits, capital improvement schedules, and manufacturer-imposed constraints. Meanwhile, to preserve the ability of old and overstressed breakers to safely interrupt faults, short-circuit currents must be kept within the limits imposed by the ratings and the age of these breakers by using the substation reconfiguration and generator commitment strategies described in this study. The immediate benefit of the above-mentioned operating strategies is a reduction of the failure probability of overstressed breakers obtained by avoiding the interruption of currents in excess of breaker ratings. Other benefits include (i) increased network reliability, (ii) restored operating margins with respect to existing equipment, and (iii) prioritized equipment upgrades that enhance the long-term planning of power systems.

A new Markov chain based energy storage model to evaluate the power supply availability of microgrids with renewable energy generation for critical loads is proposed. Since critical loads require above-average availability to ensure reliable operation during extreme events, e.g., natural disasters, using renewable energy generation has been considered to diversify sources. However, the low availability and high variability of renewable energy sources bring a challenge in achieving the required availability for critical loads. Hence, adding energy storage systems to renewable energy generation becomes vital for ensuring the generation of enough power during natural disasters. Although adding energy storage systems would instantaneously increase power supply availability, there is another critical aspect that should be carefully considered; energy storage sizing to meet certain availability must be taken into account in order to avoid oversizing or undersizing capacity, which are two undesirable conditions leading to inadequate availability or increased system cost, respectively. This dissertation proposes to develop a power supply availability framework for renewable energy generation in a given location and to suggest the optimal size of energy storage for the required availability to power critical loads. In particular, a new Markov chain based energy storage model is presented in order to model energy states in energy storage system, which provides an understanding of the nature of charge and discharge rates for energy storage that affect the system's power output. Practical applications of the model are exemplified using electrical vehicles with photovoltaic roofs. Moreover, the minimal cut sets method is used to analyze the effects of microgrid architectures on availability characteristics of the microgrid power supply in the presence of renewable energy sources and energy storage. In addition, design considerations for energy storage power electronics interfaces and a comparison of various energy storage methods are also presented. / text

Made available in DSpace on 2014-07-29T15:08:18Z (GMT). No. of bitstreams: 1 Previsao de Vazoes Naturais Diarias.pdf: 3835466 bytes, checksum: f927e5c8c3a89c73430512243b55c36c (MD5) Previous issue date: 2012-09-05 / The forecast of natural flows to hydroelectric plant reservoirs is an essential input to the planning and programming of the SIN´s operation. Various computer models are used to determine these forecasts, including physical models, statistical models and the ones developed with the RNA´s techniques. Currently, the ONS performs daily forecasts of natural flows to the UHE Tucuruí based on the univariate stochastic model named PREVIVAZH, developed by Electric Energy Research Center - Eletrobras CEPEL. Throughout the last decade, several papers have shown evolution in the application of neural networks methodology in many areas, specially in the prediction of flows on a daily, weekly and monthly basis. The goal of this dissertation is to present and calibrate a model of natural flow forecast using the RNA´s methodology, more specifically the NSRBN (Non-Linear Sigmoidal Regression Blocks Networks) (VALENCA; LUDERMIR, 2001), on a time lapse from 1 to 12 days forward to the Tucuruí Hydroelectric Plant, considering the hydrometric stations data located upstream from it s reservoir. In addition, a comparative analysis of results found throughout the calibrated neural network and the ones released by ONS is performed. The results show the advantage of the methodology of artificial neural networks on autoregressive models. The Mean Absolute Percentage Error - MAPE values obtained were, on average, 48 % lower than those released by the ONS. / A previsão de vazões naturais aos reservatórios das usinas hidrelétricas é insumo fundamental para o planejamento e operação do SIN. Diversos modelos são utilizados na determinação dessas previsões, entre os quais podem ser citados os modelos físicos, os estatísticos e aqueles baseados na técnica de Redes Neurais Artificiais. Atualmente, o ONS realiza as previsões diárias de vazões naturais para a Usina Hidrelétrica Tucuruí com base no modelo estocástico univariado denominado PREVIVAZH, desenvolvido pelo CEPEL. Ao longo da última década, muitos trabalhos têm mostrado a evolução da aplicação da metodologia de Redes Neurais Artificiais em diversas áreas e em particular na previsão de vazões naturais, para intervalos de tempo diários, semanais e mensais. O objetivo deste trabalho foi calibrar e avaliar um modelo de previsão de vazões naturais, utilizando a metodologia de RNA, mais especificamente as redes construtivas do tipo NSRBN(Non-Linear Sigmoidal Regression Blocks Networks) (VALENCA; LUDERMIR, 2001), no horizonte de 1 até 12 dias à frente, para a Usina Hidrelétrica Tucuruí, considerando as informações advindas de postos hidrométricos localizados à montante do seu reservatório. Adicionalmente, foi realizada uma análise comparativa dos resultados encontrados pela rede neural calibrada e aqueles obtidos e divulgados pelo ONS. Os resultados obtidos mostram a vantagem da metodologia de redes neurais artificiais sobre os modelos auto-regressivos. Os valores do Erro Percentual Médio Absoluto - MAPE foram, em média, 48% inferiores aos divulgados pelo ONS.