With the penetration of PV on the distribution system continually increasing, new advanced simulation methods are necessary to model the potential technical impacts of PV to the equipment and operation of the distribution system. With distributed PV, a timeseries analysis approach is necessary to more fully capture the time-varying nature of solar energy and the interaction with distribution system operations. The objective of the research is to streamline the PV interconnection process by providing more accurate methods that require less time for both the PV interconnection screening criteria and the PV interconnection impact study process. To improve the computational speed of timeseries simulations, an equivalent circuit reduction method is developed to simplify the circuit to a reduced-order model. The reduced circuit is equivalent during timeseries simulations, but it solves in a fraction of the time. The algorithm works with unbalanced multi-phase complex distribution system models, and it is shown to have high accuracy when validated against the full feeder models. An advanced PV hosting capacity simulation tool is developed and used to quantify system impacts for many PV interconnection scenarios, configurations, and locations, which can be generalized to develop improved future interconnection screening criteria. The advanced tools quantify location-specific impacts and the locational hosting capacity of potential PV interconnection locations on the feeder, including PV impact signatures and zones. A set of 50 different real distribution systems is analyzed in detail to demonstrate the range of scenarios and impacts that can occur depending on the feeder characteristics and topology. Specific methods are developed for time-series analysis, faster simulation times, distribution system equivalent circuit reduction, and PV hosting capacity analysis. The advancements presented in this thesis assist in streamlining PV interconnection studies with faster interconnection analysis times and more accurate screening criteria.

Many important decisions must be made without full information. For example, a woman may need to make a treatment decision regarding breast cancer without full knowledge of important uncertainties, such as how well she might respond to treatment. In the financial domain, in the wake of the housing crisis, the government may need to monitor the credit market and decide whether to intervene. A key input in this case would be a model to describe the chance that one person (or company) will default given that others have defaulted. However, such a model requires addressing the lack of knowledge regarding the correlation between groups or individuals. How to model and make decisions in cases where only partial information is available is a significant challenge. In the past, researchers have made arbitrary assumptions regarding the missing information. In this research, we developed a modeling procedure that can be used to analyze many possible scenarios subject to strict conditions. Specifically, we developed a new Monte Carlo simulation procedure to create a collection of joint probability distributions, all of which match whatever information we have. Using this collection of distributions, we analyzed the accuracy of different approximations such as maximum entropy or copula-models. In addition, we proposed several new approximations that outperform previous methods. The objective of this research is four-fold. First, provide a new framework for approximation models. In particular, we presented four new models to approximate joint probability distributions based on geometric attributes and compared their performance to existing methods. Second, develop a new joint distribution simulation procedure (JDSIM) to sample joint distributions from the set of all possible distributions that match available information. This procedure can then be applied to different scenarios to analyze the sensitivity of a decision or to test the accuracy of an approximation method. Third, test the accuracy of seven approximation methods under a variety of circumstances. Specifically, we addressed the following questions within the context of multivariate discrete distributions: Are there new approximations that should be considered? Which approximation is the most accurate, according to different measures? How accurate are the approximations as the number of random variables increases? How accurate are they as we change the underlying dependence structure? How does accuracy improve as we add lower-order assessments? What are the implications of these findings for decision analysis practice and research? While the above questions are easy to pose, they are challenging to answer. For Decision Analysis, the answers open a new avenue to address partial information, which bing us to the last contribution. Fourth, propose a new approach to decision making with partial information. The exploration of old and new approximations and the capability of creating large collections of joint distributions that match expert assessments provide new tools that extend the field of decision analysis. In particular, we presented two sample cases that illustrate the scope of this work and its impact on uncertain decision making. / text

Dans ce mémoire, des questions associées à la simulation en régime transitoire et à l'intégration au réseau de distribution d'une génératrice asynchrone privée (« NUIG : Non-Utility Induction Génération ») sont étudiées. L'objectif de ce travail est de vérifier l'influence de différents facteurs tels le niveau et l'emplacement de la charge et de la compensation capacitive ainsi que le type de charge sur les phénomènes d'auto-excitation de la génératrice asynchrone et les surtensions indésirables qui résultent d'un défaut et/ou d'une condition d'îlotage. Les résultats de simulation montrent que les phénomènes d'auto-excitation semblent très complexes et multidimensionnels et ils impliquent l'interaction de différents facteurs. Les courbes obtenues dépendent fortement des spécifications du système et il est difficile de dériver des critères précis qui puissent être appliqués de façon générale à tous les cas d'intégration de la génération asynchrone au réseau. Cependant, des principes directeurs pratiques pour limiter l'occurrence de l'auto-excitation ont été dérivés et selon ces principes directeurs, un système de protection et un réglage de rélai recommandés sont présentés pour l'interconnexion de génératrices asynchrones privées (« NUIG ») au réseau de distribution. / In this thesis, some topics related to the dynamic simulation and operations of Non-Utility Induction Generation (NUIG) are investigated. The objective of this thesis is to put focus on the influence of different factors such as load and capacitive compensation levels and positions as well as load type on induction generator self-excitation phenomenon and related overvoltages, resulting from a fault and/or an unwanted islanding condition. The results of simulations showed the self-excitation phenomenon appear to be very complex and multidimensional and implying the interaction of many different factors. The obtained curves and behaviors highly depend on the system specifics, and it is difficult to drive precise criteria applicable in a general manner to ail cases of generation’s integration. However, practical guidelines to limit the occurrence of self-excitation have been derived and, as per these guidelines, recommended protection System and relay settings are presented for interconnection of NUIGs to the distribution networks.