Modeling of Variable Energy Resources in Power System Reliability Assessmen

In recent years, the reliability evaluation of power systems has been receiving increasing attention. This is largely because of the ongoing changes in generation portfolios and environmental constraints. In the assessment of the increasing penetration levels of variable energy resources (VERs), such as wind turbine generators (WTGs) and photovoltaic (PV) systems, reliability methods associated with improved modeling and related phenomena have an essential role. One important factor the planning of wind power and PV systems projects is to measure the contribution of such resources to the adequacy of the power system. For power systems, the intermittent nature of VERs introduces a new level of complexity of modeling such resources using traditional reliability methods used for conventional generators. In contrast to conventional generators, VERs differ in terms of capability and dispatchability. Also, the fuel source of VERs cannot always be made available and controlled. This is due to the fact that the outputs of VERs depend mainly upon the availability of the input (wind or solar irradiation), and their outputs show a high degree of correlation. Therefore, the output of individual generators in the same farm cannot be modeled independently in the probabilistic reliability studies. The work presented in thesis develops an analytical method to model the output of large wind farms and PV systems for power system reliability assessment. Further, a reliability model of VERs that can be used for power system reliability assessment including VERs is developed. The solution of the aforementioned problems is addressed in this work by separately modeling the independent mechanical failures of generation units and the dependency on the input, and then convolving the two distributions. The resulting model includes both probability and frequency distributions of the power output of the VERs. The proposed method reduces the complexity of modeling the VERs, as it considers the effects of input dependency, correlation, and system component failures on reliability assessment. The resulting model is used to evaluate the reliability of test systems in the presence of VERs in several case studies. In power system adequacy assessment, capacity value is used to measure the contribution of VERs to the overall system adequacy. Iterative methods are traditionally used to calculate the capacity value of VERs. However, iterative methods are computationally demanding, especially for large systems. In this work, a direct, non-iterative method is developed to calculate the capacity value of VERs while considering the effect of the input uncertainty and the failure of generation units. The method developed in this work is based on augmenting the cumulative distribution function (CDF) of the generation margin (prior to adding VERs) to include the output power of VERs. From the augmented CDF of the generation margin, the capacity value is analytically determined without performing iterations. The presented method reduces modeling complexity and the computational burden associated with calculating the capacity value of VERs.