|Abstract:||The growing world-wide concern over the climate change manifests itself in the growth of grid-integrated renewable resources (RRs) to cost-effectively reduce greenhouse gas emissions and alleviate each nation’s dependence on fuel imports. However, as the penetrations of RRs deepen into the electric power grids around the world, their impacts on the grid’s resource adequacy become issues of growing concern. The marked intermittent and rapidly time-varying nature of RRs cannot be appropriately represented in the widely used time- invariant resource adequacy evaluation approach. In this thesis, we describe the development of a simulation-based resource adequacy evaluation frame- work with the capability to represent the uncertain and time-varying nature of the system loads, supply/demand resources, including those of renewable technologies. For this framework, we deploy stochastic-process-based models to effectively represent all grid-integrated resources so as to capture the intermittent, time-varying and uncertain nature of RRs and their correlation with loads and other resources. We make use of past load history and RR output data to construct the sample paths (s.p.s) associated with the stochastic process representations. The incorporation of RRs is based on the net load concept, which is defined to be the net difference between the total system load and the total RR outputs. In other words, the net load is the load that must be met by the conventional generation resources. Clearly, the net load, just as the system load and RRs, is itself a random process (r.p.) with s.p.s constructed from the s.p.s of the RR outputs and the loads. As such, the net load, in effect, captures the intermittent and time-varying nature of RRs. The time-varying framework uses Monte Carlo simulation techniques to sample the r.p.s of the loads, conventional unit availabilities and the RR outputs. In every simulation run, the efficient sampling of the r.p.s is used to construct the realizations of the outputs of the resources and loads and to evaluate the widely used resource adequacy metrics — the loss of load probability (LOLP), the loss of load hours (LOLH), the loss of load expectation (LOLE) and the expected unserved energy (EUE). The multiple Monte Carlo simulation runs provide the statistical basis for the values of these metrics. The framework provides the capability to define and evaluate additional resource adequacy metrics that are particularly appropriate for the study of the RR impacts on resource adequacy. We introduce new sensitivity indices to quantify the impacts of deepening RR penetrations on the various metrics of interest. The new indices effectively capture the marginal behavior of the adequacy metrics and provide valuable insights to grid operators and planners into how each group of RRs affects each metric for a particular system. We applied the framework to study various resource adequacy issues on a set of large-scale systems. We present representative results from our extensive application studies on a realistic large-scale system with integrated wind and solar RRs, total installed capacity 40, 000 MW and projected summer peak load 36,800 MW. The results provide detailed quantification of the behavior of the resource adequacy metrics as the RR penetrations deepen. Specifically, the results demonstrate the improvement in the grid’s resource adequacy — indicated by the declining values of the metrics — as the penetrations of wind and solar deepen. An important finding is that solar resources appear to have a significantly more pronounced impact on the metrics than wind resources. Such findings make sense because of the generally good and consistent tracking of the load by the solar generation during the summer months for the summer peaking study system. However, the behavior of all the resource adequacy metrics is characterized by significant diminution of marginal returns as the penetrations of solar and wind deepen. Moreover, the tracking ability of solar during the peak summer months is insufficient to replace additional retirements of conventional generation capacity beyond a system-dependent value. Indeed, the resource adequacy of the system begins to deteriorate, i.e., the values of the metrics increase as the conventional capacity retirement increases. Notwithstanding deepening RR penetrations for the system discussed, when the ratio of the total retired conventional capacity over the total integrated RR capacity exceeds the 0.25 value, the inability of the grid to meet the “1 day in 10 years” resource adequacy criterion becomes evident. Such limitations of RRs in their ability to substitute retired conventional capacity and to provide resource adequacy, impact the retirement schedule of fossil-fired generation units. The sensitivity studies carried out provide additional insights into the development of appropriate retirement schedules. A significant aspect of the thesis is the broad range of applications of the proposed framework to study both the short- and longer-time periods for planning, operations and other purposes. Furthermore, the framework allows for the evaluation of resource adequacy metrics for data even with different time resolutions. The proposed framework, provides a useful assessment mechanism to prepare large-grid operators in the transition to the greener electricity future.