Stochastic simulation of power systems with integrated intermittent renewable resources

We report on the development of a comprehensive, stochastic simulation methodology that provides the capability to quantify the impacts of integrated renewable resources on the power system economics, emissions and reliability variable effects over longer periods with the various sources of uncertainty explicitly represented. We model the uncertainty in the demands, the available capacity of conventional generation resources and the time-varying, intermittent renewable resources, with their temporal and spatial correlations, as discrete-time random processes. We deploy Monte Carlo simulation techniques to systematically sample these random processes and emulate the side-by-side power system and transmission-constrained day-ahead market operations. We construct the market outcome sample paths for use in the approximation of the expected values of the various metrics of interest. Our efforts to address the implementational aspects of the methodology so as to ensure computational tractability for large-scale systems over longer periods include the use of representative simulation periods, parallelization and variance reduction techniques. Applications of the approach include planning and investment studies and the formulation and analysis of policy. We illustrate the capabilities and effectiveness of the simulation approach on representative study cases on a modified WECC 240-bus system. The results provide valuable insights into the impacts of deepening penetration of wind resources.