This paper reports on the quantification of the variable effects of the integration of demand response resources (DRRs) in a power system with integrated renewable energy sources (RESs) and utility-scale energy storage systems (ESSs) with the various sources of uncertainty explicitly represented. We deploy a stochastic simulation approach based on Monte Carlo techniques to emulate the transmission-constrained hourly day ahead markets (DAMs) over longer term periods. Salient characteristics of the approach are the ability to represent the spatial and temporal correlation of the loads and the renewable resources, the ability to explicitly represent the payback characteristics of DRRs and the deployment of effective strategies to provide computational tractability for large-scale grids. We illustrate the application of the approach to a modified version of the WECC 240 bus-system to perform various study cases to evaluate the economics, emissions and reliability metrics. The studies illustrate the strong capabilities of the approach and provide insights into the impacts of deepening penetration of DRRs under different intensity levels.