There are two main types of logistical models used for long-term performance prediction of autonomous power systems:​ time series and probabilistic. Time series models are more common and are more accurate for sizing storage systems because they are able to track the state of charge. However, the computational time is usually greater than for probabilistic models. It is common for time series models to perform 1-year simulations with a 1-hour time step. This is likely because of the limited availability of high resolution data and the increase in computation time with a shorter time step. Computation time is particularly important because these types of models are often used for component size optimization which requires many model runs.

This thesis includes a sensitivity analysis examining the effect of the time step on these simulations. The results show that it can be significant, though it depends on the system configuration and site characteristics. Two probabilistic models are developed to estimate the temporal resolution error of a 1-hour simulation:​ a time series/probabilistic model and a fully probabilistic model. To demonstrate the application of and evaluate the performance of these models, two case studies are analyzed. One is for a typical residential system and one is for a system designed to provide on-site power at an aquaculture site. The results show that the time series/probabilistic model would be a useful tool if accurate distributions of the sub-hour data can be determined. Additionally, the method of cumulant arithmetic is demonstrated to be a useful technique for incorporating multiple non-Gaussian random variables into a probabilistic model, a feature other models such as Hybrid2 currently do not have. The results from the fully probabilistic model showed that some form of autocorrelation is required to account for seasonal and diurnal trends.