To address this challenge, a system of coupled models to drive a stochastic optimization process was developed and deployed. While each model attempts to deliver more accurate results, the models also quantify the inherent uncertainty in each respective component. The effort starts with weather, and builds upon the on-going work with IBM Deep Thunder, a state-of-the-art high spatial- and temporal-resolution forecasting system, customizable to meet the needs of specific weather-sensitive business decisions. It is based, in part, on the ARW core of the Weather Research and Forecasting (WRF) model. For Vermont, it is run operationally twice daily (initialized at 00 and 12 UTC) nested to 1-km horizontal resolution with high vertical resolution in the lower boundary layer for regional coverage for 48 hours. A number of model and remote sensing data sets are ingested to enable appropriate initial and boundary conditions. Three-dimensional variational data assimilation is performed around each analysis time using MADIS and EarthNetworks WeatherBug observations.
Once each execution of the weather model is completed, the results are abstracted to include key variables at the appropriate temporal and spatial resolution. The variables include both direct model output as well as diagnostic fields derived from specialized post-processing. These data then permit execution in parallel of data-driven (i.e., via statistical and machine-learning) models to predict wind and solar power, and electricity demand. All of these models operate at a granularity that enables aggregation from the 1 km computational weather grid. Hence, at the finest scale, wind power is done at the turbine level, solar at each utility-scale facility and demand at the distribution sub-station level. In addition, the demand model predicts solar power generation for distributed systems behind the meter. These models use training sets, which consist of both forecasts and hindcasts of the weather model, and historical power and other data from the utilities.
To begin to optimize the value of Vermont's generation, demand response and transmission assets, and increase utilization of renewable power, the Renewable Integration Stochastic Engine (RISE) is introduced. It uses the outputs of the aforementioned data-driven probabilistic models to assess the uncertainty in the forecasts and to predict their impact on the grid. It performs a probabilistic power flow analysis and determines the likelihood of potential congestion conditions with sufficient lead time to mitigate the impact. This can assist a utility in improving the stability of the transmission network.
This paper presents an overview of the deployed capabilities along with the results to date and the overall effectiveness of our particular approach. It also discusses on-going issues such as calibration of data and quantifying uncertainty as well as recommendations for future work.