An Integrated Approach to the Prediction of Weather, Renewable Energy Generation and Energy Demand in Vermont

Renewable energy production and energy demand have significant sensitivity to local, short term weather conditions. In Vermont there are additional challenges from an operational perspective as a result of local geography and mixed use, including complex terrain, variable weather conditions at a local scale and the distributed nature of the consumers including rural, urban and sub-urban. In our continuing work in applying advanced environmental and smarter energy analytics for utility operations we examine the development and application of an integrated system for enabling the proactive production, management and consumption of renewable energy resources in Vermont. The system is composed of several major components which include a weather prediction capability, an energy demand forecasting system, and a renewable energy power forecasting system which will provide wind and solar energy predictions. It starts with the weather forecasting model coupled to, and driving the electricity demand forecasting model and the renewables generation forecasting model. These coupled model capabilities are the basis of a unique integrated system producing smarter energy analytics for utilities in Vermont.

The weather prediction model is based upon IBM's Deep Thunder system. The Deep Thunder system is an advanced NWP capability derived from a configuration of the WRF-ARW community model, and operates in a nested configuration focused on New England. Horizontal resolution for the Vermont region is between one and two km and utilizes high vertical resolution. The configuration also includes parameterization and selection of physics options appropriate for the range of geography within the domain. This includes double-moment microphysics, NOAH land-surface modeling with soil temperature and moisture in four layers, fractional snow cover and frozen soil physics and Grell three-dimensional cumulus parameterization. The model produces 48-hour forecasts, which are updated twice daily. In addition, it incorporates a diversity of input data sets ranging from NCEP RAP for background conditions, NCEP NAM for lateral boundaries, three-dimensional variational data assimilation from several thousand surface and near-surface observation stations operated by Earth Networks, NOAA and other agencies, and surface conditions derived from remotely sensed observations provided by NASA and USGS. The surface data are particularly important for stable and accurate boundary layer and land surface modeling. We will present an overview of the integrated approach; discuss the on-going work, and some specifics of the solution. We will also discuss the overall effectiveness of our particular approach for this and related applications, issues such as calibration of data and quantifying uncertainty as well as recommendations for future work.