The New York Power Authority (NYPA) and several research partners are moving into the final phase of a solar forecasting project, funded at $1.85 million, which will be able to predict solar energy generation to improve electric grid reliability and economic efficiency of power systems, NYPA reported on June 17.
The forecasting study uses high-definition digital cameras, together with advanced weather modeling and other sources of data, to develop prediction models that can anticipate output from large solar generating facilities and smaller, rooftop, distributed solar resources.
The forecasts are based in part upon pictures of the sky analyzed to track the movement of clouds and estimate their impact on solar generation. The information can then be used by power generation operators to provide visibility of changes in solar generation and allow them to respond accordingly.
The final phase of the project will determine the benefit of deploying this forecasting technology across New York State.
The newly expanded solar forecasting network in Albany, N.Y., will mirror an already in place set of imagers on Long Island, which have shown promise for improving solar generation and load forecasting for utility operations, NYPA said.
The new solar forecasting equipment will be installed by field engineers at the University at Albany, several Albany firehouses, the New York State Energy Research and Development Authority’s headquarters and State Department of Transportation sites, the Albany airport and two additional locations that are to be determined.
Project builds on prior research
The project builds on prior research by the National Center for Atmospheric Research (NCAR) team including Brookhaven National Laboratory (BNL), located in Upton, N.Y.
NCAR developed a set of specialized numerical weather prediction models focusing on hours ahead to day-ahead forecasting, as well as machine learning models that utilize recent solar irradiance and weather information to predict the next 1-3 hours of solar irradiance at a location.
BNL developed a sky imager system to determine the movement of clouds and their impact on solar generation within the hour.
In the next phase, BNL will develop and run a forecasting model and modify the algorithms to receive and integrate data from all imagers into a regional very-short-range forecast, for both Albany and the extended Long Island network.
“In addition to expanding the ground-based imager network to the upstate region, we will continue to test and evaluate our solar forecast predictions over a full year to improve confidence in system effectiveness through a full range of seasonal variability,” said Paul Kalb, deputy chair of BNL’s Environmental and Climate Science Department.
NCAR will utilize this data, together with other data from NYS Mesonet, a statewide network of 126 weather stations operated by the University at Albany, and their advanced numerical weather prediction models and machine learning models to provide a forecast of the distributed and utility scale solar power over the entire state.
Scientists at the University’s Atmospheric Sciences Research Center will also provide their expertise and contribute to the data modeling efforts.
Central Hudson, a utility partner, will demonstrate how the results can be integrated into energy management systems and distribution management systems as well as provide situational awareness to operators.
Study results, which will be published in 2022, will help researchers understand the impact of solar power generation on net system load, forecasting and planning for online generation.
The study is funded by NYPA, NYSERDA and the U.S. Department of Energy Solar Energy Technologies Office, and co-managed by the Electric Power Research Institute.