In 2017 NCAR began a multi-phase, multi-agency effort to improve solar forecasting in New York State. The work for Phases 1 and 2 was funded by both the New York Power Authority (NYPA) and DOE, and was done in collaboration with partners at the Electric Power Research Institute (EPRI) and Brookhaven National Laboratory (BNL). Phase 3 will begin in summer 2019 and continue for three years, funded by the New York State Energy Research and Development Authority (NYSERDA) and DOE, and also include EPRI, BNL, and the University at Albany as partners.
Figure 1. WRF monthly average global horizontal irradiance (GHI) ensemble mean, valid at 1700 UTC for the WRF-Solar model runs simulating dates in July 2017 during Phase 1 of the Solar Forecasting for New York project. Locations are marked for Buffalo (BUF), Albany (ALB), Staten Island (STA), and Brookhaven National Laboratory (BNL), which are the locations targeted by BNL for installing networks of sky cameras for solar irradiance nowcasting purposes.
NCAR’s work in Phase 1 of this project used a WRF-Solar® 10-member ensemble to assess the modeled variability in solar resource, focusing on various locations around the New York at which BNL proposed to install networks of sky cameras, and comparing it to observed irradiance variability at BNL (Figure 1). In Phase 2 NCAR built on what was learned in Phase 1 to configure WRF-Solar for a one-year reforecast dataset of WRF-Solar nowcasts (0–6 h) at 3-km grid spacing over the entirety of New York State. This WRF-Solar reforecast dataset, combined with meteorological and irradiance observations at BNL, provided the training dataset for machine learning methods to blend recent observations with WRF-Solar for improved nowcasts of irradiance in the first 1–2 hours. Phase 3 will extend this work further, making the WRF-Solar and machine learning blended forecasting systems quasi-operational for demonstration purposes at multiple sites in NY, and serve as the foundation for introducing a photovoltaic (PV) solar power forecasting system for both select utility-scale sites as well as distributed PV where data are available.
Link to the Feature Story about this work.
