Speaker
Description
Increasing shares of distributed PV generation plus increased wind energy being fed into the grid challenge local grids through rising variability and peak loads, requiring accurate, high-resolution forecasts for grid flexibility. EngagePrivFlex addresses this by exploring how private households can provide flexible generation & consumption to support grid stability. The meteorological component develops post-processing and ML tools to improve weather-driven forecasts for PV and wind energy production &flexibility planning. This is done using ensemble calibration, site-specific temporal disaggregation, and conversion to per-substation aggregated power production predictions to represent local variability relevant for demand response. Meteo-forecasts are coupled with grid and consumption data to predict short-term flexibility potential, demonstrating how tailored post-processing and AI methods can translate NWP outputs into insights for smart-grid operation and renewable integration.
