Presentations

Abstract

The surge in global wind and solar energy production expects a significant energy share from these sources. Yet, their inherent variability challenges reliability for electricity generation, demanding real-time supply-demand balancing by grid operators. Strategies like regulation reserves, load-following reserves, and sub-hourly economic dispatch mitigate fluctuations. Despite efforts, the September 2021 winter storms caused substantial US power losses, amplifying supply-demand issues and triggering a blackout. Accurate extreme event forecasting is crucial. Ramp events—surges or drops in wind and solar generation—are vital for grid integration and planning. Forecasting them is complex. Deterministic forecasts offer single values but disregard atmospheric uncertainties. Probabilistic forecasts, derived from ensemble models, offer ranges with complexities. A novel approach combines ensemble WRF weather modeling and SCADA data assimilation from nearby wind farms. Assimilating data from upwind farms, especially those with ramp events, enhances predictions for downwind farms. Steps include data collection, ramp event detection, defining ensemble members, implementing observational nudging, and validation. Preliminary findings highlight advection's role in ramp event forecast horizons. Nudged ensemble members excel in detecting true events and reducing false alarms. This method aids grid and wind farm operators, bolstering power system management for energy stability and reliability.