Improving Energy Forecasting with Advanced Data Analysis
This project analyzed forecasting deficiencies in day-ahead wind and solar power predictions, highlighting their impact on grid congestion, voltage stability, and operational costs.
Challenge
Reducing forecasting errors that cause grid imbalances and cost overruns.
Approach (Methodology & Analysis)
1. Analysis of Day-Ahead Forecasting Deficiencies
Evaluated forecasting errors in wind and solar generation using historical day-ahead power forecasts from the Electric Reliability Council of Texas (ERCOT).
Identified systematic under- and over-predictions in day-ahead forecasts, leading to grid congestion and voltage instability risks.
Demonstrated that forecast aggregation can mask the severity of localized forecast errors, creating hidden vulnerabilities for system operators.
2. Quantifying Forecast Error Distributions
Analyzed the statistical properties of day-ahead forecast deviations, assessing whether wind and solar forecast errors follow normal distributions.
Compared actual wind and solar power outputs to forecasted values at 22 solar and 125 wind sites, using data from the National Renewable Energy Laboratory (NREL).
Found that forecast errors do not follow normal distributions; they are often leptokurtic (heavy-tailed) and skewed, meaning traditional Gaussian-based forecasting models may underestimate risk.
3. Assessing the Impact of Forecast Errors on Grid Stability
Modeled how forecast deviations influence transmission line congestion, voltage fluctuations, and system operating limits.
Demonstrated that uncompensated under-forecasts can trigger real-time dispatch adjustments, increasing operational costs.
Showed that over-forecasts can cause transmission constraints to be exceeded, forcing grid operators to curtail renewable generation to maintain reliability.
4. Implications for Future Forecasting Models
Recommended using alternative probability distributions (e.g., logit-normal, hyperbolic distributions) instead of normal distributions to better capture forecast uncertainty.
Suggested integrating real-time uncertainty metrics into day-ahead market operations to improve decision-making.
Highlighted the need for forecasting techniques that account for extreme weather and rapid demand fluctuations.
Key Findings & Insights
Day-Ahead Forecast Errors Are Systematic and Have Grid-Wide Impacts
Wind and solar forecasts frequently underestimate or overestimate generation, leading to grid congestion, voltage fluctuations, and increased operational costs.
Real-time balancing costs increase when forecast errors force grid operators to adjust dispatch schedules on short notice.
Traditional Forecasting Models Do Not Accurately Capture Renewable Power Variability
Wind and solar forecast errors are not normally distributed, meaning current models that assume Gaussian distributions underestimate worst-case deviations.
Heavy-tailed distributions better represent forecasting uncertainty, improving real-time dispatch efficiency.
Integrating Uncertainty Modeling into Grid Operations Can Reduce Costs and Improve Stability
Risk-aware forecasting methods can prevent costly redispatching events caused by last-minute forecast corrections.
Using alternative statistical models for forecasting uncertainty can lead to better reserve allocation and system planning decisions.
Grid Operators Must Account for Non-Normal Forecast Error Distributions in Future Planning
Developing robust forecasting models that incorporate non-Gaussian uncertainty metrics can improve real-time decision-making.
Machine learning and probabilistic forecasting techniques can enhance forecast accuracy and minimize financial losses from real-time imbalances.