Renewable Energy Analytics¶
Forecasting helps make decisions
What to forecast¶
Different participants have different needs
- Electric load
- Day-ahead prices
- Potential imbalance sign
- Regulation prices/penalties
- Potential congestion on interconnectors
- Generation from renewable sources
All these are driven by weather and climate
Use cases¶
- Definition of reserve requirements
- Unit commitment and economic dispath
- Coordination of renewables with storage
- Design of optimal trading strategies
- Electricity market clearing
- Optimal maintenance planning (especially for offshore wind farms)
Inputs to these methods are
- deterministic forecasts
- probabilistic forecasts such as quantiles intervals and predictive distributions
- probabilistic forecasts in the form of trajectory or scenarios
- Risk indices
Features for forecasting¶
- Recent power generation measurements
- Weather forecasts for upcoming future
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Other: Off-sit measurements, radar image, etc
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Short-term (<6hrs): power generation measurements are more important
- Medium-term (6-96hrs): weather forecasts are more important
- Long-term (>96hrs): weather forecasts become less important, as long-term weather forecasts are not reliable
Power Curve¶
Power curve shapes the distribution of prediction errors
| Ideal | Actual |
|---|---|
 |  |
Uncertainty¶
Causes of Non-Stationarity¶
- Seasonality
- Equipment condition
- Wind Blades cleanliness
- Solar panel cleanliness