Renewable Energy

Modelling seasonal and long-term climate variability for renewable energy asset owners.

UNDERSTANDING CLIMATE VARIABILITY

As the penetration of renewable energy increases across the National Electricity Market, seasonal and long-term climate variability will continue to have a larger impact on electricity market dynamics, such renewable energy supply and residual demand, causing large fluctuations in regional electricity prices.

As the market becomes more sophisticated, proponents must gain a more detailed understanding of the link between meteorological conditions and key climate variables – such as wind speed and solar irradiance – and the implications for regional spot market dynamics.

Seasonal and long-term climate forecasts

To solve the limitation of relying on past climatology to inform future wind and solar conditions, climate forecast models have proven to be informative in describing long-term change in the atmospheric system by linking changes in sea-surface temperature and weather patterns. These predictions may be calibrated and downscaled to create a usable local-scale forecast for wind speed and solar irradiance over seasonal (6 months) and long-term timescales (30 years).

RepuTex’s Multi-Model Ensemble (REMME) combines seasonal predictions from multiple climate forecast systems into a common framework, helping to inform a range of strategic decision making for renewable energy asset owners.

Key applications include:

  • Risk Management: Early warning and wind anomaly detection (wind droughts), such as the potential magnitude and length of low-wind periods
  • Project Development: Long-term wind outlook for a location and region, informing future changes in wind conditions in support of prospecting, project screening and capital decisions.
  • Portfolio Risk: Wind outlook at the portfolio-level based on aggregate exposure to wind volume risks and regional weather dependence.
  • Energy Storage: Understanding energy storage opportunities such as the impact of weather patterns on wholesale prices, energy shifting and arbitrage opportunities based on specific wind patterns and future conditions.
  • System-level analysis: Long-term forecasting for the impact of weather patterns on wind ramping and supply fluctuations, spot price variability, spatial and temporal meteorological dependence (i.e. wind correlations, geographic smoothing, REZ analysis, inter-connector flows, etc).
  • Trading and Price Analysis: Seasonal outlook for climate variability and alignment of forecast weather pattern frequency with regional spot market outcomes (such as wind and solar supply, demand and spot price fluctuations).

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