10:00 AM - 10:15 AM
[HRE18-05] Probabilistic Weather Prediction for Power Demand/Supply Operation Management under the High Penetration of Renewable Energy Power
Keywords:Renewable energy, probabilistic prediction, ensemble
Renewable energy power, such as from photovoltaics and wind, is volatile partly because of natural variability in weather conditions. Electric power companies have to manage the balance between supply and demand. Management of the volatility of renewable energy power is a key factor in minimizing the cost of integrating renewable energy power into electric grid systems while maintaining the required high level of reliability. For this purpose, probabilistic weather prediction for renewable energy power is one of the most cost-effective and easily implemented tools available to system operators.
In this study, we developed a regional ensemble prediction method using the Weather Research and Forecasting (WRF) model to a predict probabilistic weather. To obtain dynamically consistent perturbations with a synoptic weather pattern, both initial and lateral boundary perturbations were determined via differences between the control and an ensemble member of the Japan Meteorological Agency (JMA)'s operational one-week ensemble forecast. This method provides multiple ensemble members for Japan area with a horizontal resolution of 15 km for 75 hours at 30-minute interval outputs by downscaling the JMA's operational global forecast along with the perturbations. The predictions were able to represent various features of the high-resolution spatiotemporal distribution of precipitation affected by the intensity and location of extratropical cyclones in each ensemble member. Although the ensemble prediction method showed model bias in the mean values and variances for certain variables such as irradiance or wind speed, it has the potential to provide probabilistic information regarding the uncertainty of weather prediction.
In this study, we developed a regional ensemble prediction method using the Weather Research and Forecasting (WRF) model to a predict probabilistic weather. To obtain dynamically consistent perturbations with a synoptic weather pattern, both initial and lateral boundary perturbations were determined via differences between the control and an ensemble member of the Japan Meteorological Agency (JMA)'s operational one-week ensemble forecast. This method provides multiple ensemble members for Japan area with a horizontal resolution of 15 km for 75 hours at 30-minute interval outputs by downscaling the JMA's operational global forecast along with the perturbations. The predictions were able to represent various features of the high-resolution spatiotemporal distribution of precipitation affected by the intensity and location of extratropical cyclones in each ensemble member. Although the ensemble prediction method showed model bias in the mean values and variances for certain variables such as irradiance or wind speed, it has the potential to provide probabilistic information regarding the uncertainty of weather prediction.