JpGU-AGU Joint Meeting 2017

Presentation information

[JJ] Oral

H (Human Geosciences) » H-RE Resource and Engineering Geology

[H-RE18] [JJ] Use of earth science data toward effective applications in renewable energy

Mon. May 22, 2017 9:00 AM - 10:30 AM 202 (International Conference Hall 2F)

convener:Hideaki Ohtake(National Institute of advanced industrial and technology), Fumichika Uno(National Institute of Advanced Industrial Science and Technology), Teruhisa Shimada(Graduate School of Science and Technology, Hirosaki University), Daisuke Nohara(Central Research Institute of Electric Power Industry), Chairperson:Hideaki Ohtake(National Institute of advanced industrial and technology)

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

*Daisuke Nohara1, Masamichi Ohba1, Atsushi Hashimoto1, Shinji Kadokura1 (1.Central Research Institute of Electric Power Industry)

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.