The increasing contribution of variable weather dependent renewable energy sources (RES) to the electricity grids of European countries challenges the existing energy system in many ways. Resilient power grid and power plant management as well as viable trading at power stock exchanges are the two most obvious and known concerns which must be guaranteed even under conditions of volatile and insufficiently predictable wind and solar power. The challenge of delivering continuous probabilistic short term forecasts can be achieved by ultra-large ensemble sizes with O (1000) model runs, yielding probability density functions (pdfs) for wind and clouds respectively. At sufficient (1km) resolution this ultimately requires exascale capability, which in turn means addressing a series of technical challenges, particularly in the areas of ensemble modelling, programming models, and big data analytics. The flagship framework for accommodating these innovations will be the Ensemble for Stochastic Interpolation of Atmospheric Simulations, which will initially include the Weather Research and Forecast (WRF) model adopted to predict winds at rotor hub heights and cloud optical thickness (COT). The EURopean Air pollution Dispersion-Inverse Model (EURAD-IM) will further address the impact of aerosol-induced turbudity on solar power production. These tools will provide the meterological data needed for wind and solar day-ahead power forecasting, as well as for short-term forecasting in confluence with satellite-based cloud-motion solvers.
ESIAS-Chem is a tool for generating and controlling ultra-large ensembles of chemistry transport models for stochastic integration, exploiting a two-level parallelism, combined with a particle filter data assimilation scheme.
ESIAS-Meteo is a tool for generating and controlling ultra-large ensembles of numerical weather forecast models for stochastic integration, exploiting a two-level parallelism, combined with a particle filter data assimilation scheme.
EURAD-IM (Current forecasts can be viewed here) simulates chemistry particle transportation in local atmospheres coupled with a weather forecast application WRF. An advection-diffusion-reaction equation, with multiple solvers for chemistry, is used.
ESIAS ensemble run part will be entirely refactored with proposed technologies in EoCoE-II.
The Wind Power Management System (WPMS), see Vogt et al., and the Solar Prediction System (SPS), Saint-Drenan et al. are empirical and physical wind and solar power models used at Fraunhofer IEE to calculate German power forecasts from meteorological forecasts and satellite measurements.
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A hybrid physical and machine learning based forecast of regional wind power. S. Vogt, J. Dobschinski, T. Kanefendt, S. Otterson, and Y.M. Saint-Drenan in Fraunhofer IWES, 2016.
A probabilistic approach to the estimation of regional photovoltaic power production.
Y.M. Saint-Drenan, G. H. Good, and M. Braun in Solar Energy, 147:257-276, 2017.
PHD Thesis Philipp Franke, 2018, Univ. of Cologne (http://kups.ub.uni-koeln.de/id/eprint/8437)
PHD Thesis Jonas Berndt, 2018, Univ. of Cologne (https://kups.ub.uni-koeln.de/9098/)
Emission rate and chemical state estimation by 4-dimensional variational inversion
Elbern, H., Strunk, A., Schmidt, H., and Talagrand, O. in Atmos. Chem. Phys., 7, 3749-3769, 2007.
Elbern, H. and Schmidt, H: A four – dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling, J. Geophys. Res., 104, 18583 – 18598, 1999