Scientific Leader: Garrett Good
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. Power grid and plant management as well as power stock exchange trading are the two major areas affected by insufficiently predicted wind and solar power.
Weather forecasts play a huge role in decision-making, and the most costly situations for all stakeholders are those extreme weather events missed by conventional forecasts, like stormy winds producing excess wind power or unexpected fog blocking all photovoltaic feed-in. Even probabilistic modern forecasts that simulate tens of possible scenarios can miss these events entirely.
Ultra-large ensemble sizes of O (1000) model runs can address the challenge of capturing all events using continuous probabilistic short-term forecasts, yielding probability density functions (pdfs) for wind and clouds respectively. At sufficient (1km) resolution, this ultimately requires exascale computing 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 (ESIAS), 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 turbidity on solar power production. These tools will provide the meteorological 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.