Probabilistic forecasting of the aggregated Finnish wind energy based on the MetCoOp ensemble prediction system (MEPS)

The increasing amount of electricity generated from wind power leads to a stronger variability in electricity supply to the national electricity grids. This makes wind power forecasts a necessary component in the wind energy market. The forecasts are used to estimate the amount of energy that will be generated and prevent potential imbalances between production and consumption in electricity grids. Energy price in Finland is set every day at the Nordic energy trading stock Nordpool. Unaccounted shortage or surplus of power in the grid needs to be compensated. This may result in costs required to balance the grid.

To account for the uncertainties in wind power calculations, a probabilistic wind power forecasting tool has been developed. It is based on the MetCoOp Ensemble Prediction System (MEPS), in which the HARMONIE Numerical Weather Prediction model is run with 2.5 km horizontal resolution and 65 vertical levels. The system is operational through joint efforts of a group of Nordic countries participating in the MetCoOp cooperation.

The skills of wind power calculations, however, depend not only on the skills of the MEPS system, but also on the information on the wind farms. Detailed information on the locations of wind turbines (WT), their hub heights and technical specifications is important in adequate calculations of power production and power losses. Most essential WT’s parameters are so-called power and thrust coefficient (C_T) curves. Power curve is necessary to convert wind speed to corresponding power output. C_T curve is needed to calculate wind flow retardation by WT, also called wake effect, resulting in power losses downwind in a wind farm. Power and C_T curves, as well as other WT technical parameters, are turbine-specific and typically provided by the manufacturer under commercial terms and conditions. In power calculations over a country with a multitude of wind farms, gathering such information may be challenging.

Nowadays power curves of many WT models can be found in open sources, such as thewindpower.net and wind-turbine-models.com. However, they are still missing for some and especially new WT models. C_T curves, on the other hand, are practically not available in open sources. A statistical solution was developed to approximate both power and C_T curves using as input commonly available WT specifications. The uncertainty in power calculations associated with approximated power and C_T curves was found to be smaller than that associated with the uncertainty in the predicted wind speed.

Wind to power conversion is done for essentially all WTs installed in Finland, to produce an aggregated probabilistic wind power forecast. Wind variability is accounted by applying Gaussian smoothening to power (and C_T) curves. Wake-related losses were calculated applying Katic-Jensen wake model, with missing C_T obtained applying the above method.