Local impact of short term lidar offshore power forecasts on grid integration

Abstract
Ensemble weather prediction forecasts have been promoted by meteorologists for a long time due to their additional inherent uncertainty information. Despite this advantage over deterministic weather forecasts, their application is still limited, as knowledge of including this uncertainty information in power system operations is not widely available. ProPower is a probabilistic energy market optimisation tool, developed at DLR to demonstrate the advantages of probabilistic forecasts in cost-optimal power dispatch optimisation (Schyska, 2021 and Bents et al., 2024). This study examines whether the ProPower dispatch optimisation benefits by the application of short term lidar forecasts (nowcasts) as well as the potential benefit of more frequent market clearing updates in general.

ProPower is based on a stochastic clearing approach by (Morales et al., 2014) that anticipates balancing costs due to forecast errors. The workflow in ProPower simulates an initial probabilistic market clearing (e.g. day-ahead market) followed by several  probabilistic clearings (e.g. intraday market) based on forecast updates. The last step is the evaluation of balancing cost at delivery. For this study, simulations have been performed on a simplified transmission grid topology covering all Germany. ECMWF ensemble forecasts (Leutbecher and Palmer, 2007) are used as input for an initial market clearing and intraday clearings. The latest intraday forecast for the wind park Amrumbank-West is a probabilistic short term lidar power forecast. The clearing is performed 5 to 30 minutes before delivery to capture ramps in wind power caused by local wind flows that are not resolved in NWP forecasts. ERA5 reanalysis data and on-site power measurements at the wind park are used to compute balancing costs due to forecast errors. This approach aims to demonstrate the benefit of offshore lidar power forecasts on reduced power dispatch at neighbouring nodes and less congested power lines. The advantage of using lidar forecast in dispatch optimisation for a simplified network with five nodes has already been demonstrated by (Bents et al., 2025).

Acknowledgment

Lidar forecasts provided by ForWind – Center for Wind Energy Research of the Universities of Oldenburg, Hannover and Bremen.

Funded by BMWK (Windramp II, ref. No. 03EE3101).

References

Bents,H.M., von Bremen,L. and Schyska,B.U. (2024): Using weather forecast uncertainty minimises electricity costs in low flexibility power systems,  23rd Wind and Solar Integration Workshop, WIW 2024.

Bents,H.M., von Bremen,L, Schyska,B.U. and Rott,A. (2025) Evaluating the benefit of probabilistic Lidar-based wind power forecasts in power systems management. D-A-CH 2025, Meteorology Conference, 2025-06-23 - 2025-06-27, Bern, Schweiz.

Leutbecher,M., and Palmer,T.N. (2007): Ensemble forecasting, Journal of Computational Physics, 227

Morales,J.M., Zugno,M., Pineda,S., and Pinson,P. (2014): Electricity Market Clearing with
Improved Scheduling of Stochastic Production, European Journal of Operational Research, 253(3)

Schyska,B.U. (2021): Coaction of Input Parameters and Model Sensitivities in Numerical Power System Modeling PhD thesis, Oldenburg: Carl-von-Ossietzky Universität Oldenburg