The Sensitivity of Power System Expansion Models

Power system expansion models are a widely used tool for planning powersystems, especially considering the integration of large shares of renewableresources. The backbone of these models is an optimization problem, whichdepends on a number of economic and technical parameters. Although theseparameters contain significant uncertainties, the sensitivity of power systemmodels to these uncertainties is barely investigated. In this work, we introduce a novel method to quantify the sensitivity ofpower system models to different model parameters based on measuring theadditional cost arising from misallocating generation capacities. The value ofthis method is proven by three prominent test cases: the definition of capitalcost, different weather periods and different spatial and temporal resolutions.We find that the model is most sensitive to the temporal resolution. Fur-thermore, we explain why the spatial resolution is of minor importance andwhy the underlying weather data should be chosen carefully

Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Decreasing gate closure times on the electricity stock exchange market and the rising share of renewables in today’s energy system causes an increasing demand for very short-term power forecasts. While the potential of dual-Doppler radar data for that purpose was recently shown, the utilization of single-Doppler lidar measurements needs to be explored further to make remote-sensing-based very short-term forecasts more feasible for offshore sites. The aim of this work was to develop a lidar-based forecasting methodology, which addresses a lidar's comparatively low scanning speed. We developed a lidar-based forecast methodology using horizontal plan position indicator (PPI) lidar scans. It comprises a filtering methodology to recover data at far ranges, a wind field reconstruction, a time synchronization to account for time shifts within the lidar scans and a wind speed extrapolation to hub height. Applying the methodology to seven free-flow turbines in the offshore wind farm Global Tech I revealed the model’s ability to outperform the benchmark persistence during unstable stratification, in terms of deterministic as well as probabilistic scores. The performance during stable and neutral situations was significantly lower, which we attribute mainly to errors in the extrapolation of wind speed to hub height

Lidar-based minute-scale offshore wind speed forecasts analysed under different atmospheric conditions

In recent years, the potential of remote sensing-based minute-scale forecasts to improve the integration of wind power into our energy system has been shown. In lidar-based forecasts, the wind speed is extrapolated from the measuring to the forecast height, i.e. the wind turbines hub height, by assuming a stability-corrected logarithmic wind profile. The objective of this paper is the significant reduction of large forecasting errors associated with the height extrapolation. Hence, we introduce two new approaches and characterise their skill under different atmospheric conditions. The first one is based on an empirical set of parameters derived from lidar data and operational wind turbine data. The second approach derives the wind speed tendency of two consecutive forecasts at the measuring height and applies this to operational wind speed data at hub height. We identified the uncertainty in stability estimates and measurement height as the main cause for large extrapolation errors of the existing lidar-based forecast. Monte Carlo simulations revealed the new approaches low sensitivity to uncertainty in lidar data processing, propagation and height extrapolation. Forecasting errors of a 5-minute-ahead wind speed forecast of free flow turbines at an offshore wind farm were significantly reduced for the two newly developed methods as compared to the existing forecast during stable atmospheric conditions. Persistence could be outperformed during unstable and neutral atmospheric conditions and for situations with higher turbulence intensity. Overall, we found lidar-based forecasts to be less sensitive to atmospheric conditions than persistence. We discuss the importance of accurate vertical wind speed profile estimation, the advantages and shortcomings of the two newly introduced methods and their skill compared to persistence. In conclusion, the additional use of wind turbine operational data can significantly improve minute-scale lidar-based forecasts. We further conclude that the characterisation of forecast skill dependent on atmospheric conditions can be valuable for decision-making processes

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km × 31 km to 1 km × 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our approach, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets

Very short-term forecast of near-coastal flow using scanning lidars

Wind measurements can reduce the uncertainty in the prediction of wind energy production. Today, commercially available scanning lidars can scan the atmosphere up to several kilometres. Here, we use lidar measurements to forecast near-coastal winds with lead times of 5 min. Using Taylor's frozen turbulence hypothesis together with local topographic corrections, we demonstrate that wind speeds at a downstream position can be forecast by using measurements from a scanning lidar performed upstream in a very short-term horizon. The study covers 10 periods characterised by neutral and stable atmospheric conditions. Our methodology shows smaller forecasting errors than those of the persistence method and the autoregressive integrated moving average (ARIMA) model. We discuss the applicability of this forecasting technique with regards to the characteristics of the lidar trajectories, the site-specific conditions and the atmospheric stability