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Summary Optimization models are a widely used tool in academia. In order to build these models, various parameters need to be specified, and often, simplifications are necessary to ensure the tractability of the models; both of which introduce uncertainty about the model results. However, a widely accepted way to quantify how these uncertainties propagate does not exist. Using the example of power system expansion modeling, we show that uncertainty propagation in optimization models can systematically be described by quantifying the sensitivity to different model parameters and model designs. We quantify the sensitivity based on a misallocation measure with clearly defined mathematical properties for two prominent examples: the cost of capital and different model resolutions. When used to disclose sensitivity information in power system studies our approach can contribute to openness and transparency in power system research. It is found that power system models are particularly sensitive to the temporal resolution of the underlying time series.

Energy system models rely on accurate weather information to capture spatio-temporal characteristics of renewable energy generation. Whereas energy system models are often solved with high abstraction of the actual energy system, meteorological data from reanalysis or satellites provides rich gridded information of the weather. The mapping from meteorological data to renewable energy generation usually relies on major assumptions as for solar photovoltaic energy the photovoltaic module parameters. In this study, we show that these assumptions lead to large deviations between reported and estimated energy as shown in case of photovoltaic energy feed-ins in Germany. To decrease these deviations, we propose a novel gradient-based end-to-end framework which is able to learn local representative photovoltaic capacity factors from aggregated reported transmission system operator feed-ins. As part of the end-to-end framework, we compare physical and neural network model formulations to obtain a functional mapping from meteorological data to photovoltaic capacity factors. We show that all developed methods have better performance than commonly used reference methods. The neural network shows remarkable success to predict the aggregated Transmission System Operator photovoltaic energy feed-ins leading to an accurate, unbiased prediction model. However, choosing the neural network model is not always the strictly preferred choice as it depends on the use case: Operational use cases may decide for the neural network implementation due to its higher accuracy whereas academic settings prefer the physical model due to its high interpretability and transferability. In this talk, we discuss the development of the end-to-end framework which we believe is highly relevant due to its energy-meteorological and methodological contributions.

Due to the increasing share of wind energy in the power system, minute-scale wind power forecasts have gained importance. Remote sensing-based approaches have proven to be a promising alternative to statistical methods and thus need to be further developed towards an operational use, aiming to increase their forecast availability and skill. Therefore, the contribution of this paper is to extend lidar-based forecasts to a methodology for observer-based probabilistic power forecasts of individual wind turbines and aggregated wind farm power. To do so, lidar-based forecasts are combined with SCADA-based forecasts that advect wind vectors derived from wind turbine operational data. After a calibration, forecasts of individual turbines are aggregated to a probabilistic power forecast of turbine subsets by means of a copula approach. We found that combining the lidar- and SCADA-based forecasts significantly improved both forecast skill and forecast availability of a 5-minute ahead probabilistic power forecast at an offshore wind farm. Calibration further increased the forecast skill. Calibrated observer-based forecasts outperformed the benchmark persistence for unstable atmospheric conditions. The aggregation of probabilistic forecasts of turbine subsets revealed the potential of the copula approach. We discuss the skill, robustness and dependency on atmospheric conditions of the individual forecasts, the value of the observer-based forecast, its calibration and aggregation and more generally the value of minute-scale power forecasts of offshore wind. In conclusion, combining different data sources to an observer-based forecast is beneficial in all regarded cases. For an operational use one should distinguish between and adapt to atmospheric stability.

Predicting the occurrence of strong and sudden variations in wind power, so‐called ramp events, has become one of the main challenges for the operation of power systems with large shares of wind power. In this paper, we investigate 14 ramp events of different magnitudes and minute‐scale durations observed by a dual‐Doppler radar system at the Westermost Rough offshore wind farm. The identified ramps are characterised using radar observations, turbine data and data from the Weather Research and Forecasting (WRF) model. A remote sensing‐based forecasting methodology that propagates wind speeds upstream of wake‐free turbines is extended here to the whole farm, by including corrections for wake effects. The methodology aims to probabilistically forecast the wind turbines' power in the form of density forecasts. The ability to predict ramp events of different magnitudes is evaluated and compared with probabilistic statistical and physical benchmarks. During the observed ramp events, the remote sensing‐based forecasting model strongly outperforms the benchmarks. We show here that remote sensing observations such as radar data can significantly enhance very short‐term forecasts of wind power.

Increasing amounts of high-velocity spatio-temporal data reinforce the need for clustering algorithms which are effective for big data processing and data reduction. As currently applied spatio-temporal clustering algorithms have certain drawbacks regarding the comparability of the results, we propose an alternative spatio-temporal clustering technique which is based on empirical spatial correlations over time. As a key feature, CorClustST makes it easily possible to compare and interpret clustering results for different scenarios such as multiple underlying variables or varying time frames. In a test case, we show that the clustering strategy successfully identifies increasing spatial correlations of wind power forecast errors in Europe for longer forecast horizons. An extension of the clustering algorithm is finally presented which allows for a large-scale parallel implementation and helps to circumvent memory limitations. The proposed method will especially be helpful for researchers who aim to preprocess big spatio-temporal datasets and who intend to compare clustering results and spatial dependencies for different scenarios.

The formation and dissipation of clouds are one of the longest studied and yet least understood phenomenon in nature. This is crucial in atmospheric and climate science as clouds have a significant impact on radiative forcing. In numerical weather prediction, solar radiation forecasts have lower skill than other parameters as temperature forecasts despite recent progresses. This study aims at better understanding cloud situations over Europe and how solar radiation forecast errors are related to these situations. Therefore, an enhanced cloud class algorithm based on unsupervised Deep Learning and hierarchical clustering is introduced. By using the MODIS optical cloud thickness product, the algorithm is able to classify 14 different daily cloud situations which are applied on defined tile regions (approximately 70,000 km²) of Europe. These different classes differ in both optical cloud phase and the overall structure of the cloud shape. The usefulness of the cloud classes is illustrated by showing regional differences of cloud type frequencies over the last 20 years. To better understand solar radiation forecast errors, the cloud classes are assigned to ECMWF IFS clearness day-ahead forecast errors. We show that high-water content and mixed-cloud phase situations lead to highest absolute forecast errors for single sites. Summed up over an area, we observe an accumulation of forecast errors for mixed-cloud phase situations whereas for other cloud situations forecast errors are more likely to cancel each other out (e.g. broken high-water content clouds). This study is useful for researchers and practitioners to better understand situations of high solar radiation errors by using the developed cloud product.

With the increasing penetration of renewable energy capacities in the European energy system, the electricity generators have shifted from centralized power plants to decentralized, weather-dependent wind turbines and photovoltaic systems. Energy system models now rely on skillful weather data to estimate renewable energy feedins on electricity bus levels. These feedins are usually calculated by bilinearly interpolating the closest atmospheric model grid points to the electricity network bus locations. This comes to the cost of averaging multiple atmospheric model grid points reducing overall atmospheric model variability. In addition, electricity grids are often modeled in clustered representations in terms of number of lines and buses. The number of buses is usually much smaller than the number of atmospheric model grid points and therefore some grid points and their characteristics may not be taken into account in highly clustered networks. So far, this interconnection between atmospheric model resolution and electricity grid topology has not been widely investigated.This study approaches the question if and to what extent the atmospheric model resolution affects the energy system model results. The regional reanalysis COSMO-REA6 is used as a reference data and its resolution is artificially reduced. This allows to compare the loss of information (mainly variability) due to a lower grid point resolution. The weather data is then used within different energy system network topologies to determine the corresponding renewable energy feedins at bus levels. A subsequent optimal power flow model estimates the impact on energy system metrics as storage usage and economic dispatch costs to further understand the relationship between atmospheric model resolution and energy system model topology. This study provides useful insights to choose the appropriate resolution of the atmospheric model input given an energy system model.

Cloudiness is a difficult parameter to forecast and has improved relatively little over the last decade in numerical weather prediction models as the EMCWF IFS. However, surface downward solar radiation forecast (ssrd) errors are becoming more important with higher penetration of photovoltaics in Europe as forecasts errors induce power imbalances that might lead to high balancing costs. This study continues recent approaches to better understand clouds using satellite images with Deep Learning. Unlike other studies which focus on shallow trade wind cumulus clouds over the ocean, this study investigates the European land area. To better understand the clouds, we use the daily MODIS optical cloud thickness product which shows both water and ice phase of the cloud. This allows to consider both cloud structure and cloud formation during learning. It is also much easier to distinguish between snow and cloud in contrast to using visible bands. Methodologically, it uses the Unsupervised Learning approach tile2vec to derive a lower dimensional representation of the clouds. Three cloud regions with two similar neighboring tiles and one tile from a different time and location are sampled to learn lower-rank embeddings. In contrast to the initial tile2vec implementation, this study does not sample arbitrarily distant tiles but uses the fractal dimension of the clouds in a pseudo-random sampling fashion to improve model learning.The usefulness of the cloud segments is shown by applying them in a case study to investigate statistical properties of ssrd forecast errors over Europe which are derived from hourly ECMWF IFS forecasts and ERA5 reanalysis data. This study shows how Unsupervised Learning has high potential despite its relatively low usage compared to Supervised Learning in academia. It further shows, how the generated land cloud product can be used to better characterize ssrd forecast errors over Europe.

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.

Power system expansion models are a widely used tool for planning power systems, especially considering the integration of renewable resources. Studies using these models form the basis for far-reaching political decisions. The backbone of power system models is an optimization problem, which depends on a number of economic and technical parameters. Although these parameters contain significant uncertainties, a consistent way to quantify the sensitivity to these uncertainties does not yet exist. Here, we analyze and quantify the sensitivity of a power system expansion model to the meteorological parameter time series based on a novel misallocation metric. We find that the sensitivity to the weather data is in the same order of magnitude as the sensitivity to the definition of cost. By comparing different climatic periods both from a meteorological perspective and with respect to the impacts on the optimal power system design we can, additionally, identify representative weather years and periods which should rather not be used for expansion planning.

Vietnam has tremendous potential for renewable energy. However, its renewable generation mix remains in its infancy, while demand grows rapidly year after year. In this work, we present an open dataset to model the Vietnamese electricity system implemented in Python for Power System Analysis (PyPSA) and use it to analyse a Vietnamese power development plan until 2030. We analyse two different meteorological datasets, the ERA5 and MERRA-2 reanalyses and use them to costoptimise a future Vietnamese power system under an official power development plan as boundary condition and compare the findings. We show that differences in the results are small for power systems mostly based on conventional thermal generation.

Decreasing gate closure times on the electricity stock exchange market and the rising share of renewables in today's energy system cause an increasing demand for very short-term power forecasts. While the potential of dual-Doppler radar data for that purpose was recently shown, the utilisation 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 synchronisation 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.

The Vietnamese Power system is expected to expand considerably in upcoming decades. However, pathways towards higher shares of renewables ought to be investigated. In this work, we investigate a highly renewable Vietnamese power system by jointly optimising the expansion of renewable generation facilities and the transmission grid. We show that in the cost-optimal case, highest amounts of wind capacities are installed in southern Vietnam and solar photovoltaics (PV) in central Vietnam. In addition, we show that transmission has the potential to reduce levelised cost of electricity by approximately 10%.

A large offshore observational dataset from stations across the North and Baltic Seas is used to investigate the planetary boundary-layer wind characteristics and their coherence, correlation and power spectra. The data from thirteen sites, with pairs of sites at horizontal distances of 4 to 848 km, are analyzed for typical wind turbine nacelle heights. Mean wind characteristics, correlation and coherence are also calculated for analogous wind data from simulations with the Weather Research and Forecasting (WRF) model. Results indicate a general good agreement for the coherence calculated based on measurements and the WRF-derived time series. By normalizing the frequency axes with the distance and mean wind speed, it can be demonstrated that, even for data with a wide range of distances, the coherence is a function of the frequency, mean wind and distance, which is consistent with earlier studies. However the correlation coefficient as a function of distance calculated from WRF is higher than observed in the measurements. For the power spectra, wind speed and wind speed step change distribution, the results for all sites are quite similar. The land masses strongly influence the individual wind direction distribution at each site. The ability of the WRF model to reproduce the coherence of the measurements demonstrates that its output can be used to estimate the coherence of fluctuations for the integration of offshore energy. The power spectra of WRF time series underestimates the high-frequency fluctuations. Due to the large number of measurement sites, the results can be used for further plausibility validation for mesoscale model runs over the sea.

Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of dual-Doppler radar observations of wind speed and direction to derive five-minute ahead deterministic and probabilistic forecasts of wind power. An advection-based technique is introduced, which estimates the predictive densities of wind speed at the target wind turbine. In a case study, the proposed methodology is used to forecast the power generated by seven turbines in the North Sea with a temporal resolution of one minute. The radar-based forecast outperforms the persistence and climatology benchmarks in terms of overall forecasting skill. Results indicate that when a large spatial coverage of the inflow of the wind turbine is available, the proposed methodology is also able to generate reliable density forecasts. Future perspectives on the application of Doppler radar observations for very short-term wind power forecasting are discussed in this paper.

Wind measurements can reduce the uncertainty in the prediction of wind energy production. Nowadays, 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 five minutes. 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 ten periods characterized by neutral and stable atmospheric conditions. Our methodology shows smaller forecasting errors than those of the persistence method and the 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.

The objective of this paper is to compare probabilistic 100-m wind speed forecasts, which are relevant for wind energy applications, from different regional and global ensemble prediction systems (EPSs) at six measurement towers in central Europe and to evaluate the benefits of combining single-model ensembles into multimodel ensembles. The global 51-member EPS from the European Centre for Medium-Range Weather Forecasts (ECMWF EPS) is compared against the Consortium for Small-Scale Modelling’s (COSMO) limited-area 16-member EPS (COSMO-LEPS) and a regional, high-resolution 20-member EPS centered over Germany (COSMO-DE EPS). The ensemble forecasts are calibrated with univariate (wind speed) ensemble model output statistics (EMOS) and bivariate (wind vector) recursive and adaptive calibration (AUV). The multimodel ensembles are constructed by pooling together raw or best-calibrated ensemble forecasts. An additional postprocessing of these multimodel ensembles with both EMOS and AUV is also tested. The best-performing calibration methodology for ECMWF EPS is AUV, while EMOS performs better than AUV for the calibration of COSMO-DE EPS. COSMO-LEPS has similar skill when calibrated with both EMOS and AUV. The AUV ECMWF EPS outperforms the EMOS COSMO-LEPS and COSMO-DE EPS for deterministic and probabilistic wind speed forecast skill. For most thresholds, ECMWF EPS has a comparable reliability and sharpness but higher discrimination ability. Multimodel ensembles, which are constructed by pooling together the best-calibrated EPSs, improve the skill relative to the AUV ECMWF EPS. An analysis of the error correlation among the EPSs indicates that multimodel ensemble skill can be considerably higher when the error correlation is low.

Unlike deterministic forecasts, probabilistic predictions provide estimates of uncertainty, which is an additional value for decision-making. Previous studies have proposed the analog ensemble (AnEn), which is a technique to generate uncertainty information from a purely deterministic forecast. The objective of this study is to improve the AnEn performance for wind power forecasts by developing static and dynamic weighting strategies, which optimize the predictor combination with a brute-force continuous ranked probability score (CRPS) minimization and a principal component analysis (PCA) of the predictors. Predictors are taken from the high-resolution deterministic forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF), including forecasts of wind at several heights, geopotential height, pressure, and temperature, among others. The weighting strategies are compared at five wind farms in Europe and the U.S. situated in regions with different terrain complexity, both on and offshore, and significantly improve the deterministic and probabilistic AnEn forecast performance compared to the AnEn with 10‑m wind speed and direction as predictors and compared to PCA-based approaches. The AnEn methodology also provides reliable estimation of the forecast uncertainty. The optimized predictor combinations are strongly dependent on terrain complexity, local wind regimes, and atmospheric stratification. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, the AnEn performance is improved by up to 20 % at on and offshore sites.

With increasing wind power capacity, accurate uncertainty forecasts get more and more important for grid integration. The uncertainty of forecasts can be quantified by ensemble forecasts. We use ensemble forecasts from the COSMO-DE EPS to generate short-term ensemble forecasts of regionally aggregated wind power. The wind power forecasts are generated by an optimised regional power curve model that is based on minimum score estimation and leads to wind power forecasts with small deterministic errors. Remaining bias and dispersion errors in the wind power forecasts are removed by statistical post-processing (also called calibration) with ensemble model output statistics and the temporal rank correlation of the raw ensemble is maintained by ensemble copula coupling. The verification of raw and calibrated ensembles shows both strong improvements by calibration and the benefit of ensuring time consistency with ensemble copula coupling. The improvements are indicated by the multivariate energy score as well as in a proposed univariate verification approach that is based on integrated wind power forecast and measurement trajectories. Slight deficits in time consistency of the forecasts remain because the theoretical assumptions of ensemble copula coupling are not always fulfilled as the COSMO-DE EPS is based on distinguishable ensemble members. The more training days are used for calibration against measurements of regionally aggregated wind power, the lower is the improvement by calibration which contradicts former results for different variables like wind speed.

International audience; This chapter shows some test cases from the EU project SAFEWIND on different ways that the planning phase of wind farms can benefit from early knowledge of wind power predictability. The interest is focused on day-ahead short-term forecasting, as the most relevant range for TSOs and wind energy traders. We shall use the term predictability assessment when we quantify wind predictability during the planning phase of a wind farm. This can be associated to the prediction of the hub-height wind speed (the meteorological wind resource), the gross wind power from a specified turbine power-curve model, or the net wind power output, which includes wind farm losses due to wake effects and other environmental aspects that are site and wind farm layout specific. Within the SAFEWIND project, wind power predictability is evaluated to suggest potential benefits during the planning phase of wind farm deployment. Predictability assessment can be useful, for instance, to anticipate operational costs due to market imbalance for energy traders, costs related to prediction of offshore weather windows for turbine installation or maintenance, or to benefit from portfolio effects when aggregating wind farms that operate in uncorrelated wind climates. While the weight of predictability for a wind farm developer is low in the decision-making process compared to the capacity factor, it remains a useful indicator of the quality of wind for grid integration purposes. A series of case studies are presented in various European regions to present tools for wind power predictability assessment at different scales.

Ensemble forecasts are a valuable addition to deterministic wind forecasts since they allow the quantification of forecast uncertainties. To remove common deficiencies of ensemble forecasts such as biases and ensemble spread deficits, various postprocessing methods for the calibration of wind speed (univariate calibration) and wind vector (bivariate calibration) ensemble forecasts have been developed in recent years. The objective of this paper is to compare the performance of state-of-the-art calibration methods at distinct off- and onshore sites in central Europe. The aim is to identify calibration- and site-dependent improvements in forecast skill over uncalibrated 100-m ensemble forecasts from the ECMWF Ensemble Prediction System. The ensemble forecasts were evaluated at four onshore and three offshore measurement towers in central Europe at 100-m height for lead times up to 5 days. The results show that the recursive and adaptive wind vector calibration (AUV) outperforms calibration methods such as univariate ensemble model output statistics (EMOS), bivariate EMOS, variance deficit calibration, and ensemble copula coupling in terms of the root-mean-square error and continuous ranked probability score at almost all sites. It was found that exponential downweighting of past measurements in AUV contributes to higher forecast skill since similar downweighting approaches in the other calibration methods improved forecast skill. Proposing a bidimensional bias correction in bivariate EMOS similar to the approach taken in AUV yields bivariate EMOS skill at onshore sites that is similar to AUV skill. Deterministic and probabilistic improvements are usually much lower at offshore sites and increase with increasing complexity of the site characteristics since systematic forecast errors and ensemble underdispersion are larger at high-roughness sites.

Utilizing a vanadium redox flow battery (VRFB) for better market integration of wind power at a single wind farm level was evaluated. A model which combines a VRFB unit and a medium sized (10 MW) wind farm was developed and the battery was utilized to compensate for the deviations resulting from the forecast errors in an electricity market bidding structure. VRFB software model which was introduced in our previous paper was integrated with real wind power data, power forecasts and market data based on the Spanish electricity market. Economy of the system was evaluated by financial assessments which were done by considering the VRFB costs and the amount of deviation penalty payments resulting from forecast inaccuracies.

The renewable power generation aggregated across Europe exhibits strong seasonal behaviors. Wind power generation is much stronger in winter than in summer. The opposite is true for solar power generation. In a future Europe with a very high share of renewable power generation those two opposite behaviors are able to counterbalance each other to a certain extent to follow the seasonal load curve. The best point of counterbalancing represents the seasonal optimal mix between wind and solar power generation. It leads to a pronounced minimum in required stored energy. For a 100% renewable Europe the seasonal optimal mix becomes 55% wind and 45% solar power generation. For less than 100% renewable scenarios the fraction of wind power generation increases and that of solar power generation decreases.

The fluctuation of photovoltaic and wind power generation is directly related to the weather variability. In this study, 8 years of data from a weather model were used to simulate the spatial and temporal characteristics of photovoltaic and wind power generation in Europe. Imbalances in the power system between photovoltaic (PV) plus wind power generation and consumption are investigated in a full (energy) supply scenario. Two different approaches in spatial aggregation are analyzed (i) unlimited crossborder power flows to simulate one European control zone and (ii) no crossborder power flows, i.e. deviations between generation and consumption are balanced on a regional level. On all investigated time scales the fluctuation of imbalances can be reduced by 50% in a common European control zone, i.e. power flows smooth out spatial differences in PV and wind power generation on a European wide grid very effectively. Consequently, less energy from storage facilities is required to meet imbalances, e.g. storage losses are reduced by about 50%. The optimal mix between PV and wind energy in the power system depends on the time scale. While on the monthly time scale roughly 40% of PV is favourable to minimize the variance of imbalances, the optimal share of PV is only 20% on the hourly time scale, because of the strong impact of the diurnal cycle on PV generation.

Disponible sur internet : http://www.ewec2006proceedings.info/allfiles2/968_Ewec2006fullpaper.pdf; International audience

Currently, wind power faces heterogeneous conditions for market integration throughout Europe, given both differing wind power and power system characteristics as well as according regulatory frameworks. This paper discusses marketing options and assesses the influence of market rules on the economic value of wind power for an Austrian case study. Special emphasis is thereby put on options for short-term wind power forecasting and trade. Results indicate that market integration can be improved considerably by implementing continuous day-ahead markets in Central European Countries as a first step. Under such framework conditions also simple forecast approaches provide a significant added value.

Smoothing of wind power forecast errors is well-known for large areas. Comparable effects within a wind farm are investigated in this paper. A Neural Network was taken to predict the power output of a wind farm in north-western Germany comprising 17 turbines. A comparison was done between an algorithm that fits mean wind and mean power data of the wind farm and a second algorithm that fits wind and power data individually for each turbine. The evaluation of root mean square errors (RMSE) shows that relative small smoothing effects occur. However, it can be shown for this wind farm that individual calculations have the advantage that only a few turbines are needed to give better results than the use of mean data. Furthermore different results occurred if predicted wind speeds are directly fitted to observed wind power or if predicted wind speeds are first fitted to observed wind speeds and then applied to a power curve. The first approach gives slightly better RMSE values, the bias improves considerably.

Large-scale wind farms will play an important role in the future worldwide energy supply. However, with increasing wind power penetration all stakeholders on the electricity market will ask for more skilful wind power predictions regarding save grid integration and to increase the economic value of wind power. A Neural Network is used to calculate Model Output Statistics (MOS) for each individual forecast model (ECMWF and HIRLAM) and to model the aggregated power curve of the Middelgrunden offshore wind farm. We showed that the combination of two NWP models clearly outperforms the better single model. The normalized day-ahead RMSE forecast error for Middelgrunden can be reduced by 1 % compared to single ECMWF. This is a relative improvement of 6 %. For lead times >24h it is worthwhile to use a more sophisticated model combination approach than simple linear weighting. The investigated principle component regression is able to extract the uncorrelated information from two NWP forecasts. The spread of Ensemble Predictions is related to the skill of wind power forecasts. Simple contingency diagrams show that low spread corresponds is more often related to low forecast errors and high spread to large forecast errors.

Ethanol is a commodity with potential in both the car fuel sector, thanks to its properties of transportability and efficient combustibility, and in the chemical sector because of its chemical structure and reactivity. However, because the compound is competing with established fuels and chemicals for existing markets rather than creating entirely new ones, there are many practical constraints on its widespread use. If it could be produced cheaply enough in large quantities, and if technology could be developed for its efficient use, then ethanol's potential might be realised. However, potential users of ethanol would be unlikely to develop new processes without a guaranteed supply of cheap ethanol. On the other hand, producers of ethanol have little incentive to improve processes radically without the incentive of a large market. This vicious circle could be broken by political intervention but the intervention must have a valid basis in technology — and not be merely based on political expediency.

We investigate the optimal distribution of wind and photovoltaic generation facilities in Europe. For this purpose, feed-in data covering Europe for the renewable sources wind, photovoltaics and hydro are used. Together with historical load data and a transmission model, a simplified European power system is simulated. The results show that wind should be placed in countries on the shores of the North-Sea while the picture for PV is complex: A band from Germany to Italy over Switzerland is optimal. Furthermore, we show that hydro power has little influence on the optimal distribution.

document disponible en suivant ce lien : http://www.ewec2006proceedings.info/allfiles2/968_Ewec2006fullpaper.pdf; International audience; Due to the large dimensions of offshore wind farms, their electricity production must be known well in advance to allow an efficient integration of wind energy into the European electricity grid. For this purpose short-term wind power prediction systems which are already in operation for onshore sites have to be adapted to offshore conditions, which has been a major objective of the EU-project ANEMOS. The paper presents the offshore results of the project partners in a cumulative way. In general, it has been found that the accuracies of wind speed predictions for the offshore sites Horns Rev and FiNO1 are similar or better than for single onshore sites considering that the mean producible power is twice as high as onshore. A weighted combination of two forecast sources leads to reduced errors. A regional forecast of the aggregated power output of all projected sites in the German Bight with a total capacity of 25 GW benefits from spatial smoothing effects by an error reduction factor of 0.73, showing an RMSE of 3GW. An aggregated forecast for the sum of on- and offshore production in Germany with a total capacity of 50GW would benefit from an error reduction factor of 0.43, leading to an RMSE of 3.5 GW. The project partners also investigated the most important parameters which influence the wind speed profile offshore. A new air-sea-interaction model for calculating marine wind speed profiles was developed, i.e. the theory of inertially coupled wind profiles (ICWP). Evaluation with Horns Rev and FINO1 data showed good agreement, especially regarding wind shears. Next, emphasis was given on modelling spatio-temporal characteristics in large offshore farms. New approaches were developed to model wakes behind such farms. Wake losses are anticipated to be at least 5-10% of power output. Wind speed recovery can be predicted to occur between 2 and 15 km downwind of such farms according to the model type chosen. Also, a comparison of mesoscale model results with WAsP predictions was performed to quantify gradients of wind speed over large wind farms. Moreover, the contribution of satellite data in offshore prediction was studied. For the complex situation in the Strait of Gibraltar, a semi-empirical model was developed. Finally, various physical and statistical (i.e. neural networks) models were calibrated on power data from two offshore wind farms: Tunoe and Middelgrunden in Denmark.