西班牙可再生能源中心风功率预测

1、Pedro Correia: Laura Fras: Ivn Moya: Wind Resource Assessment and Prediction - EPR The Numerical Weather Prediction models(NWP), have been traditionally used to predict the real state of earths atmosphere. The initial state of the atmosphere (ANALYSIS) is reproduced using measurements from satellite

2、s, weather stations, etc, and converted to a regular grid that can be used to feed the mesoescale model. By resolving the primitive equations with that input data, the NWP models can predict the weather in the future. Theres different types of atmospheric models:Global:Global Forecasting system (GFS

3、) from Ncep/NCAR and the ECMWF Global model, .Regional:Skiron; Eta Model; WRF; MM5,Hirlam; Aladin,.NWP: Mesoescale Models Short Description Mesoescale model that uses the base of the ETA model.Requires an UNIX Operating System;It is able to use the weather input data from;GFS (Global Forecasting Sys

4、tem);NCEP/NCAR Reanalysis 1;ECMWF (Global Model)CENER works with SKIRON since October, 2005.CENER works with SKIRON since October, 2005.It was first configured to run real-time forecasts, allowing CENER to obtain daily weather predictions.From some time now, the model is also been used to wind and d

5、irect solar radiation resource assessment in an wide range of locations throughout the globe.To generate wind/solar radiation maps, its desirable to run SKIRON as long as possible (more than 5 years), in order to obtain the long term behavior of the different meteorological variables, such as pressu

6、re, wind velocity and direction (at several heights above ground level), direct solar radiation, temperature,etc.NWP: Mesoescale Models SKIRON SKIRON: Real-time predictionsIts executed in 16 processors with an horizon of 180ph - 5h 30minHorizontal Resolution: 0.1x0.1 (10 kmx10km) - 341x281 ptsVertic

7、al Resolution: 38 Eta vertical levels. No nesting Temporal resolution: output frequency = 1h (180 daily files)Daily download and storage of: GFS 12UTC, SST, Snow cover and Snow depthBackup system: The same model configuration, in different machines which are located in another area. This allows us t

8、o guarantee the clients forecasts in case of a failure in the main system (power failure, computer malfunction, network problems, etc). Wind Forecast and Wind Energy production using SKIRON SKIRON: Real-time Real-time domain from October 2005 until November 2009. Domain since November 2009 until now

9、.Wind Forecast and Wind Energy production using SKIRON In the electricity grid at any moment balance must be maintained between electricity consumption and generation - otherwise disturbances in power quality or supply may occur. Wind generation is a direct function of wind speed and, in contrast to

10、 conventional generation systems, is not easily dispatchable. Fluctuations of wind generation thus receive a great amount of attention. Managing the variability of wind generation is the key aspect associated to the optimal integration of that renewable energy into electricity grids. Reason for wind

11、 power forecasts Statistical prediction methods are based on one or several models (linear and non-linear) that establish the relation between historical values of power, as well as historical and forecast values of meteorological variables, and wind power measurements. Model parameters are estimate

12、d from a set of past available data, and they are regularly updated during online operation by accounting for any newly available information (i.e. meteorological forecasts and power measurements).Statistical approach to wind power FORECASTS FOR THE DAILY MARKET. LocalPred is operational since 2001

13、and has been continuously developed since then. Reliability and accuracy are the main characteristics of the system. Reliability is based on the redundancy of: Hardware. Input data. Processes. Accuracy is obtained through the combination of forecasts with different information: “multi-model ensemble

14、”. Support Vector Machine technology, PCA algorithms, data quality control. Forecasts for offshore wind farms: Wind farm energy production. Waves (WAM4 high resolution wave forecasts). reduced visibility.Operational since 2001DESCRIPTION OF LOCALPRED FORECASTING SYSTEMGFSSKIRONECMWFPCA MOS ENSEMBLEM

15、OS 2DELIVERYSVMZAMUDIOCENERMultimodel ensembleDESCRIPTION OF LOCALPRED FORECASTING SYSTEMCombination Algorithm DESCRIPTION OF LOCALPRED FORECASTING SYSTEM LocalPred includes a combination algorithm developed in collaboration with DTU-IMM. The level of improvement depends on the error of the individu

16、al forecasts and on the level of correlation between them. The combination is able to improve the best individual forecast.wind farms clustering analysisEvolution error index vs the number of agreggated wind farmswind farms clustering analysisEconomic impact obtainedby the aggregation of wind farmsF

17、ORECASTS FOR THE INTRADAY MARKETSIntra-daily market process. Focus on very short-term forecasts The actual Spanish electrical market, allows us to correct the wind energy forecasting presented in the daily market by means of the intradaily market. This market is organized into six sessions and agent

18、s that have previously participated in the daily market can present new program of production. The new predictions must be presented between the opening and closing hours of the session. Thus, in each intradaily session, we correct a maximum of five predictions, and taking into account the closing h

19、our of the session, we can use from fourth to eighth step ahead. Therefore the importance of the short time forecast and so the need of a short time model falls here.Intra-daily market process. Focus on very short-term forecasts A new model for short-term prediction has been developed taking into ac

20、count the Spanish market rules. This model is focused in short forecasting horizons. First, it uses online power production data of the wind farms to build different time series models (Box-Jenkins methodology and a version of Holt Winters Algorithm). On the other hand, it utilizes existing forecast

21、s for the daily market produced by CENERs LocalPred model based on mesoscale NWP and MOS corrections. Finally it implements a combination algorithm that offers the optimal forecast for each horizon.Methodology of the short time forecasting model of CENERResults obtained We present the results obtain

22、ed from the CENER short time model applied on a medium wind farm from Spain between February and December. We present the improvement of the new model against the persistence as short time prediction and against the daily market forecasting.EUROPEAN PROJECTS R+D European projects (VI and VII Framewo

23、rk Program):UPWIND “Finding design solutions for very large wind turbines”POWWOW “Prediction Of Waves, Wakes and Offshore Wind”ANEMOS Development of a Next Generation Wind Resource Forecasting System for the Large-Scale Integration of Onshore and Offshore Wind Farms ANEMOS.PLUS “Advanced Tools for t

24、he Management of Electricity Grids with Large-Scale Wind Generation”SAFEWIND “Multi-scale data assimilation, advanced wind modelling and forecasting with emphasis to extreme weather situations for a safe large-scale wind power integration” PUBLICATIONS1 Mart, I., Nielsen, T. S., Madsen, H., et al. P

25、rediction models in complex terrain. Proceedings of the European Wind Energy Conference. Copenhagen, July 2001.2 Mart, I., Usaola, J. et al. Wind power prediction in complex terrain. LocalPred and Siprelico. Proceedings of the European Wind Energy conference, June 2003.3 Mart, I. et al. Wind power p

26、rediction in complex terrain: from the synoptic scale to the local scale. “The science of making torque from wind”. Delft. The Netherlands, 2004.4 M.Gastn,L.Fras,M.J.San Isidro,I.Mart. Exploring the limits of wind farm grouping for prediction error compensation. EWEC 2006.5 L. Fras, M. Gastn, I. Mar

27、t .A new model for wind energy forecasting focused in the intra-daily markets. EWEC 2007.6 L.Fras, E.Pascal, U.Irigoyen, E.Cantero, Y.Loureiro, S.Lozano, P.M. Fernandes, I.Mart.Support Vector Machines in the wind energy framework. A new model for wind energy forecasting. EWEC 2009. The daily SKIRON

28、forecasts can be visited at . Cloud Cover, Snow, Wind, Temperature, Precipitation, Local forecasts to main cities, etc.Skiron real-time meteorological products: Anomalies: Wind and/or Energy Density:Anomalies maps with an 1kmx1km resolution. It is necessary to possess a simulated database with SKIRO

29、N to be able to calculate these maps.Its possible to calculate the anomalies for every desired period (daily, weekly, monthly, seasonally, yearly, .etc)The reference period used to obtain these product can also be changed accordingly to the clients needs.Theres also the possibility to generate varia

30、bility maps to help identify unstable(regarding wind resource) regions.They are delivered in GIS format with several layers attached (Anomaly; topography, wind farm locations, etc)Skiron real-time meteorological products: Figure Interpretation: At the Ebro Valley, the February wind velocity was 20%

31、less than the last 6 years mean.Types of anomalies: Wind and Energy DensityMonthlyYearlySeasonly.Advantage!: Allows to indentify in a very intuitive way, if a given region had registed above/under average winds. Easy to identify a possible cause for over/under production of a wind farm.Skiron real-t

32、ime meteorological products-AnomaliesThe use of mesoescale models for wind resource assessment is a recent activity.The standard methodology may vary, accordingly with the model user, but the goal is the same: To take advantage of the NWP models capacity to predict wind.Methodology? - The main goal

33、is to determine the wind climatology instead of the real-time prediction. Instead of predict in the future, long periods (years) of time are simulated using a stored input data archive (GFS, Reanalysis).SKIRON: Wind resource AssessmentThe methodologies used in these climatic simulations can be very

34、different, but some aspects are common: 1.Initial DataThe model used to calculate the wind map needs initial input data and initial boundary conditions. There are few available sources of that information to such long periods: Reanalysis (NCAR/NCEP, ECMWF, JRA) or the stored outputs from the GFS and

35、 ECMWF models.2.Climatic Simulation.It is necessary to obtain a long term representative simulation of the area in question.The only available options are trying to simulate the largest available period: 5, 10, 15 or more years, or simulate a climatological representative year to the region of inter

36、est.SKIRON: Wind resource AssessmentExamples of methodologies used to obtain a representative Wind Atlas.SKIRON: Wind resource AssessmentCENER test case:SKIRON: Wind resource AssessmentIn Cener case, 24 weather stations in Navarra, that fullfill all the requirements, have been carefully selected and

37、 the results have been analyzed taking into consideration the complexity of the terrain, mean wind velocity and station.MAE, RMSE and Bias were calculated:If we look only to the simulations with reanalysis data, it can be stated that the lowest MAE is achieved with the 0,03x0,03 resolution, but the

38、lowest Bias is achieved in the 0,1x0,1 resolution. If we analyze all the simulations made, its easy to see that the optimal configuration to the SKIRON model is GFS as input data and an horizontal resolution of 0,05x0,05. SKIRON: Wind resource Assessment“Climatic” Simulations: Horizontal Resolution:

39、 0.05x0.05 (5 kmx5km)Vertical Resolutions: 50 Eta vertical levels. No nestingTemporal resolution: output frequency = 1h (48h horizon)Inputs: GFS 12UTC, SST, Snow cover and Snow depthAvailable period at CENER (from June 2003 until now 8 years) CENER computacional Resources:618 processors640 GB RAMUni

40、x Operating SystemSKIRON: Wind resource AssessmentMEASUREDSKIRON0.05 x 0.05GFS1 x 1HOURLYWINDVALUESVALIDATIONFILTERINGWINDMAPHOURLYTIME SERIESGISWEBSERVICESKIRON: Wind resource Assessment01/01/2004:.48 hourly maps31/12/2010:.48 hourly maps.(First run)(Last run)(1274 runs)To run each year, 182 simula

41、tions, each one with an 48 hours prediction horizon are launched. So the number of simulations needed to run each case are : N Years x 182 = N SimulationsSKIRON: Wind resource Assessment+7(y) x 365(d) x 24(h) = 61320 hourly wind mapsThe result from all the simulations is one wind map for every hour

42、in the chosen period.ADVANTAGE!: The possibility to validate the wind map with real measures. SKIRON: Wind resource AssessmentIn Tunisia, CENER used the measurements from 17 weather stations, with anemometers installed at 20m and 40m. That allowed us to perform an exhaustive validation of CENER meth

43、odology to obtain wind maps and virtual series. TUNISIA WIND MAPSKIRON: Wind resource Assessment TunisiaIn the Great Lakes Wind Map, CENER used wind measures from 50 weather stations, most of them had anemometers at an 10m height. This was a good test case to validate the simulated wind, both onshor

44、e and offshore.SKIRON: Wind resource Assessment Great LakesWith the goal of validate the offshore virtual series generated with SKIRON, a wind map for the North Sea was generated: FINO Validation:6 months: January-June 2006Data coverage: 90%SKIRON:Resolution: 0.05, 50 vertical levels,1hrForecast max

45、imum horizon: 48hrMeasures:Wind at: 33,40,50,70,80,90mTemperature at: 30,40,50,70,100mRH at: 33,50,90m Tower effect corrected in the wind values.SKIRON: Wind resource Assessment Offshore FinoExcellent results both in Velocity and directionSKIRON: Wind resource Assessment Offshore FinoSonicsCupsFino1

46、54.014N6.5905ESKIRON: Wind resource Assessment Offshore FinoSKIRON: Wind resource Assessment Offshore Fino Mean Wind Map from the desired region with a final horizontal resolution of 1kmx1km(Inverse distance weighted interpolation); The mean wind could be calculated at hub height; Wind roses and pro

47、bability distribution in representative points in jpg format. Delivered in GIS format with several layers of information (Mean Wind, Topography, Protected areas; Electric grid, etc)POLAND WIND MAPTUNISIA WIND MAPNORTH SEA WIND MAPEAST EUROPE WIND MAPSKIRON: Wind resource Assessment Offshore Derived

48、productsGIS Format compatible KMZ Format Google Earth SKIRON: Wind resource Assessment Offshore Derived products Energy Density MapsMean Energy Maps from the desired region at an 1kmx1km horizontal resolution.The energy is calculated, point by point, to every hour using the pressure and temperature

49、simulated with SKIRON. This means that the air density used its also calculated and not averaged to the entire domain.The energy density obtained is representative of the simulated period.Its calculated at the hub height.Its delivered in GIS format (Energy density + standard layers) Weibull paramete

50、rs maps,Individual maps for the parameters (A, k) to the entire simulated region at a final horizontal resolution of 1kmx1km. Also delivered in GIS format, with the standard information.SKIRON: Wind resource Assessment Offshore Derived productsCENER simulated domains :Wind:Iberian Peninsula; Poland;

51、 Romania; Great Lakes; Mexico; Central America, Chile; Peru; Fino, Brazil (NE Region)Solar Radiation:Australia; North Africa; United EmiratesCENER Simulated Domains (Wind/Solar Radiation Maps)With the goal to suppress the lack of measurements in some locations, CENER has developed and validated a me

52、thodology capable of generating virtual wind series (velocity and direction), energy density, temperature, pressure, etc, using the mesoescale model SKIRON.In order to obtain the wind series at a desired location, hourly wind outputs from SKIRON are used as input to the microscale model WasP and the

53、n corrected by the Ad factor given by the WasP/CFD simulation.Wind resource Assessment Downscaling and Virtual Wind SeriesAdvantage!: Allows to detect the local effects caused by the local topography that SKIRON cant resolve.SKIRON0.05 x 0.05HOURLYWINDVALUESWAsPSRTMAd FACTORHOURLYTIME SERIESHIGH RESOLUTION WUND MAPSWind resource Assessment Downscaling and Virtual Wind SeriesHourly wind outputs from SKIRON are used as