Solarstromprognosen für Übertragungsnetzbetreiber

Transcription

1 Solarstromprognosen für Übertragungsnetzbetreiber Elke Lorenz, Jan Kühnert, Annette Hammer, Detlev Heienmann Universität Oldenburg 1

2 Outline grid integration of photovoltaic power (PV) in Germany overview regional PV power prediction system* irradiance forecasting numerical weather predictions cloud motion vectors from satellite data combination of forecast models PV power forecasting summary & outlook *operational system in cooperation with meteocontrol GmbH 2

3 New structure of electricity supply system controllable demand driven weather dependent supply driven new methods for balancing demand and supply necessary 3

4 Contribution of PV systems to electricity supply in Germany installed PV power (end 2012): 32 GW peak in Germany up to 42% of electricity demand from PV 22 GW strong variability of solar and wind power 4

5 Balancing of solar power according to renewable energy sources act mandatory purchase of all available renewable electricity by grid operators marketing and balancing of PV power by transmission system operators (TSOs) based on their respective share of the all German electricity supply (horizontal burden sharing) control areas of transmission system operators additional option of direct marketing of PV power currently plays a minor role need for regional forecasts 5

6 Balancing of solar power input (RES act) by transmission system operators day-ahead: selling of PV power at the European Power Exchange(EPEX) hourly contingents 12:00 for the next day intra-day: trading of electricity at the EPEX hours or 15-minute periods until 45 minutes before delivery forecast requirements hourly forecasts for the next day forecasts with 15 min resolution for the next hours remaining deviations are adjusted with costly balancing power high forecast accuracy very important 6

7 OVERVIEW ON PV POWER PREDICTION SCHEME forecast horizon irradiance measurement PV power measurement Satellite data hours optimized, sitespecific irradiance prediction power prediction PV simulation up-scaling regional PV power prediction ECWMF forecast days representative PV system sites PV system description module type nominal power Tilt and orientation.. 7

8 Numerical Weather Prediction (NWP) description of atmospheric processes by prognostic equations (conservation of momentum, energy and mass,..) ECMWF, R. Hagedorn 8

9 Numerical Weather Prediction (NWP) description of atmospheric processes by prognostic equations (conservation of momentum, energy and mass,..) solving equations numerically on a grid ECMWF, R. Hagedorn 9

10 Numerical Weather Prediction (NWP) description of atmospheric processes by prognostic equations (conservation of momentum, energy and mass,..) solving equations numerically on a grid starting the forecast: initial conditions from worldwide meteorological observations ECMWF, R. Hagedorn 10

11 ECMWF global model irradiance forecast: irradiance forecast and measurement stations (DWD and Meteomedia GmbH) used spatial resolution: 25km x 25km temporal resolution: 3h post processing: hourly forecasts: combination with a clearsky-model adaptation to historic measurements: correction of systematic overestimation for variable clouds situations , 11:00 ECMWF: European Center for Medium-Range Weather Forecasts 11

12 Evaluation of regional irradiance forecasts: data sets irradiance measurements of more than 200 stations in Germany (DWD and meteomedia GmbH) period: July April 2012 Forecasts: ECMWF intra-day and day-ahead irradiance forecasts 12

13 Evaluation of mean forecasts for Germany: satellite based vs NWP based irradiance forecast

14 Evaluation of ECMWF based irradiance forecasts mean Germany single sites intra-day 35W/m 2 96W/m 2 day-ahead 43W/m 2 107W/m 2 rmse = 1 N N i = 1 only daylight hours ( I meas I pred 2 )

15 AUSWERTUNG Example: extreme values of forecast errors ECMWF irradiance forecast 15

16 : fog in southern Germany 16

17 Irradiance from satellite data cloud detection based on Meteosat satellite images Meteosat Second Generation (high resolution visible range) spatial resolution: 1.2km x 2.2 km temporal resolution: 15min visible broadband channel

18 Irradiance prediction based on satellite data cloud detection based on Meteosat satellite images Meteosat Second Generation (high resolution visible range) detection of cloud motion vectors (CMV) extrapolation of cloud motion to predict future cloud situation

19 Irradiance prediction based on satellite data cloud detection based on Meteosat satellite images satellite derived irradiance maps detection of cloud motion vectors (CMV) extrapolation of cloud motion to predict future cloud situation irradiance calculation from predicted cloud images 200W/m 2 900W/m 2

20 Atmospheric extinction processes extraterrestrial radiation ozone... air molecules... Rayleigh scattering Clear sky model: I clear = f(geometry, air molecules, ozone,aerosols, water vapour) trace gases... aerosols... water vapor... absorption scattering, absorption clear sky irradiance: I clear

21 Atmospheric extinction processes extraterrestrial radiation ozone... air molecules... Rayleigh scattering trace gases (CO 2,...)... scattering absorption....clouds aerosols... scattering, absorption k*:clear sky index, describes cloud transmissivity water vapour... absorption clear sky irradiance: I clear global irradiance: I=k* I clear

22 Heliosat method satellite images: bright clouds dark surface clear sky model cloud index images: measure of cloudiness 0 W/m W/m 2 global irradiance 1000 W/m 2

23 detection of cloud motion t 0-15min t 0 basic assumption: cloud structures remain constant during motion identification of similar patterns in consecutive images: minimizing rmse between rectangular areas

24 detection of cloud motion t 0-15min t 0 basic assumption: cloud structures remain constant during motion identification of similar patterns in consecutive images: minimizing rmse between rectangular areas

25 forecast of cloud motion t 0-15min t 0 forecast image: t 0 +x*15min Extrapolation of motion smoothing motion vector field

26 Evaluation of regional irradiance forecasts: data sets irradiance measurements of more than 200 stations in Germany (DWD and meteomedia GmbH) period: July April 2012 forecasts irradiance forecasts based on satellite data up to 5 hours ahead ECMWF based intra-day irradiance forecasts for comparison: persistence of cloud situation 26

27 Evaluation of mean forecasts for Germany : satellite based vs NWP based irradiance forecast satellite based forecasts generally show better agreement with measured values than NWP based forecasts for several hours ahead

28 Evaluation of mean forecasts for Germany: satellite based vs NWP based irradiance forecast intra- day NWP forecast: rmse ECMWF =35W/m 2 hour-ahead MVF forecast: rmse MVF =14W/m 2 rmse = ( ) N I 2 pred I 1 = i 1 N meas

29 Evaluation of mean forecasts for Germany: satellite based vs NWP based irradiance forecast intra- day NWP forecast: rmse ECMWF =35W/m 2 2 hours-ahead MVF forecast: rmse MVF = 20W/m 2

30 Evaluation of mean forecasts for Germany: satellite based vs NWP based irradiance forecast intra- day NWP forecast: rmse ECMWF =35W/m 2 3 hours-ahead MVF forecast: rmse MVF = 25W/m 2 outliers due to formation or dissolution of cloud fields

31 Evaluation of mean forecasts for Germany: satellite based vs NWP based irradiance forecast intra- day NWP forecast: rmse ECMWF =45W/m 2 3 hours-ahead MVF forecast: rmse MVF = 25W/m 2 outliers due to formation or dissolution of cloud fields large errors for for solar elevation < 10 due to use of satellite images in the visible range currently only forecasts with solar elevation > 10 usable

32 Statistic evaluation: rmse in dependence of forecast horizon MVF forecasts better than NWP based forecast up to 4 hours ahead MVF forecasts better than persistence of cloud transmissivity derived from measured irradiance data from 2 hours onwards calculation time of MVF: sunel > 10 only hours with all forecast horizons available included

33 Satellite data irradiance measurement PV power measurement ECWMF forecast optimized, sitespecific irradiance prediction power prediction PV simulation up-scaling regional PV power prediction DWD forecast representative PV system sites PV system description module type nominal power Tilt and orientation.. 33

34 Combination of 2 NWP forecasts with satellite based forecasts ECWMF, DWD and MVF forecasts combination of the three models: I pred,combi3 =a I ECMWF,pp + b I DWD,pp + c I MVFpp + d coefficients a,b,c,d are fitted to measured data using the last 30 days for each hour separately period: evaluation restricted to hours, all forecasts are available 34

35 Evaluation: combination of ECMWF,DWD and MVF forecasts I in W/m 2 date

36 Evaluation: 3 hour ahead forecasts combination of ECMWF,DWD and MVF mean Germany rmse ECMWF 41W/m 2 rmse MVF 34W/m 2 rmse combi 25W/m 2 combination much better than single models cc error(ecwmf,dwd) =0.51, cc error(ecwmf,mvf) =0.29 improvement* vs ECMWF improvement* vs MVF * ) 39% 26% rrrr rrr rrrr nnn rrrr rrr

37 Evaluation: 3 hour ahead forecasts combination of ECMWF,DWD and MVF mean Germany, rmse Δ max ECMWF 41W/m 2 167W/m 2 MVF 34W/m 2-177W/m 2 combi 25W/m 2 113W/m 2 combination much better than single models: in particular outliers are reduced

38 Evaluation: rmse in dependence of forecast horizon CMV forecasts better than NWP based forecast up to 4 hours ahead large improvement with combined model calculation time of MVF: sunel > 10 only hours with all models available included in dependence on forecast horizon

39 Evaluation: rmse in dependence of forecast horizon improvement with combined forecasts more pronounced for regional average forecasts

40 Summary irradiance predictions accuracy of day-ahead predictions is mainly determined by NWP model forecasts: largest forecast errors for fog irradiance forecasts based on cloud motion vectors from satellite data significantly better than NWP based forecasts up to 4 hours ahead significant improvement by combination different forecast models with statistical methods regional forecasts show much higher accuracy than single site forecasts: depending on forecast model rmse for German average of single site rmse 40

41 OVERVIEW ON PV POWER PREDICTION SCHEME PV power prediction forecast horizon Satellite data irradiance measurement PV power measurement hours optimized, sitespecific irradiance prediction power prediction PV simulation up-scaling regional PV power prediction ECMWF irradiance forecast days representative PV system sites PV system description ECMWF: European Center for Medium-Range Weather Forecasts module type nominal power Tilt and orientation.. 41

42 Power prediction model irradiance on tilted plane (parametric model 1 ) PV simulation parametric model for MPP efficiency of modules 2 η MPP (I tilt,t) inverter model additional losses power forecast for single PV systems 1) Klucher et al, ) Beyer et al,

43 Regional power prediction: up-scaling operational forecast transpower 50Hertz description of energy production of thousands of systems without detailed system information: representative subsets: Pall, nom P all, pred = Prep, pred P rep, nom almost no loss in accuracy, if the representative subset correctly reflects the basic properties of the complete data set 43

44 Regional power prediction: up-scaling description of energy production of thousands of systems without detailed system information: representative subsets: Pall, nom P all, pred = Prep, pred P rep, nom almost no loss in accuracy, if the representative subset correctly reflects the basic properties of the complete data set distribution of installed power system orientations 44

45 Evaluation of regional PV power prediction: data sets control area of 50Hertz period: December 2011 November 2012 operational NWP based forecasts comparison to measured power information on feed-in from all systems not available 45

46 Evaluation of regional PV power prediction: data sets control area of 50Hertz period: December 2011 November 2012 operational NWP based forecasts comparison to measured power information on feed-in from all systems not available upscaling using systems of monitoring data base of meteocontrol GmbH 46

47 Evaluation of operational PV power forecast changing weather conditions are generally predicted well 47

48 AUSWERTUNG PV PROGNOSE 2012 Evaluation of operational PV power forecast rmse intraday : 4.9% rmse day-ahead :5.7% only daylight values,normaliazation to installed power

49 AUSWERTUNG PV PROGNOSE 2012 Evaluation of operational PV power forecast annual course

50 AUSWERTUNG PV PROGNOSE 2012 Evaluation of operational PV power forecast annual course largest rmse values Feb-April 2012 fog and snow

51 Detection of snow cover on PV systems 51

52 Empirical approach to predict PV power during winter forecast of snow covered modules based on: temperature forecasts forecast of snow depth PV power measurements for the previous days

53 AUSWERTUNG PV PROGNOSE 2012 Evaluation of snow detection large improvement- but still need for further development

54 AUSWERTUNG PV PROGNOSE 2012 Evaluation of snow detection large improvement- but still need for further development

55 Summary PV power prediction contributes to successful grid integration of 32 GW peak PV power in Germany different methods of irradiance forecasting for different forecast horizons: numerical weather prediction for day-ahead forecasts cloud motion vectors from satellite images or hour-ahead forecasts strong potential for improvement by combining different forecasts with statistical methods

56 Summary conversion of irradiance to PV power use of standard PV simulation tools upscaling to regional power: information on installed power is essential Detection of snow on PV systems need for improvement: fog and snow situations research is required for different steps of PV power forecasting chain e.g. cooperation with weather services, improvement of satellite based forecasts, statistical methods to combine and improve irradiance and PV power forecast models

57 Vielen Dank für Ihre Aufmerksamkeit! 57