energy metro

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Energy MeteorologyInstitute of Physics / ForWindCarl von Ossietzky Universität Oldenburg

Detlev Heinemann

National Cheng Kung University, Tainan, Taiwan – 9 September 2015

Meteorological Research in Support of Renewable Energies

ENERGY METEOROLOGY

DAAD/NCKU Summer School – Lecture 1

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OVERVIEW


ENERGY METEOROLOGY

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! Renewables in the German power sector today

! Consequences for meteorology

! Energy Meteorology in Oldenburg

! 3 Examples! Solar power forecasting (-> photovoltaics)! Small-scale variability of solar irradiance!

Wind ow characteristics in wind farms



NET POWER PRODUCTION, 2014

GERMAN POWER SECTOR TODAY


ENERGY METEOROLOGY

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Sources: B. Burger (Fraunhofer ISE), DESTATIS, Leipziger Strombörse EEX

Changes to 2013

Nuclear Brown coal Hard coal Natural gas Wind Solar Biomass Hydro

Nuclear Brown coal Hard coal Natural gas Wind Solar Biomass Hydro

10,0 % 6,4 % 10,5 % 3,6 %

30,5 %



NET POWER PRODUCTION FROM SOLAR & WIND, 2014/15


ENERGY METEOROLOGY

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Sources: Fraunhofer ISE, BSW, DEWI

! Installed capacity, end of June 2015: PV: 39,2 GW Wind: 38,9 GW (compare with average load 2014: ~59 GW)

!Power production 2014: PV: 32,8 TWh (6,4 % of net electricity consumption)

Wind: 51,4 TWh (10,0 % of net electricity consumption)

! Maximum combined production from wind & solar (30.03.2015):

43,35 GW = 66 % of load (PV 13,46 GW, Wind 29,89 GW)

! Partial supply of load from wind and solar up to 50 % each

! Problems: variability, peak load

! Excess production very likely to occur soon





ENERGY METEOROLOGY

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Sources: B. Burger (Fraunhofer ISE), Leipziger Strombörse EEX

SOLARMax: 4,84 TWh

Min:0,40 TWh

MONTHLY PRODUCTION SOLAR, WIND, 2014 (TWh)

Min:2,34 TWh

Max: 8,85 TWh

WIND





ENERGY METEOROLOGY

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SOLAR & WIND Max: 9,25 TWh

MONTHLY PRODUCTION SOLAR, WIND, 2014 (TWh)

Min:4,96 TWh





WEEKLY PRODUCTION SOLAR, WIND, 2014 (TWh)


ENERGY METEOROLOGY

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WIND

SOLAR

SOLAR & WIND

Max: 1,26 TWhMin: 0,06 TWh






http://slidepdf.com/reader/full/energy-metro 8/44National Cheng Kung University, Tainan, Taiwan – 9 September 2015

ENERGY METEOROLOGY

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DAILY PRODUCTION SOLAR, WIND, 2014 (TWh)Max: 0,212 TWhMin: 0,003 TWh



SOLAR

WIND

SOLAR & WIND





ENERGY METEOROLOGY

Conventional energy conversion:" Availability of fuels is nite but known" Energy uxes are highly adjustable

WhyEnergy Meteorology?

9National Cheng Kung University, Tainan, Taiwan – 9 September 2015



ENERGY METEOROLOGY


Renewable energies:" Nearly innite energy source" Availability of ‘fuel’ uncertain and only

known on the average

Daytime

S o

l a r

i r r a

d i a

n c e

S o ur c

e: Mi k

eZ

eh n

er

WhyEnergy Meteorology?



CHARACTERISTICS

TRANSFORMATION OF THE ENERGY SECTOR

! Change from demand-driven to supply-oriented energy system

secured availability of conventional fuels <—> largely non-

deterministic availability of uctuating renewables! Increased complexity of the system (—> and of the number of degrees of freedom for an optimal control) many di # erent generating technologies, storage, bi- directionality, demand side management, energy trade, …

! High importance of ‘commodity’ Information especially of meteorological content...


ENERGY METEOROLOGY

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ENERGY METEOROLOGY

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DIRECT INFLUENCING ...

! Planning of solar and wind power plantsprecise knowledge of available energy (potential) at a givenlocation

! Economic operation of these plantsprecise forecasts of the actual available energy

! Development of next generation of systems and technologydetailed specication of relevant meteorological conditions

Ex.: turbulence -> mechanical loads of wind turbines spectrum of solar irradiance -> photovoltaics

An important – and new – constraint for future energy supplysystems are its temporal and spatial varying production rates.




ENERGY METEOROLOGY IN OLDENBURG


ENERGY METEOROLOGY

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" Background: Energy systems research in Physics since around 1982" Modeling of systems, its components & performance aiming at

understanding system behavior and optimization" With more and more detailed modeling the role of system

constraints became more important" Meteorology became more and more important" Since 1995: New research topic “Energy Meteorology“" Presently approx. 25-30 scientists

" Highly interdisciplinary: meteorologists, physicists, computerscientists, …



ACTIVITIES (approx. 30 scientists)

ENERGY METEOROLOGY IN OLDENBURG


ENERGY METEOROLOGY

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Method development " Surface solar irradiance from

satellite data" Measurement and modeling of

spectral irradiance" Sky scanner system for short-term

cloud motion" Wind ow modelling in wind farms

(Ex.: Large Eddy Simulation)" Parameterization of marine boundary

layer processes" Forecasting schemes for wind and

solar power in various time scales" Statistical characterization of

correlated wind and solar elds withhigh spatial and temporal resolution

Applications" Operational forecasting of wind & solar

power" Spatio-temporal balancing e # ects (PV,

wind) in grid integration" Variability of the solar spectrum and

inuence on PV energy yields" Measurement & modeling of spatial

variability of solar irradiance" Influence of uctuations in wind &

solar power production on gridperformance" Wake e # ects in large wind farms" High-res resource estimation



ENERGY METEOROLOGY

ForWind @ Oldenburg: 3 research groups within the Institute of Physics


Wind Energy Systems (WESys)

" Dynamics of complete WECs(numerical & experimental)

" Control & monitoring ofWECsand wind farms

" Integrated system design ofWECs

" LIDAR

Martin Kühn

Energy Meteorology (EnMet)

" Forecasting

" Simulation of large-scalewind power" Small- & meso-scale

simulation of atmosphericow (LES,..)

" Wind farm modeling" Marine atmospheric

boundary layer

Detlev Heinemann

Turbulence, Wind Energyand Stochastics (TWiSt)

" Modeling of turbulent windelds

" Wind tunnel experiments" Stochastic simulation of

WEC behaviour" Rotor aerodynamics" Interaction of wind and WEC

Joachim Peinke

Fraunhofer IWES Project Group „Computational Fluid and System Dynamics“ (CFSD)

FORWIND – CENTER FOR WIND ENERGY RESEARCH



ENERGY METEOROLOGY

FORWIND – CENTER FOR WIND ENERGY RESEARCH

Wind Energy Meteorology: 3 interconnected research elds


Offshore meteorology &resource modeling

" Modeling of the marine ABL

" Micro-scale models for windprofiles over sea (ICWP)

" Modeling the influence ofthermal stability and wavefield on the wind field (andvice versa)

" Meso-scale modeling (WRF,COSMO)

" Modeling of wind farmeffects and „regional powercurves“ for analysis of windpower integration in Europa(On&Offshore)

Jens Tambke

Small-scale wind eldsimulation

" Modeling of the turbulent

ABL and of mesoscale pro-cesses over various surfaces" Large Eddy Simulation

(PALM)" Mesoscale modeling (WRF,

COSMO)" Parameterization of the

inuence of WECs innumerical models

" Simulation of turbulent owin wind farms

" Engineering model FLaP(Farm Layout Program)

Gerald Steinfeld

Wind power simulation andforecasting

" Wind power forecasting

" Validation of meteorologicalforecast models

" Large-scale simulation ofwind power grid integration

" Ensemble forecasting" Extreme-Forecast-Index for

wind power grid integration

" Wind and poweructuations

Lüder von Bremen



OVERVIEW


ENERGY METEOROLOGY

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! Renewables in the German power sector today

! Consequences for meteorology

! Energy Meteorology in Oldenburg

! 3 Examples! Solar power forecasting (-> photovoltaics)! Small-scale variability of solar irradiance!

Wind ow characteristics in wind farms



BY TRANSMISSION SYSTEM OPERATOR (TSO)

" Market (TSOs, energy trader, wind/solar plant operator)" Power stock market: EEG direct marketing 24-48 h" Intraday trading (12-24h)

" Control power market (not realized)" Grid Management (TSOs, DSOs)

" Grid operation, congestion management (0-12 h)" Recall of control power (0-1h)" Grid maintenance planning (2-7 days)

Forecast requirements

i. Hourly forecasts

for the next dayii. Forecasts with 15 min. resolution for the next hours

POWER MARKET: TREATMENT OF PV POWER


ENERGY METEOROLOGY

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OVERVIEW OF SCHEME

REGIONAL PV POWER PREDICTION


ENERGY METEOROLOGY

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RegionalPV power forecast

Simulation ofPV power

Solar irradianceforecast

Numericalweather prediction

(NWP)

Cloud motionfrom satellite

PV powermeasurements



ENERGY METEOROLOGY

Meteosat Second Generation (HR VIS)" Cloud index from

Meteosat images(Heliosat method)

Resolution:

MSG (HRV):! 1.2 km x 2.2 km

(Germany)! 15 Minuten


IRRADIANCE FORECASTING BASED ON

SATELLITE IMAGES

ENERGY METEOROLOGY



ENERGY METEOROLOGY


" Cloud index fromMeteosat images(Heliosat method)

" ‘Cloud motionvectors‘ (CMV) fromidentication of patternin consecutive images

" Extrapolation of cloudmotion to forecast next

cloud index image

Meteosat Second Generation (HR VIS)


SATELLITE IMAGES

ENERGY METEOROLOGY



ENERGY METEOROLOGY

Satellite-based solar irradiance map

200W/m 2 900W/m 2


" Cloud index fromMeteosat images(Heliosat method)

" ‘Cloud motionvectors‘ (CMV) fromidentication of patternin consecutive images

" Extrapolation of cloudmotion to forecast next

cloud index image

! Solar irradiance from

forecasts of cloud indeximages using the

Heliosat method


SATELLITE IMAGES

ENERGY METEOROLOGY



ENERGY METEOROLOGY

I i n W / m 2


Combination of forecast models with linear regression:

Ipred,combi = aI ECMWF + b I DWD + c I CMV + d

coe $ cients a,b,c,d from measurements of recent 30 days, separate for each hour

EVALUATION: COMBINATION OF ECMWF,

DWD AND CMV FORECASTS

ENERGY METEOROLOGY



ENERGY METEOROLOGY

! CMV forecasts better than

NWP based forecasts up to

4 hours ahead! CMV forecasts better than

persistence (of cloud situation)

from 2 hours on

! Significant improvement with

combination of forecasts

! Next steps:- extending the temporal scale

towards nowcasting with sky

imaging - merging NWP dynamics into

CMV method

EVALUATION: RMSE DEPENDING ON

FORECAST HORIZON


ENERGY METEOROLOGY



ENERGY METEOROLOGY


SKY IMAGERY FOR EVALUATION OF SPATIAL

IRRADIANCE FLUCTUATIONS AND VERYSHORT-TERM FORECASTING

Original JPG image1900 x 1900 pxevery 10s

Analyzed colors:Red-Blue-ratio RBR

White px: RBR ~ 1Blue px: RBR < 1

Weighted RBRby means of clear sky info

Total cloud cover

ENERGY METEOROLOGY



ENERGY METEOROLOGY


SKY IMAGERY – CLOUD SHADOWS

From cloud cover toshadows:

1. Consider orientationof imager

2. Lens function -> 2Dclouds

3. Estimation of cloudheight or ceilometermeasurement

4. Geometric

calculation ofposition of clouds

ENERGY METEOROLOGY



ENERGY METEOROLOGY


SKY IMAGERY – CLOUD MOTION VECTORS1. Corner Detection

(Shi-Tomasi-Algorithm):

Find good points to track

(mask horizon and sun region)

2. Optical ow(Lucas-Kanade-Algorithm):

Find the points in the subsequent image

ENERGY METEOROLOGY



ENERGY METEOROLOGY


HD(CP)2 Project‘Wolken- und Niederschlags-prozesse im Klimasystem‘

Pyranometer networkApril - Juli 201310 x 8 km 2

~100 pyranometer

3 SupersitesApril - MaiWind lidars, Ramanlidars, Micro-waveradiometer Cloud radar

300x radiosondes

8 km

EXPERIMENTAL EVALUATION OF SPATIAL

IRRADIANCE FLUCTUATIONS

ENERGY METEOROLOGY




EXPERIMENTAL EVALUATION OF SPATIAL

IRRADIANCE FLUCTUATIONS

Large potential for detailed measurement of spatial variability across large

solar power plants (CSP, PV)" high resolution in time and space" cheap sensors" maintenance!

ENERGY METEOROLOGY




PRO ECTION OF CLOUD SHADOWS AT THE SURFACE

ENERGY METEOROLOGY



Wakes behind wind turbines or windfarms influence downstream turbinesby:" reduced power output by lower

wind speeds in the wake" enhanced mechanical loads by

higher turbulence in the wake

Influenced by:" farm geometry" thermal stratification" surface roughness" orography

WAKES IN WIND FARMS


ENERGY METEOROLOGY




LES of wind farm ow& wind farm wakes

Validation with measurements

High-res Large Eddy

Simulation (LES) ofsingle wakes

Wake interaction withatmospheric boundary layer

Parameterization of wind farm

wakes in mesoscale models

SMALL-SCALE WIND FIELD MODELLING

ENERGY METEOROLOGY




ADM ADM-R ALM

1 WEA 1,8 h 2,9 h 37 h5 WEA 5,3 h 10,5 h (> 100 h)

Computing time with 1024 CPUs for 30min simulation

Actuator disk model(ADM )

Actuator line model(ALM )

Improved ADM(ADM-R )

Near wake Far wake

Wind turbine parameterizationin LES model PALM

SMALL-SCALE WIND FIELD MODELLING

•

ENERGY METEOROLOGY



" Di# erent ow patternin near wake, smallerdi # erences in farwake

" Similar results forALM and ADM-R

" Mean vertical owinduced by buoyancyin ALM and ADM-R

" Wake asymmetrydue to tower,inuence of nacellenegligible

u/u inflow w in m/s

WIND TURBINE PARAMETERIZATION FOR

SINGLE WAKES


ENERGY METEOROLOGY




LES enables analysis of wake dynamics

EXAMPLE: SINGLE WAKE SIMULATION WITH

LES MODEL PALM & ALM

ENERGY METEOROLOGY




EXAMPLE: SINGLE WAKE SIMULATION WITH

LES MODEL PALM & ALM

ENERGY METEOROLOGY



Stability Farm efciencyUnstable 74%Neutral 66%Stable 61%

Example: Horns Rev O # shore wind farm

Wind speed: 8 ms -1 ,Wind direction: W

Jensen (2007)

INFLUENCE OF ATMOSPHERIC STABILITY


ENERGY METEOROLOGY



Example: Power output of wind turbines in ‘alpha ventus‘ wind farmDörenkämper et al. (2012)

Large differences even forsingle wind turbines

Reasons:- Measurement at hub

height is notrepresentative for energyflux through rotor plane

- Energy flux differs fordifferent stabilities due todifferent wind shear

INFLUENCE OF ATMOSPHERIC STABILITY


ENERGY METEOROLOGY



" Filter: 360 ± 4°, 12 ± 1,5 m/s" mostly neutral to unstable stratication

(winter, T air < T water )" LES shows realistic reduction of power

16 km nort of Darß-Zingst21 WTG à 2,3 MW (Siemens SWT 2.3-93)Hub height: 67 m, Rotor diameter: 93 m

COMPARISON OF LES SIMULATIONS WITH

TURBINE DATA SCADA


ENERGY METEOROLOGY



Wind farm inow fromdi # erent directions

SIMULATION OF FLOW CONDITIONS IN

WIND FARM ‘ALPHA VENTUS‘


ENERGY METEOROLOGY





WIND FARM ‘ALPHA VENTUS‘Result: u/u inow , time averaged, inow direction: 255°

ENERGY METEOROLOGY





WIND FARM ‘ALPHA VENTUS‘Result: u/u inow , time averaged, inow direction: 270°

ENERGY METEOROLOGY





WIND FARM ‘ALPHA VENTUS‘

ENERGY METEOROLOGY



SUMMARY

Meteorological constraints will increasingly inuence ourenergy supply.

The value of uctuating power from solar and wind stronglydepends on the quality of information on these uxes.

To provide methods and data at the interface between energysystems and meteorology is the aim of research and development

in Energy Meteorology .

energy metro

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