Wind power forecast accuracy under icing conditions General approach, practical applications and options for considering effects of wind turbine icing Michael Durstewitz, Jan Dobschinski, Zouhair Khadiri-Yazami ISET Germany mdurstewitz@iset.uni-kassel.de 1
Outline Introduction Wind energy development in Germany Challenges of grid integration Forecasting of Wind power Influences of icing situations to forecast quality Conclusions 2
Three Figures 23 GW installed wind capacity 33 TWh production Jan Oct 08 7 % of el. consumption in 2007 3
Wind power development in Germany 25.000 18.290 20.470 22.092 MW 20.000 16.480 14.500 15.000 11.850 8.680 10.000 6.050 100 170 320 2.040 2.830 1.090 1.520 610 4.380 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 5.000 0 4
Electricity production by wind 99 07 40 TWh 35 30 25 20 15 10 5 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 gesamt 5,9 TWh 8,8 TWh 10,9 TWh 17,1 TWh 19,2 TWh 26,8 TWh 26,1 TWh 30,2 TWh 38,5 TWh 5
Wind power installations and TSO regions TSO mission e.on - balancing of generation and demand - grid stability voltage, frequency - provide reserve power (UCTE: 3000 MW primary control reserve ) VET 20000 18000 16000 14000 konv. Erzeugung RWE Leistung [MW] 12000 10000 8000 6000 4000 Old school: conventional production = load EnBW 2000 0 0 24 48 72 96 120 144 168 Stunden 6
Situation today Leistung [MW] 20000 18000 16000 14000 12000 10000 8000 6000 4000 Windenenergie konv. Erzeugung Hundreds of wind farms and single turbines Fluctuating power generation Non-dispatchable generators Privileged production of RE Today: conventional production = load wind energy 2000 0 0 24 48 72 96 120 144 168 Stunden Purchase commitment by utilities consequences for stable and reliable grid operation Information about actual state of wind power feed-in + short & medium term forecast is essential Foto: NEG Micon 7
Situation tomorrow results of DENA study 60000 50000 load w/o wind load incl. wind Load profile & wind generation 2015 40000 Power [MW] 30000 20000 10000 0 conv. generation = load wind generation 0 0 24 48 72 96 120 144 Hours No additional conventional generation is necessary => Grid management (frequency, voltage control,...) needed 8
low load + high wind limitations of grid capacity 9
The wind power predicton method at ISET Numerical weather Prediction (NWPs) ws i wd i T i P i i=1..n P i (t-1) P i (t-2) P i (t-3) i=1..n ANN i normalized power 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun time measurement prediction Wind power prediction for representative wind farm i Recent wind power feed-in of representative wind farms N = number of representative windfarms 10
Transformation algortihm sub-division of the control zone into small sectors Wind power feed-in of sector i P = k s * A * P i i j j ij j 4.5 P sum( t) = Pi ( t) i measurement prediction 3.5 2.5 1.5 0 Sat Sun Mon Tue Wed Thu Fri Sat time OM in operation 11
Prediction results 24 h forecast 12000 10000 Online Forecast D+1 8000 Power [MW] 6000 4000 2000 0 14.1 15.1 16.1 17.1 18.1 19.1 20.1 21.1 Day Wind generation online, day-ahead forecast 12
Prediction results 4 h forecast 12000 10000 Online Forecast 4H 8000 Power [MW] 6000 4000 2000 0 14.1 15.1 16.1 17.1 18.1 19.1 20.1 21.1 Day Wind generation online, 4 hour forecast 13
Prediction results 2 h forecast 12000 10000 Online Forecast 2H 8000 Power [MW] 6000 4000 2000 0 14.1 15.1 16.1 17.1 18.1 19.1 20.1 21.1 Day Wind generation online, 2 hour forecast 14
Wind Farm Control, Prediction Systems AI-based Prediction System WPMS E.ON Netz, Vattenfall Europe Transmission, RWE Transportnetz Strom, EnBW Transportnetze Verbund Austria TERNA Italien Egypt, Zafarana UK, National Grid Wind FarmCluster Management WCMS E.ON Netz Vattenfall Europe Transmission Red Electrica Espana Redes Energeticias Nacionais 15
Integration of wind power into the energy supply TSO dispatch centre WPMS screen 16
Prediction Methods I/Ro 1/2002 17
Wind & ice Reduced aerodynamic efficiency Faulty sensor signals 18
kw 700 600 500 400 300 200 100 Turbine reaction High winds Heavy snowfall Mismatch of power and measured wind speed Jan '08 Dec '07 0 0 4 8 12 m/s 16 19
Google earth Forecast errors caused by icing???? Task: Analyse forecast quality with measured results Selected sites: Mountainous regions Elev. 300 700 m asl Example: WP Ulrichstein/Vogelsberg sev. types / ~ 11 MW tot Elevation ~600 m asl Low mountain range Several icing reports Data: Measured P (subst) DWD-Analysis data 20
WP Ulrichstein / Temperature profile (2004 2008 JAN, FEB, DEC ) Frequency distribution of ambient temperature DEC JAN DEC JAN 12% FEB FEB 10% 8% 6% 4% -15-10 -5 0 5 10 15 T / C Span of T and avg. temp. by months 2% 0% -15-12 -9-6 -3 0 3 6 9 12 15 T / C 21
P(v) @ substation Ulrichstein / 60 avg. T>0 C P [MW] V [m/s] Power characteristic 22
P(v) @ substation Ulrichstein / 60 avg. T 0 C P [MW] V [m/s] Power characteristic 23
P(v) @ substation Ulrichstein / 60 avg. Power characteristic 24
Measured time series example 1 v w P forecast C P measured 25
Measured time series example 2 26
Distribution of observed forecast errors vs. temperature 30 20 10 mean error % 0-15 -10-5 0 5 10 15-10 -20-30 -40 site: Ulrichstein elev.: ~ 600 m asl -50 T / C 27
Conclusions 1 / outlook WPMS is a powerful tool for integrating wind power Reliability of results has to be checked for low temperatures Next steps: Identification and analysis of data from other sites P max, other parameter, icing footprint definition Integration into ANN training procedure Automatic ice detection-procedure (measurements & forecasts) Probability of icing risk, expected losses of power output Warning system for icing events Verification of results Implementation / update of WPMS 28
Conclusions 2 Forecasting is difficult especially when considering the future! 29
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