Wind power forecast accuracy under icing conditions

Transcription

1 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 1

2 Outline Introduction Wind energy development in Germany Challenges of grid integration Forecasting of Wind power Influences of icing situations to forecast quality Conclusions 2

3 Three Figures 23 GW installed wind capacity 33 TWh production Jan Oct 08 7 % of el. consumption in

4 Wind power development in Germany MW

5 Electricity production by wind TWh 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

6 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 konv. Erzeugung RWE Leistung [MW] Old school: conventional production = load EnBW Stunden 6

7 Situation today Leistung [MW] 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 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

8 Situation tomorrow results of DENA study load w/o wind load incl. wind Load profile & wind generation Power [MW] conv. generation = load wind generation Hours No additional conventional generation is necessary => Grid management (frequency, voltage control,...) needed 8

9 low load + high wind limitations of grid capacity 9

10 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 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

11 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 Sat Sun Mon Tue Wed Thu Fri Sat time OM in operation 11

12 Prediction results 24 h forecast Online Forecast D Power [MW] Day Wind generation online, day-ahead forecast 12

13 Prediction results 4 h forecast Online Forecast 4H 8000 Power [MW] Day Wind generation online, 4 hour forecast 13

14 Prediction results 2 h forecast Online Forecast 2H 8000 Power [MW] Day Wind generation online, 2 hour forecast 14

15 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

16 Integration of wind power into the energy supply TSO dispatch centre WPMS screen 16

17 Prediction Methods I/Ro 1/

18 Wind & ice Reduced aerodynamic efficiency Faulty sensor signals 18

19 kw Turbine reaction High winds Heavy snowfall Mismatch of power and measured wind speed Jan '08 Dec ' m/s 16 19

20 Google earth Forecast errors caused by icing???? Task: Analyse forecast quality with measured results Selected sites: Mountainous regions Elev 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

21 WP Ulrichstein / Temperature profile ( JAN, FEB, DEC ) Frequency distribution of ambient temperature DEC JAN DEC JAN 12% FEB FEB 10% 8% 6% 4% T / C Span of T and avg. temp. by months 2% 0% T / C 21

22 substation Ulrichstein / 60 avg. T>0 C P [MW] V [m/s] Power characteristic 22

23 substation Ulrichstein / 60 avg. T 0 C P [MW] V [m/s] Power characteristic 23

24 substation Ulrichstein / 60 avg. Power characteristic 24

25 Measured time series example 1 v w P forecast C P measured 25

26 Measured time series example 2 26

27 Distribution of observed forecast errors vs. temperature mean error % site: Ulrichstein elev.: ~ 600 m asl -50 T / C 27

28 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

29 Conclusions 2 Forecasting is difficult especially when considering the future! 29

30 Thank you for your attention! More information on: Institut für Solare Energieversorgungstechnik e.v. Electrical Engineering and Systems Technology for the Use of Renewable Energies and the Decentral Power Supply Applications-oriented Research and Development Wind Energy Photovoltaics Bio Energy Hydro Power and Marine Energies Energy Conversion and Storages Static Converters Hybrid Systems Energy Economy 30