RED ELÉCTRICA DE ESPAÑA Tracking wind farm output in real time Wind power prediction experience Gerardo González Morales Metering Europe Barcelona 20 th September 2005
Introduction Spanish objective for 2010: Electricity with renewable origin: 29,4% Wind energy: necessary to achieve it Transport investment related with wind power generation 2
Installed wind power in Spain Installed Wind Power (MW) 10000 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 47 51 76 130 206 448 774 3200 2431 1542 4700 5800 6200 9300 92 93 94 95 96 97 98 99 00 01 02 03 04 05 3
Necessity of the project High number of wind plants projects Septiembre 2005: 9.300 MW December 2007: 14.000 MW December 2010: 20.000 MW Minimize the impact of wind generation on system operation 4
450 250 2960 300 1140 3000 3200 1063 50 MENORCA 89 14 MALLORCA 2000 4500 2300 IBIZA FORMENTERA 372 2,6 LA PALMA GOME RA HIERRO 0,3 10,5 82 A R C H I P I É L A G O C A N A R I O FUERTEVENTURA TENERIF GRAN CANARIA 95 LANZAROTE 11,5 16,4 3315 R E D E L É C T R I C A D E E S PA Ñ A Eólicos Penin-Española : 22.900 MW Eólicos I.Baleares : 103 MW Eólicos I.Canarias : 218,3 MW Eólicos Portugal : 2.000 MW 5
Wind power generation impact on System Operation Features: Forecasting difficulty Absence/Lack of schedule: need of adequate system reserve Problems to participate in the existing market structure Power oscillations 6
Forecast using P-wD P Forecast Curve P-w D=0 v 0 Curve P-w D=180 w w w D Wind forecast (w, D) 7
Sipreólico calendar May 2000 Wind farm generation forecast November 2000 Feasibility report February 2001 SIPREÓLICO beginning July 2001 Preliminary versions. Prototype December 2001 SIPREÓLICO. Prototype Present moment December 2002 Sipreolico debugged tool (V1)... Improvements SIPREÓLICO 8
System operators with short term wind power forecast Denmark Western/Occidental system Eastern/oriental system E.ON (Germany) California Creta (Greece) Spain Red Eléctrica de España Carlos III University (Madrid) 9
Wind power prediction System operator: Generation necessities Grid constraints Reserve planning Real-time operation Distributors Market demand Wind farm Maintenance Production to offer in the market 10
Short term prediction (48 hours) Results: Hourly forecasts Input data: Meteorological forecasts (wind speed: magnitude and direction) Hourly wind power Methods: Physical Statistical 11
Metered wind generation (80%) 12
Metered real wind generation and estimation 13
Estimated real wind generation 14
Estimated real wind generation (Nov 6th 2002) 15
Generation - load Wind power Other special generation 16
Electricity load Wind power Other special generation 17
Generation - load differences Wind power Other special generation 18
Prediction tool at Red Eléctrica de España Real time wind generation Meteorological forecast System operation REE. CECOEL 19
SIPREOLICO tool features Currently running on a PC MATLAB Input meteorological forecasts from the National Metheorological Institute Output data: hourly wind power forecasts for the following day 20
Tool description Real time wind power Data processing Forecasts treatment Meteorological forecasts New wind farms Wind farms database Prediction algorithm Results processing Turbines database Real P-w curves Statistical corrections Historical data Result graphs Report 1 Report 2 Report N 21
Prediction module Input data Available data Common: Extra: Turbine P- w curve Location Meteorological forecasts A1 A2 A3 Historical data Delayed data Real time data H0 common H1 common + A1 H2 common + A2 H3 common + A3 Simple prediction Real power curves Temporal series 22
Meteorological forecasts H0,2 H0,5 23
Hirlam data 24
Models Models: historical power + non parametric cycle + Power - wind (speed, direction) curve. Daily cyclic effect Wind speed (and direction) effect Model 10 Model 11 25
Meteorological forecasts comparison The final accuracy of the prediction process depends on the meteorological forecasts quality. COEFICIENT E R2 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 PRED. VIENTO-II COMBINACIÓN DE PREDICCIONES EN ALAIZ (JULIO 2001) PRED. VIENTO-I 0 0 5 10 15 20 25 30 35 40 45 50 HORIZONTE DE PREDICCIÓN 26
40 Relative error (Pediction/Production) 06/06/2005-06/12/2005 R e la tiv e e rro r (% ) 30 20 10 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 Prediction horizon (hours) 27
800 Mean error 06/06/2005-06/12/2005 700 Mean error (MW) 600 500 400 300 200 100 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 Prediction horizon (hours) 28
1 N a v a r r a 0. 9 0. 8 0. 7 Error medio relativo 0. 6 0. 5 0. 4 0. 3 0. 2 0. 1 0 1 0 2 0 3 0 4 0 H o r i z o n t e d e p r e d i c c i ó n 29
Wind generation (March 2002 to February 2005) Pow e r (M W) Est imat ed hour ly mean power Inst alled power 8 500 8 000 7 500 7 000 6 500 6 000 5 500 5 000 4 500 4 000 3 500 3 000 2 500 2 000 1 500 1 000 500 0 mar-02 abr-02 may-02 jun-02 jul-02 ago-02 sep-02 oct-02 nov-02 dic-02 ene-03 feb-03 mar-03 abr-03 may-03 jun-03 jul-03 ago-03 sep-03 oct-03 nov-03 dic-03 ene-04 feb-04 mar-04 abr-04 may-04 jun-04 jul-04 ago-04 sep-04 oct-04 nov-04 dic-04 ene-05 feb-05 30
Wind generation (March 2002 to February 2005) 100 90 80 70 60 50 40 30 20 10 0 Generation by percentage Hourly mean power/ Installed power (%) 31
Frequency distribution (Spanish peninsula) Frequency (%) W ind generation frecuency distribution 14 12 10 8 6 4 2 0 0 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 75-80 80-85 85-90 90-95 95-100 Power (%) 32
Frequency distribution (Spanish peninsula) Generation percentage 80 70 60 50 40 30 20 10 0 0% 20% 40% 60% 80% 100% Probability to overcome this generation 33
95-100 Frequency distribution (Navarra) 20 18 16 14 12 10 8 6 4 2 0 34 85-90 Frequency (%) 0 5-10 15-20 25-30 35-40 45-50 55-60 65-70 75-80 Power (%)
Frequency distribution (Navarra) Generation percentage 100 90 80 70 60 50 40 30 20 10 0 0% 20% 40% 60% 80% 100% Probability to overcome this generation 35
Maximum wind power values Máxima Máxima Zona Fecha % 08/04/2005 1: Andalucía Occidental 22-dic-03 92 64 2: Andalucía Oriental 29-jul-02 100 64 3: Aragón 10-oct-02 102 85 4: Asturias 19-ene-05 100 27 5: Cantabria 0 0 6: Castilla-La Mancha Occidental 15-feb-05 87 12 7: Castilla-La Mancha Oriental 23-may-02 97 73 8: Castilla-León 03-mar-03 70 67 9: Cataluña 11-oct-02 100 74 10: Extremadura 0 0 11:Galicia Norte 17-mar-02 101 72 12: Galicia Sur 23-oct-04 100 89 13: La Rioja 05-oct-02 100 90 14: Madrid 0 0 15: Murcia 0 0 16: Navarra 04-may-05 85 72 17: País Vasco 0 0 18: Valencia 29-ene-05 97 20 TOTAL España Peninsular 08-abr-05 71 71 36
Conclusion To improve the wind power integration in the electrical system is necessary to develop more accuracy forecast models THANK YOU VERY MUCH FOR YOUR ATTENTION 37