Tracking wind farm output in real time. Wind power prediction experience
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1 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
2 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
3 Installed wind power in Spain Installed Wind Power (MW)
4 Necessity of the project High number of wind plants projects Septiembre 2005: MW December 2007: MW December 2010: MW Minimize the impact of wind generation on system operation 4
5 MENORCA MALLORCA 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, R E D E L É C T R I C A D E E S PA Ñ A Eólicos Penin-Española : MW Eólicos I.Baleares : 103 MW Eólicos I.Canarias : 218,3 MW Eólicos Portugal : MW 5
6 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
7 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
8 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
9 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
10 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
11 Short term prediction (48 hours) Results: Hourly forecasts Input data: Meteorological forecasts (wind speed: magnitude and direction) Hourly wind power Methods: Physical Statistical 11
12 Metered wind generation (80%) 12
13 Metered real wind generation and estimation 13
14 Estimated real wind generation 14
15 Estimated real wind generation (Nov 6th 2002) 15
16 Generation - load Wind power Other special generation 16
17 Electricity load Wind power Other special generation 17
18 Generation - load differences Wind power Other special generation 18
19 Prediction tool at Red Eléctrica de España Real time wind generation Meteorological forecast System operation REE. CECOEL 19
20 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
21 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
22 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
23 Meteorological forecasts H0,2 H0,5 23
24 Hirlam data 24
25 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
26 Meteorological forecasts comparison The final accuracy of the prediction process depends on the meteorological forecasts quality. COEFICIENT E R PRED. VIENTO-II COMBINACIÓN DE PREDICCIONES EN ALAIZ (JULIO 2001) PRED. VIENTO-I HORIZONTE DE PREDICCIÓN 26
27 40 Relative error (Pediction/Production) 06/06/ /12/2005 R e la tiv e e rro r (% ) Prediction horizon (hours) 27
28 800 Mean error 06/06/ /12/ Mean error (MW) Prediction horizon (hours) 28
29 1 N a v a r r a Error medio relativo H o r i z o n t e d e p r e d i c c i ó n 29
30 Wind generation (March 2002 to February 2005) Pow e r (M W) Est imat ed hour ly mean power Inst alled power 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
31 Wind generation (March 2002 to February 2005) Generation by percentage Hourly mean power/ Installed power (%) 31
32 Frequency distribution (Spanish peninsula) Frequency (%) W ind generation frecuency distribution Power (%) 32
33 Frequency distribution (Spanish peninsula) Generation percentage % 20% 40% 60% 80% 100% Probability to overcome this generation 33
34 Frequency distribution (Navarra) Frequency (%) Power (%)
35 Frequency distribution (Navarra) Generation percentage % 20% 40% 60% 80% 100% Probability to overcome this generation 35
36 Maximum wind power values Máxima Máxima Zona Fecha % 08/04/2005 1: Andalucía Occidental 22-dic : Andalucía Oriental 29-jul : Aragón 10-oct : Asturias 19-ene : Cantabria 0 0 6: Castilla-La Mancha Occidental 15-feb : Castilla-La Mancha Oriental 23-may : Castilla-León 03-mar : Cataluña 11-oct : Extremadura :Galicia Norte 17-mar : Galicia Sur 23-oct : La Rioja 05-oct : Madrid : Murcia : Navarra 04-may : País Vasco : Valencia 29-ene TOTAL España Peninsular 08-abr
37 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
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