Wind Power Forecast based on ARX Model with Multi Time Scale Parameter

Transcription

1 Wind Power Forecast based on ARX Model with Multi Time Scale Parameter Atsushi YAMAGUCHI Research Associate, Takeshi ISHIHARA Professor,

2 Typical Model flow NWP data Geographic information Prediction of wind speed Prediction of power SCADA data Mesoscale Model Microscale Model etc. Wind farm power curve Neural network etc. Correction of predicted power ARX model MOS etc. Final forecast

3 Existing studies on wind power forecasting Model Country Year Type WPPT DK 1991 WF power curve + ARX Prediktor DK 1994 Physical WF power curve + MOS WPMS DE 2000 Neural Network Previento DE 1999 Analytical wind + Physical WF power curve e-wind US 1999 Mesoscale model + Power Curve + MOS The effect of the wind prediction model on the power prediction is not clarified. Extreme weather event (typhoon / lightning) results in the trouble of the operation of the wind farm, causing the sudden decrease of the output power. This is the large source of the prediction error.

4 Objective To clarify the effect of the spatial resolution of the wind prediction in complex terrain To develop a model which enables to take the sudden change of the output from the wind farm due to the operational trouble into account

5 Wind Farms WF A,B,C,D,E WF F WF H,I WF G Grid1 Δxy = 8km Grid2 Δxy = 2km Grid3 Δxy = 1km Tohoku area consists of nine wind farms (total capacity 243MW) are focused in this research and online measurement system of the power (kw) was constructed.

6 Mesoscale model RAMS Regional Atmospheric Modeling System developed by the Colorado State University. Numerical Weather Prediction data data by by Japan Meteorological Agency Horizontal resolution 40km Temporal resolution 3hours Mesoscale model RAMS Conservation of momentum, heat etc. Physical model, turbulence model Terrain, landuse, SST database Regional Wind Horizontal resolution 1km Temporal resolution 30 minutes.

7 Power / Rated Power(%) Forecast by mesoscale model 実測 20km 1km 0 10/01 10/03 10/05 10/07 Time 10/09 10/11 10/13 RMSE(%) km 1km RMSE n pred meas P 2 i Pi i1 n Forecast 予測時間 horizon ( 時間 ) Prediction error was was reduced from from 19% 19% to to 17% 17% for for hours ahead forecast

8 Power curve model Predicted wind speed is converted into power by power curve model P f u, PC pred pred tk t tk t tk t t: initial time k: forecast horizon Power curve is estimated dynamically by weighted least square method (Nielsen 1999) using measured power and predicted wind speed of the past. 102 tk 2 f s s f k0 s1k ts meas arg min f u P f u f = f is called the forgetting factor. f = was adopted for power curve corresponds to the time scale of around 41.7 days Days ago 20 0

9 Estimated power curve Wind Farm A For SE and NW wind, the output shows larger value due to the effect of topography. For NE and SW wind, the output decreases due to the wake effect. Wind farm farm power curve can can take take the the effect of of topography and and wake Output (MW) N 0 NE E Wind Direction SE S SW W NW Wind Speed (m/s)

10 ARX model For the final prediction of the power, predicted power is corrected by using most recent measurement data. [ARX (AutoRegression model with exogenius inout) model] P a k P b k P pred meas pc tkt t Model parameters a(k) and b(k) are estimated similarly using the forgetting factor ab = 0.998, corresponds to days. tkt a k tk ts meas meas pc arg min ab Ps akps k bkps b k s 1 k This This approach can can change the the parameter dynamically capable of of expressing the the seasonal or or inter inter annual change of of power curve and and parameters Sudden change of of the the power due due to to operational trouble cannot be be predicted.

11 Problem of the power curve model Power / Rated Power(%) meas Exisiting Model 0 03/30 04/01 04/03 04/05 04/07 04/09 04/11 04/13 Sudden decrease of of the the power due due to to the the operational trouble cannot be be predicted

12 Multi time scale model To account for the operational condition of the wind farm, a new parameter c t was introduced. PC pred pred P PC pred pred P tk t f utk t, tk t tk tct f utk t, tk t 2 pred t ts meas pred t arg min c s s, s c s1 c P c f u 1 For For the the estimation of of c t, t, different time time scale was was used. c = c was was adopted, which corresponds to to days representing shorter time time scale f = c = Days ago 20 0

13 Optimization of the parameter c To find the optimum value of forgetting factor c, different forgetting factors were used for the forecast RMSE[%] λc The The optimum value exists, which is is around for for c. c.

14 Time series of the prediction 100 meas ARXM model Exisiting Model Power / Rated Power(%) /30 04/01 04/03 04/05 04/07 04/09 04/11 04/13 Proposed model can can predict the the sudden decrease of of the the output of of the the power.

15 RMSE of the proposed model Conventional study This study RMSE (%) Tohoku area July, 2006 RMSE n pred meas P 2 i Pi i1 n Forecast Horizon (hours) The The prediction error was was reduced significantly by by proposed model

16 Effect of the length of the training period Forecast for September 2007 was investigated by changing the length of the training period RMSE[%] week 2 weeks 1 month 2 months 3 months 6 months Forecast horizon (h) 3 months or or more training period is is enough to to obtain stable model parameters.

17 Conclusions Mesoscale model is useful for the wind power forecasting in complex terrain. The prediction error was reduced from 19% to 17% for 12 hours ahead forecasts by mesoscale simulation with 2km resolution. Proposed multi time scale parameter model enabled to predict the operating condition of the wind farm and the prediction accuracy increased compared to the conventional model. The optimum time scales exist for the estimation of different model parameters. The forgetting factor used for the estimation of c is much shorter than the one used for the estimation of the power curve. For the training of the model, at least three months is required to estimate the stable model parameters.

18 Acknowledgement This research is a part of the Development of wind power generation forecasting system based on weather forecast ( ), funded by New Energy and Industrial Technology Development Organization, Japan (NEDO).

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20 12 10 RMSE[%] 週間 2 週間 1 ヶ月 2 ヶ月 3 ヶ月 6 ヶ月 予測時間 [h]

21 Background In some utilities, the penetration of wind energy is quite large. For further penetration of wind energy, forecasting would be needed. This presentation focuses on the forecasting of the wind power for a utility. (Regional forecasting) Wind Capacity / Max. Load Hokkaido Tohoku Tokyo 174MW / 6,000MW 367MW / 16,000MW 57MW / 60,000MW East Japan 50Hz area

22 Day ahead forecast and hourly forecast Day Day ahead ahead forecast Forecast time Forecast horizon 00:00 06:00 12:00 00:00 12:00 00:00 Day ahead forecast is carried out once a day at 06:00 a.m. for the next day Main application: unit commitment, market Hourly forecast Forecast time Forecast horizon : 00:00 12:00 00:00 12:00 Hourly forecast is carried out every 30minutes for next 24 hours. Main application: load following, regulation

23 WPPT NWP data SCADA data WF WF power curve and and the the parameters for for power prediction model is is estimated using adoptive method WF power curve model Power prediction model Predicted power P f u,, k pc pred pred tk tkt tkt,, P a k P b k P pp pred meas pred pc tk tkt t tkt tk 2ht k 2h c tkt, kcos c tkt, ksin c pred s pred tk WF WF power curve and and the the parameters are are the the function of of the the wind wind direction

24 The outline of the project A national R&D project, Development of Wind Power Prediction Models Based on Numerical Weather Prediction has been carried out since October 2005 until March The object of the project is to develop a practical wind power prediction system for power system operation. The Project is funded by New Energy and Industrial Technology Development Organization (NEDO) and cooperated by five organizations. (UT) Itochu Techno-Solutions (CTC) Central Research Institute for Electric Power Industry (CRIEPI) Japan Weather Association (JWA) E & E Solutions (EES)

25 The concept of the project To meet any type of requirement in the future, two types of model will be developed. 数値気象予報データ NWP 単機出力 / ナセル風速データ SCADA a c b d ファーム Ⅲ WF 総出力データ SCADA ファームⅢ ファームⅠ ファームⅣ ファームⅡ 数値気象予報データ NWP 単機出力予測値 a b c d WF 総出力予測値ファームⅠ ファームⅡ ファームⅢ ファームⅣ ファーム Ⅲ WF 総出力予測値 (a+b+c+d) エリア総出力予測値 (Ⅰ+Ⅱ+Ⅲ+Ⅳ) WF model Area model Based on detailed SCADA information. Based on limited SCADA information For wind farm owners. For grid operators.

26 The contents of the project Measurement (both on-line and off-line) EES Development of wind power prediction model CRIEPI + JWA (WF model), UT + CTC (Area model) Verification and error analysis of the model CRIEPI (WF model), UT (Area model) On line implementation of the model JWA (WF model), CTC (Area model) Development of the platform for wind power prediction model CTC, UT Guideline for wind power prediction CRIEPI, UT

27 Error Analysis In order to show the performance of the models, following measures were used. ME n i1 P pred i n P meas i Mean Error (bias) RMSE n pred meas P 2 i Pi i1 n Root Mean Square Error ME and RMSE is calculated as a non dimensional value (%) relative to the total nominal power.