Multi Time Scale Wind Energy Forecasting Model based on Meteorological Simulation and Onsite Measurement

Transcription

1 Multi Time Scale Wind Energy Forecasting Model based on Meteorological Simulation and Onsite Measurement Kota ENOKI, Takeshi ISHIHARA, Atsushi YAMAGUCHI, Yukinari FUKUMOTO, The University of Tokyo Tokyo Electric Company

2 Introduction 2/19 For electric power supply system, demand and supply have to be balanced. The wind power forecasting can contribute to increase the wind power penetration. Some problems are still left 1. In Japan, the surrounding terrain of wind farms is very complex. 2. Unexpected stop is not predicted because operational condition is not considered.

3 Review 3/19 Model Development Description Complex terrain Operational conditions Predikor 1993~ Denmark Physical model based on WAsP WPPT 1991~ Denmark Statistical model with consideration of wind direction ewind 23~ USA Mesoscale model + statistic approach. Considerations of complex terrain is not enough. No model considers operational conditions.

4 Objectives of this study 4/19 Propose a new physical downscaling model based on mesoscale simulation and microscale model to consider the effect of complex terrain. Propose Multi Time Scale Wind Energy Forecasting Model that uses two different time scales to consider the operational condition.

5 Description of site and data 5/19 Hachijo Wind Power Plant (Tokyo Electric Company) Consists of one 5kW WT. Located in small island 3km south of Tokyo. Located at the side of a mountain and surrounding terrain is very complex. Power and nacelle wind speed are measured on-line through SCADA system. NWP data JMA provides NWP data called RSM. Horizontal Time Resolution Initial Time Prediction Horizon Arrival 4km 3hr (Pressure Level) 2km 1hr (Surface Level), 12(UTC) 51hr 6hrs after the initial time

6 Physical Down Scaling Model 6/19 First, NWP(Numerical Weather Prediction) data is downscaled by a mesoscale meteorological model. Mesoscale wind prediction is further downscaled by a microscale model. Finally, we get the local wind speed prediction. NWP data Mesoscale Simulation Nonlinear Microscale Model Wind Speed Prediction data Physical Down Scaling Model

7 Mesoscale Meteorological Simulation 7/19 As a mesoscale model RAMS developed by the Colorado States University was used. Governing equations are: non-hydrostatic Reynoldsaveraged primitive equations, a thermodynamic equation and a water species mixing ratio equation. By three level nesting, NWP data with 2km resolution is downscaled to 1km resolution. 4k m u u u u ' u u u u v w fv Km Km Km t x y z x x x y y z z v v v v ' v v v u v w fu Km Km Km t x y z y x x y y z z w w w w ' g v ' w w w u v w Km Km Km t x y z z x x y y z z 4k m Grid Size 8km 44k m 18k m ' R u v w t cv x y z 44k m 18k m Grid Size 2km Grid Size 1km

8 Microscale Model MASCOT is a nonlinear microscale model based on computational fluid dynamics. Reynolds averaged equations with standard k- model was used as governing equations. u j t x j u uu p u t x x x x i j i i j i j j 2 2 k u u i j uu i j kij 2C 3 x j x i k uk j t k ui uu i j t xj xj k xj xj 2 u j t ui C1 uu i j C2 t xj xj xj k xj k uu i j 8/19 To apply the result of microscale model, Idealizing and Realizing Approach was used. Physical Downscaling Model

9 IRA (Idealizing and Realizing Approach) 9/19 In IRA, two simulations are carried out. One with coarse terrain and the other with fine terrain. The wind speed ratio for each wind direction at the target site is calculated. Coarse Grid (1km resolution same as finest grid of RAMS) Fine Grid (5 m resolution) This wind speed ratio is applied to the wind speed by mesoscale model to obtain the wind speed considering fine grid. C( ) u u inflow fine coarse RAMS RAMS ufine C( z 123 ) uz 4 Physical Downscaling Model

10 Result 1/19 Wind Speed (m/s) Observation Mesoscale Model Microscale Model Nov.24 Day Ahead Prediction 11/3 11/5 11/7 11/9 11/11 11/13 11/15 11/17 Date Error (m/s) Yearly RMSE of Predictions (May.24-Apr.25) Forecast Horizon (hr) Mesoscale Model Microscale Model RMSE n pred meas u 2 i ui i1 The microscale simulation gives better results compare to the mesoscale simulation, especially for long term forecasts. n Physical Downscaling Model

11 Analysis of the Error 11/19 RMSE can be decomposed to three components rmse bias sdbias disp i xpred, ixmeas, i sdbias ( xpred) ( xmeas) bias disp 2 ( xpred) ( xmeas)(1 r( xpred, xmeas)) Error (m/s) RMSE Bias Component Mesoscale Microscale Forecast Horizon (hr) Error (m/s) RMSE Dispersion Component Mesoscale Microscale Forecast Horizon (hr) Microscale downscaling can reduce the bias component. The dispersion component which relates to the phase error could not be reduced by physical downscaling. The yearly prediction bias is reduced to.12 m/sec by proposed approach from 1.32m/sec mesoscale downscaling. Physical Downscaling Model

12 Conventional Wind Power Prediction 12/19 Wind Speed Prediction PC Dow P fpc u tkt t k t ( ) P Modified k PC k a P t k t tk t b P Obs t Power Curve Model Combine Observation Data with Predicted Power Data Wind Power Prediction Power Generation (kw) Observation Power Curve Wind Speed (m/s) Power Curve function based on measurement Power Power predicted by Downscaled Wind Combined Forecasting Latest Observation Time The concept of onsite measurement combination Downscaled wind speed is converted to power by the Power Curve function f PC. Forecasted power is combined with on-line measurement to reduce the forecast error for very short term. Multi Time Scale Forecasting Model

13 Time scale 13/19 To identify the power curve function or parameters (a k, b k ), Recursive Least Square with Exponential Forgetting is used. The forgetting factor λ determines the weight of the past data in the least square estimation. In the conventional model, only one forgetting factor is used, which is usually around λ=.999, equivalent to the time scale of 2.8 months. This time scale explains the seasonal change of the power curve and the parameters. RLSwEF Ey x θ s θ T s Argmin effective i t s1 Effective number of data N y 2 s xsθ t s T i 1 1 Weight Effect of Foregetting Factor λ = Observation Number Multi Time Scale Forecasting Model

14 Operational Problems 14/19 5 Jul Three hour ahead prediction Power Generation (kw Jul 28-Jul 29-Jul 3-Jul Date Observation General Conventional Model (3 hr ahead prediction) The wind turbine experiences unexpected stop. This change in the operational condition has much shorter time scale than the seasonal change. Different time scale should be introduced to consider this operational condition. Multi Time Scale Forecasting Model

15 Proposal of multi time scale model A new parameter to describe the operational condition is introduced. P c P Modified tkt t PC tkt C t is estimated from past values of C by Recursive Least Square with exponential forgetting. Measured c P / f ( u ) pc Measured Wind Speed Prediction Power Curve Model Model for detecting the operational condition of the WT Combine Observation Data with Predicted Power Data Wind Power Prediction 15/19 Multi Time Scale Forecasting Model

16 Time scale for C t 16/19 In this study, the forgetting factor λ=.8 is chosen for the estimation of Ct, which is much smaller than the forgetting factors for the other parameters. Effect of Foregetting Factor λ =.999 λ= Weight Observation Number Multi Time Scale Forecasting Model

17 Prediction result 17/19 5 Jul hour ahead prediction Power Generation (kw Jul 28-Jul 29-Jul 3-Jul Date Observation Proposed Model (3 hr ahead prediction) General Commercial Model (3 hr ahead prediction) The proposed model can consider the operational condition Multi Time Scale Forecasting Model

18 Monthly prediction error 18/19 Error / Rated Power (% Monthly RMSE of Two Prediction Model Proposed Model Conventional Model May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr Month The proposed model gives better results compared to the conventional model especially in the month that unexpected stop occur frequently. Multi Time Scale Forecasting Model

19 Conclusion 19/19 The yearly prediction bias is reduced to.12 m/sec from 1.32m/sec by the proposed approach in which the effect of complex terrain is considered. The multi time scale power forecasting model gives better prediction than the conventional models, especially in the month that unexpected stop occur frequently.

20 Thank you for your kind attention!

21 Forgetting Factor RMSE RMSE Dispersion Component λ=1. λ=.999 λ=.995 Downscaled λ=1. λ=.999 λ=.995 Downscaled Error (m/s) Forecast Horizon (hr) Error (m/s) Forecast Horizon (hr) Error (m/s) RMSE Bias Component λ=1. λ=.999 λ=.995 Downscaled Forecast Horizon (hr) By combination of observation, dispersion error become small, especially in short forecast horizon. Relatively small λ makes dispersion large. Large value of λ means stability. But total bias become large.

22 Transition of Combination Parameters Short Term Power Prediction Model

23 Verification of Combination (λ=.999) 23/19 RMSE Downscaled Combined Mothod RMSE Bias Component Error (m/s) Forecast Horizon (hr) RMSE Dispersion Component Error (m/s) Forecast Horizon (hr) Error (m/s) Forecast Horizon (hr) By combining observation, dispersion error which could not be reduced by physical downscaling became small for short term. The bias component is also reduced for whole horizon. Short Term Power Prediction Model

24 Wind Power Prediction Model 24/19 Downscaled wind speed prediction is converted to power prediction by the Power Curve Model. Forecasted power is modified by coupling with latest observed power statistically. Short Term Power Prediction Model