A Turbine Hub Height Wind Speed Consensus Forecasting System

rap.ucar.edu

A Turbine Hub Height Wind Speed Consensus Forecasting System

A Turbine Hub Height Wind

Speed Consensus Forecasting

System

NCAR’s Wind Energy Prediction System

Developed for Xcel Energy

Bill Myers

Seth Linden

National Center for Atmospheric Research

3/2/2011


Xcel Energy Service Areas

Wind Farms (50+)

~3000 Turbines

(growing)

~ 3.75 GW (Wind)

~10% Wind

3.4 million customers

(electric)

Annual revenue $11B

Copyright 2010 University Corporation for Atmospheric Research


NCAR Wind Energy Prediction System

Xcel Energy Configuration

NCEP Data

NAM

GFS

RUC

MOS

Observations

Wind Farm Data

Nacelle wind speed

Generator power

Node power

Met tower

Availability

CSV Data

Operator GUI

WRF RTFDDA

System

Ensemble

RTFDDA

System

Dynamic,

Integrated

Forecast

System

(DICast ® )

Wind to Energy

Conversion

Subsystem

Meteorologist

GUI

Environment

Canada

GEM Global

GEM Regional

WRF Model Output

Copyright 2010 University Corporation for Atmospheric Research

3


Xcel DICast ® System

• Overall Xcel system goal:

• Generate accurate forecasts of power

• Approach taken by NCAR :

• First generate accurate forecasts of wind speed at

the hub height of each turbine

• Then derive power from individual turbines from

wind speed

• As a consensus point forecast system, DICast was a

logical choice to generate the HH wind speed

forecasts

• DICast tries to predict Nacelle wind speed sensor

value

3/2/2011


Xcel DICast ® System Diagram

Data

Ingest

GFS DMOS

NAM DMOS

RUC DMOS

RT-FDDA WRF DMOS

.

.

GEM DMOS

Ensemble Mean

Integrator

Post

Processing

Wind Speed

Forecasts

3/2/2011

DMOS means Dynamic Model Output Statistics


Xcel DICast ® System

Hub Height Wind Speed not explicitly

predicted by NWP models

Like other DICast variables (POP, etc)

Predictors relevant to HH Wind Speed

must be derived from NWP data

All attempt to directly predict HH Wspd

Use 10m Wspd and P-level Wspds

3/2/2011


Hub Height Wind Speed Predictors

Second P-level

above

1 : Interpolate between 10m and first P-

level above winds

2 : Extrapolate* up from 10m winds

3 : Extrapolate* down from next P-level

above winds

etc

First P-level above

Hub

Hgt

10m wind level

First P-level below

* log-based extrapolation with roughness

dependent on land-use

3/2/2011


Dynamic MOS

• Linear regression-based statistical method

• Similar to NWS MOS, but regressions built

dynamically

• New equations generated each week

System learns based on recent data

• Can be applied to any NWP forecast model fairly

easily

• Tuned forecasts can be generated quickly for new

sites

• Uses “default equations” if statistical model fails

3/2/2011


3/2/2011

NAM-DMOS Performance


Xcel DICast ® System Diagram

Data

Ingest

GFS DMOS

NAM DMOS

RUC DMOS

RT-FDDA WRF DMOS

.

.

GEM DMOS

Ensemble Mean

Integrator

Post

Processing

Wind Speed

Forecasts

3/2/2011

DMOS means Dynamic Model Output Statistics


Forecast Integrator Objectives

To combine forecasts from a set of models:

• Discovers the “best” combination of forecast modules

for a given forecast time and location.

• Computationally simple and robust.

• Can easily adapt to the addition of new modules or

removal of obsolete modules.

System learns as weights are updated daily

• Weights nudged in direction of gradient in weight space

3/2/2011


Forecast Integrator

Forecast error as function of W 1 & W 2

1

W 2

W 2 (i)

Integration Step

3/2/2011

0

0

W 1

W 1 (i)

1


Integrator Performance

3/2/2011

Copyright 2010 University Corporation for Atmospheric Research


3/2/2011

Conclusions

DICast reduces forecast errors with multiple

optimization steps

DMOS provides optimized forecasts from

individual NWP models

Integrated forecasts better than forecasts

from each individual NWP model

Summer predictions more difficult than

fall/winter

High resolution modeling by itself is not the

optimal solution to wind energy forecasting

DICast is a robust technology providing

documented forecast improvements

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