European project, demo “NETFLEX”...Data mining exploration resulted in multi-linear...

European project, demo “NETFLEX” For large integration of renewables using DLR, PST and PMU.

8 February 2012

�2010 to 2013 , budget : 57 M €�26 partners (many TSO’s , large actors, some universiti es)

�2020 european electrical energy goal = 20% renewables , 20% energy savings , 20% less CO2

TWENTIES = Transmission system operation With large penetration of Wind and other Renewables Electricity Sources in Networks by means of Innovative tools and

integrated energy solutions.

20% renewables , 20% energy savings , 20% less CO2

�2 demos with DLR and wind farms (one in Spain, one in CWE)

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NETFLEX Demo locationDLR Location selection in Belgium(ELIA/ULg)

NORTH SEA

10 Ampacimon sensorsinstalled on a two circuit 150 kV line Brugge-SLijkens

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BELGIUM

DLR sensor installation

Installation of 10 DLR Ampacimon sensors on 150 kV line in Belgium (to cover two circuits of about 30 km each)

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Weather forecast model Ampacity forecast model

Topoclimatology University of Liège

1 2 3 4

Netflex demo with DLR sensors. Step 1 : ampacity forecast based on meteo

forecast only (one day ahead)

Stochastictools

24H Ampacity

Stochastictools

24H Ampacity

Ampacity forecast

24HImplementation

Ampacity forecast

24HImplementation

Weatherstation tuning

24H

Weatherstation tuning

24H

Effective windtuning at sensor

location24H

Effective windtuning at sensor

location24H

1 2 3 4

Updated every 6 hours� 5

1.1 Météo Forecast : how it works worldwide

Météo numerical forecastprocessing

GFS (mesh of 50km/every 3h)

WRF (10km/30min)( 2km/10min)

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1.2 One-Day ahead meteo forecast : downsizing

• Analyses were carried out on a 220kV line in Nantes

• One year of data (2009-2010) at our disposal

• On regional model, appropriate tuning is required for lo w wind speed

forecast (very much related to local data)

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Example. Basic real time data used for validation.Sag changes in real time as direct sensor output

11.5

12

12.5

13

13.5

sag

[m]

Sag measured on a 225 kV OVL (Nantes, France), 2010

Actual sag (Ampacimon sensor)

Max. allowed sag

11.5

12

12.5

13

13.5

sag

[m]

Sag measured on a 225 kV OVL (Nantes, France), 2010

Actual sag (Ampacimon sensor)

Max. allowed sag

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Summer period (one week) : sag output from Ampacimon sensor. (max sag 13.5 m)

20/05 21/05 22/05 23/05 24/05 25/05 26/05

10.5

11

Time [dd/mm]02/02 03/02 04/02 05/02 06/02 07/02 08/02

10

10.5

11

Time [dd/mm]

Winter period (one week) : sag output from Ampacimon sensor. (max sag 13.5 m)

From sag to effective weather conditions then to ampaci ty

Example of outputs. One-Day ahead forecast based on meteo only (step 1)

2000

2500

3000[A

mpe

re]

Real-time ampacity & forecasts on a 225 kV OVL (Nantes, France), 2010

RT Ampacity (Ampacimon sensor)

1-day ahead forecast : solely weather-basedActual current

Summer period

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20/05 21/05 22/05 23/05 24/05 25/05 26/050

500

1000

1500

Time [dd/mm]

[Am

pere

]

static rating = 990 A

Summer period(one week) part 1

Example of outputs. One-Day ahead forecast based on meteo only

Winter period2000

2500

3000

3500

[Am

pere

]




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Winter period(one week) part 1

02/02 03/02 04/02 05/02 06/02 07/02 08/020

500

1000

1500

2000

Time [dd/mm]

[Am

pere

]


1. Statistical and temporal analyses

2. Learning machine algorithms

3. Multi -Linear regressions

Step 2. One-Day ahead ampacity forecast updating using data mining

3. Multi -Linear regressions

...(AmpaFO)α(ambT)α(dirv)α(v)ααAmpa i4i3i2i10i +++++=

n1,...,i =

Find

samples

α

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1000

1500

2000

2500SEASON 2010, 220kV line, Nantes (France), module 15

Nb

of o

ccur

ence

s

2.1 Data mining first update : ML regression Summer (May-Sept 2010)

1000

1500

2000


Nb

of o

ccur

ence

s

-1000 -800 -600 -400 -200 0 200 400 600 800 10000

500

(RT-Forecast) ampacity error

Nb

of o

ccur

ence

s

Error mean: -95 AStandard deviation : 225 A

Error mean: 6 AStandard deviation : 220 A

-1000 -800 -600 -400 -200 0 200 400 600 800 10000

500


Nb

of o

ccur

ence

s

MULTI-LINEAR regression

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Example of outputs. The ampacity first update , ste p 2 from black to green curves (summer)

Summer period

2000

2500

3000Real-time ampacity & forecasts on a 225 kV OVL (Nantes, France), 2010



1-day ahead forecast : multi-linear regression

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20/05 21/05 22/05 23/05 24/05 25/05 26/050

500

1000

1500

Time [dd/mm]

[Am

pere

]


Example of outputs. (winter)the ampacity first update : from black to green cur ves

Winter period

2500

3000




1-day ahead forecast : multi-linear regression

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02/02 03/02 04/02 05/02 06/02 07/02 08/020

500

1000

1500

2000

Time [dd/mm]

[Am

pere

]


2.2 Data mining second update : histogram push

1000

1500

2000


Nb

of o

ccur

ence

s

Summer (May-Sept 2010)

1000

1500

2000


Nb

of o

ccur

ence

s

-500 0 500 1000 15000

500


Nb

of o

ccur

ence

s

-1000 -800 -600 -400 -200 0 200 400 600 800 10000

500


Nb

of o

ccur

ence

s

MULTI-LINEAR regression« push »

Standard deviation : 220 A98% of conservative ampacity

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Example of outputs. (summer)Step 3, Final Output : Ampacity forecast one day

ahead blue curve

Summer period

2000

2500




1-day ahead forecast : TWENTIES (98% confidence)

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20/05 21/05 22/05 23/05 24/05 25/05 26/050

500

1000

1500

Time [dd/mm]

[Am

pere

]


RT Ampacity : mean 165%; forecasted secure ampacity : 125%

Example of outputs. (winter)Final Output : Ampacity forecast one day ahead : bl ue

curve

Winter period2000

2500

3000

3500

[Am

pere

]




1-day ahead forecast : TWENTIES (98% confidence)

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02/02 03/02 04/02 05/02 06/02 07/02 08/020

500

1000

1500

2000

Time [dd/mm]

[Am

pere

]


RT Ampacity : mean 165%; forecasted secure ampacity : 125%

Intra-day Forecasting(one hour ahead without meteo informations)

1000

1200

1400

1600

1800cu

rren

t [A

] & A

mpa

city

[A

]november 2010

RT currentseasonal limitforecasted AmpacityRT ampacity

11/24 11/25 11/26 11/27 11/28 11/290

200

400

600

800

1000

time [dd/mm]

curr

ent

[A] &

Am

paci

ty [

A]

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Other typical outputs :How about wind speed on power lines critical

spans during wind generation offshore ?

Occurrence of an onshore (on power line location worst critical span) perpendicular windspeed when the offshore windspeed blows at 10 m/s or more (Statistics of 2010)

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SIEMENS

CORESOserver

WAMS Smart-PFC

DLR

Global architecture at CORESO for Active network management using DLR

SIEMENSapplication server

Predictive and RT algorithms

Location : Brussels

Ampacimonunits

Location : Brugge

Weather stationsobservations

ELIART data

Location : Schaerbeek

Weather forecast server

Location : Liège/InternetLocation : Internet

� Another target of NETFLEX is to include in the glob al management system, the potential role of PST (phase shift transformers exi sting) and PMU (phase monitoring unit

� « advanced dynamic network management »

0) CORESO estimate one day ahead potential congesti ons into CWE

1) DLR is giving new thermal limits for critical lines (both in real time and 24 h

Combination of DLR/PST and PMU

1) DLR is giving new thermal limits for critical lines (both in real time and 24 h forecast), some potential congestions may dissapear .

2) PST optimisation scheme is proposing to redirect ac tive power flow into the supervised region (Central Western Region) taking i nto account DLR limits. This corresponds to optimised angle change at some PST transformers to limit congestion, if any forecasted one is possible as evaluated one day ahead.

3) PMU is checking if stability limits are still ok (r eal time) with new power flows and actions may be taken in case of potential probl em.

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� Data mining exploration resulted in multi-linear regression and histogrampush to get a reliable ampacity forecast one day ahead.

� Specific tuning of weather forecastwith emphasis on low windspeed

Conclusions

with emphasis on low windspeed

� This approach allows (on our Demosite) an average DLR >= 125% of staticrating, with 98% confidence.

� Combining DLR, PST and using PMU helps a lot in large penetration of renewables .

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European project, demo “NETFLEX”...Data mining exploration resulted in multi-linear...

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