Hotspot analyses for

Dynamic Power Line Rating

Dirk Malda

Willy Zittersteijn, Melanie Hoffmann, Jelle Wisse

EWEA workshop Leuven, October 2nd 2015

• Technical explanation of MeteoGroup’s Dynamic

Line Rating (DLR) model

• Probability forecasts for reducing risks

• Hotspot analysis to refine DLR

• Hotspot observations used for improved forecast

• Future Outlook

Outline

Transport capacity model

• 𝑰 transport capacity

• 𝑹 line resistance

line properties

• 𝑺𝒊𝒏 solar radiation

geographical location, date and

time

orientation of line

• 𝑳𝒐𝒖𝒕 outgoing longw radiation

Tline

• 𝑳𝒊𝒏 incoming longw radiation

TT, N

• 𝑯 sensible heat flux

TT, Tline, FF

4

Sin

>> Lout

Lin >

H

𝐼2𝑅 = 𝐻 − 𝑆𝑖𝑛 + 𝐿𝑜𝑢𝑡 − 𝐿𝑖𝑛

𝐼 =𝐻 − 𝑆𝑖𝑛 + 𝐿𝑜𝑢𝑡 − 𝐿𝑖𝑛

𝑅

Shortwave radiation on line

5

E

S

W

N

E

S

W

N

Capacity is strongly related to wind speed

0.6 m/s

0 oC

35 oC

The lowest capacity determines the line capacity!

Dynamic line rate model

Dynamic line rate model

Cigré

Land use (roughness length)

Weather forecast

Transport capacity forecast

Probability forecast

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

4

5

6

7

8

9

10

11

12

13

14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Temperature

Wind speed

Transport capacity

4

6

8

10

12

14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

80% Chance of exceeding

Most likely value

20% Chance of exceeding

Cigré

Monte Carlo

Dynamic line rate model in forecast modeTransport capacityTransport capacity and wind speed (m/s) 2010-06-08 21:00

Validation of reliability forecast for Dynamic Line Rating

Risk!

Safe, conservative

approach

Perfect forecast, but close to risk areaMeteoGroup Model:

efficiency approach

Example of

perfect forecast:

80% of measured

line capacity is

above 80%

confidence level

forecast

Steps to become more accurate

1. Determine critical line sections (hotspots)

2. Installing real time (weather) monitoring on critical sections

3. Use weather data to calibrate the model forecast

Improving the accuracy by determination of

hotspots

Defining hotspots: Selection of surrounding weather stations

Defining hotspots: Using 10 years of historical observations of surrounding weather

stations

• Weather observation data are

downscaled towards the power

lines

• Downscaling method takes into

account:

a) local information along the lines

b) local situation of weather stations

c) actual weather

Defining hotspots – low wind speeds

N

E

S

W

NWNE

SESW

• Roughness method:

High roughness means

low wind speed Low

roughness

High

roughness

Location of pylon

Current fixed capacity based on line

properties

Higher max

Ampacity

Lower max

Ampacity

Higher max

Ampacity

Lower max

Ampacity

Higher max

Ampacity

• Line capacities are

not always

homogeneous!

Average windspeed June July and August

2.0 3.4 m/s

Average daily maximum temperature June July

and August20.0 24.0°C

Average relative capacity

1.40 1.50

Limited section count

20

Number of hours in June, July and August a section is the limited

section

Detailed focus

Followed by visit

Final hotspots

23

Weather stationSolar panel

Height: 10 m above

ground level

Installation of weather station

Use observations to improve dynamic

line rating forecastMOS

equations

MOS

forecast

Downscaled

forecast

Meteobase

forecast

Historical

OBS

Historical

MODEL

Topographical

information

Edit

meteorologist

+

MOS

equations

Actual

MODEL

Actual

OBS+ +

MOS

forecast +

Downscaled

forecast +

Dynamic line rating model

In July is 60% of the time more than 150% relative capacity available

>150% capacity

Example: Capacity throughout the year related to

hotspot analyses

>125% capacity

Future outlook