Use and interpretation of medium to extended Use and interpretation of medium to extended range productsrange products

ECMWF, Reading, 14th of November 2005

drs. Stefan Meulemans, MSc

Sempra Energy Europe Ltd.

London

smeulemans@sempracommodities.com

The Energy Markets

Major global player in metals, polymers, gas, oil and electricity markets

Offices in New York, London, Houston, San Diego, Singapore, Geneva, Calgary and Toronto

Sempra Energy Europe Ltd.Sempra Energy Europe Ltd.

Speculative trading on gas, oil and electricity marketsUsing information to anticipate market movesWeather major fundamental

Energy marketsEnergy markets

Assessing the supply and demand of the main energy sourcesDetermining a price based on all available fundamentalsOften volatile markets compared with traditional markets

Futures and forwardsFutures and forwards

Day ahead (EC)Week ahead (EC)Month Ahead (EC)Cal ahead

Daily QENOYc1 [Candle]08/03/2005 - 07/06/2005 (GMT)[Professional]

14 21 31 07 14 21 28 06 13 24 31 07Mar 05 Apr 05 May 05

PriceEUR

27.3

27.6

27.9

28.2

28.5

28.8

29.1

29.4

29.7

30.3

30.6

30.9

31.2

31.5

31.8

32.1

32.4

32.7

27

30

33

QENOYc1, Last Trade, Candle06/06/2005 31.90 32.25 31.80 32.23

Supply and demandSupply and demand

Supply and demand determine the forward electricity pricesTraded contracts are day-ahead, week-ahead, month-ahead etc.Supply: wind and hydro Demand: temperature important

Nordic marketsNordic markets

Weather crucial due to very high hydro capacityMarket very dependent on ECMWF model

Hydro Hydro

ECMWF forecasts have to be transposed into a precipitation energy forecastWe also need an estimate of the snow cover and the expected melt, based on the temperatureAlps, Pyrenees and Scandinavia

Hydro Reservoirs ScandinaviaHydro Reservoirs Scandinavia

Example Q106 Nord PoolExample Q106 Nord PoolDaily QENOQc1 [Candle, MA 14, WMA 14, EMA 14, RSI 14, RSIMA 14,14]

13/06/2005 - 14/11/2005 (GMT)[Professional]

17 27 04 11 18 25 01 08 15 22 29 05 12 19 26 03 10 17 24 31 07 14Jun 05 Jul 05 Aug 05 Sep 05 Oct 05 Nov 05

PriceEUR

37

38

39

41

42

43

44

45

40

QENOQc1, Last Trade, Candle10/11/2005 39.00 39.15 38.70 39.10QENOQc1, C lose(Last Trade), MA 1410/11/2005 38.84QENOQc1, C lose(Last Trade), W MA 1410/11/2005 38.65QENOQc1, C lose(Last Trade), EMA 1410/11/2005 38.91

Dry weather in NordicDry weather in Nordic……Cal 03 in 2002

0.0050.00

100.00150.00200.00250.00300.00350.00400.00

02/0

1/20

02

02/0

2/20

02

02/0

3/20

02

02/0

4/20

02

02/0

5/20

02

02/0

6/20

02

02/0

7/20

02

02/0

8/20

02

02/0

9/20

02

02/1

0/20

02

02/1

1/20

02

02/1

2/20

02

Wet weather in NordicWet weather in Nordic……Cal 05 in 2004

150.00

170.00

190.00

210.00

230.00

250.00

270.00

290.00

02/0

1/20

04

02/0

2/20

04

02/0

3/20

04

02/0

4/20

04

02/0

5/20

04

02/0

6/20

04

02/0

7/20

04

02/0

8/20

04

02/0

9/20

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02/1

0/20

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02/1

1/20

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02/1

2/20

04

ECMWF Nord PoolECMWF Nord Pool

Data is directly given in GWh. One run moves the market. Ensemble very important.

Countries around Pyrenees and Alps only influenced by major hydro events, as wind and non-renewable power production is more widely used

Example Point Carbon Example Point Carbon

Precipitation energy and date Histogram precipitation energy total

NAO outlook NAO outlook

Development of Nordic NAOECMWF data day 10 or beyond to calculate the NAOCurrent NAO value known, so not of interest

Nordic NAO day 10 based on EC ensemble (12Z and 00Z)

-2-1012345

19/1

0/20

05

22/1

0/20

05

25/1

0/20

05

28/1

0/20

05

31/1

0/20

05

03/1

1/20

05

06/1

1/20

05

09/1

1/20

05

ECMWF cluster outlookECMWF cluster outlook

WindWind

Short range deterministic –storms!Mid range ensemble ECMWF

Installed wind capacity

Wind production forecastsWind production forecasts

Short term models for day-ahead wind production. Error should be low.First days deterministic ECMWF modelFurther ahead ensemble ECMWFProbabilistic wind forecast?Germany, Denmark and SpainUnit MWh/h

Example Point CarbonExample Point Carbon

Wind production in MWh and date

TemperatureTemperature

Very well correlated to heating and cooling demandWater temperature forecasts with ECMWF data Development of demand outlooks

Point Carbon example

Cold spell and NBP gasCold spell and NBP gasDaily QGA2NB [Line, EMA 14, MA 14, WMA 14, RSI 14, RSIMA 14,14]

21/06/2004 - 21/11/2005 (GMT)[Professional]

Jul Sep Nov Jan Mar May Jul Sep Nov2004 2005

PriceGBp

25

35

45

55

65

75

85

95

105

20

30

40

50

60

70

80

90

100

110

QGA2NB, C lose(Last Trade), Line10/11/2005 46.00QGA2NB, C lose(Last Trade), EMA 1410/11/2005 39.97QGA2NB, C lose(Last Trade), MA 1410/11/2005 37.67QGA2NB, C lose(Last Trade), W MA 1410/11/2005 40.66

Day ahead German electricityDay ahead German electricityDaily QDE-B-1D=RWEE [Line]

21/12/2004 - 08/06/2005 (GMT)[Professional]

03 17 31 14 28 14 28 11 25 09 23 06Dec 04 Jan 05 Feb 05 Mar 05 Apr 05 May 05

PriceEUR

20

40

60

80

0

100

QDE-B-1D=RWEE, Close(Last Quote), Line06/06/2005 42.00

Demand modelsDemand models

Main variable temperature

Demand industry and basic load

Day of the week and holidays

In case of electricity hourly load calculation

SempraSempra’’ss gas modelgas model

223.5

0

221.0

9

234.8

0

207.5

0

220.0

3

226.3

5

197.4

8

188.1

0 191.5

8

207.6

4

216.6

0

217.3

0 220.5

9

219.8

4

170

180

190

200

210

220

230

240

250

11-Ju

n

12-Ju

n

13-Ju

n

14-Ju

n

DA - 15

-Jun

BOW -

16-Ju

nBOW

- 17

-Jun

WE - 1

8-Jun

WE - 1

9-Jun

WDNW

- 20-J

unW

DNW - 2

1-Jun

WDNW

- 22-J

unW

DNW - 2

3-Jun

WDNW

- 24-J

un

-40

-20

0

20

40

60

Oil marketsOil markets

ECMWF tropical model. New hurricane track forecast immediately moves the market.

Weekly inventories oil and natural gas make the market move. Weather data important.

Katrina and the GulfKatrina and the Gulf

Depending on track, certain oil product are influencedProbability outlook very important

Brent oil price since AprilBrent oil price since April

21 28 06 13 20 27 06 13 20 27 04 11 18 25 01 08 15 22 30 06 13 20 27 04 11 18 25 01 08 15Apr 05 May 05 Jun 05 Jul 05 Aug 05 Sep 05 Oct 05 Nov 05

PriceUScBbl

4950

5050

5150

5250

5350

5450

5550

5650

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6350

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6800

QLCOZ5, Last Trade, Bar10/11/2005 5,651 5,675 5,566 5,583QLCOZ5, Close(Last Trade), EMA 1410/11/2005 5,832QLCOZ5, Close(Last Trade), MA 1410/11/2005 5,851QLCOZ5, Close(Last Trade), WMA 1410/11/2005 5,813

European gasoline contractEuropean gasoline contract

17 21 25 29 03 07 11 15 19 23 27 31 04 08 12 16 20 24 28 01 05 09 13 17 21 25 29 03 07 11 15 19 23 27 31 04 08Jun 05 Jul 05 Aug 05 Sep 05 Oct 05 Nov 05

PriceUSDT

510

520

530

540

550

560

570

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QPU-1M-ARA, Last Quote, Bar08/11/2005 518.25 523.50 518.25 523.50QPU-1M-ARA, Close(Last Quote), EMA 1408/11/2005 547.06QPU-1M-ARA, Close(Last Quote), MA 1408/11/2005 541.98QPU-1M-ARA, Close(Last Quote), WMA 1408/11/2005 538.22

Emissions marketEmissions market

Emission markets originated from Kyoto agreementEach country has a maximum allowance to emit CO2

If country emits more than allowed, it has to buy allowances, and vice versa

Emissions marketEmissions market

Weather also important to anticipate emissions output

ECMWF data can be translated in expected C02 output from power stations

Emissions marketEmissions marketDaily QCFIZ5 [Line, EMA 14, MA 14, WMA 14, RSI 14, RSIMA 14,14]

27/04/2005 - 14/11/2005 (GMT)[Professional]

04 11 18 25 02 09 16 23 30 07 14 21 28 04 11 18 25 02 09 16 23 30 07 14 21 28 04 11May 05 Jun 05 Jul 05 Aug 05 Sep 05 Oct 05 Nov 05

PriceEUc

1350

1450

1550

1650

1750

1850

1950

2050

2150

2250

2350

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QCFIZ5, Close(Last Trade), Line10/11/2005 2,265QCFIZ5, Close(Last Trade), EMA 1410/11/2005 2,202QCFIZ5, Close(Last Trade), MA 1410/11/2005 2,178QCFIZ5, Close(Last Trade), WMA 1410/11/2005 2,189

Weather derivativesWeather derivatives

Directly linked to weather outlook. Trading HDD, CAT and precipitation indices.

Weather markets about to develop significantly

Weather derivatives also important to hedge other energy contracts

Converting data in HDDConverting data in HDD

Sempra directly converts the model data in different possible scenario’s for HDD and CAT.

Mid- and long-range forecasts all needed

Risk management Risk management Use of long-term probabilistic outlooks

Assess risk of certain energy scenario’s

Try to have view on weeks- and months-ahead

Probabilistic longProbabilistic long--range outlookrange outlook

Reduced risk on mid-range positionsNow particularly for temperature and precipitation, but wind would be interesting tooWhen signal is strong, it makes sense to take significant long or short positions

SummarySummary

Mid- to long-range model data very relevant to different energy commodities

Importance of also considering the energyindustry when models would be enhanced. E.g. probabilistic data

Development of 30-days ensembles wouldinteresting