ANALYSING AND COMPARING ELECTRICITY SPOT PRICES

MSc. Student: HACA (GHICA) Andreea Valentina Supervisor: Professor MOISA ALTAR

2007

Goals

Analyzes the dynamic of the spot price for electricity on three European exchanges

Present the relevant factors that influence the price, such as long term mean, seasonality and mean reverting

Captures the intra-day correlation between the hours for Romanian market to see the if the peak hour imposed are relevant.

Content

Description of Romanian Power Market

Operator

Day Ahead Market price model

Base load index results

Peak load index results

Hourly spot price model: Romanian evidence

Conclusion

Romanian Power Market Operator (OPCOM)

The Romanian electricity market was fully liberalized

from 1 July 2007

OPCOM was established in 2000 – mandatory spot

market

In 2005 the new day ahead market (DAM) system

was launched

DAM is a voluntary market administrated by OPCOM

In near future OPCOM will take the roll of Central

Counterparty for DAM

Day ahead model price

Lucia and Schwartz (2002) establish the base for

analyzing electricity spot prices

The behavior of spot price (Pt) is described by two

components.

1. Deterministic component (f(t)), fully predictable -

captures the electricity price behavior such as

deterministic trend and seasonality.

2. Stochastic component (St) - a diffusion stochastic

process which follows a stationary mean reversing

process.

lnPt = f(t) + St

Day ahead model price – Stochastic component

Captures the movement of prices outside the deterministic behavior.

d St = - αStdt + σdZwhere

α is the speed with which the price revert to the long time mean α >0.dZ it’s the increment of the standard Brownian motion Zt. the long time mean, μ, was included in the deterministic component the mean reversing process is model from a deviation from zero

To estimate the stochastic process using discreet information it’s needed to formulate the model in a discreet way.

St = (1- α) St-1 +εt where

t takes values from 0 to N, the error term is normal random variable with mean 0 and variance σ2.

captures the predictable and regulates behavior – include:

long term mean μ (the level at which the price tend on long term)

variation of mean for weekend

for every month

f(t) = μ + β*Dt +

where Dt is 1 for weekend day and legal holiday and 0 in the

rest

Mit is 1 for all the day of month i and 0 in rest

Day ahead model price – Deterministic component

iti

iM

12

2

Data analysis – Base load index

Maximum price

77.91 euro/MWh OPCOM

177.85 euro/MWh EXAA

34.35 euro/MWh Gielda

Minimum price

5.71 euro/MWh OPCOM

13.60 euro/MWh EXAA

21.05 euro/MWh Gielda

Data analysis – Base load index

negative skewness show that the probability to have on OPCOM and Gielda extreme low prices is bigger than the normal distribution probability

positive skewness indicate that probability to have extreme high prices at EXAA is bigger that the normal distribution probability

Results – base load index

Toward the model proposed by Lucia and

Schwartz I split the dummy variable sets for

weekend in two dummy variables, one sets for

Sunday (D1) and one sets for Saturday (D2)

I excluded the dummies set for months,

because on analyzed series the coefficients for

months weren’t significant

lnP(t) = μ * α + β1*(D1(t) + (α-1)*D1(t-1)) +

+β2*(D2(t) + (α-1)*D2(t-1)) – α*lnP(t-1)+εt

Results – base load index - OPCOM

C(4) coefficient of dummy sets for Saturday is insignificant - which price tend (the long term mean) is not different from the week day

C(3) coefficient of the dummy sets for Sunday is significant, and the minus shows that the long term mean for Sunday is smaller than that for the week day.

C(2) coefficient is the long term mean at which converge the price log, its value is 3.72, which means 41.43 euro/MWh

C(1) is the speed with which the price, after taking a extreme value, return to the long time mean

Results – base load index - EXAA

Both dummy coefficients are significant and with minus, which shows that the prices in weekend are smaller than in week day

long term mean is 3.89 which mean 48.88 euro/MWh, lower with 0.51 for Sunday and with 0.25 for Saturday

Results – base load index - Gielda

Coefficient of dummy variable sets for Saturday is not significant

long time mean is 29.85 euro/MWh, and for Sunday 28.39 euro/MWh.

Results – base load index

Autocorrelation of residual term

for the three exchanges estimations the value of

Durbin Watson test is near to 2, this means that the first

level autocorrelation is not present

applying Ljung-Box test/Q correlogram is obvious

that it’s present a 7th lag autocorrelation - can be explain

by the seasonal character of electricity prices, the prices

for day t aren’t correlated with the prices from day t-1, but

with the prices from day t a week before.

White heteroskedasticity test - for all series the value of

F statistics is higher that its critical value computed through

=@qchisq(.95,5) → residual are heteroskedastics.

Results – base load index

For analyzing the influence of the cold season on electricity

prices I included another dummy variable (D3) sets with value 1

for all day in period October – March and 0 in rest

lnP(t) = μ * α + β1*(D1(t) + (α-1)*D1(t-1)) + β2*(D2(t) +

+(α-1)*D2(t-1)) + β3*(D3(t) + (α-1)*D3(t-1)) – α*lnP(t-1) + εt

Only for Romania in the cold season the electricity prices

are higher than in the warm one. For Polish and Austrian

markets the coefficient for the dummy variable sets for cold

season isn’t significant.

In Romania for cold season the long term mean is 45.43

euro/MWh.

Results – base load index

Re-estimating equation

•New estimation base on significant term and introduction of an

autoregressive term for 7th lag

lnP(t) = μ * α + β1 * (D1(t) + (α-1) * D1(t-1)) + β2 * (D2(t) +

+(α-1) * D2(t-1)) + β3*(D3(t) + (α-1)*D3(t-1)) – α * lnP(t-

-1) + +*lnP(t-7) + εt

* With blue are common terms for all exchanges

Results – base load index – OPCOM

All coefficients are significant: long time mean is 35.83

euro/MWh, for the cold season is 45.31 euro/MWh and for

Sunday is 25.02 euro/MWh.

Ljung-Box test

(correlogram of residual -21

lags) - autocorrelation is not

present for any lag

heterokedasticity the F

statistic value of White test

is F = 8.71, below to it’s

critical value → the residual

series is homoskedastic.

Results – base load index – EXAA

model use is GARCH (1,1) with residual series distributed t Student’s.

all coefficients are significant

long term mean is 50.35 euro/MWh, for Saturday is 40.83 euro/MWh and for Sunday is 31.48 euro/MWh

Ljung-Box test (correlogram of residual) - autocorrelation is not present

Big difference between base load and peak load index

The biggest – EXAA, where the mean price for the analyze period is 59.45 euro/MWh for peak hour and 47.71 euro/MWh for base load, this means a difference above 11 euro/MWh.

On Romanian market the difference isn’t so remarkable as for EXAA, but is for 5 euro/MWh, the mean for peak being 47.09 euro/MWh.

Data analysis – Peak load index

In Poland is notice the smallest difference between base load and peak load index, almost 2 euro/MWh.

Results – peak load index – OPCOM

Following the same model as

for base load index the result

are:

•the long term mean for warm

season is 38.22 euro/MWh and

for the cold season is 51.62

euro/MWh

computing Ljung – Box test

→ the residual series isn’t

autocorelated

White test → the residual

series is homesckedastic.

Results – peak load index – EXAA

Using the model Garch (1,1)

the final result are:

•A long term mean is 59.21

euro/MWh, were Sunday is

31.48 euro/MWh and Saturday

44.65 euro/MWh.

•Speed with which the price

return to the long term mean is

higher then for base load, but

steel the smallest between the

exchanges

Results – peak load index – Gielda

For peak load the coefficient

for dummy sets for cold

season is significant and goes

to a 2.5% above the long tern

tendency of the price.

Computing:

Ljung-Box test →

residual series isn’t

autocorrelated.

White test → the

residual series is

homesckedastic.

Hourly spot Model: Romanian evidence

To capture the intra-day correlation of the hours I use a panel

worksheet and I analyzed the correlogram of residual result from

LS estimation using coefficient covariance method cross- section

SUR (which permit cross section correlation between residuals ).

lnPh (t) = fh (t) + Sh (t)

fh (t)= μ0 + μh + Σ βd*Dumd

Sh (t) = (1- αh) * Sh (t-1) + εh(t)

for seeing only the variation from the long time mean a

restriction is impose αh = α0

Hourly spot Model: Romanian evidence

computing ADF test for each series, comparing calculated value of

t statistic with the critical value → series are stationary, and the key

characteristic stationary series is that are mean reverting

Hourly spot Model: Romanian evidence

μ0 is 3.51 meaning 33.60 euro/MWh, the speed with which price return to

the mean α0 (C2) is 0.41.

coefficients of the dummy variables set for each day βd (C3-C8). Beside C(4) coefficient (the coefficient of dummy variable sets for Sunday) which shows that Sunday prices are lower, the rest of them has a positive value which show that in the other day prices tend to a value bigger than the mean level.

Coefficients from C(9) to C(31) represent hourly deviation from the long time mean. μh takes value between -0,53, for hour 4 (3:00-4:00) to 0,41, for hour 21 (20:00-21:00). So the smallest price is for hour four, and the biggest one for hour nine.

I can notice also that for interval 1-7 the price tendency is below the mean and for hour 8 the coefficient is not significant.

In correlation matrix computed for residual series was pointed out the values bigger than 0.5 which shows a significant correlation. I can be observed that are some hourly blocks which are strongly correlated. The strongest correlation is between hour 11 and 12 with a value of 0.94.

Conclusion

capture the price behavior and the one of the factors which

influences the price.

Long term mean

Seasonality

Weekly

Monthly → not significant

Seasonal (significant for Romania and only Peak

load index for Poland )

Conclusion

In Austria, as a develop country, prices for weekend are

significantly lower than for the working day, and we cannot

see a difference between cold and warm season.

The polish exchange according to its behavior I can say

that is still in development phase. The volume traded on this

exchange is smaller comparing with the one traded on other

exchanges. The price on this exchange has a low volatility

and is very small comparing with the other exchange.

Conclusion

Regarding the Romanian Power Market Operator through

price deterministic component I capture the differences from the

tendency of the price on long term for Sunday and Saturday and

also for cold season. I can see that for Sunday the price tendency is

below the long time mean, but not for Saturday. This means that

Saturday is a working day in Romania. In the cold season the price

tendency is higher the long term mean. The speed with which the

price revert to the long time mean, captured by the stochastic part

of the model much bigger that in Austria, the price return to its long

time mean three time faster than in Austria.

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*** Operatorul Pieţei de Energie Electrică din România. www.opcom.ro*** Towarowa Gielda Energii, Polish Power Exchange. www.polpx.pl*** Energy Exchange Austria. www.exaa.at*** www.riskglossary.com*** www.puc_rio.br

Thank you for your attention