Relation between wind and electricity prices in a deregulated market ...

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Relation between wind and electricity prices in a deregulated market ...

Relation between wind and electricity prices in a deregulated

market: the case of Ireland

Valeria Di Cosmo ∗ and Laura Malaguzzi Valeri †

Laura Malaguzzi Valeri:

Research Officer, Economic and Social Research Institute

Adjunct lecturer, Trinity College Dublin

Whitaker Square, Dublin 2, Ireland

laura.malaguzzivaleri@esri.ie

1 Introduction

In this paper we are interested in evaluating how increasing wind generation affects a deregulated

electricity system with capacity payments. A fundamental part of this analysis requires the

identification of the effect of wind on the wholesale price of electricity. We use the Irish Single

Electricity Market (SEM) as a test system.

We start by analyzing historic data for the SEM between 2008 and 2011. The SEM encompasses

the electricity systems of both the Republic of Ireland and Northern Ireland, making it a crossjurisdiction,

cross-currency system. The SEM is a compulsory pool system, where plants bid their

short-run marginal costs and are called to generate on the basis of the merit order: plants that

provide lower bids are called to generate before more expensive plants and in each period total

generation is equal to total demand.

As in many other jurisdictions, there has been an increased desire to increase renewable energy

in electricity in Ireland and Northern Ireland.

The contribution of renewable energy to overall

energy demand has been estimated to be around 4% in 2011 for the Republic of Ireland, but

its target under the European Directive (2009/28/EC) is to achieve a 16% penetration. 1

Government plan of 2011 (http://www.taoiseach.gov.ie/eng/Publications/Publications_

Archive/Publications_2011/Programme_for_Government_2011.pdf) recognises that electricity

generation from renewable sources offers one of the most effective ways to reduce Ireland’s greenhouse

gas emissions (GHGs). The same document declares that renewables will have to account

for about 40% of electricity demand if Ireland is to meet its targets. Installed wind capacity is

therefore expected to continue rising in the near future.

∗ Trinity College and Economic and Social Research Institute, Dublin.

† Funding from the Energy Policy Research Centre is gratefully acknowledged.

1 The Directive is available at (http://eur-lex.europa.eu/LexUriServ/LexUriServ.douri=OJ:L:2009:140:

0016:0062:en:PDF)

The

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The Irish dataset is particularly well-suited to our analysis for several reasons: first, the island

has limited interconnection with other systems allowing us to identify the effect of wind more

easily. Second, it has experienced a large increase in installed wind capacity, more than doubling

from about 900MW at the end of 2007 to more than 2000MW at the end of 2011. Third, it is

a compulsory pool system and therefore the published data refer to almost all of the electricity

traded in the SEM.

Extensive data on the system are available from the beginning of the SEM in November 2007.

The SEM is a compulsory pool system, where every generator with a capacity larger than 10MW

has to offer electricity. Similarly, all buyers have to buy from the pool. We are therefore able to

simulate our model based on complete system data. This is particularly interesting considering

that other studies, such as Nicholson and Porter (2012) and Woo et al. (2011) report results for

the balancing market of the Texas ERCOT system, where only 5 per cent of electricity exchanges

take place, the rest taking place through bilateral agreements.

Most analyses of the effect of wind generation on prices to date approach the problem by simulating

the effects of increased generation on a given system (e.g Traber and Kemfert 2011). This paper

differs from previous work by providing an econometric analysis of the historic effect of wind. Knittel

and Roberts (2005) study hourly electricity prices in California in 2000-2001 using a methodology

that highlights the time series characteristics of the data (the Generalised Autoregressive Conditional

Heteroskedasticity model, more commonly known by its acronym GARCH). O’Mahoney and

Denny (2011) analyse the SEM data, but only for 2009, using a time series methodology. We argue,

following Härdle and Trück (2010) and Huisman et al. (2007), that a better approach to hourly

data is to consider each hour separately. Generators typically provide hourly bids for the following

day at the same time. In fact, in the SEM, although the price is set in each half hour based on

actual bids and demand for that period, generators provide the same set of price/quantity pairs

for every period of the day. Given this setup, it is reasonable to think of each hour as a separate

contract period.

The system marginal price (SMP) is the sum of the shadow price, determined by the marginal

plant that is called to produce, and uplift. The uplift is the payment made to plants to avoid shortrun

losses and covers the cost of turning the plant on and generating the first MWh of electricity.

In this paper we specifically study the effect of wind generation on the shadow price and find that

wind generation is inversely correlated with the shadow price.

The rest of the paper is organised as follows. Section 2 introduces the SEM in more detail.

Section 3 explains our methodology and describes the data. Section 4 reports and comments the

results and finally Section 5 concludes.

2 The SEM

In the Irish system, the system marginal price (SMP) is determined by the bid provided by the

marginal plant – defined as shadow price– and the value of uplift. The marginal plant is the most

expensive plant needed to meet demand in every given period. Plants are stacked according to

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their bids, from cheapest to most expensive, and plants are called to generate in that order up

until they produce enough to service existing demand. The uplift measures the amount of money

generators have to be paid in addition to the shadow price in order to avoid short-run losses. The

bid reflects the short run marginal costs of a plant and includes the costs of fuel and carbon dioxide

emission permits needed to generate a megawatthour (MWh) of electricity. On top of the short-run

payments, power plants also receive capacity payments, designed to cover additional capital costs.

The regulation authority monitors the market through the market monitoring unit. Power

plants are required to bid their short run marginal cost in line with the bidding code of practice

available from the regulator’s website (http:www.allislandproject.org). Moreover, there is

a system of future contracts in the form of contracts for differences (CFD), created to enhance

competition between generators both in the Republic of Ireland and in Northern Ireland.

The presence of wind on the system raises some interesting questions related to security of

supply. As the system cannot rely on wind alone (it can have large and sudden variations), in

any given period the TSO curtails up to 50% of wind capacity to ensure that a sufficient amount

of thermal electricity generation is always available. If wind is curtailed and thermal plants that

would not otherwise be called to generate have to produce electricity, they receive ”constrained

payments” that cover their marginal cost of generation. In this case thermal power plants need to

increase or decrease generation less frequently. Ramping costs of thermal power plants are therefore

reduced by wind curtailment and the associated payments. Other market risks are also relatively

low in the current market. For example, firm strategic behaviour is limited by the bidding code of

practice. As a result, the forward market associated with the SEM is not well developed.

3 Methodology

3.1 Data

We create a dataset of hourly information on hourly electricity generation, demand, plant availability,

and daily fuel and carbon costs for the last 4 years.

Most of the data is downloaded directly from the system operator, SEM-o, including final

demand and availability of generators in each period of time. 2 Quarter-hourly wind generation for

the Republic of Ireland comes from EirGrid and half-hourly wind generation for Northern Ireland

comes from SONI, the system operator of Northern Ireland. We aggregate both series to hourly

levels.

Information on prices comes from Datastream. Specifically, coal prices are represented by the

API2 prices traded on the London market, converted in euro using daily exchange rates from

Datastream. Gas prices are from the UK HUB. All information on prices is on a daily basis. Since

fuels are traded Monday through Friday, whereas electricity is traded on weekends as well, we set

the weekend prices equal to those of the previous Friday. Carbon dioxide prices are spot prices,

taken from BlueNext (www.bluenext.eu). In cases where Bluenext values are missing, they are

2 We are currently examining the cause of fluctuations in plant availability information and will cross check with

monthly data on availability published by EirGrid.

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supplemented with carbon spot prices from Reuters. Since carbon dioxide permits are not traded

on weekends, we assume that the cost of carbon on weekends is fixed at the previous Friday’s value.

Table 3.1 reports summary statistics for our dataset.

Variable Obs Mean Std Min Max

Shadow price (€/MWh) 35064 49.83 23.55 0.00 494.56

Loads 35064 3983.04 894.89 1885.65 6822.30

Wind generation (MWh) 35064 429.19 357.42 1.89 1833.22

Gas(€/MWh) 35064 34.20 10.75 8.26 57.40

Oil (€/MWh) 35064 108.39 26.95 46.12 160.40

Coal(€/MWh) 35064 12.88 3.72 7.29 23.84

Generation margin (MWh) 35057 3363.83 1102.93 -2954.16 6783.89

Co2 price (€/MWh) 35040 14.84 5.25 0.02 28.73

We build the monthly capacity of wind series based on system operator files that specify the

size and initial connection date of all wind farms in the Republic of Ireland and Northern Ireland.

Figure 1: Relation between shadow price and generation fuels

Fig.(1) shows the dynamics of electricity price and of the main fuels used in the generation

process. All the fuel prices refer to the previous day, since generators submit bids for electricity

generation the day prior to generation. The electricity price series shows a structural break at

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the beginning of 2009, following the collapse of oil (and gas) prices in the summer of 2008. 3 We

therefore estimate the model excluding the data prior to the identified structural break, in order

to exclude potential sources of errors in our model.

Electricity generated from wind is produced at a marginal cost of 0, given that wind itself is

free. This means that as the amount of wind generation increases, we expect it to have a dampening

effect on the shadow price of electricity. On the other hand, we expect a positive relation between

loads and shadow price ,because as demand increases, more expensive plants will be called on to

generate. The following figure shows the scatterplot between shadow price and loads (part a) and

shadow price and wind (part b):

Figure 2: Electricity, wind and system loads

(a) Shadow price and loads

(b) Shadow price and wind

The raw data shown in Fig.(2) suggest a linear relation between shadow price and wind and

a non-linear relation between shadow price and system loads. The correlation between wind and

shadow price in our sample is equal to -0.06 while the correlation between shadow price and loads

is 0.43.

3.2 Estimation

With hourly electricity data, use of a simple time series regressions is suboptimal. Härdle and

Trück (2010), Huisman et al. (2007), Guthrie and Videbeck (2007) and Weron (2008) show that

hourly prices can be considered as different contracts stipulated during the same day and should

be analysed separately. In the SEM, generators submit bids in the form of price/quantity pairs

that are fixed during one day, but can vary between days, another reason to consider the hourly

outcomes separately. This does not mean that prices in one hour can be analysed independently

from those in the previous hour. Prices across hours will be correlated and we need to take that

into account.

3 See Di Cosmo and Malaguzzi Valeri (2012)

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One way of addressing this set of estimations is to consider them as a system of seemingly

unrelated regressions (SUR), as proposed by Zellner (1962). We estimate a simultaneous system

of equations, where residuals are correlated across groups (in our case hours of the day). The nonlinear

relation that emerges between shadow prices and system loads shown in Fig (2) is due to high

level of demands in the system, which at times are associated with scarce power plant availability.

As demand increases, the system forces the less efficient power plants to run, and then the price

increases. When the margin between available generation and demand is low or negative, the price

can increase dramatically.

To take this into account we include a variable that measures the capacity margin, which is

the difference between total power plant availability on the system and system loads. We also

include separate variables for periods of high demand allowing demand to affect shadow prices non

linearly. 4 Given all of the considerations above, we estimate the following system of equations:


P 1,d = α 1 + ∑ h βh 1 Lh 1,d + γ 1W 1,d + ∑ j ζj 1 F j 1,d−1 + µ iCO d−1 + θ 1 mar 1,d + ∑ κ s D1 s + ɛ 1,d

... ⎪⎨

P i,d = α i + ∑ h βh i Lh i,d + γ iW i,d + ∑ j ζj i F j

i,d−1 + µ iCO d−1 + θ i mar i,d + ∑ κ s Di s + ɛ i,d

...

⎪⎩

P N,d = α N + ∑ h βh N Lh N,d + γ NW N,d + ∑ j ζj N F j N,d−1 + µ iCO d−1 + θ N mar N,d + ∑ κ s DN s + ɛ N,d

(1)

As mentioned above, the shadow price P i,d in hour i of day d, depends on: the load L 1,d , which

is allowed to have different effects depending on the intensity of demand h; the previous day’s

fuel prices F , where j indexes the fuel; the previous day’s carbon dioxide permit prices CO, the

generation margin mar; wind generation W ; and finally a set of dummies D to account for the

weekend and the months. There are N = 24 equations in the system, one for every hour of the day.

According to Baltagi (2008), cross-sectional dependence is a problem in macro panels with long

time series. Ignoring possible correlations of regression disturbances over time and between subjects

can lead to biased coefficients. We test the presence of correlation of the residuals in Eq1 with the

Breush-Pagan test, which is a Lagrance Multiplier (LM) test. We reject the null hypothesis of lack

of correlation between the residuals, as the χ 2 associated to the statistic is equal to 16665.926 with

an associated p-value equal to 0. We then follow the methodology proposed by Zellner (1962) to

account for the correlation between the residuals and use a two step procedure. In the first step,

the system of equations described by Eq1 is estimated by OLS. In the second step the parameters

of the system are estimated by Feasible Generalised Least Squares (FGLS), using the variancecovariance

matrix estimated in the first step. We also test for the presence of autocorrelation in

the residuals; as the T dimension of our system is quite high (we have 1460 observations for each

4 We tried different variables to approximate the non-linear relationship between loads and the shadow price. We

compare the properties of the residual under the different specifications; inclusion of separate variables at times of

high demand minimizes the residuals of the estimation. Specifically, our estimation includes separate variables for

periods where demand is larger than 4750MW, 5000MW 5250MW and 5300MW.

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hours considered in the system of equations, as we have daily observations repeated for 4 years), the

presence of autocorrelation in the residuals causes the estimated standard errors of the coefficients

to be smaller than their ’true’ value and the R-squared to be higher. Following Wooldridge (2002)

we reject the null hypothesis of lack of autocorrelation between the residuals. 5 Finally, we check

the stationarity of the price series. If the shadow price series were non-stationary, our estimated

coefficients could be picking up a spurious relation between the shadow price and some regressors

that was simply due to a potentially common trend over time. In our case the Im-Pesaran-Shin

test (Im and Shin 2003) rejects the null hypothesis of unit root in our endogenous variables at the

1% level. 6 We therefore conclude that we do not need to be concerned about this issue.

4 Results

Using hourly observations we find a small but significant negative effect of wind generation on

shadow prices, as expected. The scale of the effect is not very large.

In order to compare our

results to papers that do not analyse the relation separately for each hour, we calculate the weighted

average of the wind coefficient, with weights given by the loads of the corresponding hour. Our

coefficient is equal to -0.004,hich can be interpreted as saying that every 100MW increase in wind

generation (equal to about 25% of the average wind generation in our sample) will lead to a decrease

of the shadow price equal to 0.4 €/MWh, or about 0.8% of its average value in our sample. This is

almost half as small as the -0.009 value found by O’Mahoney and Denny (2011) when analysing 2009

data. The difference can be either ascribed to the different sample considered or the methodology

adopted, as O’Mahoney and Denny (2011) use a purely time series approach to address the question.

Table 1 reports results for select variables.

In the SEM, peak demand occurs typically between 10 a.m. and 12p.m. in the middle of the

day and between 5 and 7 p.m. in the evening (corresponding to hours 17-19 in our analysis). The

evening peak is the overall daily peak in the winter, whereas the morning peak is the daily peak

during summer months.

Our analysis shows that fuel prices are positively related to the electricity prices, as expected.

It is interesting to note that oil has a statistically significant effect on the shadow price only during

peak hours (e.g. in hour 17). Oil power plants tend to be peaking power plants, or power plants

that are quick to turn on and off, but relatively inefficient, in the sense that they need a lot of fuel

to generate 1 MWh of electricity. Usually, they therefore enter the merit order only when loads

are extremely high, which is consistent with our results. The impact of peak loads is captured

by the variables that assumes the value of the loads when they are in the last four deciles of the

load distribution. These variables are positive and significant during the peak hours. Finally, the

capacity margin is always negative and significant. This means that high level of demand (low

values of capacity margin) are associated to higher electricity prices.

5 The χ 2 associated to the statistic is equal to 79.547 with a p-value equal to zero.

6 The χ 2 associated to the statistic is equal to -20.9432.

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Table 1: Estimation results

Hour Constant Loads Wind Gas d−1 Coal d−1 Oil d−1 GenMargin

1 0.459 0.003** 0.001 0.653*** 0.379 0.007 -0.001*

(3.965) (0.001) (0.001) (0.067) (0.296) (0.019) (0)

2 1.186 0.003*** -0.001 0.600*** 0.562** 0.029* -0.001***

(2.437) (0.001) (0) (0.041) (0.181) (0.011) (0)

3 -2.838 0.005*** -0.001 0.593*** 0.403 0.036 -0.001**

(4.778) (0.001) (0.001) (0.077) (0.339) (0.021) (0)

4 2.005 0.003** -0.005*** 0.583*** 0.419 0.011 -0.001***

(3.283) (0.001) (0.001) (0.057) (0.25) (0.016) (0)

5 6.619 0.002 -0.007*** 0.493*** 0.621* -0.001 -0.001***

(3.949) (0.001) (0.001) (0.067) (0.295) (0.018) (0)

6 6.565 0.002 -0.008*** 0.537*** 0.445 -0.001 -0.001***

(3.595) (0.001) (0.001) (0.064) (0.282) (0.018) (0)

7 7.541* 0.001 -0.009*** 0.527*** 0.399 0.011 -0.001***

(3.451) (0.001) (0.001) (0.062) (0.274) (0.017) (0)

8 -0.033 0.002*** -0.003*** 0.671*** 0.406* 0.013 -0.001***

(2.147) (0) (0) (0.042) (0.188) (0.012) (0)

9 -0.641 0.003*** -0.003*** 0.606*** 0.805* 0.011 -0.002***

(3.828) (0.001) (0.001) (0.072) (0.319) (0.02) (0)

10 3.326 0.003** -0.003** 0.632*** 0.109 0.049 -0.002***

(7.233) (0.001) (0.001) (0.132) (0.585) (0.037) (0.001)

11 -0.337 0.006*** -0.002 0.447*** 0.989 0.003 -0.003***

(7.982) (0.001) (0.001) (0.126) (0.559) (0.035) (0.001)

12 -1.214 0.006** -0.003** 0.500*** 0.413 0.039 -0.003***

(8.927) (0.002) (0.001) (0.12) (0.529) (0.033) (0.001)

13 -6.107 0.007*** -0.003** 0.408** 0.851 0.024 -0.002***

(10.649) (0.002) (0.001) (0.13) (0.575) (0.035) (0.001)

14 -15.301 0.010** -0.003 0.599** 0.585 0.006 -0.005***

(19.13) (0.004) (0.002) (0.225) (0.992) (0.061) (0.001)

15 9.752 0.003* -0.002** 0.412*** 0.45 0.06 -0.003***

(7.489) (0.001) (0.001) (0.11) (0.491) (0.031) (0)

16 11.878* 0.003*** -0.002*** 0.419*** 0.578 0.053* -0.003***

(4.615) (0.001) (0) (0.085) (0.38) (0.024) (0)

17 4.789 0.004*** -0.003*** 0.410*** 0.483 0.067** -0.002***

(4.227) (0.001) (0) (0.081) (0.363) (0.023) (0)

18 -3.893 0.005** -0.002* 0.561*** 0.524 -0.005 -0.002***

(9.566) (0.002) (0.001) (0.164) (0.732) (0.046) (0.001)

19 1.703 0.009 -0.010** 0.609 0.997 -0.028 -0.006**

(26.859) (0.005) (0.003) (0.403) (1.776) (0.109) (0.002)

20 10.386 0.007 -0.005* 1.335*** -1.758 -0.035 -0.004**

(19.077) (0.004) (0.002) (0.281) (1.242) (0.077) (0.001)

21 20.194 0.002 -0.008*** 0.826** -0.554 0.079 -0.004***

(17.038) (0.004) (0.002) (0.253) (1.119) (0.069) (0.001)

22 -1.211 0.005 -0.003 0.744*** 0.428 -0.041 -0.003**

(13.917) (0.003) (0.002) (0.202) (0.893) (0.055) (0.001)

23 12.049 0.002 -0.003 0.712*** -0.594 0.079 -0.002***

(11.785) (0.003) (0.001) (0.15) (0.663) (0.042) (0.001)

24 13.764*** 0.001 -0.002*** 0.489*** 0.621** 0.035** -0.001***

(3.195) (0.001) (0) (0.045) (0.2) (0.013) (0)

* p < 0.1; ** p < 0.05; *** p < 0.01

The regression includes high load variables, carbon dioxide prices, and dummies for months and weekends. Complete

results are available from the authors.

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The carbon price coefficient is always positive and significantly different from zero, as expected.

Carbon prices enter the cost function of thermal plants. When burning fossil fuels, some carbon

dioxide is released in the atmosphere and have to be covered through the acquisition of carbon

dioxide permits on the part of the plants.

Finally, the monthly dummies are positive and significant. The weekend dummy is strongly

significant in the first hours of the day, as during the weekend restaurants and pubs stay open

longer than during the working days.

In conclusion, after accounting for a fairly flexible specification we find a consistently negative

effect of wind on shadow prices in the SEM, especially at times when demand is not extremely low.

The effect is not extremely large, but it is still likely to affect profits of all plants, both renewables

and thermal.

5 Conclusions

In this paper we have analysed how wind generation influences the electricity shadow price in the

Irish Single Electricity Market. We control for other variables that can theoretically affect shadow

price levels, including the level of demand, the prices of the main fuels (coal, gas, oil), the price of

carbon dioxide permits, and the generation margin, that measures how much extra generation is

available in each period, in addition to dummy variables for weekends and the different months of

the year. We estimate a system of hourly equations by FGLS, correcting for the presence of serial

correlation and find a small but significant negative effect of wind generation on shadow prices. We

also find positive and statistically significant effects of the main fuel prices on loads on the shadow

price, as expected.

Our results show that controlling for all other determinants of the shadow price, the effect of

wind is negative and significant, and on average equal to -0.004, which can be interpreted as saying

that every 100MW increase in wind generation (equal to about 25% of the average wind generation

in our sample) will lead to a decrease of the shadow price equal to 0.4 €/MWh, or about 0.8% of

its average value in our sample.

At times when generation capacity is abundant, this dampening effect is likely to be captured

by consumers. At times when there is need for more generation investment, however, the lower

expected profits might act as a deterrent to potential investors.

As wind increases, it is more likely that it will be curtailed in order for the system to maintain

its reliability. On such occasions, wind and thermal plant generators receive constraint payments

designed to compensate them for the adjustments they need to make in the face of curtailments.

We plan on studying how wind generation affects such constraint payments and identify any policy

implications.

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