Effects of Wind Power Forecasts on Supply
Bidding Strategies in the Spanish electricity
market auction
Cristina Ballester
Department of Financial Economics
Universitat de València
Dolores Furió
Department of Financial Economics
Universitat de València
Abstract:
The development and integration of renewable electricity production has been a target in most
countries as a consequence of a great climate awareness. In this work, it is used a panel data
model with the aim to identify ex-post evidence of strategic bidding by generators in the dayahead market, distinguishing between generation technology (thermal, combined cycle, nuclear,
hydroelectric and renewables), as a function of wind power forecast (WPF) together with other
explanative variables such as carbon prices and hydro-electric water reservoirs. Additionally,
the offered prices by the identified strategic units are modified according to the estimated results
in order to simulate the auction process and provide a quantification of the effect of the trading
strategies based on the WPF on the day-ahead market marginal price and volatility. To do so,
the Spanish market has been chosen as a paradigmatic example due to the huge increase in
renewable installed capacity and renewable generation observed throughout the recent years.
The results obtained show a strategic behavior that causes an increase in the marginal price,
leaving it in a level higher than the intuitively expected one in a context with notable wind
penetration. The findings obtained in the present work are of interest to practitioners and
regulators, given that they come to shed light on the way the inclusion of renewable generation
into the electricity market has altered the trading strategies in the day-ahead market by the
supply market participants.
JEL Code: L94, Q42, Q48.
Keywords: wind power forecast, strategic bidding, panel data model
1. Introduction
Electricity from renewable energy sources has received public support in many countries all
over the world as a consequence of a greater climate awareness. In the Spanish case, the
development and integration of renewable electricity production in the electricity market has
been a target for the regulator for the last decade. Tables 1 and 2 show the annual figures of
installed power capacity and electric energy balance, respectively, from 2007 to 2013 in Spain,
distinguishing by generation technology. As can be observed, for instance, the installed capacity
of wind power increased around a 68% in the mentioned interval, whereas the amount of wind
generation did it a 98%, being responsible for the 20% of the overall generation during 2013.
This sustained growth has meant a substantial change in the generation mix from conventional
energy sources to renewables, which will likely have an impact on spot prices.
[Insert Table 1 about here]
[Insert Table 2 about here]
There can be found in literature a number of studies that analyses the impact of an increasing
renewable electricity production on the level of spot prices. In all of them a common pattern is
detected consisting of a decrease in spot prices as a consequence of the increase in renewable
production. This is due to the auction mechanism itself that it is based on a merit order dispatch
system, commonly used in electricity markets. Thus, sellers and buyers, the day before the
delivery day, submit quantity-price bids to the auction market. The bids are ranked by price and
the spot or clearing price is set when the supply aggregate curve matches the demand aggregate
curve. Thereby, the generators with lower marginal costs, like renewables, can bid at lower
prices, being normally located in the base of the merit-order, and they are among the first bids
matched in the auction. As a consequence, an increase in renewables is expected to shift he
supply curve in such a way that the spot price is set at lower levels. This has been called in
literature the merit-order effect of renewables. This effect has been highlighted in previous
studies (Saenz de Miera et al (2008), Gelabert et al (2011), Gil et al (2012), Holttinen (2004),
Sensfu et al (2008), Ballester and Furió (2015) among others2).
2For
a complete overview of past research on the merit-order effect of renewables see Würzburg et al (2013).
A reduction in spot prices is usually welcome by consumers and regulators. In fact, it
undoubtedly implies savings for the former, and it can compensate the economic effort to
finance the support for renewables.
However, several concerns related to the supposedly intermittent and somewhat unmanaged
nature of some (mainly wind and solar) renewable generation sources arise. For instance, wind
production heavily depends on the wind speed and direction. Many voices claim that this
intermittency in the output of renewables is completely undesirable because, on the one hand, it
will likely be transferred to electricity prices with the result of an increase in uncertainty and in
a greater price risk. And, on the other hand, because lower spot prices also means lower
revenues for the so-called conventional generators3, being this especially true for the generators
with higher variable costs who may obtain lesser incomes not only due to lower prices but also
due to lower production if their production is (at least partially) replaced by production coming
from renewable sources.
These conventional generators, as are assumed to use much more secure and reliable power
sources, have got an institutional response to their problem in many electricity markets by
means of the so-called capacity payments, which after all can be defined as a stable source of
revenues for being there, ready to produce when necessary. Therefore, capacity payments are
aimed to compensate fossil fuel fired plants for providing backup power at times of either peak
demand or low renewable output.
Our point is that, together with that institutional answer for the sake of system security and
reliability, such a dramatic change in the electricity generation mix will have made generators
reconsider their trading strategies when bidding and incorporate the new information available
in the market in their decision-taking process.
In this work, it is used a panel data model with the aim to identify ex-post evidence of strategic
bidding by generators, distinguishing between generation technology (thermal, combined cycle,
nuclear, hydroelectric and renewables), as a function of wind power forecast (WPF) together
with other explanative variables such as carbon prices and hydro-electric water reservoirs.
Additionally, the offered prices by the identified strategic units are modified according to the
estimated results in order to simulate the auction process and provide a quantification of the
effect of the trading strategies based on the WPF on the day-ahead market marginal price and
volatility. To do so, the Spanish market has been chosen as a paradigmatic example due to the
3
Conventional generators are those different from those that produce power using renewable sources.
huge increase in wind installed capacity and wind generation observed throughout the recent
years (Tables 1 and 2).
Agent-based models have been used in literature to capture the complexity of the electricity
markets (Weidlich and Veit (2008)), complemented with numerical exercises to analyse the
strategy bidding in electricity markets (Gountis and Bakirtzis (2004), Veit et al (2009), Twomey
and Neuhoff (2010) and Li and Shi (2012)). Twomey and Neuhoff (2010) highlight the
existence of market power that allows conventional generators to obtain extra-profits by
pushing prices below competitive levels in times of large wind power production and increasing
prices, otherwise. Green and Vasilakos (2010) empirically test the theoretical model of Twomey
and Neuhoff (2010) by applying it to the British wholesale market and conclude that prices can
be more volatile and higher in scenarios with market power. Li and Shi (2012) analyse the
bidding optimization of a wind generator. Their results lead them to conclude that wind
generation firms can increase its net earnings by improving wind forecasting accuracy.
This document is organized as follows. In section 2 it is described the dataset used. In section 3,
firstly, a panel data model is used to detect strategic bidding for supply linked to wind power
forecasts in the day-ahead market, distinguishing by generation technology type. Secondly, it is
approximated a quantification of the estimated impact of the detected trading strategies
performed by any technology generation group on price and volatility. Section 5 summarises the
results and concludes.
2. Data
The data set covers the period from January 1, 2010 to December 31, 2013 and comprises the
following time-series data:
-
The quantity-price offers submitted by market participants to the day-ahead Spanish
electricity market, in order to sell or buy energy, by delivery hour (including matched
and non-matched offers). This data are available at the website of OMIE
(www.omie.es).
-
The last Wind Power Forecasts at an hourly frequency available before the deadline for
submitting bids to the day-ahead market auction. This data are published at the website
of REE (www.ree.es).
-
The day-ahead market marginal hourly prices (www.omie.es).
-
European Emission Allowances (EUAs) futures prices corresponding to next December
maturity with a daily frequency, available at Reuters database.
-
Hydroelectric water reservoirs data with a weekly frequency, available at Reuters
database.
3. Empirical Analysis
3.1. Strategic bidding for electricity supply in the day-ahead electricity market
As previously mentioned, bids made by wind generators usually are among the first matched in
the day-ahead market auction. This is due to the market mechanism itself, a merit-order dispatch
procedure in which the technologies with lower variable costs (like wind and other renewable
sources) can submit bids with lower offered prices and be among the first ones to be matched.
Thus, when there is more wind, more offers of other technologies may result unmatched. As a
consequence, it is expected that renewable and non-renewable generators adopt a strategic
behaviour when submitting bids to the auction market depending on wind power forecasts to
optimize benefits.
In this section, we aim to find out whether the wind has become a key variable and, in such a
case, the way that the different generator types use that information when designing their
auction trading strategies. To do so, we use a panel data model with the average offered price by
each generation technology as the dependent variable and the wind power forecast, WPF,
(available at the website of REE just before the deadline to submit bids for the auction dayahead market) as one of the dependent variables. The panel data model also allows us to
difference away the specific effects of each hour.
To enrich the analysis, we also include other explanatory variables such as the European
Emission Allowances futures prices (EUAs), the hydro-electric water reservoir levels (WR) and
a dummy variable to capture the business-day effect. According to the classification made at the
OMIE webpage, bids from generators have been grouped into the following categories:
Combined Cycled (CC), Coal, Fuel-gas and Fuel-oil thermal plants (CT), Hydro-electric (CH),
Nuclear (CN) and the last one including renewable technologies, mainly wind and solar (CR10).
Let us consider the following panel data model in which the cross section is the delivery hour
i=1,2,…,24:
10
This category is denoted by OG (other generation) at the OMIE webpage. It also includes bids coming from
cogeneration and surplus production but these latter are really of residual importance because of their relatively
scarce associated volume.
PMOe,t-1,t,i = e + e*WPFt-1,t,i+eDLt+e*EUAt-2+e,t-1WRt-1+ue,t,i (1)
ue,t,i=ve,i+e,t,i
where PMOe,t-1,t,i is the average supply offered price on average by the group e of generators
sharing the same generation technology submitted at a particular day (day t-1) for delivering
electricity at the day-ahead (day t) during the hour i; WPFt-1,t,i denotes the last published wind
power forecast at the website of REE before the deadline time for submitting bids to the auction
day-ahead market for delivering electricity during the hour i at the day t, though known at day t1; DLt is a dummy variable that is equal to 1 if t is a business day and 0, otherwise; EUAt-2 is the
European emission allowances next December maturity futures closing prices at the day t-2 and
WRt-1 is the hydro-electric water reservoirs at the day t-1. The stochastic component, ueti, is a
process made up of two components: e,i, which is assumed to be independent over the days
though it allows for cross-sectional covariance between the hours, and e,t,I, which is the usual
homoscedastic component, normally distributed N(0,)12.
The estimation results are shown in Table 3. The F-Test null hypothesis is H0: vi=0, i.e., all
coefficients of vi are zero. This hypothesis is rejected for all the generation technology plant
types, indicating that there is evidence of a component in the second equation of (1) that
depends on the delivery hour, whereas it does not depend on the delivery day. Therefore, the
panel data model as it is defined in (1) is suitable for all the considered cases, i.e. for all the
involved generation technology plant groups.
[Insert Table 3 about here]
The estimated e parameter is statistically significant for every generation technology group, e,
indicating that the wind production forecast has become relevant information for all the supply
market participants. Furthermore, its value is negative for every considered group e, with the
only exception of thermal generation (e=CT).
The negative value of the e parameter may be an a priori expected result, given that the
expected marginal price is supposed to decrease with the increase in wind production. In such a
situation, a logical reaction from bidders would consist in offering lower prices so as not to
become unmatched. However, according to our results, the thermal generation plants would
have been offering their production at higher prices, maybe trying to compensate the likely less
12
A one-way fixed effects panel model as defined in Green (2002).
required thermal production by increasing the auction resulting prices but, at the same time,
incurring in a higher risk of not being matched.
The business dummy variable, γe, is statistically significant and positive for hydro-electric and
nuclear generation plants and negative for thermal and combined cycled plants, whereas it is not
significantly different from zero for the case of renewable generation plants. The positive value
for the business dummy variable implies that hydro-electric and nuclear generation plants offer
their production at higher prices for delivery hours belonging to business days. Surprisingly, the
opposite holds for combined cycled and thermal plants. This results must be properly adjusted
with the ve,i parameter value for each generation technology group e and hour i. The hour 9 has
been dropped from the analysis to avoid perfect multicollinearity.
On the one hand, combined cycled and thermal plants submit their bids at remarkably higher
prices during the early hours of the day (from the first to the eighth and seventh hour,
respectively) when the electricity demand levels are the lowest, and as said before, even lower
whether the delivery hour belongs to a business day.
On the other hand, according to our results, hydro-electric and renewable generation plants
submit slightly higher price bids into the market auction for the first delivery eight hours of a
day, though in the case of the former, the offered prices are even higher for business days
whereas for the latter, no matter whether it is a business or a non-business day.
Finally, the nuclear generation plants offer higher prices for business days and there can be also
observed small differences between hours, being lower the first ten hours and the very last one.
Another potentially key variable that we have chosen to take into account when analyzing the
impact of renewable generation into the electricity market is carbon prices. At the end of each
year, firms must deliver the equivalent number of allowances for its excess emissions. Then,
they must be provided with a number of emission allowances which will depend on their
pollution (derived from their production) levels. Firms that need to increase their volume of
emissions must buy the corresponding permits to do it. Thereby, it should be expected that
market participants may have internalized carbon prices into their decision making process.
How may carbon prices affect the trading strategies of the different generation technology plant
groups participating in the market? As detailed in the Data section, the price series used in the
present study corresponds to the ICE ECX European Emission Allowances next December
maturity closing futures prices at t-2. We use lagged prices because at the closure time of the
auction which takes place at t-1 (for delivering electricity at t), the available closing prices are
those of the previous trading session, i.e. at t-2.
As a first conclusion, it appears evident that carbon prices do have an impact on the offered
price by all the considered generation technology groups. The estimated φe parameter value is
highly statistically significantly different from zero in all cases. The obtained sign of this
coefficient also brings us interesting insights. Thus, it is positive for the case of thermal plants
group as well as for renewable plants group, meaning that they bid at higher prices when carbon
prices increase. Thermal plants are the most directly affected by the carbon allowances prices,
since they are the ones that may need to pay for them. Therefore, it is logical that they may
should internalize that cost into their bids. The strategy followed by the rest of players,
however, seems to have been different depending on the generation technology group. Knowing
that the resulting marginal price may be high due to the expected push effect when bidding by
thermal plants, most of them (combined cycled, hydro-electric and nuclear plants) may simply
decide to bid at lower prices in order to assure to be matched, given that at the end all the
matched production will be paid at the marginal price. The group of renewable generation
plants, however, may have chosen a relatively more aggressive strategy by submitting offers at
higher prices, though the gap between the average offered prices by thermal and renewable
generation plants is commonly too large to compete for setting the marginal price in normal
conditions.
The last explanatory variable included in the panel data model is the hydro-electric water
reservoirs, ωe,t-1. As only weekly data were available, each data is repeated seven daily periods.
The way in which larger water reservoirs can impact on prices is very similar to that of more
wind. In fact, more water reservoirs will imply higher capacity to produce electricity, namely
higher supply. Then, in times of water reservoir excess, hydro-electric generators, with very low
variable costs, will be able to bid into the market auction at lower prices. The estimated results
are consistent with that idea, with the only exception of that of the coefficient for the thermal
generation plants. In fact, the obtained ω parameter value is significantly negative for all the
generation technology plants groups meaning that bid prices would decrease with hydro-electric
water reservoirs, except for the thermal generation plants for which the estimated ω value is
shown to be significantly positive.
To conclude, as a consequence of variations in the wind production forecast, carbon prices and
hydro-electric water reservoirs, combined cycled, hydro-electric, nuclear and renewable
generation plants seem to submit their price offers consistently with what would be expected
based on the strategy to be matched in the market auction.
However, some astonishing results have also been found for the studied sample period. Firstly,
average offered prices by combined cycle, thermal and renewable generation plants for a
particular hour are systematically lower if delivery takes place on a business day.
Secondly, average offered prices by all the involved generation plant groups except for the
nuclear appear to be higher for the first eight delivery hours.
Thirdly, thermal generation plants submit bids at higher prices when (i) carbon prices are
expected to be higher, which is perfectly understandable given that these plants may wish to
transfer this extra cost to prices, but with regards to the following they seem to behave as if they
wish to avoid being matched in the day-ahead auction. In fact, they submit bids at higher prices
when (ii) wind production forecast is larger and when (iii) there are more hydro-electric water
reservoirs. Their offered prices are also higher, on average, for non-business days and from hour
1 to hour 7.
3.2 Quantifying the impact on the marginal price
As previously seen, the supply bidders react based on the wind production forecast in a different
manner depending on the generation technology group. Now we aim to quantify the isolated
effect of the different strategies followed by market participants due to the entrance of wind
generation (using the expected wind production for the delivery hour) into the market during the
last year of the sample period. To do so, we simulate the hour marginal price resulting from the
day-ahead auction market by substituting the actual offered price of each bid of a particular
generation technology group with a new one which the previously estimated effect of the wind
power forecast has been deducted from. The new (fictitious) offered price will be computed
according to the following formula:
(2)
where P̂aєe,t-1,t,i denotes the fictitious offered price; Paєe,t-1,t,i is the actual offered price submitted
the day t-1 by the market participant a of the generation technology group e for the hour i of
the delivery day t; βe is the estimated parameter obtained for the generation technology group e
in the previous section and displayed in Table 3, and WPFt-1,t,i is the wind power forecast known
at day t-1 for the hour i of the day-ahead, t.
The simulation exercise starts by ranking all the offered prices for delivering electricity during a
particular hour of the day ahead included in the modified aggregate supply curve, i.e. the one
which has replaced the offered price by the generation technology e with that obtained
following (2), during 2013. It is of note that throughout this exercise, all the bids are considered
to be simple, i.e. without complex conditions, in order to simplify the algorithm that solves the
auction13. The simulated marginal price will be the price that satisfies the registered electricity
demand.
With the aim to isolate the impact of the wind power forecast on marginal prices through the
bids submitted by each generation technology plants group, the simulation exercise is carried
out five times, once for each generation technology group. Figure 1 depicts the actual and
simulated prices for each of the technology generation group and hour. Differences between
actual and simulated prices are displayed in Table 4.
[Insert Figure 1 about here]
[Insert Table 4 about here]
The average actual marginal price during 2013 was 44.04 Eur/MWh, whereas the average
simulated marginal price after removing the estimated effect of the wind power forecast on the
offered prices by the thermal generation group accordingly to the formula (2), amounts to 42.85
Eur/MWh. Therefore, the overall daily effect for the year 2013 that may be attributable to the
thermal generation bids may be quantified, on average, in an increase of 1.19 Euros/MWh.
Distinguishing between hours, the average increase in the marginal price would oscillate
between 0.87 Euros/MWh (hour 3) and 1.47 Euros/MWh (hours 17 and 24).
With regards to the remainder generation technology groups, as the estimated beta values are
statistically negative, the effect of the trading originated by the wind production forecast
13
According to the market rules, complex (economical or technical) conditions can be incorporated in the so-called
complex bids and are those that include one or some of the following: indivisibility, load gradients, minimum income
and scheduled stop.
translates into a decrease in average marginal prices. In particular, the decrease in the marginal
price, on average, attributable to combined cycled, hydro-electric and nuclear generation groups
bidding as a consequence of the wind production forecasts, would have been of 0.16
Euros/MWh each, whereas the decrease in marginal prices would have reached to 0.37
Euros/MWh when isolating the effect of renewable generation group.
The overall effect, on average, is positive, meaning that the aggregated impact of the bidding
strategies carried out by the combined cycled, hydro-electric, nuclear and renewable generation
groups offering lower prices and pushing marginal prices down exceeded the effect of the
bidding strategies implemented by the thermal generation group, which was just the opposite.
Then, the conclusion here would be that marginal prices resulting from the day-ahead market
auction may have been remarkably lower as a consequence of the entrance of wind generation
in the market, if it hadn´t been for the strategic bidding of thermal generation plants.
[Insert Figure 2 about here]
[Insert Table 5 about here]
Additionally, we have tested whether the bidding strategies may have had consequences on the
marginal price volatility, measured through the standard deviation. Figure 2 depicts the actual
and simulated marginal price standard deviation, whereas Table 5 show the differences between
them. According to the obtained results, the bidding strategies carried out by the different
technology generation groups involved in the present study exclusively taking into account the
wind production forecasts would have contributed to increase the marginal price volatility, with
the only exception of the thermal technology group which may have helped to reduce it.
4. Conclusions
As a consequence of the inclusion of renewable production coming from renewable sources into
the electricity systems, and due to the auction mechanism itself, it should be expected a decrease
in the day-ahead auction marginal prices. Within this new framework, generators that use power
sources with higher variable costs have denounced the lesser revenues received because of the
lower marginal prices as well as the lesser amount of electricity finally matched in the auction
and have even alerted about the need to close their plants, demanding to the regulator concerned
to take actions in this regard.
From the regulator’s point of view, electricity generation by conventional power sources such as
nuclear, fossil fuels and hydro-electric is more reliable than that from renewables due to the
intermittency of this latter. Thereby, his/her arguments are that the former could not be entirely
substituted by the latter, since according to the demand, in days without enough wind or number
of hours of sun, non-intermittent generating technologies will be needed, at least as a backup.
Therefore, as long as electricity cannot be stored, both types of generation sources are required
to complement each other14. Thus, the conventional generators receive capacity payments to
stand by to be available to produce electricity, if needed. In Spain, it is the Ministerial Order
ITC/3127/201115, later modified by the Royal Decree-Law 9/2013 that justify the existence and
establishes the rules of these regulated payments, which were designed to be a compensation
with the aim to incentivize the presence of available generation capacity from the so called
conventional generators.
In this work, we have analyzed whether the trading strategies translated into the offered prices
submitted to the day-ahead market auction may have been affected by the wind power forecasts,
among other factors such as the carbon prices and the hydro-electric water reservoirs. The
analysis is enriched distinguishing by technology generation group and using hourly data that
allows us to obtain insights derived from the differences between different load profiles that
correspond to different demand levels.
We have obtained very interesting findings. On the one hand, as a consequence of variations in
the wind production forecast, carbon prices and hydro-electric water reservoirs, combined
cycled, hydro-electric, nuclear and renewable generation plants seem to submit their price offers
consistently with what would be expected following the strategy to be matched in the market
auction.
However, on the other hand, some astonishing results have also been found for the studied
sample period.
-
Firstly, average offered prices by combined cycle, thermal and renewable generation
plants for a particular hour are systematically lower if delivery takes place on a business
day.
-
Secondly, average offered prices by all the involved generation plant groups except for
the nuclear appear to be higher for the first eight delivery hours.
-
Thirdly, thermal generation plants submit bids at higher prices when (i) wind production
forecast is larger, when (ii) carbon prices are expected to be higher and when (iii) there
are more hydro-electric water reservoirs. Their offered prices are also higher, on
14There
are works that explore technological options for the transition to a 100% renewable energy in the future.
(Elliston et al (2012) among others). However, at the moment, conventional energy is needed.
15
Previously, the Ministerial Order ITC/2794/2007 regulated similar payments called power guarantee payments.
average, for non-business days and from hour 1 to hour 7. Thus, the average offered
prices by the thermal plants would have pushed prices up when bidding and with
regards to (i) and (iii), it seems that they behave as if they wish to avoid being matched.
Additionally, we have simulated the algorithm that solves the day-ahead market auction by
substituting the actual offered prices by each generation plant of a particular technology
generation group for fictitious offered prices, which the estimated effect of wind production
forecasts has been deducted from. Thereby, we provide an approximate quantification of such
an effect on the market auction marginal price level and volatility, differentiating by the effect
potentially caused by each technology generation group.
The overall effect, on average, is positive, meaning that the aggregated impact of the bidding
strategies carried out by the combined cycled, hydro-electric, nuclear and renewable generation
groups offering lower prices and pushing marginal prices down exceeded the effect of the
bidding strategies implemented by the thermal generation group, which was just the opposite.
The findings obtained in the present work are of interest to practitioners and regulators, given
that they come to shed light on the way the inclusion of renewable generation into the electricity
market has altered the trading strategies in the day-ahead market by the supply market
participants. Furthermore, from the auction resulting price simulation, it is found that prices
may have been remarkably lower as a consequence of the entrance of wind generation in the
market, if it hadn´t been for the strategic bidding of thermal generation plants.
An extension of this analysis focused on the subsequent segments of the spot market,
particularly, on the intraday market and on the real time market are left for further research.
Acknowledgements
The authors gratefully acknowledge the financial support of the Spanish Ministry of Education
and Science (project ECO2013-40816-P).
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TABLES
Table 1
Installed Power Capacity (MW) from 2007 to 2013
(www.ree.es). Last accessed March 2015.
Hydraulic
Nuclear
Coal
Fuel + Gas
Combined Cycle
Other hydraulic
Wind
Fotovoltaic
T hermal Solar
T hermal Renewable
T hermal non renewable/Cogeneration/ Others
T otal
2007
17,507.30
7,729.11
11,894.79
7,542.55
22,390.25
1,871.49
13,667.82
636.93
11.02
588.17
6,617.31
90,456.74
2008
17,555.42
7,729.11
11,897.13
7,161.10
23,105.03
1,981.13
16,117.99
3,352.55
60.92
634.57
6,870.29
96,465.24
2009
17,555.42
7,729.11
11,897.13
5,994.58
24,503.01
2,022.91
18,869.00
3,398.10
232.22
782.12
7,076.79
100,060.39
2010
17,564.63
7,790.38
11,918.11
5,145.44
27,146.39
2,036.94
19,715.31
3,838.45
532.02
821.13
7,240.04
103,748.84
2011
17,571.99
7,865.99
12,158.11
3,717.33
27,171.21
2,042.40
21,174.90
4,259.35
998.62
887.07
7,317.65
105,164.61
2012
17,786.40
7,865.99
11,623.77
3,428.73
27,206.47
2,042.76
22,765.85
4,559.53
1,950.02
975.41
7,280.70
107,485.64
2013
17,785.98
7,865.99
11,641.23
3,498.37
27,206.47
2,105.70
23,002.30
4,667.03
2,299.52
980.05
7,200.37
108,253.01
2012
19,454.73
61,470.16
57,661.60
7,541.49
42,510.47
-8,511.61
4,646.34
48,508.34
8,202.09
3,444.13
4,754.77
33,767.25
283,449.75
-5,022.55
-11,199.95
267,227.25
2013
33,970.28
56,827.39
42,397.79
7,002.18
28,671.93
-7,053.51
7,102.20
54,713.25
8,326.92
4,441.53
5,074.70
32,296.38
273,771.03
-5,957.85
-6,732.14
261,081.04
Table 2
Annual Electrical Energy Balance (GWh) from 2007 to 2013.
(www.ree.es). Last accessed March 2015.
Hydraulic
Nuclear
Coal
Fuel + Gas
Combined Cycle
Generation Consumptions
Other hydraulic
Wind
Fotovoltaic
T hermal Solar
T hermal Renewable
Cogeneration/ Others
Net Generation
Pump Consumption
International Exchange Balance
T otal
2007
26,351.89
55,102.47
75,027.85
10,784.48
72,307.14
-9,634.62
4,126.50
27,611.65
483.90
7.63
2,588.97
23,450.43
288,208.29
-4,432.29
-5,750.47
278,025.54
2008
21,428.20
58,973.42
49,646.83
10,690.97
95,528.68
-9,256.95
4,639.82
32,159.82
2,497.96
15.38
2,868.71
26,721.15
295,913.98
-3,802.50
-11,039.59
281,071.89
2009
23,862.23
52,761.04
37,311.24
10,056.01
82,239.39
-7,999.11
5,454.07
38,252.83
6,072.39
129.82
3,317.34
28,600.73
280,057.99
-3,794.19
-8,086.41
268,177.39
2010
38,652.87
61,989.95
25,478.01
9,552.96
68,595.33
-7,572.09
6,824.32
43,545.33
6,422.77
691.62
3,332.36
30,973.32
288,486.75
-4,457.78
-8,332.68
275,696.29
2011
27,571.15
57,731.36
46,518.61
7,479.95
55,139.86
-8,128.95
5,295.99
42,465.29
7,425.12
1,832.36
4,317.99
32,318.80
279,967.53
-3,214.96
-6,090.13
270,662.44
Table 3
PMOe,t-1,t,i = e + e*WPFt-1,t,i+eDLt+e*EUAt-2+eWRt-1+ue,t,i
ue,t,i=ve,i+e,t,i
F test results of the one-way fixed effects model OLS estimation (1). F statistic under the null
hypothesis,H0: vi=0, where vi are the parameters of the fixed effects, one for each hour of the delivery
day, assuming v9=0. PMOe,t-1,t,i is the average supply offered price on average by the group e of generators
sharing the same generation technology submitted at a particular day (day t-1) for delivering electricity at
the day-ahead (day t) during the hour I; WPFt-1,t,i denotes the wind power forecast known at day t-1 for
delivering electricity during the hour i at the day t; DLt is a dummy variable that equals to 1 if t is a
business day and 0, otherwise; EUAt-2 refers to the European emission allowances next December
maturity futures closing prices at the day t-2 (EUAt-1) and WRt-1 denotes the hydro-electric water
reservoirs at the day t-1. The stochastic component, ueti, is a process made up of two components: e,i,
which is assumed to be independent over the days but it allows for cross-sectional covariance between the
hours and e,t,I, which is the usual homoscedastic component, normally distributed N(0,). The considered
generation technology groups are: combined cycled generators (CC), thermal (CT), hydraulics (CH),
nuclear (CN) and renewable (CR). Statistical significance at the 1% level is denoted by ** and at the 5%
by *.
Hour / Total
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
CT
1.19
1.20
1.31
0.87
1.01
0.98
1.15
1.40
1.24
1.17
1.03
1.09
1.07
1.14
1.21
1.24
1.35
1.47
1.38
1.37
1.12
1.06
1.01
1.16
1.47
Difference in average price (Actual - Simulated)
CC
CH
CN
CR
-0.16
-0.16
-0.16
-0.37
-0.25
-2.07
-0.20
-0.34
-0.22
-1.80
-0.22
-0.53
-0.33
-1.85
-0.45
-0.80
-0.33
-1.79
-0.32
-0.79
-0.28
-1.67
-0.35
-0.80
-0.23
-1.36
-0.24
-0.68
-0.14
-1.35
-0.18
-0.53
-0.17
-1.49
-0.15
-0.34
-0.13
-1.55
-0.14
-0.33
-0.18
-1.82
-0.18
-0.31
-0.09
-1.73
-0.08
-0.23
-0.11
-1.75
-0.09
-0.26
-0.09
-1.84
-0.11
-0.24
-0.09
-1.63
-0.08
-0.24
-0.12
-1.81
-0.10
-0.34
-0.11
-1.69
-0.10
-0.32
-0.12
-1.73
-0.09
-0.33
-0.11
-1.76
-0.12
-0.29
-0.10
-1.93
-0.10
-0.28
-0.09
-2.12
-0.10
-0.24
-0.17
-2.23
-0.17
-0.28
-0.10
-2.14
-0.09
-0.16
-0.11
-2.09
-0.09
-0.17
-0.12
-1.60
-0.08
-0.24
Difference in price volatility (Actual - Simulated)
CT
CC
CH
CN
CR
-0.33
0.06
0.54
0.13
0.39
-0.31
0.12
0.63
0.14
0.34
-0.02
0.06
1.13
0.18
0.46
0.18
0.11
1.44
0.26
0.62
0.17
0.08
1.46
0.17
0.58
0.10
0.02
1.33
0.18
0.50
-0.01
0.04
1.07
0.17
0.53
-0.03
0.00
1.04
0.14
0.47
-0.25
0.08
0.50
0.12
0.38
-0.28
0.05
0.38
0.13
0.37
-0.41
0.09
0.35
0.14
0.33
-0.51
0.05
0.41
0.08
0.29
-0.62
0.06
0.55
0.08
0.28
-0.62
0.03
0.42
0.10
0.26
-0.62
0.04
0.68
0.09
0.29
-0.49
0.04
0.73
0.08
0.40
-0.50
0.04
0.88
0.11
0.39
-0.54
0.07
0.81
0.11
0.40
-0.54
0.03
0.46
0.12
0.33
-0.55
0.02
0.37
0.09
0.28
-0.49
0.03
0.26
0.07
0.23
-0.52
-0.06
0.19
0.01
0.14
-0.69
-0.03
0.03
-0.01
0.05
-0.64
-0.03
0.15
0.03
0.06
-0.50
-0.01
0.47
0.07
0.19
Table 4
Displays the difference between the marginal price and the simulated spot price as well as the difference
between the standard deviation of the marginal price and the standard deviation of the simulated
marginal price, distinguishing by technology generation group. It is shown the daily difference together
ith the difference per hour for the whole year 2013. The considered technology generation groups are:
combined cycled generators (CC), thermal (CT), hydro-electric (CH), nuclear (CN) and renewable (CR).
Statistical significance at the 1% level is denoted by ** and at the 5% by *.
Hour / Total
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
CT
1.19
1.20
1.31
0.87
1.01
0.98
1.15
1.40
1.24
1.17
1.03
1.09
1.07
1.14
1.21
1.24
1.35
1.47
1.38
1.37
1.12
1.06
1.01
1.16
1.47
Difference in average price (Actual - Simulated)
CC
CH
CN
CR
-0.16
-0.16
-0.16
-0.37
-0.25
-2.07
-0.20
-0.34
-0.22
-1.80
-0.22
-0.53
-0.33
-1.85
-0.45
-0.80
-0.33
-1.79
-0.32
-0.79
-0.28
-1.67
-0.35
-0.80
-0.23
-1.36
-0.24
-0.68
-0.14
-1.35
-0.18
-0.53
-0.17
-1.49
-0.15
-0.34
-0.13
-1.55
-0.14
-0.33
-0.18
-1.82
-0.18
-0.31
-0.09
-1.73
-0.08
-0.23
-0.11
-1.75
-0.09
-0.26
-0.09
-1.84
-0.11
-0.24
-0.09
-1.63
-0.08
-0.24
-0.12
-1.81
-0.10
-0.34
-0.11
-1.69
-0.10
-0.32
-0.12
-1.73
-0.09
-0.33
-0.11
-1.76
-0.12
-0.29
-0.10
-1.93
-0.10
-0.28
-0.09
-2.12
-0.10
-0.24
-0.17
-2.23
-0.17
-0.28
-0.10
-2.14
-0.09
-0.16
-0.11
-2.09
-0.09
-0.17
-0.12
-1.60
-0.08
-0.24
Difference in price volatility (Actual - Simulated)
CT
CC
CH
CN
CR
-0.33
0.06
0.54
0.13
0.39
-0.31
0.12
0.63
0.14
0.34
-0.02
0.06
1.13
0.18
0.46
0.18
0.11
1.44
0.26
0.62
0.17
0.08
1.46
0.17
0.58
0.10
0.02
1.33
0.18
0.50
-0.01
0.04
1.07
0.17
0.53
-0.03
0.00
1.04
0.14
0.47
-0.25
0.08
0.50
0.12
0.38
-0.28
0.05
0.38
0.13
0.37
-0.41
0.09
0.35
0.14
0.33
-0.51
0.05
0.41
0.08
0.29
-0.62
0.06
0.55
0.08
0.28
-0.62
0.03
0.42
0.10
0.26
-0.62
0.04
0.68
0.09
0.29
-0.49
0.04
0.73
0.08
0.40
-0.50
0.04
0.88
0.11
0.39
-0.54
0.07
0.81
0.11
0.40
-0.54
0.03
0.46
0.12
0.33
-0.55
0.02
0.37
0.09
0.28
-0.49
0.03
0.26
0.07
0.23
-0.52
-0.06
0.19
0.01
0.14
-0.69
-0.03
0.03
-0.01
0.05
-0.64
-0.03
0.15
0.03
0.06
-0.50
-0.01
0.47
0.07
0.19
FIGURES
Figure 1.
Displays the average hourly marginal price (P) and the hourly simulated marginal prices
obtained after substituting the actual supply curve for the fictitious supply curve which contains
offered prices that have been modified to ignore the estimated effect of the wind power forecasts
on them by each technology generation group. The considered groups are: combined cycled
(CC), thermal (CT), hydraulic (CH), nuclear (CN), and mostly renewable (CR). Prices
expressed in Euros/MWh.
60
55
50
45
40
35
30
25
0
1
2
3
4
5
6
7
P
8
9
CT
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
CH
CN
CC
CR
Figure 2.
Displays the average hourly marginal price standard deviation (P) and the hourly simulated
marginal price (obtained after substituting the actual supply curve for the fictitious supply curve
which contains offered prices that have been modified to ignore the estimated effect of the wind
power forecasts on them by each technology generation group) standard deviation. The
considered groups are: combined cycled (CC), thermal (CT), hydraulic (CH), nuclear (CN),
and mostly renewable (CR).
23
22
21
20
19
18
17
16
15
0
1
2
3
4
5
6
7
P
8
9
CT
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
CH
CN
CC
CR