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you are encouraged to use recent and relavent IEEE papers

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th International Conference on Green Technology and Sustainable Development (GTSD)

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Abstract – The Vietnamese government have plan to develop the
wind farms with the expected capacity of 6 GW by 2030. With the
high penetration of wind power into power system, wind power
forecasting is essentially needed for a power generation
balancing in power system operation and electricity market.
However, such a tool is currently not available in Vietnamese
wind farms as well as electricity market. Therefore, a short-term
wind power forecasting tool for 24 hours has been created to fill
in this gap, using artificial neural network technique. The neural
network has been trained with past data recorded from 201

5

to
2017 at Tuy Phong wind farm in Binh Thuan province of Viet
Nam. It has been tested for wind power prediction with the input
data from hourly weather forecast for the same wind farm. The
tool can be used for short-term wind power forecasting in
Vietnamese power system in a foreseeable future.

Keywords: power system; wind farm; wind power forecasting;
neural network; electricity market.

I. NECESITY OF WIND POWER FORECASTING

Today, the integration of wind power into the existing
grid is a big issue in power system operation. For the system
operators, power generation curve of wind turbines is a
necessary information in the power sources balancing. From
the dispatchers’ point of view, wind power forecast errors
will impact the system net imbalances when the share of
wind power increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
electricity market [1]. In the deregulated market, day-ahead
electricity spot prices are also affected by day-ahead wind
power forecasting [2]. Wind power forecasting is also
essential in reducing the power curtailment, supporting the
ancillary service. However, due to uncertainty of wind speed
and weather factors, the wind power is not easy to predict.

In recent years, many wind power forecasting methods
have been proposed. In [3], a review of different approaches
for short-term wind power forecasting has been introduced

,

including statistical and physical methods with different
models such as WPMS, WPPT, Prediktor, Zephyr, WPFS,
ANEMOS, ARMINES, Ewind, Sipreolico. In [4], [5], the
methods, models of wind power forecasting and its impact on

*Research supported by Gesellschaft fuer Internationale

Zusammenarbeit GmbH (GIZ).
D. T. Viet is with the University of Danang, Vietnam (email:

dtviet@ac.udn.vn).
V. V. Phuong is with the University of Danang, Vietnam (email:

phuongvv@cpc.vn).
D. M. Quan is with the University of Danang, Vietnam (email:

dmquan@dut.udn.vn).
A. Kies is with the Frankfurt Institute for Advanced Studies, Germany

(email: kies@fias uni-frankfurt.de).
B. U. Schyska is with the Carl von Ossietzky Universität Oldenburg,

Germany (email: bruno.schyska@uni-oldenburg.de).
Y. K. Wu is with the National Chung-Cheng University, Taiwan (email:

allenwu@ccu.edu.tw).

the electricity market and power systems have been
presented. The practice and experience of short-term wind
power forecasting accuracy and uncertainty in Finland has
also been investigated [1].

In general, the equation for wind power P (W) of each
wind turbine is given by the formula (1):

P = (1/2)ρ×A×Cp×Ng×Nb×V
3 (1)

where ρ: air density (kg/m3), A: rotor swept area (m2), Cp:
coefficient of performance, V: wind speed (m/s), Ng:
generator efficiency, Nb: gear box bearing efficiency [6].

Unfortunately, many multiplication factors in the formula
(1) are uncertain. It leads to uncertainty in relationship
between wind speed and wind power of each wind turbine
[7].

II. WIND POWER FORECASTING IN VIETNAM

A. Wind power in Vietnam

Vietnam is considered to have high potential for wind
energy. The wind energy potential of Vietnam is shown in
Table 1, Fig. 1 and Fig. 2 [8]:

TABLE 1. WIND ENERGY POTENTIAL OF VIET NAM AT 80 M ABOVE
GROUND LEVEL

Average
wind
speed
(m/s)

<4 4-5 5-6 6-7 7-8 8-9 >9

Area
(km2)

95,916 70,868 40,473 2,435 220 20 1

Area
(%)

45.7 33.8 19.3 1.2 0.1 0.01 0

Potential
(MW)

956,161 708,678 404,732 24,351 2,202 200 10

The development of wind power has been paid attention
by both the Vietnamese government and investors. The
national renewable energy development strategy by 2030,
which was approved by the Vietnamese Prime Minister,
emphasizes the role of wind power in particular. Expectations
about installed wind power capacity are 800 MW in 2020;
2,000 MW in 2025 and around 6,000 MW by 2030 [9], [10].

By 2017, 160 MW of wind power capacity has been
installed, some large wind farms with capacity and year of
beginning operation are listed – Tuy Phong: 30 MW (2009);
Bac Lieu: 16 MW (2013), and 99.2 MW (2016); Phu Lac: 24
MW (2016); Phu Quy: 6 MW (2013) [8].

A Short-Term Wind Power Forecasting Tool for Vietnamese Wind
Farms and Electricity Market*

Dinh Thanh Viet, Vo Van Phuong, Minh Quan Duong, Alexander Kies,
Bruno U. Schyska and Yuan Kang Wu

978-1-5386-5126-1/18/$31.00 ©2018 IEEE

2018 4th International Conference on Green Technology and Sustainable Development (GTSD)

131

Figure 1. Wind resource map of Vietnam at the height of 80 m

Figure 2. Representative wind profile for the three regions in Vietnam

B. Wind power forecasting in Vietnam

At present, there is no effective tool for predicting wind
power in Vietnam. With the increasing integration of wind
energy into the Vietnamese power system, the projected
capacity of wind power plays an important role in supporting
the optimal operation of wind power plants as well as the
electricity market.

The forecast error of the whole wind farm will be much
affected by the forecast errors from all wind turbines as a
sum. For the electricity market operators, the predicted power

of the whole wind farm at the point of coupling into the
power grid is needed, rather than the sum of predicted powers
of all turbines. In this paper, the approach of wind power
forecasting for the whole wind farm will be investigated.

III. SHORT-TERM WIND POWER FORECASTING
USING NEURAL NETWORK

A. Neural network

A neural network is a multi-input, multi-output system,
consisting of an input layer, one or two hidden layers and an
output layer. Each class uses a number of neurons, and each
neuron in a layer is connected to neurons in the adjacent
layers with different weights. The architecture of the typical
neural network is shown in Fig. 3 [11], [18].

Figure 3. Neural network structure

where input X = (x1, x2, …, xd), output O = (o1, o2, …, on). The
signal is fed into the input layer, passing through the hidden
layer and to the output layer. In a neural network, each
neuron (except neuron at the input layer) receives and
processes stimuli (inputs) from other neurons. Each input is
first multiplied by the corresponding weight, then the
resulting products are added to produce a weighted sum,
which is passed through a neuron activation function to
produce the output of the neuron [11], [12].

B. Feedforward neural network

A feedforward neural network usually has one or more
hidden layers of sigmoid neurons followed by a linear
neurons output layer. The paper uses the model of a
feedforward neural network as described in Fig. 4. The input
layer consists of 3 neurons of historical wind speed,
temperature and wind power. The neural network has 20
neurons in the hidden layer and 01 neuron in the output layer.

The neural network may be used as a general function
approximator. With enough neurons in the hidden layer, any
function with a specific number of discontinuities arbitrarily
can be approximated well. The algorithm for building the
neural network – wind power forecasting model is shown in

2018 4th International Conference on Green Technology and Sustainable Development (GTSD)

132

Fig. 5. The historical data from wind farm, including wind
power, wind speed and temperature, are loaded into the
program and stored as a matrix of forecasting variable.
Because of different operation scenarios, the historical data
may not correctly reflect the relationship between wind
power to wind speed and temperature. In few cases, historical
data in practice may show negative values of wind power
during generator starting time. Therefore, a data
preprocessing is needed for reducing forecasting error. The
historical data is used for training the forecasting neural
network.

Figure 4. Feedforward neural network.

C. Wind power forecasting model

From the formula (1), it is obvious that generating power
of each wind turbine depends largely on the wind speed. The
temperature of environment is chosen as second input,
affecting on the output power [13], [14], sum of output
powers from the turbines serves as another input for neural
network wind power forecasting model (Fig. 6). The input
data is used to forecast the generating power of the wind
farm. This model is designed for Vietnamese wind farms’
power forecasting (short name: VWPF).

IV. CASE STUDY: WIND POWER FORECASTING FOR
TUY PHONG WIND FARM

A. Simulation data

Tuy Phong is the first large-scale wind farm in Vietnam
with a total capacity of 30MW, including 20 turbines of
Fuhrländer, each turbine has a height of 85m, a blade
diameter of 77m, and capacity of 1.5MW. The research is
based on real data on wind speed and wind power production
at Tuy Phong wind farm for 3 years. Collected data from
January 1, 2015 to December 31, 2017 was used for wind
power forecasting.

The data in the forecasting model VWPF includes wind
speed, environmental temperature and wind power, which are
collected every hour, represented by 24 lines per day.
Example of data on October 7, 2017 for wind power
forecasting is shown in Table 2.

The data set is divided into two data subsets:

� Data subset 1 from 01/01/2015 to 30/10/2017 is used
to train the neural network. This is a database with

data collected in 24,816 hours, almost 3 years, large
enough for the neural network training purpose.

� Data subset 2 from 01/11/2017 to 31/12/2017 is used
to compare the forecast results with the collected
actual data for the error evaluation purpose.

Figure 5. Algorithm for building the neural network – wind power

forecasting model VWPF.

Figure 6. Wind power forecasting model VWPF

2018 4th International Conference on Green Technology and Sustainable Development (GTSD)

133

TABLE 2. EXAMPLE OF DATA ON OCTOBER 7, 2017

Date Hour
Tempera-
ture ( )

Wind
speed
(m/s)

Wind
power (W)

07/10/2017 1 24 3.56 272,299

07/10/2017 2 24 3.51 268,449

07/10/2017 3 24 3.52 266,840

07/10/2017 4 25 3.93 596,442

07/10/2017 5 25 3.9 572,175

07/10/2017 6 27 4.05 767,869

07/10/2017 7 27 3.96 772,839

07/10/2017 8 32 5.54 2,067,089

07/10/2017 9 32 5.47 2,137,362

07/10/2017 10 32 5.35 2,071,529

07/10/2017 11 32 5.4 2,044,341

07/10/2017 12 32 5.28 2,237,072

07/10/2017 13 32 5.25 2,105,929

07/10/2017 14 32 5.34 2,143,538

07/10/2017 15 30 4.68 1,683,934

07/10/2017 16 29 4.04 767,723

07/10/2017 17 24 4.53 1,632,532

07/10/2017 18 24 5.29 2,165,775

07/10/2017 19 24 5.77 2,943,629

07/10/2017 20 24 5.89 2,947,676

07/10/2017 21 24 6.13 3,200,192

07/10/2017 22 24 5.98 3,200,290

07/10/2017 23 25 5.75 3,084,564

08/10/2017 0 25 5.73 3,065,696

B. Error evaluation

The error measures – Mean Absolute Percent Error
(MAPE), Mean Absolute Error (MAE) and Root Mean
Square Error (RMSE) are used to evaluate the accuracy of the
forecasting model [15], [16].

The MAPE represents the accuracy of the model as a
percentage of the error, calculated according to formula (4):

(4)

where:

Preal: actual power output of the wind power plant,

Ppred: generation power according to forecasting model,

N: number of forecasting data.

The MAE shows the accuracy of the model in the same
unit of measure as the predicted data. This index is used to
evaluate the margin of error and is calculated according to the
formula (5):

(5)

In order to evaluate MAE in percentage for comparison
between different models, we can use Normalized Mean
Absolute Error (NMAE) (6):

(6)

where Pinst is the wind farm installed capacity.

The RMSE is the standard deviation of the prediction
errors (residuals). This is also a frequently used measure of
the differences between values forecasted by a model and the
values actually observed. The RMSE is calculated by the
formula (7):

(7)

Similarly, we can use Normalized Root Mean Square
Error (NRMSE) in percentage (8):

(8)

C. Result

By putting Tuy Phong wind farm data into the model and
implementing neural network training, the forecasted model
has been received and shown in Fig. 7, where the blue line
represents the predicted wind power, the red line represents
the actual generated wind power from the data subset 1. Fig.
7 shows a part of the snapshots for series of predicted time,
the total number of snapshots is 23,856 hours (from 1/1/2015
to 20/9/2017).

The forecasting model VWPF is validated with data from
any date in the data subset 2 for 92 days during period from
01/10/2017 to 31/12/2017. The real data of previous day is
updated and included into the training database for the next
day wind power forecast. As an example, the forecast on
November 26, 2017 (the lowest wind speed was 8.39 m/s and
the highest was 15.44 m/s with the steady change of wind
speed throughout the day) is shown in Fig. 8. The error in the
graph represents difference between real wind power and
predicted wind power. The measures for the accuracy of the
forecast result on November 26, 2017 are: MAPE=4.72%;
NMAE=4.66%; NRMSE=5.66%.

Forecast results for December 17, 2017 with the speed
changes from 6.15 m/s to 14.47 m/s are shown in Fig. 9. The
measures for the accuracy of the forecast result on
17/12/2017 are: MAPE=5.44%; NMAE=4.74%;
NRMSE=6.24%.

The wind power day-ahead forecast results in one week
from 25/12/2017 to 31/12/2017 and relevant forecast error
are shown in Fig.10. The forecast error or difference between
the observed and the forecast wind power for one week from
25/12/2017 to 31/12/2017 is evaluated by the following
values: MAPE=8.78%; NMAE=4.14%; NRMSE=4.58%

2018 4th International Conference on Green Technology and Sustainable Development (GTSD)

134

Based on the results of the forecasted wind power from
VWPF, we have a practical range of errors as in Table 3:

TABLE 3. RANGE OF ERRORS FROM PRACTICAL TESTING
WITH THE MODEL VWPF

No MAPE (%) NMAE (%) NRMSE (%)

1 8.05 5.52 7.28

2 4.72 4.66 5.66

3 5.13 5.13 6.20

4 10.06 5.98 7.32

5 7.03 5.59 7.70

6 6.07 4.07 4.09

7 5.44 4.74 6.24

8 9.64 5.95 8.00

9 5.54 5.94 7.70

Average 6.85 5.29 6.69

Figure 7. Wind power forecasting result after neural network training.

Figure 8. Forecasted versus actual wind power on 26/11/2017

Figure 9. Forecasted versus actual wind power on 17/12/2017

Summary of error range from Table 3: MAPE = 4.72-
10.06%, NMAE = 4.07-5.98%, NRMSE = 4.09-8.00%.

2018 4th International Conference on Green Technology and Sustainable Development (GTSD)

135

Figure 10. Forecasted versus actual wind power during one week from

25/12/2017 to 31/12/2017

TABLE 4. COMPARISON THE FORECAST ERROR INDICES OF
THE PROPOSED MODEL (VWPF) WITH SOME OTHER MODELS

Forecasting Model
Error indices

MAPE
(%)

NMAE
(%)

NRMSE
(%)

Persistence 14.43 6.18 7.99
BPNN 14.35 5.98 7.53

RBFNN 12.73 5.94 7.40
ANFIS 14.92 6.24 8.03
NNPSO 11.51 5.35 6.59

WT+BPNN 12.19 5.77 7.18
WT+RBFNN 11.18 5.62 6.95
WT+ANFIS 12.58 5.86 7.67
WT+NNPSO 8.19 4.86 6.28

VWPF 6.85 5.29 6.69
Table 4 showed comparison between the forecast errors

of the proposed model with some other published models
[17]. The error indices in different seasons [17] were
recalculated as the average values. From Table 4, we find that
the error indices between of the VWPF model is relatively
smaller in comparison with most of the published wind
power forecasting models. It proves that the VWPF model
provides reliable forecasting results.

V. CONCLUSION
In the paper, a model of the wind power forecasting

(VWPF) is developed for this need in Vietnam. The power
system operators are usually interested in the forecasting of
the whole wind farm’s power, which is generated into the
power system, rather than forecast power of each wind
turbine. It shows advantage and effectiveness of the
developed model in power prediction for the whole wind
farm, which is well appropriate for dispatcher working as
well as electricity market operator. The neural network
prediction model can be used for short-time wind power
forecasting (hour-ahead, day-ahead, and week-ahead). The
forecasting model has been applied for estimating the wind
power output of the Tuy Phong wind power plant in Binh
Thuan province, Vietnam. The predicted results were
evaluated with the average forecast error indices
MAPE=6.85%, NMAE=5.29%, NRMSE=6.69%. The

forecast error indices, showing the high accuracy of the
model, are relatively smaller in comparison with most of
similar research models (Table 4). Application of artificial
intelligence technique at the connected point of the wind
farm to the power grid proved effectiveness of this approach.
This wind power forecasting tool can be applied not only for
Tuy Phong wind farm, but also for the others in Vietnam.

ACKNOWLEDGMENT

This work is part of the R&D Project “Analysis of the
Large Scale Integration of Renewable Power into the Future
Vietnamese Power System”, financed by Gesellschaft fuer
Internationale Zusammenarbeit GmbH (GIZ, 2016-2018).

REFERENCES

[1] H. Holttinnen, J. Miettinen, S. Sillanpää “Wind power forecasting
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[2] T. Jónsson,; P. Pinson, H. Madsen, “On the market impact of wind
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[3] X. Wang, P. Guo, X. Huang, “A Review of Wind Power Forecasting
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[4] G. Kariniotakis, Renewable Energy Forecasting: From Models to
Applications, 1st ed., Woodhead Publishing, 2011.

[5] G. Giebel, R. Brownsword, G. Kariniotakis, M. Denhard, C. Draxl,
“The State of the Art in Short-Term Prediction of Wind Power: A
Literature Overview”, 2nd ed., 2011.

[6] A. Sarkar, D. K. Behera, “Wind Turbine Blade Efficiency and Power
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[7] D.M. Quan, E. Ogliari, F. Grimaccia, S. Leva, M. Mussetta, “Hybrid
model for hourly forecast of photovoltaic and wind power”, 2013
IEEE International Conference on Fuzzy Systems, p.p 1-6, 2013.

[8] N. Q. Khanh, “Analysis of future generation capacity scenarios for
Vietnam”, Green Innovation and Development Centre (GreenID),
Vietnam, 2017.

[9] The Vietnamese Prime Minister, “Approving the development
strategy of renewable energy of Vietnam by 2030 with a vision to
2050”, Decision No. 2068/QD-TTg dated November 25, 2015.

[10] A. Kies, B. Schyska, D. T. Viet, L. Bremen, D. Heinemann, S.
Schramm, “Large-Scale Integration of Renewable Power Sources into
the Vietnamese Power System”, Energy Procedia, vol. 125, pp. 207–
213, 2017.

[11] S. Samarasingh, Neural Networks for Applied Sciences and
Engineering: From Fundamentals to Complex Pattern Recognition,
CRC Oress, Taylor & Francis Group, Boca Raton, 2007.

[12] The MathWorks Inc, Neural Network Toolbox User’s Guide, 2014.
[13] A. Singh ; K. Gurtej ; G. Jain ; F. Nayyar ; M. M. Tripathi, Short

term wind speed and power forecasting in Indian and UK wind power
farms, 2016 IEEE 7th Power India International Conference
(PIICON),

[14] Salih Mohammed Salih, Mohammed Qasim Taha, Mohammed K.
Alawsaj, “Performance analysis of wind turbine systems under
different parameters effect”, International Journal of Energy and
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[15] D. B. Alencar, C. M. Affonso, R. C. L. Oliveira, J. L. M. Rodríguez, J.
C. Leite and J. C. R. Filho, “Different Models for Forecasting Wind
Power Generation: Case Study”, Energies, 2017.

[16] X. Zhao, S. Wang, T. Li, “Review of Evaluation Criteria and Main
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761 – 769, 2011.

[17] P. Mandala, H. Zareipourb, W. D. Rosehart, Forecasting Aggregated
Wind Power Pro duction of Multiple Wind Farms Using Hybrid
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[18] S. Haykin, Neural Networks and Learning Machines, 3rd ed., Pearson
Education Inc, 2009.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 4, OCTOBER 2012 809

Comparison of Wind Energy Support Policy
and Electricity Market Design in Europe,

the United States, and Australia
Néstor Aparicio, Member, IEEE, Iain MacGill, Member, IEEE, Juan Rivier Abbad, Member, IEEE, and

Hector Beltra

n

Abstract—This paper is intended to fill a gap in the current lit-
erature comparing and contrasting the experience of a number of
Europeancountries,U.S. states, andAustraliawithregard towind
energysupportpolicyandelectricitymarketdesign.Aswindpene-
trations increase, thenatureof thesearrangementsbecomesan in-
creasingly importantdeterminantofhoweffectivelyandefficiently
thisgeneration is integrated into theelectricity industry.Thejuris-
dictions considered in this paper exhibit a range of wind support
policy measures from feed-in tariffs to green certificates, and elec-
tricity industry arrangements including vertically integrated utili-
ties,bilateral tradingwithnetpools,aswellasgrosswholesalepoo

l

markets. We consider the challenges that various countries and
states have faced as wind generation expanded and how they have
responded. Findings include the limitations of traditional feed-in
tariffsathigherwindpenetrationsbecausetheyshieldwindproject
developersandoperators fromthe implicationsof theirgeneration
on wider electricity market operation. With regard to market de-
sign, wind forecasting and predispatch requirements are particu-
larly important for forward markets, whereas the formal involve-
ment of wind in scheduling and ancillary services (balancing and
contingencies) is key for real-time markets.

Index Terms—Balancing markets, electricity market design, re-
newable energy policy, wind energy.

I. INTRODUCTION

P OLICY measures to support greater wind energy havehadademonstrated impacton itsdevelopment indifferent
jurisdictions around the world. Experience to date suggests
that feed-in tariff (FIT) policies have been the most successful
approach in rapidly expanding wind generation capacity, as
demonstrated incountries includingDenmarkandSpain,which
now have world leading wind energy penetrations [1]. This ap-
proach, however, may cause increasing integration challenges
for the electricity industry as wind penetrations continue to

Manuscript received August 30, 2011; revised June 08, 2012; accepted July
06, 2012. Date of publication September 10, 2012; date of current version
September 14, 2012. This work was supported in part by the Universitat
Jaume I under Grant P1·1A2008-11. N. Aparicio’s research visit to the Centre
of Energy and Environmental Markets, which kindly offered him a visiting
position, was supported by the Universitat Jaume I under Grant E-2008-06.
N. Aparicio and H. Beltran are with the Area of Electrical Engineering, Uni-

versitat Jaume I, 12071 Castelló de la Plana, Spain (e-mail: aparicio@uji.es).
I. MacGill is with the School of Electrical Engineering and Telecommuni-

cations and Centre for Energy and Environmental Markets, University of New
South Wales, Sydney 2052, Australia (e-mail: i.macgill@unsw.edu.au).
J. Rivier Abbad is with Iberdrola Renovables, 28033 Madrid, Spain (e-mail:

jrivier@iberdrola.es).
Color versions of oneormore of thefigures in this paper are available online

at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TSTE.2012.220877

1

rise. The value of electricity within a power system varies over
time, by location and subject to uncertainties reflecting, in ag-
gregate, the changing costs and benefits of all generations and
end-users. There have been worldwide moves over the last two
decades to restructure electricity industries so that generators
and end-users see price signals that more appropriately reflect
these underlying industry economics. In their simplest form,
FIT schemes can effectively shield project developers from
such energy market signals through a fixed payment for each
MWh of renewable generation independent of the value it ac-
tually provides for the industry at that time and location within
the network [2]. Simplified tendering processes awarded to
projects on the basis of lowest required government payments
per MWh of renewable generation, which were adopted in
countries such as Ireland and China, can have similar impacts.
Other policy approaches such as renewable electricity pro-

duction taxcreditsas seen in theU.S., and tradablegreencertifi-
cates as seen in a number of European countries and Australia,
provide another approach for supporting wind energy. By com-
parison, these can ensure that wind farm developers and opera-
tors are still incentivizedbyelectricitymarket “signals” tomax-
imize overall industry value.
The last few years has seen important developments in a

number of countries that can help us better understand these
issues. For example, Denmark and Spain have moved from
a conventional FIT to a tariff premium above the electricity
market price, the latter with additional arrangements that cap
potential incomes to wind generators. The UK Renewables
Obligation scheme now appears to be driving greater industry
development, especially in offshore projects. The U.S. Federal
production tax credits and state-based renewableportfolio stan-
dardshavealsodrivenvery significant if sometimesboom–bust
winduptake,particularly inTexaswithaquarterof thatnation’s
installed capacity. Table I shows the total wind energy installed
capacity at the end of 2010 in the regions considered in this
paper together with their proportion of electricity consumption
now supplied by wind energy.
Wind generation penetrations have now reached significant

levels (from 10%–20%) in countries such as Denmark and
Spain, and states such as South Australia and Iowa. This, in
turn, has driven changes in electricity market design and wider
policyarrangements in thesecountries inorder tobettermanage
the major contributions of highly variable and only somewhat
predictablewindgenerationwithin theirpowersystems.Formal
participation by wind generation in electricity market dispatch
and ancillary services may be limited to day-ahead markets

1949-3029/$31.00 © 2012 IEEE

810 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 4, OCTOBER 201

2

TABLE I
TOTAL INSTALLED WIND ENERGY CAPACITY AT THE END OF 2010 AND
THE PROPORTION OF ELECTRICITY CONSUMPTION SUPPLIED IN 201

0

FOR SELECTED COUNTRIES AND REGIONS

or include real-time markets and even ancillary services such
as voltage and frequency control. Improved wind forecasting
systems have reduced prediction errors, whereas delayed gate
closures and active demand participation have decreased the
potential energy imbalances that have to be resolved by the
industry. Other electricity market arrangements that may affect
wind energy are charges due to imbalances settlement, addi-
tional income due to capacity recognition and rewards when
wind generators reduce their output following orders from
transmission system and market operators.
This paper draws together someof thekeyexperiences, chal-

lenges, and responses to growing wind penetrations in selected
jurisdictions within Europe, the United States, and Australia.
These are by no means the only countries from which lessons
might be drawn, or where significant wind industry develop-
ment is underway. However, they do represent important and
interesting examples of some key current and possible future
wind markets, and the range of support policy approaches and
electricitymarket arrangements thatmaybeemployed to facili-
tate highwindpenetrations.The selected states inAustralia and
theU.S.are thosewith thehighest installedwindcapacities.The
paper is divided into four further sections. Section II presents
the main support policies in place for wind in these selected ju-
risdictions. Section III provides an overview of their different
electricity market arrangements. Section IV discusses the inter-
actions between wind energy and the support policies and elec-
tricity markets considered in the previous two sections. Finally,
conclusions are presented in Section V.

II. SUPPORT POLICIES

A wide range of policy mechanisms to support wind energy,
or renewable energy more generally, have been used by dif-
ferent jurisdictions over recent decades. A general assessment
of the available support policy mechanisms and their potential
strengthsandweaknessescanbefoundin[3].Fourgeneralwind
energy support policy mechanisms are considered here. The ju-
risdictionscovered in thispaper thathaveoptedforeachof these
four approaches, together with their particular characteristics,
are described below and summarized in Table II.

TABLE II
RENEWABLE ENERGY SUPPORT POLICIES IN DIFFERENT REGIONS

A. Tender Schemes

The theoretical basis of tender schemes is highly
promising—governments can set a target of installed capacity
or total public expenditure, and invite prospective project de-
velopers to submit project tenders that specify the government
support—capital $ or $/MWh—required to proceed. Gov-
ernments can then choose the lowest cost project providers.
Unfortunately, the experience to date with tender-based ap-
proaches is mixed. For example, a number of countries opted
for this approach in order to drive initial deployment of re-
newable energies but abandoned it some years later. In 1990,
the UK introduced the Non Fossil Fuel Obligation mainly as a
policy to support the nuclear industry although it also drove the
installation of a number of wind farms. Ireland introduced its
tender scheme in 1996 based on the UK model, but abandoned
it ten years later because it failed to reach its set targets [4]. It
is also notable that China adopted a franchise tender program
in 2003 that was abandoned in 2009 for onshore projects.
What proved to be irrationally low bids offered by competing
developers led to the Government selecting projects with such
lowrequired support prices that the“winning”proponentswere
later unwilling or unable to actually undertake their projects
[5]. However, China kept a tender scheme for offshore wind

APARICIO et al.: COMPARISON OF WIND ENERGY SUPPORT POLICY AND ELECTRICITY MARKET DESIGN 811

farms and Denmark opened a scheme in2005, also for offshore
wind, with very good results [6].
A number of U.S. states use tender-based processes for their

renewable portfolio standard although these may also be based
around the use of tradable certificates as outlined in the next
section.The jurisdictionsusing such tendershavealsoachieved
mixed success [7].

B. Feed-in Tariffs

Feed-in tariff schemes adopted by Denmark, Germany, and
Spain have without doubt been the primary drivers of their
significant wind energy deployment over the last two decades.
Initial policy settings in these three countries, announced in the
1990s,were similar and relatively simple schemeswith a single
tariff for all renewable producers. These FIT schemes provided
an electricity consumer funded fixed price for each MWh of
generation over a given time period. Any project meeting
the scheme requirements was eligible for this payment. As
wind installed capacity started to rise, however, these policies
have been significantly amended. Each country has followed
different strategies.
Germany has decided to make important changes in the FIT

scheme, includingfixeddegression,anequalizationschemethat
tries to compensate the differences in wind resources between
regions, and higher tariffs for repowering and offshore wind
farms.The latest amendment of theGermanRenewableEnergy
Act, in forcesinceJanuary1,2012,has increased thedegression
for both onshore and offshore projects. However, the reduction
inoffshore tariffswillnotbeapplicableuntil 2018 insteadof the
originally proposed 2015. An optional accelerated repayment
model which offers a higher initial tariff for a reduced number
of years has also been introduced for offshore wind farms. This
amendmentalso introducedanewmarketpremium,openingthe
possibility fordirect selling(ordirectmarketing),whichhas im-
portant implications for the generators that participate, as it is
shown latter in the paper. German amendments have managed
tokeepannual increases in installedcapacity relativelyconstant
over the past decade as Fig. 1 shows.
Denmark phased out its FIT scheme in 2000. After a transi-

tionperiod, it adoptedaschemewhere the“feed in” tariff isnow
afixedpremiumpaymentaboveandbeyondwhat thewindfarm
projects earn from the electricity market. In Denmark’s case,
wind generators connected to the grid after January 2003 must
sell their production to the electricity market. Fig. 1 shows that
new installed capacity rapidly declined following this change.
However, andasnotedabove, the tenderingprocess foroffshore
wind farms has driven more than 200 MW of new capacity in
both 2009 and in 2010, and in 2013 the Denmark’s largest off-
shore wind farm with 400 MW will start operation and is ex-
pected to supply 4% of the country’s demand. This will help to
meet the Danish target of 50% from wind by 2020.
Spain introduced theoptionofwind farms takinganFITpre-

mium in addition to energy market prices in 1998, earlier than
Germany, but also kept the conventional FIT mechanism. Thus
wind energy producers are able to choose between both remu-
neration schemes and switch between them every 12 months.
No wind energy producers initially decided to participate in the

Fig. 1. Annual increases in installed wind capacity in Spain, Germany, and
Denmark from 2002 to 2011. Sources: Respective national wind associations.

electricity market so an amendment in 2004 provided extra in-
centives to switch. By the end of 2006, around 90% of wind
generationcapacitybid in themarketas thosearrangementspro-
vided higher revenues than the conventional FIT. Finally, fur-
ther amendments, announced in 2007, modified the tariffs in-
troducing a cap and a floor in the sum of market price plus pre-
mium, variable degression depending on inflation, and lower
tariffs once a technology target has been reached. Many wind
energy developers accelerated the installation of their projects
in order to complete them before this amendment came into ef-
fect at the beginning of 2008. The year 2007, therefore, saw a
record increase in installed capacity, as shown inFig. 1.Thede-
crease in new installed capacity over the last two years is due
to the“pre-assignation” register introducedby theSpanishgov-
ernment in 2010. A limited number of projects are approved in
order to ensure Spain does not surpass its targets for the wind.
Moreover, in January 2012, the government announced a tem-
porary moratorium that freezes policy support for any new re-
newable energy project due to the impacts of the Global Finan-
cial Crisis.
Ireland and China have now replaced their tender schemes

withFITs.InIreland,FITshavebecomethemainmechanismfor
supporting wind energy. Offshore wind farms have had higher
tariffs since2008 [4]. InAugust 2009,China announced itsfirst
FIT scheme for onshore wind with different tariffs that depend
on thewind resources and investment conditions in eachof four
regions [8].

812 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 4, OCTOBER 20

12

C. Quota System/Tradable Certificates

Aquota systemisusually relatedwith tradable renewableen-
ergy certificates (or credits) and has different names depending
on the country. In the UK, the scheme that came into force in
2002 is known as Renewables Obligation (RO). Wind genera-
tors receivedaRenewableObligationcertificate (ROC)foreach
MWh of electricity generated. A 2008 amendment introduced
the concept of ROC banding in order to give additional ROCs
to emerging technologies. Thus, onshore wind, included in the
“reference” band, receives 1.0 ROC per MWh, while offshore
wind, which is included in the “postdemonstration” band, ini-
tially received 1.5 ROCs per MWh. The 2009 budget raised it
to2ROCsfor2009/10and1.75 for2010/11.TheROCsaresold
to electricity suppliers in order to fulfil a mandated obligation
placed upon them by the government according to its national
renewable energy quota or target. Suppliers can either present
enough ROCs to cover their obligations or they can pay for any
shortfall into a buyout fund. The Renewables Obligation Order
2009 introduced significant changes. It requests the Secretary
of State to announce the obligation level six months preceding
an obligation period. This obligation level is the greater value
of either the number of ROCs needed to meet a fixed target of
ROCs/MWh or a headroom that is calculated as the ROCs ex-
pected to be issued according to the amount of renewable elec-
tricity expected to be generated, uplifted by 10%. This “guar-
anteed headroom” mechanism sets an effective floor for ROC
prices once the obligation is reached. In the case where ROC
supply exceeds theobligateddemand, therefore, theROsystem
will then effectively operate in a similar manner to an FIT pre-
mium mechanism.
TheAustralianGovernment’sMandatoryRenewableEnergy

Target (MRET) commenced operation in 2001 as the world’s
first renewable energy certificate trading scheme [2]. It requires
all Australian electricity retailers and wholesale electricity cus-
tomers to source an increasing amount of their electricity from
newrenewablegeneration sources.The liablepartieswithin the
Australian electricity market are electricity retailers and those
large consumers who purchase directly from the wholesale
market. The “additional renewable electricity” that the liable
parties are required to acquire was originally intended to be
equivalent to 2% of their electricity purchases by 2010. The
Renewable Energy Target (RET) announced in 2009 raised the
requirement to 20% by 2020. Targets to date have been easily
met and the costs seem reasonable by international standards
[2].
Some State Governments have also set jurisdictional renew-

able goals although they are not backed with specific obliga-
tions. For example, in 2007, South Australia set a 20% target
for2014. In June2011, it hasbeenalreadymet.Thestate, there-
fore, set a new goal of 33% by 2020 [9]. Victoria, which has
the second highest installed wind capacity, has a 2020 goal of
25%, with a minimum of 20% for wind energy. However, it is
intended that the Federal RET provide almost all wind project
support in Australia. A number of changes have been made to
the schemeover its decadeof operationbeyondagreater target,
including separate arrangements for large-scale and small-scale
renewable energy systems [2].

Currently, there isnoafederalquotaschemein theU.S.How-
ever, 29 states, the District of Columbia, and Puerto Rico have
a Renewable Portfolio Standard (RPS), and eight states have a
renewable portfolio goal.
Texas adopted both an RPS and a renewable energy credit

(REC) trading program in 1999 [10]. The RPS target was
2000 MW of new renewable generation by 2009, in addition
to the 880 MW installed at the time. It was raised in 2005 to
5880 MW by 2015, where 500 MW must be resources other
than wind, and to 10000 MW by 2025. According to the 2009
compliance report, Texas had already surpassed its 2025 target
by 2009. The Electric Reliability Council of Texas (ERCOT

)

acts as the program administrator of the REC trading program.
Iowa passed one of the earliest renewable energy laws in

the U.S. in 1983. It allocated 105 MW of renewable gener-
ating capacity between the two Iowan investor-owned utilities:
Mid-AmericanEnergyCompany (MEC)andAlliantEnergy In-
terstate Power and Light (IPL) [10]. As Table I shows, the re-
quirementhasbeenclearly surpassed sobothutilitieshavebeen
authorized to export RECs by participating in Midwest Renew-
ableEnergyTrackingSystem, Inc. (M-RETS).Renewablegen-
eratorsusedformeetingtheRPSarenotallowedtoexportRECs
in order to avoid double-counting [11]. A voluntary goal of
1000MWofwindenergycapacityby2010,establishedin200

1,

has also been easily exceeded. Section 476.53 in Code of Iowa
(2009) provides that it is the intent of the general assembly to
attract the development of electric power generating facilities
within the state. Thus, when eligible new electric generation is
constructed by a rate-regulated public utility, the Iowa Utilities
Board, upon request, must specify in advance the ratemaking
principles that will apply when the costs of the new installa-
tionare included in electricity rates. InMarch2009, pursuant to
section 476.53, MEC filed an application for determination of
advance ratemakingprinciples for up to1001MWofnewwind
generation to be built in Iowa from 2009 through 2012. In De-
cember 2009, the Board took up MEC’s proposal. As a result,
MEC has installed significant wind generation capacity and is
expected to have a total 2284 MW by the end of 2012.
Minnesota introduced twoseparateRPSpolicies in2007,one

for theutilityXcelEnergyand the second for other electric util-
ities. The latter includes public utilities providing electric ser-
vice, generation, and transmission cooperative electric associa-
tions, municipal power agencies, and power districts operating
in the state [10]. The RPS for Xcel Energy requires 30% of its
total retail electricity sales in Minnesota to come from renew-
able sources by 2020. It included a minimum of 25% for wind
energybut aStateSenateBill passed in2009addedamaximum
of 1% from solar to this requirement. Thus, at least 24% must
come from windenergy, up to1% maycome from solar energy,
and the other 5% may come from other eligible technologies.
TheRPSforotherutilities requires25%of their total retail elec-
tricity sales in Minnesota to come from renewable sources by
2020 without any technology minimums. Minnesota has been
included in the M-RETS since 2008. This tracking system pro-
gram ascribes the same amount of credits to all eligible tech-
nologies independently of the state where electricity is gener-
ated.XcelEnergyisnotallowedtosellRECstootherMinnesota
utilities for RPS-compliance purposes until 2021.

APARICIO et al.: COMPARISON OF WIND ENERGY SUPPORT POLICY AND ELECTRICITY MARKET DESIGN 81

3

California launched an RPS in 2002 with a target for its
electric utilities to have 20% of their retail sales derived from
eligible renewable energy resources in 2010 [10]. Senate Bill
X1-2, enacted in 2011, raised the requirement to 33% by
2020. The Bill also established three categories, also known
as buckets, of RPS-eligible electricity applicable to contracts
executed from June 2010. The decision adopted in December
2011 by the California Public Utilities Commission provides
detailed requirements for the three categories. Category one is
for electricity that is from an RPS-eligible generation installa-
tion that has its first point of interconnection with a California
Balancing Authority (CBA); scheduled from an RPS-eligible
generation installation into a CBA without substituting elec-
tricity; or dynamically transferred to a CBA. Category two is
for electricity that is firmed and shaped, providing incremental
electricity scheduled into a CBA. Category three is for those
transactions that do not meet the criteria of any of the two
previous categories, including unbundled RECs. There are
limitations on the amount of generation procured in categories
two and three that become increasingly narrow over time.
Categoryonegeneration is required tobeaminimumof50%of
the total for the compliance period ending in 2013, 65% for the
compliance period ending in 2016, and 75% thereafter. Until
the portfolio content categories become sufficiently clear, the
utilities are preferencing power purchase agreements (PPAs)
with installations that definitely belong to category one. Given
California’s increasing target, it is envisaged that PPA opportu-
nities in categories two and three will expand in time, and with
greater clarity on the arrangements.
The Western Renewable Energy Generation Information

System (WREGIS) tracks the renewable energy generated in
the region covered by the Western Electricity Coordinating
Council (WECC), California included. WREGIS issues cer-
tificates for every REC generated, which can be used to verify
compliance with state RPS. However, currently WREGIS is
not able to track the three new portfolio content categories.

D. Tax Credits

The U.S. Federal production tax credit (PTC) for wind has
had a checkered history over the past decade. The latest exten-
sionof2009providesan incometaxcreditof2.2¢/kWhuntil the
endof2012while addinganumberofprovisions.Taxpayers el-
igible for the PTC are allowed to take a business energy invest-
ment tax credit (ITC) equal to30% of the construction costs for
the installation or to receive a cash grant of equivalent value if
construction began by the end of 2011. Before 2009, installa-
tion owners that did not generate enough taxable income were
unable to utilize PTC so they had to monetize the PTC through
tax equity investors.
The PTC, which applies for the first ten years of electricity

production [12], has been remarkably successful in supporting
wind deployment when it has been in place. However, during
the periodic lapses of the PTC prior to congressional renewal,
the state-based RPS mechanisms alone were not able to sustain
the growth of wind power [13]. As it represents a credit against
passive income, the PTC has a significant resemblance to an
FIT premium. In fact, both are a fixed cash incentive provided
to each kWh generated by wind.

Finland is the only EU country which uses tax incentives as
the main support scheme for renewable energies. This policy,
however, has not been effective for wind development [6].

III. OVERVIEW OF ELECTRICITY MARKETS
AROUND THE WORLD

Jurisdictionsaround theworldhave takenawide rangeofap-
proaches to electricity industry restructuring over the past three
decades. Care must, therefore, be taken when comparing in-
dustry approaches and performance. Generally, countries with
restructured electricity industries have both forward markets
and real-time markets. In the forward markets, electricity is
traded either centrally on a power exchange or bilaterally di-
rectly between market participants. A key role for the forward
markets is to support unit commitment of those thermal genera-
tors that require generation scheduling aday prior to energyde-
livery. In theday-aheadmarkets,electricity is traded in intervals
(settlementperiods) thatmaybeonehour longor lessdepending
on the market design. A key challenge for these arrangements
is that unexpected generator outages or changes in demand can
take place between the closing of the day-ahead market and de-
livery thenextday. Intradaymarketspermitmarketparticipants
to trade closer to thedelivery time, upuntil just prior to thegate
closure. Intraday markets are commonly a continuous trading
market that operates with a gate closure set one hour ahead of
the settlement period [14]. After gate closure, it is no longer
possible to change bids and offers for the settlement period.
Fromgateclosureuntil real-time, thedifferencebetweensupply
and demand is continuously balanced through real-time mar-
kets,where transmissionsystemoperators (TSOs)—orindepen-
dent system operators (ISOs) or regional transmission organi-
zations (RTOs)—purchase the energy needed to match supply
to demand and to solve any network constraints. These orga-
nizations also have a responsibility for imbalance settlement.
Chargesapply togenerators if theirenergydeliveriesdiffer from
theoffers submitted to themarket.Thepayment dependson the
price system. This may be a two-price system, which has one
price for imbalanceswith thesamesignas thesystemnet imbal-
ance (prejudicial for the system since they contribute to the net
imbalance)andadifferentprice (lower) for imbalanceswithop-
posite sign (beneficial since they counteract the net imbalance);
or a one-price system, which has only one price for all imbal-
ances. Normally, the two-price system is used, since it encour-
ages market participants to limit their imbalances according to
whether they add to, or subtract from, net system imbalances.
Toensurepower systemsecurityand reliability, ancillaryser-

vices are needed, including frequency and voltage control and
black-start capability. TSOs or their equivalent organizations
purchase ancillary services from service providers. Frequency
control ancillary services are commonly tradedonamarket that
has marked similarities to the electricity market.
Gate closure, the duration of settlement periods, and imbal-

ance settlement arrangements are all potentially very important
for wind energy integration. The brief descriptions of elec-
tricity market arrangements for each of the jurisdictions that
we are considering here, therefore, pay special attention to
these features.

814 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 4, OCTOBER 2012

A. Denmar

k

The four countries comprising the Nordic region (i.e., Den-
mark,Sweden,Norway,andFinland)areamongthefirst tohave
restructured their electricity industries. In 1993, they connected
their individual markets creating Nord Pool Spot, which was
the world’s firstmultinational power exchange. Nord PoolSpot
trades 74% of the electricity generated in the Nordic region.
The rest is traded through bilateral contracts. It also operates a
day-ahead market called Elspot, and an intraday market called
Elbas. Elspot trades in one-hour intervals and closes at 12:00.1

It introduced different area prices in order to deal with network
congestion between countries and within Norway [15]. Elbas
is a continuous intraday market that covers the Nordic Region,
Germany, and Estonia with a gate closure one hour before de-
livery. This intraday market is becoming increasingly signifi-
cant as more wind energy enters the grid given that imbalances
between its day-ahead contracts and produced volumes often
needtobeoffset [15].Asdiscussedbelow, thesemarketarrange-
ments are now being changed in ways that affect wind energy
directly.
Since April 2011, the gate closure for trading in Germany

has been reduced to 30 min. Negative prices have also been
introduced inallmarket areas sincemid-February2011 inorder
to price oversupply [16].
Each local TSO has a different set of ancillary services costs

according to their procurement arrangements and reserve re-
quirements. Energinet.dk is the Danish TSO and purchases dif-
ferent ancillary services inWesternandEasternDenmarkas the
former is synchronouslyconnected to theUCTEsystemand the
latter to the Nordel system. Denmark settles imbalances using
a two-price system. Norway used one-price system until 2008.
Since then, all Nordic countries have used a two-price system
forproduction imbalances andaone-price systemforconsump-
tion imbalances [17].

B. Spain

Spainbelongs,withPortugal, toMercado Ibéricode laElect-
ricidad (MIBEL)—the IberianElectricityMarket.Eachcountry
may have different prices (splitting) in the case of transmis-
sion restrictions. All market participants can either arrange bi-
lateral physical contracts or participate in a day-ahead market.
As with the Danish market, this day-ahead market is divided
into one-hour settlement periods and closes at 12:00. After the
day-aheadscheduling,generatorpositionscanbeadjusted in the
intradaymarket.Rather thanbeingacontinuousmarket, it is di-
vided into six sessions.Eachsessionhasadifferentgate closure
(around2hours)aswell asadifferent timeofdeliveryscope(up
to 9 hours in the sixth session).
The provision of ancillary services follows the general rules

common in theUCTEsystem.Theprimarycontrol is a compul-
sory service shared across all generators and without remuner-
ation. The secondary control is performed within control areas
according to the requirements, in MW, set by the Spanish TSO
(Red Eléctrica de España). The generators willing to offer this

1This time of day and the following ones are in 24-h notation.

service bid their power available and a market clearing process
calculates the price per MW. The price is paid even if their ser-
vicesarenot required(availability).The tertiarycontrol restores
the secondary control reserves under emergencyconditions and
while it is optional for generators to formally participate, the
TSO can call upon any generator should it be required. By con-
trast with secondary control reserves, tertiary control services
areonlypaid for ifused. Imbalancesare settledwitha two-price
system.

C. Germany

Most wind generation is not scheduled and, instead, is intro-
duced into the electricity market through one of the country’s
fourTSOs.Thedistributionnetworkoperators transfer thewind
generationforafixedprice to their respectiveTSO,which trans-
forms the load fluctuating profiles into standard load profiles
which are sold to all utilities [18]. Customers pay an average
tariff to utilities. According to [18], this profile transformation
mechanism isnot fully transparent andnot cost-optimized.Fur-
thermore, as the costs of the profile transformation are com-
pletelypassedthroughtothenetworkcustomers, there isnoeco-
nomic incentive to minimize these costs. This mechanism has
thereforebeenargued to represent an importantweaknessof the
GermanFITscheme.However, theamendmentapplicable from
January2012 [19]mayhelp inaddressing thesedrawbacks.Re-
newablegenerators candecideonamonthlybasis to change the
remunerationmechanismsandparticipate indirect selling in the
electricity market. They have to forecast their production and
are directly charged for their imbalances. The additional costs
are covered by an extra premium known as management pre-
mium.
TSOssharewindenergy imbalances,which reduces theneed

for reserves. In order to manage this equitably, it is allocated
proportionally to theTSOs’according to their consumption,not
their installed wind power.

D. United Kingdom

The British Electricity Trading and Transmission Arrange-
ments (BETTA) cover England, Wales, and Scotland. Northern
Ireland has been part of the Single Electricity Market together
with the Republic of Ireland (see Section III-E) since 2007. In
BETTA, over 90% of electricity is traded through unrestricted
bilateral contracts. A power exchange permits market partici-
pants tofine tune their contractedpositions.Gate closure is cur-
rently set one hour ahead of each half hourly settlement period.
ELEXON, the Balancing and Settlement Code Company, uses
a two-price system [20].

E. Ireland

The Single Electricity Market (SEM) commenced trading in
IrelandandNorthern Irelandonanall-islandbasis inNovember
2007. SEM is a mandatory power exchange where all Ireland’s
electricity must be traded. It has only a day-ahead market with
a gate closure at 10:00. Energy is settled weekly. SEM plans to
develop an intraday market [21].

APARICIO et al.: COMPARISON OF WIND ENERGY SUPPORT POLICY AND ELECTRICITY MARKET DESIGN 81

5

F. Australia

The Australian National Electricity Market (NEM) includes
all states and territories other than Western Australia and the
NorthernTerritory. Itscenterpiece isasetof regionalgross-pool
spot energy and ancillary services markets that solve a secu-
rity-constrained dispatch every 5 min. The Australian Energy
MarketOperator (AEMO) is thewholesalemarket operator and
TSO for the entire system. Regions are currently located at all
borders between states within the NEM. All generating plants
of greater than30-MWcapacity (except intermittent generation
including wind) are required to participate as scheduled gen-
erators and submit offers to sell or bids to buy energy (and/or
ancillary services) in the NEM dispatch process. The predis-
patch processes forecasts up to 40 hours ahead of real time and
provides public forecasts of energy and ancillary service prices
and (privately to each dispatchable participant) dispatch levels
based on participant bids and offers, the demand forecasts and
the estimated effects of dispatch constraints. Demand is per-
mitted to participate directly in the wholesale market; however,
nearly all end-users interface with the market through an elec-
tricity retailer [2].
There are eight Frequency Control Ancillary Services

(FCAS) markets to provide load following (raise and lower)
and three contingency responses of different speed (raise and
lower) between the 5-min energy dispatches. Market dispatch
co-optimizes energy and FCAS bids and offers to establish re-
gional prices for both energy and FCAS for each 5-min period.
Commercial trading is based on these prices averaged over
30 min. Locational pricing within regions is achieved using
averaged loss factors. Importantly all generators are permitted
to change their offers (rebid) just prior to each 5-min dispatch.
Furthermore, the only commercially significant prices in the
NEM are these averaged30-minprices—the predispatchprices
are advisory only. Note also that the NEM is an energy-only
market and participants are required to manage their own unit
commitment and other intertemporal scheduling challenges
(within a range of technical dispatch constraints).

G. Texas

ERCOT manages the electricity industry arrangements sup-
plying 85% of Texas demand and covering 75% of state land
area. The ERCOT control area is not synchronously connected
to either the Eastern or Western Interconnection. However, it
can exchange about 860 MW through dc links. In December
2010, ERCOT switched from a zonal market to a nodal market
in order to improve price signals and dispatch efficiencies and
assign localcongestiondirectly[22].Theday-aheadmarketem-
ploys a co-optimization engine that uses both energy and ancil-
lary services offers to calculate the energy schedules and ca-
pacity awards. ERCOT closes this market at 14:30.
The real-timemarket is called security constrainedeconomic

dispatch(SCED).ERCOTgenerally runs theSCEDevery5min
using offers by individual resources and actual shift factors by
each resourceoneach transmissionelement.The settlementpe-
riods are 15 min long.

H. Minnesota and Iowa

Minnesota and Iowa belong to the Midwest ISO (MISO),
which operates a day-ahead market and a real-time and op-
erating reserves market. They coexist with both financial and
physical bilateral transactions between industry participants.
The day-ahead market simultaneously clears energy and op-
erating reserves on a co-optimized basis for every one-hour
settlement period. Security constrained unit commitment
(SCUC) and SCED algorithms ensure the scheduling of ade-
quate resources [23].
The real-time and operating reserves market uses an SCED

algorithm to simultaneously balance supply and demand and
to meet operating reserves requirements amongst other actions.
The gate closure is set to only 30 min ahead of

delivery.

InMarch2011,an importantchangecame intoeffectwith the

creation of a new category of resources called Dispatchable In-
termittentResources (DIRs).Thiscategoryonlyapplies towind
farmsandallows themtovoluntarilyparticipate in the real-time
market fromJune2011,where theyareeligible tosupplyenergy
but not operating reserves. From September 2011, DIR imbal-
ances are settled similarly to conventional generators although
only when an 8% tolerance band is exceeded, with a minimum
of 6 MWh and a maximum of 30 MWh, for four or more con-
secutive 5-min intervals within an hour. Wind generators are
exempt of imbalance charges in cases of force majeure, such as
extreme winds.

I. California

The California ISO (CAISO) has three day-ahead processes:
a market power mitigation determination, integrated forward
market, and residual unit commitment. A bid from a market
participant that fails the market power test is automatically re-
duced to the reference level price of that participant, and the
system determines the minimal and most efficient schedule of
generation to address local reliability. The integrated forward
market simultaneously analyzes the energy and ancillary ser-
vices market to determine the transmission capacity required
(congestion management) and confirm the reserves that will be
needed to balance supply and demand based on supply and de-
mand bids. It ensures generation meets load and that all final
schedules are feasible with respect to transmission constraints
as well as ancillary services requirements. When forecast load
is not met in the integrated forward market, the residual unit
commitment process enables CAISO to procure additional ca-
pacity by identifying the least cost resources available [24].
The real-time market produces energy to balance instanta-

neous demand, reduce supply if demand falls, offer ancillary
services as needed and, in extreme conditions, curtail demand.
The gate closure is set 75 min ahead of delivery. The market
has two unit commitment mechanisms. Real-time unit commit-
ment assigns fast- and short-start units in 15-min intervals and
looks forward 15 min, while short-term unit commitment as-
signs short- and medium-start units every hour and looks for-
ward three hours beyond the settlement period every15min. In
real-time, the economic dispatch process dispatches imbalance
volumes and energy from ancillary services. It runs automat-
ically and dispatches every 5 min for a single 5-min interval.

816 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 4, OCTOBER 2012

Under certain contingency situations, CAISO may dispatch for
a single 10-min interval.

IV. WIND ENERGY PARTICIPATION IN ELECTRICITY MARKETS

A compilation of some key themes for wind energy integra-
tion coming out of the individual jurisdictional experiences is
presented as follows.

A. Wind Energy Forecasts and Generation Scheduling

Windgeneratorsparticipatinginday-aheadmarketsmustpre-
dict theiroutputbeforemarketclosetime(varyingbetween9.5h
in ERCOT to 40 h ahead of delivery in some other markets).
Theday-aheadand longerwind forecasts required for suchgen-
eration scheduling are not sufficiently precise and two possible
alternatives have been put forward to help manage this. In the
first case, adopted in Germany (not for renewable generators
that participate in direct selling in the electricity market), Aus-
tralia, MISO, ERCOT, and CAISO, the output of all wind gen-
erators is predictedover a rangeof timehorizons throughacen-
tralized forecasting system. In the second approach, adopted in
Denmark, Spain, and the UK, numerous prediction companies
compete to provide forecasts for wind farm clients [25]. Note
that such wind energy forecasts have value to all market partic-
ipants, not just the wind farms—another argument for centrally
provided forecasting services.
Even in industries where wind is not required to partici-

pate in forward markets there is considerable value in wind
farms and other market participants having useful day-ahead
forecasts. These can play a role in derivative market trading
around the future spot price, maintenance scheduling of wind
farms, unit commitment strategies of thermal plant, and pro-
duction scheduling of hydro. There is also considerable value
in useful short-term forecasts that assist in generator bidding
in the real-time spot markets. For example, recent changes
in the Australian market arrangements have more formally
incorporated wind farms into market scheduling and ancillary
services arrangements through a semischeduled classification.
There are similarities with the new DIR category within the
MISO arrangements that was described in the previous section.

B. Imbalance Settlement

Imbalance settlement is probably the aspect of electricity
markets design that has highest impact on wind energy [17],
[26]. Ingeneral, systemandmarketoperatorscalculate such im-
balances and settle them according to either a one- or two-price
system. This determines how the total balancing costs are
distributed and how incentives are given to market participants
[27]. With the one-price system used for balance settlement
in Norway before 2008, the balance costs of wind energy
were negligible as long as its random variability assured that
positive imbalances from some wind generation were compen-
sated by negative ones [28]. Since 2008, all Nordic countries
have used a two-price system for production imbalances and
a one-price system for consumption imbalances [17]. Spain
originally applied a different one-price system that charged for

all imbalance volumes independently of their direction. Now it
uses this two-price system: zero costs for producers that do not
contribute to the system net imbalance and a penalty for those
that do. Denmark, the UK, and Australia (only with Regulating
FCAS)also runa“causerpaysprocedure” inorder to assign the
cost of the regulating power to those market participants who
are responsible for the imbalance. Note, however, that there
are inevitably difficulties in assigning such responsibilities
given the complex nature of electricity industry operation. This
kind of arrangement may impose significant charges on wind
energy so some tolerance in energy imbalance is often applied
and the prices paid by wind are often lower than those paid by
conventional

generation.

In Germany, full responsibility for balancing wind genera-

tion is assumedby theTSOs. Indeed, they are in chargeof fore-
casting, scheduling, and balancing. In the U.S., Federal order
890assists intermittent generationwithmoreflexiblebalancing
settlement [29]. For example, DIRs in MISO are only charged
if an 8% tolerance band is exceeded for four or more consec-
utive 5-min intervals within an hour, whereas CAISO has the
Participating IntermittentResourceProgram(PIRP).Windgen-
erators that participate in the PIRP have better arrangements.
Energy imbalances are netted on a monthly basis and settled at
a monthly weighted market-clearing price.
InAustralia, thehybrid5/30mingross spotmarketwithasso-

ciated frequency control ancillary services (FCAS) seems rea-
sonably supportive of wind integration [2].
Reduced gate closures permit rebidding up to few minutes

from delivery whereas in many intraday markets it is possible
to reschedule with updated forecasts of only one hour (or less
as in MISO). However, this can reduce liquidity; especially in
the case of market power (CAISO has market power mitiga-
tion). If themarket is openuntil very close togate closure,wind
generators are able to better forecast and manage the energy
that they actually produce. However, the closer to real time,
the fewer the conventional generators that may be available to
help wind generators to correct their position. Continuous mar-
kets permit changes to bids closer to real time (for example, in
the UK until one hour before). In Spain, by contrast, the six in-
traday sessions have delivery scopes up to 9 hours. There are
higher forecast errors; however, there is also a greater willing-
nessand interestamongst thegenerators tochange theirposition
(including all wind generators), so the market is likely to have
greater liquidity [18]. Experience to date suggests that intraday
pricesdiffer little fromtheday-aheadprices.So thismore liquid
market permits generators to improve their bids and offers at
littlecostwhilealsoreducingregulationrequirements.Note that
gross pool arrangements such as those of the Australian NEM
resolveshort-termsupply–demandbalancewith thecompulsory
involvement of all generation and load.
Wind imbalances are significantly reduced by aggregating

the bids of wind farms over geographically dispersed locations
and large areas [26]. Active demand participation is also useful
in reducing imbalances. FERC Order 719 considers electricity
market accepting bids from demand response resources, on a
basis comparable to any others, for ancillary services that are
acquired in a competitive bidding process.

APARICIO et al.: COMPARISON OF WIND ENERGY SUPPORT POLICY AND ELECTRICITY MARKET DESIGN 817

C. Curtailment

An excess of wind generation may cause system operating
problems such as transmission line overloading or insufficient
regulation reserves that force TSOs or their equivalent organi-
zations toorder real-timecurtailment towindgenerators during
normal operation. Wind generators that reduce power may be
rewarded for this, depending on the electricity market arrange-
ments. When rewards are given, wind generators typically earn
a percentage of what they could have generated. In Ireland it
is 100% [30] while in Spain it is just 15%. Another possibility
to reduce over-production is to permit negative wholesale
market prices [27]. Prices in electricity markets typically have
a zero-floor limit. Elbas has accepted negative prices since
2009 while Australia and some U.S. electricity markets have
permittednegativepriceswellbefore this.CAISOhasproposed
to lower the bid floor from $30/MWh to $150/MWh, then
to $300/MWh. Spain may consider accepting it only for
downward regulation provision.

V. CONCLUSION

Wind generation penetrations have reached significant levels
in some countries and states around the world. In almost all
cases this has required changes in both policy support mech-
anisms and electricity market design in order to better manage
wind energy. Tender schemes have been successful only with
offshore wind. FIT tariff premiums are now the predominant
scheme as a market oriented transition from conventional FITs.
Denmark made the transition mandatory whereas Spain, and
Germany since 2012, introduced incentives. For the case of
Spain, theypersuadedaround90%ofwindgenerators to switch
from FITs. The “guaranteed headroom” mechanism introduced
in the UK is a way to transform the Renewables Obligation
mechanism into FIT premiums given an oversupply of ROCs.
The PTC can also be considered as a form of FIT premiums.
Quota-based approacheshavehadmore limited applicationand
mixed success to date.
With regard to electricity market arrangements, imbalance

settlement is probably the element of electricity market design
thathas thehighest impactonwindgeneration. Itdependsonthe
price system, the specificarrangements for intermittent sources,
possibilities to aggregate bids of wind farms over geographi-
cally dispersed sites and large areas, active demand participa-
tion, and gate closure. Small gate closures permit scheduling
with reduced forecast errors. However, this reduces liquidity.
In conclusion, there are complex and changing interactions

between a) the desired policy objectives of increasing wind
energy generation or renewable energy more generally, b) the
chosen policy approaches applied to facilitate greater deploy-
ment, and c) the commercial and regulatory arrangements that
govern how such wind energy is integrated into existing elec-
tricity industries. It is evident that the challenges of appropriate
policy and electricity market arrangements grow as wind pen-
etrations increase. Some clear trends have emerged with those
countries now experiencing high penetrations. These include
the need to have wind farms more formally participating in
the electricity market mechanisms that manage supply-demand
balanceover the immediate to longer-term.While thismight be

seen as an impediment to greater wind deployment, it is better
understood as the inevitable process of wind transitioning from
a small industry contributor that can be ignored as “negative
load,” to a seriousplayer that cangreatly help in addressingour
growing energy security and climate change challenges within
the electricity sector.

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[13] C.-J. Yang, E. Williams, and J. Monast, Wind Power: Barriers and
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Gómez, L. Söder, G. Strbac, A. Shakoor, J. Ricardo, J. C. Smith, M.
Milligan, and E. Ela, Design and Operation of Power Systems With
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Phase One 2006–2008, Aug. 2008.

[18] C.Klessmann,C.Nabe, andK.Burges, “Pros andconsof exposing re-
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[21] SEMO, Single Electricity Market Operator [Online]. Available: http://
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[24] CAISO,Market Processes [Online].Available: http://www.caiso.com/
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[25] J. Rivier Abbad, “Electricity market participation of wind farms: The
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[26] F. V. Hulle, N. Fichaux, A.-F. Sinner, P. E. Morthorst, J. Munksgaard,
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[27] C. Hiroux and M. Saguan, “Large-scale wind power in European elec-
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[28] A. Helander, H. Holttinen, and J. Paatero, “Impact of wind power on
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[29] NERC, Accommodating High Levels of Variable Generation Apr.
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[30] T. Ackermann, G. Ancell, L. D. Borup, P. B. Eriksen, B. Ernst, F.
Groome, M. Lange, C. Mohrlen, A. G. Orths, J. O’Sullivan, and M.
de la Torre, “Where the wind blows,” IEEE Power Energy Mag., vol.
7, no. 6, pp. 65–75, Nov./Dec. 2009.

Néstor Aparicio (S’06–M’12) received the M.Sc.
degree fromtheUniversity JaumeIofCastelló (UJI),
Castelló de la Plana, Spain, in 2002, and the Ph.D.
degree from Universidad Politécnica de Valencia,
Spain, in 2011.
He is an Assistant Professor of the Electrical

Engineering Area at Universitat Jaume I, with re-
search interests in thegrid integrationofwind-power
generators. For 6 months, he visited the Institute
of Energy Technology of Aalborg, Denmark and
the Centre for Energy and Environmental Markets

(CEEM), Sydney, Australia in 2006 and 2008, respectively.

Iain MacGill (M’10) is an Associate Professor in
the School of Electrical Engineering and Telecom-
munications at the University of New South Wales,
Sydney, Australia, and Joint Director (Engineering)
for the University’s Centre for Energy and Environ-
mental Markets (CEEM). His teaching and research
interests at UNSW include electricity industry
restructuring and the Australian National Electricity
Market, sustainable energy technologies, renewable
energy integration into power systems, and energy
policy.

Juan Rivier Abbad (M’99) received the Electronic
Engineering degree from the Universidad Pontificia
Comillas,Madrid, in1992,andthePh.D.degreefrom
the same University in 1999.
He joined the Instituto de Investigación Tec-

nológica (IIT) in 1992 as a research fellow and the
Electrical Department of the Engineering School
(ICAI) in 1999 as an assistant professor, both at the
Universidad Pontificia Comillas. He was a Visiting
Research Fellow at the Centre for Energy and
Environmental Markets (CEEM) of the University

of New South Wales, Australia, during the academic year 2005/06. He is
currently the Energy Management Responsible at Iberdrola Renovables. He
has experience in industry joint research projects in the field of electric energy
systems in collaboration with international and Spanish utilities, and with
energy regulatory commissions. His areas of interest are regulation, operation,
and integrationof renewableenergysourcesgenerators, andelectricitymarkets.

Hector Beltran received the M.Sc. degree in indus-
trialengineering, in2004, fromtheUniversitatJaume
I (UJI),Castellóde laPlana,Spain, and thePh.D.de-
gree inelectricalengineering, in2011, fromtheTech-
nicalUniversityofCatalonia (UPC),Terrassa,Spain.
During 2003, he worked at the European Centre

forNuclearResearch (CERN),Geneva,Switzerland.
From 2004 to 2006, he worked as a Researcher at
theElectronicandEnergyDepartmentsof theEnergy
Technologycal Institute (ITE),València,Spain.Since
2006, he is an Assistant Professor in the Electrical

Engineering Area at UJI. Meanwhile, he visited the Institute of Energy Tech-
nology, Aalborg University, Denmark (for 6 months), and the Renewable Ener-
gies Electric Systems Research Group at the Technical University of Catalonia
(UPC),Spain(for9months).Hiscurrent researchinterests includemassivepho-
tovoltaic integration into the grid, energy-storage systems, and microgrids.

1

Effects of increasing wind power penetration
on the physical operation

of large electricity market systems
Bernd Klöckl, Member, IEEE, and Pierre Pinson

Abstract—This contribution describes indirect coupling effects
between wind power infeed and physical operation of power
market systems by means of qualitative hypotheses, backed by
suited exploratory data analyses. As an example, the case of a
central European TSO located in the vicinity of a control block
with high wind penetration is demonstrated. It shows, based
on established methods of computational statistics, considerable
nonlinear effects on cross border power flows and transmission
system flows of that control block, conditional on increasing
wind power penetration in an interconnected market system. The
observed effects are theoretically explained through the influence
of the wind infeed on the behaviour of market participants
and attributed to indirect coupling between wind power and
conventional generation in adjacent control blocks.

Index Terms—Wind power, energy market, cross border flows,
principal component analysis.

I. INTRODUCTION

T HE rapidly increasing share of wind generation in theEuropean energy markets, together with further progress
in their liberalization has led to a number of direct and indirect

effects of wind power injection on flow patterns in the trans-

mission systems and has increased the operational challenges

for TSOs. The developments throughout the last years have

shown that increasing the share of wind generation beyond

certain levels has created the need for further investigations

on the following issues:

1) connection issues and grid codes (technical)

2) high concentrations of volatile infeeds in certain areas,

e.g. close to windy coast lines, leading to the need for

infrastructure reinforcements, especially in the transmis-

sion system (technical)

3) implications of day-ahead wind forecasts on the whole-

sale electricity prices in a given market area (economic)

4) coupling of these implications to adjacent markets and

TSOs (economic)

5) increasing wide-scale influence of wind generation on

the behaviour of conventional generation (economic)

6) thus increasing operational challenges for TSOs beyond

the effect of the wind infeed itself (techno-economic).

The first two points above are currently being extensively

treated by planning and operation staff of DSOs and TSOs,

and the last point is experienced more and more by the grid

B. Klöckl is with the Market Management Department of Verbund Austrian
Power Grid, Vienna, Austria, EU (e-mail: bernd.kloeckl@verbund.at)

P. Pinson is with the Technical University of Denmark, DTU Informatics,
Kgs. Lyngby, Denmark, EU (e-mail: pp@imm.dtu.dk)

operation staff. In this paper, the authors elaborate on the

points 3 to 5, which is a simplified functional chain creating a

feedback loop between technical and economic issues of wind

generation.

The remainder of the paper is structured in the following

way: First, a general overview of the EU transmission system

with respect to the market operation and a glimpse on the

most obvious effects of wind infeed is given. Then, a suited

statistical analysis method for the problem set is derived. This

analysis is exemplified on the measurements taken from the

system of a central European TSO being indirectly affected

by large wind power penetrations in adjacent markets. The

conclusions attempt to relate the observed effects to future

investigation needs.

II. EFFECTS OF WIND POWER ON MARKET PRICES

A. The EU market and transmission system

Fig. 1. Overview of the ETSO member states and the related EU market
zones. The geographical distance between northern Germany and the APG
block as indicated is approximately 750 km.

The European transmission system operators are organized

in ETSO [1], which is an association that covers five different

synchronous zones. The largest zone of the EU transmission

network is the UCTE grid, that links all national transmission

systems of continental Europe except for the Baltic states

(Fig. 1). It is one of the largest synchronous systems in the

world with an installed generation capacity of approximately

650 GW [2]. For the time being, the different national trans-

mission networks represent market zones that are technically

© CIGRE2009 978-2-85873-080-3

2

connected by sets of tie lines and commercially linked by

allocation mechanisms for the cross-border transmission ca-

pacities.

B. Observed interaction of wind generation and spot prices

Fig. 2. Potential shift of the market settlement price on the aggregated merit
order curve of a price zone in dependence on wind generation. Quantiles of
the merit order curve (e.g. a 90% quantile) are indicated as an illustration of
the uncertainty of the market settlement prices as a consequence of market
imperfections and other factors.

Recently, the effect of wind power on market prices has

been discussed for the cases of market areas with high wind

penetration. Generally, there seems to be an agreement that

wind generation lowers the spot market prices, while the

mechanisms behind are not entirely clear. For a first general

treatment of the effect, see e.g. [3]. In [4], the reduction of

price in dependence on wind generation is described. This

does not come as a big surprise since wind generation in the

German system is prioritized in the dispatch and can thus

be regarded as a negative load. In [5], this is interpreted

as an effect on the activation of the merit order curves of

the GENCOs, meaning that in the presence of wind there

is increased probability that expensive power plants will not

settle the spot prices. This effect is then opposed to the costs

of the renewable energy support scheme1. See Fig. 2 for an

graphical illustration of the mechanism. Jóhnsson [6] provides

detailed modeling recommendations for the price reduction

effect applied to the Danish case and states that the decisive

variable for the reduction effect is clearly the wind power

prediction, rather than the actual production at the time of

delivery.

In addition to the findings cited above, it can be shown that

there is a second effect of high wind generation on the market

prices which has not yet been discussed in the literature. One

would assume that, especially for market zones with priority

wind dispatch, the variable to look at is simply a fictitious

expected system load,

Lf ic = L − P̂w, (1)
1However, the reduction of energy price for the end consumers must not

be confused with the calculation of the total socio-economic benefit, which
has to take into account also the financial impact on the GENCOs, the wind
turbine manufacturers etc.

where L is the system load (the consumption within the market
area), and P̂w is the predicted wind generation. Fig. 3 shows
the German EEX spot prices for 2006-07 in a logarithmic scale

plotted against Lf ic. It can be observed that for equal Lf ic,
the mean prices at high wind generation are still slightly lower

and that price spikes virtually do not occur in periods of high

forecasted wind penetration

r̂w = P̂w/L. (2)

Fig. 3. EEX market prices for 2006 and 2007 in dependence on different
levels of Lf ic (Sources: [2], [7], [8], [9], [10]), plotted for events with a
wind penetration below 7.8% (red) and above 7.8% (green), where 7.8% is
the mean of the wind penetration in the period. The logarithmic scale for the
spot price is depicted on the l.h.s., while the plot on the r.h.s. shows the mean
values in a linear scale. It can be observed that for the region in which the
resulting loads to be covered by the market are equal, the price is still always
lower for high r̂w . This might be caused by price expectations of the traders
or by the fact that not the entire volume of energy consumed in Germany is
traded at the power exchange.

There is little doubt that this general price damping effect of

predicted wind infeed has an influence not only on the affected

market zone itself, but also on adjacent interconnected areas

with correlated market prices. In the following, we would like

to explain how this can be detected from observed data.

III. METHODOLOGY: CONDITIONAL MULTIVARIATE DATA

ANALYSIS

Analysing the operation of a complex system may translate

to simultaneously studying the behaviour of a large number

of possibly redundant variables in a multivariate data analysis

framework. At a given time t the measured values for this
set of m variables are gathered in a single vector Xt. For
the example of the present study, these variables may be

cross-border flows or flows on transmission systems. Owing

to the instantaneous nature of electricity transmission, flow

data recorded at different points in the horizontal network

may often cloud the true underlying mechanisms by containing

redundant information and noise components that arise from

mechanisms other than the one that is subject to investigation.

A possibility to alleviate this problem is to employ Principal

Component Analysis (PCA) for dimension reduction. This will

3

be described in a first part below. One can then work in a

reduced basis defined by principal components, and develop

conditional parametric models for capturing the nonlinear

effects of the influential variables of interest, e.g. forecasted

wind power penetration, on the flows.

A. Dimension reduction with Principal Component Analysis
(PCA)

PCA is a classical method in multivariate data analysis,

which allows one to reduce the dimension of the problem at

hand, and to potentially work in a reduced orthonormal basis

defined by the set of (uncorrelated) principal components. For

a nice introduction to multivariate data analysis and PCA, the

reader is referred to [11], while more extensive mathematical

developments may be found in e.g. [12]. Consider a number

N of measured flow values Xt (being of dimension m), and
define X̃t the centered and standardized version of Xt. This
simply means that for each dimension of X̃t one has

X̃t,j = τj (Xt,j )

=

Xt,j − X̄

j

σj
, j = 1, . . . , m (3)

where X̄j and σj are the mean and standard deviation of
the jth flow variable. We will denote by τ the simultaneous
application of the τj transformations to all components of Xt.

Finding the principal components for the dataset considered

may be performed by diagonalizing the covariance matrix of

the data, given as

R =
1
N

N

∑

i=1

X̃tX̃
�
t (4)

By arranging the eigenvalues in decreasing order and identi-

fying the corresponding eigenvectors, one obtains the set of

principal components. Using the average eigenvalue method

[12, pp. 348], the set of retained principal components are

those for which the related eigenvalue is larger than the mean

eigenvalue of R. By writing Yi (i = 1, . . . , n, n < m) these principal components, one then obtains an orthonormal basis in

which X̃t can be written as linear combination of the principal
components,

X̃t =
n∑

i=1

αiYi + �t, ∀t (5)

plus �t which is a m-dimensional centered noise of finite
variance. By comparing the eigenvalues corresponding to the

retained principal components to the trace of R (i.e. the
sum of all eigenvalues), one can determine the degree of

explanation of the variance in the data, giving a hint on the

information content of the remaining noise. Note that the

principal components Yi can be seen as orthogonal modes
explaining the interrelated variations of the flows considered,

while

P = [Y1 Y2 . . . Yn] (6)

can be seen as the projection matrix allowing to project

standardized flow values X̃t in the basis defined by the
principal components.

B. Conditional parametric models for local smoothing

The model in Eq. (5) permits one to express the flows as

a linear combination of the principal components Yi. Such
models can be extended in order to account for the potential

nonlinear effects of influential variables on the flows. Denote

by ut the values of these influential variables at time t. They
may include forecast wind power penetration, fictitious load or

the spot price on the EEX market for instance. The dimension

of ut should be kept low (say, lower than 3) owing to the so-
called curse of dimensionality. The model of Eq. (5) is then

extended to

X̃t =
n∑
i=1

αi(ut)Yi + �t, ∀t (7)

where the αi coefficients are not constant anymore, but instead
coefficient functions of the set of influential variables ut. �t
is still a m-dimensional centered noise of finite variance.

We do not describe here the detail of the method for

estimating the coefficient functions. In general, the challenge

is to define a fitting procedure for the coefficients αi(ut) that
allows to consistently eliminate �t in Eq. (7) (i.e. to regard
it as noise component). The basis for their estimation can be

found in e.g. [13]. No assumption is made about the shape of

the coefficient functions, except that they are continuous and

suffiently smooth for being locally approximated. The method

for their estimation consists of approximating them locally

at a number of fitting points with first order polynomials,

and of using weighted least squares for determining the

polynomial coefficients. Different variants of the method for

their estimation can be found in e.g. [14], [15].

C. Identifying trend surfaces

There may be different ways of using the estimated coef-

ficient functions in Eq. (7) for analysing the impact of the

defined influential variables on the flows. One can in a first

stage analyse the estimated α̂i functions themselves in order
to see how influential variables act on the contribution of the

various identified modes to the observed flows. Alternatively,

it may be easier to go back to the original flow variables and

to show what is the mean effect of the influential variables

in the variations of the various flows considered. It is this

alternative that will be preferred in the following. Indeed, by

simply discarding the noise term in Eq. (7), projecting the α̂

i

functions back to the basis in which X̃ is defined, and using
the inverse τ transformation for getting back to the original
X variables, one obtains

X̂(u) = τ −1 (Pα̂i(u)) (8)

which defines the mean flows as a function of the influential

variables u. Variations in such mean flows as a function of u
can be seen as trends induced by u, which can be for instance
a trend induced by forecast wind power penetration. Examples

of such trend surfaces will be given and discussed below.

4

IV. METHODOLOGY APPLICATION EXAMPLE

A. Influential variables in the analysis

For this study, a closer look is taken at the control block

of Austria, operated by the TSO APG (Fig. 1). The installed

generation capacity in Austria is more than 19 GW (12 G

W

in hydro power units), while the maximum load is less than

10 GW [2]. In spite of this excess of installed generation

capacity, the block has shifted its overall characteristics from

export to import throughout the last years, which can be caused

by a number of factors. The block is physically linked to six

different control blocks and a total of nine control zones.

The complexity of such structures is demanding a concise

top-down approach based on recorded data. Effects caused

by the following influential variables were subject to detailed

investigations:

1) fictitious load (Eq. (1)) in Germany, and

2) forecasted wind power penetration in Germany (Eq. (2)),

both together generating an indirect effect on generation and

trading within the APG block via market-related effects. The

trends of cross-border flows and APG system flows were

subsequently analysed for 2006-07 data according to the

methodology outlined in Sec. III above. The vector ut as
introduced in Eq. (7) then includes the measured values for

these 2 influential variables at time t. In parallel, the vector
Xt of flow values measured at time t may either relate to
the overall block balance (being one-dimensional in this case,

thus not needing the PCA step of the methodology introduced

above), or gathering the set of cross-border flows (m = 6), or
finally the flows on transmission lines (m = 23).

B. Flow patterns as a result of wind power

Fig. 4. Trend of the block balance of APG in dependence on the wind power
penetration in Germany.

1) Control block balance: The control block of APG is
closely linked to the control block of Germany, due to high

transmission capacities and mutual benefits in the generation

mixes the two block are able to share. In particular, the pumped

hydro facilities within the APG area serve as buffer for low

Fig. 5. Trend of the block balance of APG in dependence on the wind power
penetration and the fictitious load in Germany. Due to computational reasons,
the three-dimensional fitting procedure cannot be applied with suffcient
numerical reliability up to the full measured wind power penetration of 40%.

price energy injected into the German block. The question

is if a general tendency for a relation between the wind

penetration in Germany and the export/import balance of APG

can be found. Since there are many different simultaneously

relevant influential variables, the method described in Sec. III

has been used for the identification of the influence of the

wind power penetration alone. The result is depicted in Fig.

4

and shows an interesting feature: The general trend indicates

that for zero wind penetration in Germany, there is even a

slight export to be expected from the APG block. For higher

penetrations, the sign turns to negative, that is, the consumers

and pumped hydro plants clearly start to import “green”

energy. Disintegrating this information to the influence of the

wind power penetration and the ficititious load in Germany

separately results in the trend surface plot shown in Fig. 5.

It shows the trends of the export/import balance of APG in

dependence on the wind power penetration and the fictitious

load in Germany at the same time.

2) Physical cross border flows: The cross-border flow
trends of the APG block have been identified following the

methodology outlined in Sec. III. The dimension reduction

part of the methodology has led to the identification of

2

modes explaining 60% of the variations in the data. The

resulting trend computations are shown in Figs. 6, 7 and 8.

The analysis indicates that for high wind penetration and low

load in Germany, the northern flowgates of APG (Germany and

Czech Republic) are importing considerably more energy than

in periods of low wind penetration and high load in Germany.

For the export flow at a southern flowgate to Switzerland (and

then further to Italy), the opposite is the case, which is a clear

indication of wind energy transit through the APG block.

3) Unintended cross border flows: The unintended cross-
border flow at a flowgate is defined as the difference between

the physical flow and the scheduled flow. It turns out that the

level of wind power penetration influences the probability of

unintended exchanges on APG’s borders. Again, the dimension

5

Fig. 6. Trend of the physical cross-border flow at the border between APG
and Germany dependent on the wind power penetration and the fictitious load
in Germany.

Fig. 7. Trend of the physical cross-border flow at the border between
APG and Czech Republic dependent on the wind power penetration and the
fictitious load in Germany.

reduction part of the methodology has led to the identification

of 2 modes, which explain here 67% of the variance in the

data. As an example, Fig. 9 shows the trends at the German

border. There is a nonlinear dependence on the fictitiuos

load, showing a peak at a certain value, and a roughly linear

and clearly positive dependence on the German wind power

penetration. Here, it should be noted that a part of such effects

comes from the zonal market model and is influenced by

the impedance relations in the horizontal network. That is,

the overall unintended exchange of the APG block is widely

unaffected by the wind power penetration.

4) Transmission line flows: The 4 identified modes (ex-
plaining 75% of the variance of the original data) of some

important 400-kV-systems have been analysed in order to

distinguish between more and less volatile flow patterns. In

Fig. 10, these modes are plotted for 23 systems. Following the

Fig. 8. Trend of the physical cross-border flow at the border between APG
and Switzerland dependent on the wind power penetration and the fictitious
load in Germany.

Fig. 9. Trend of the unintended exchange at the border between APG and
Germany dependent on the wind power penetration and the fictitious load in
Germany.

introduction of PCA in Section III-A, most of the variations

of the flows on the 400-kV-systems can be explained and

expressed as a linear combination of these 4 modes. Visual

inspection can already tell which systems have more flow

variations. It can be seen that e.g. system No. 15 shows distin-

guished volatile behaviour, since exhibiting larger magnitude

of variations in the various modes. Fig. 11 reveals that the

flow on this line shows a clear nonlinear dependence on the

wind power penetration and the fictitious load in Germany.

V. CONCLUSIONS

The market-related indirect influence of high local wind

penetration on physical variables in other locations of large

transmission systems has been demonstrated for an EU control

block. The theoretically suspected effect has been separated

from other influences by means of a classical multivariate data

6

Fig. 10. Modes of 23 400-kV-systems in the APG block identified by PCA
as described in Sec. III.

Fig. 11. Trend of the flow on one selected APG system (No.15) dependent
on the wind power penetration and the fictitious load in Germany.

analysis method. Since the future development of renewables

in the EU is highly ambitious, there will be the need for a

projection of the observed nonlinearities to future renewables

penetrations. Top-down approaches based on measured data

as the one outlined in this paper seem to be a practical and

reliable way to approach this problem. In the light of the

demonstrated statistical evidence for still relatively small wind

power penetrations incorporated into the market via a simple

mechanism, future research activities should increasingly fo-

cus on smart market design for renewables and other new

energy technologies.

REFERENCES

[1] European Transmission System Operators (ETSO). information online.
http://www.etso-net.org, 2009.

[2] Union for the Co-ordination of Transmission of Electricity (UCTE).
values online. http://www.ucte.org, 2009.

[3] T. Ackermann (Ed.) and P.E.Mothorst. Wind Power in Power Systems,
chapter Economic Aspects of Wind Power in Power Systems, page
401 ff. Wiley, 2005.

[4] J. Neubarth, O. Woll, C. Weber, and M. Gerecht. Beeinflussung der
Spotmarktpreise durch Windstromerzeugung. Energiewirtschaftliche
Tagesfragen, 56:42–45, 2006.

[5] F. Sensfuß and M. Ragwitz. Analyse des Preiseffekts der Stromerzeu-
gung aus erneuerbaren Energien auf die Börsenpreise im deutschen
Stromhandel. Technical report, Fraunhofer ISI, 2007.

[6] T. Jónsson. Forecasting of Electricity Prices Accounting for Wind Power
Predictions. Master’s thesis, Technical University of Denmark, Lyngby,
2008.

[7] European Energy Exchange. values online. http://www.eex.com, 2009.
[8] E-ON Netz. values online. http://www.eon-netz.com, 2009.
[9] RWE Transportnetz Strom. values online.

http://www.rwetransportnetzstrom.com, 2009.
[10] Vattenfall Europe Transmission. values online. http://www.vattenfall.de,

2009.
[11] J. Lattin, J.D. Carroll, and P.E. Green. Analyzing Multivariate Data.

Duxbury Applied Series, 2003.
[12] A.C. Rencher. Multivariate Statistical Inference and Applications. Wiley

Series in Probability and Statistics, 1998.
[13] W. Cleveland and S. Devlin. Locally Weighted Regression: An Approach

to Regression Analysis by Local Fitting. Journal of the American
Statistical Association, 83(403):596–610, 1988.

[14] H.Aa. Nielsen, T.S. Nielsen, A.K. Joensen, H. Madsen, and J. Holst.
Tracking Time-varying Coefficient Functions. Int. J. Adapt. Control,
14:813–828, 2000.

[15] P. Pinson, H.Aa. Nielsen, H. Madsen, and T.S. Nielsen. Local Linear
Regression with Adaptive Orthogonal Fitting for the Wind Power
Application. Stat. Comput., 18:59–71, 2008.

Bernd Klöckl (M’02) received the M.Sc. degree in
Electrical Power Engineering from Graz University
of Technology, Austria, in 2001 and the Ph.D. degree
from ETH Zurich, Switzerland, in 2007, where he
was research associate and lecturer for renewables
from 2002 to 2006. From 2006 to 2007, he headed
the grid section of the Austrian Association of Elec-
tricity Companies, Vienna, Austria. Since 2007, he is
with the national TSO APG, responsible for research
in market models and cross-border tariffication in
ETSO. Dr. Klöckl is member of IEEE and CIGRÉ.

Pierre Pinson received the M.Sc. degree in Applied
Mathematics from the National Institute for Applied
Sciences (INSA Toulouse, France) in 2002 and the
Ph.D. degree in Energetic from Ecole des Mines de
Paris in 2006. He is currently with the Informat-
ics and Mathematical Modeling department of the
Technical University of Denmark as an Associate
Professor. His research interests include among oth-
ers forecasting, uncertainty estimation, optimization
under uncertainty, decision sciences, and renewable
energies.

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65
 

Electricity Price Variations in a Power System with
Wind Energy Penetration

Avinash D.

M. Tech. Student, Electrical Engineering Department
National Institute of Technology, Kurukshetra

Haryana, India
avinash.6474@yahoo.co.in

Shelly Vadhera
Assoc. Prof., Electrical Engineering Department
National Institute of Technology, Kurukshetra

Haryana, India
shelly_vadhera@rediffmail.com

Abstract — This paper aims to give the variations of electricity

price by considering the effect of wind energy integrated into the
system and also the system variations such as load demand,
outages and congestion. The electricity market is cleared by using
a two stage stochastic optimization model in which first stage
represents the scheduled case and the second stage represents the
deviations from the scheduled case caused by uncertainty in the
production of wind energy, this stage is considered as the
balancing market operation. IEEE 24 bus test system is
considered in different system conditions with 26.3% of wind
energy penetration to study the price variations.

Keywords — Congestion, Electricity prices, Stochastic
optimization, Wind energy.

I. INTRODUCTION
Wind energy is been considered as the best alternative

compared with the other renewable energy sources, according
to Global Wind Energy Council, (GWEC) [1] 2014 was a
record year for wind industry crossing the 50 GW of global
annual installation, with this the cumulative installed capacity
of wind energy at the end of 2014 around the world is about
369,597 MW [1].

This shows the increased integration of wind energy into
the power system network which affects the various parameters
like electricity prices, congestion and reliability. The electricity
market considered in this paper is based on pool operation
where the market is cleared in two stages one in day ahead
stage, that is 24 hours before the actual operation and the other
in balancing stage where the adjustments to the scheduled case
are considered. Balancing market operations are carried at the
actual time of dispatch which varies according to the wind
production level and other uncertainties. A mathematical model
is presented in [2] to co optimize the energy and reserve, at a
given load demand to get the prices in two stages and to
schedule the reserves according to the wind variations.

In [3] a literature review is presented for the European
Wind Energy Association (EWEA) to show the decrease of
spot prices because of wind power production, which is
verified by a regression analysis. The correlation between the
two adjacent wind farms and how one wind farm affects the
other is considered in [4] using a stochastic programming
optimization technique. The wind integration also creates the

problem in planning and scheduling the units, in recent years
combined Energy Storage System with wind energy as in [5]
are proposed to decrease the effect of uncertainty of wind
production, several other methods such as demand response in
which demand responds to the variations of prices, especially
in peak hours and in wind power generation scenarios are
developed [6] and in [7] game theory approach is used for
planning problems. With respect to smart grid applications
there are some new approaches developed incorporating
orthogonal frequency division multiple access (OFDMA) in [8
– 10]. Pricing based on stable matching algorithm and
message-passing algorithm for data redistribution market in
wireless networks is considered in [11]. In this paper stochastic
programming is used to schedule the units, since prices are
dual variables of the power balance constraints they can be
obtained at both the day ahead and balancing stages.

Section II of the paper describes the modeling of stochastic
programming technique which is used to clear the electricity
market, whereas section III gives the results for IEEE 24 bus
test system and section IV concludes the paper.

II. ELECTRICITY MARKET MODEL
Here, pool operation of the market is considered as one

sided bidding in which sellers submit their bids to the pool
operator, based on the load demand, the generation and
reserves are scheduled to obtain the minimum cost of
operation. DC load flow method is used to simplify the model
and hence the model can be solved by Linear Programming
(LP) techniques

A. Motivation
Stochastic programming can be used for the decision

making problems in which uncertainty is present [12], with the
integration of wind energy the uncertainty in power generation
is increased along with the other uncertainties such as load
variations and electricity prices. The uncertain wind power data
is given as a set of possible scenarios, each having certain
probability at a given point of time, the output consists of
schedule of the units and respective prices for each and every
scenario at all the considered time periods.

978-1-4673-6540-6/15/$31.00 ©2015 IEEE

IEEE INDICON 2015 1570168503

1

B. Algorithm
• Scheduled wind generation is included in the day

ahead market stage where as the deviations of Wind
generation are considered as low wind and high wind
scenarios in balancing market stage, for each time
period

• Wind generation is modeled as the negative load
demand

• Reserve bidding is included simultaneously with the
generation scheduling trading according to [13]

• A day schedule is divided into 24 time periods each
indicating 1 hour of operation, thus the solution gives
a dispatch schedule for each hour for the upcoming
day

C. Formulation
Minimize

( )⎥

⎦

⎤
⎢
⎣

⎡
−∏

+

++

∑∑

∑

∈

∈

∈

Kk

D
kst

D
kt

U
kst

U
kt

Ss
s

D
kt

RD
kt

U
kt

RU
kt

Kk
ktkt

rCrC

RCRCPC

TtLV
Ll

shed
lst

LOL
l

Ss
s ∈∀⎥

⎦
⎤
⎢
⎣

⎡
∏+ ∑∑

∈∈

: (1)

Subject to

( ) 000 =−−−+ ∑∑∑∑
Φ∈

Φ∈Φ∈Φ∈

mtnt
m

nm
l

nt
q

sh
qt

k
kt

M
n

L
n

Q
n

K
n

bLWP δδ

: , , (2)
( ) ( )∑∑∑

Φ∈Φ∈Φ∈

−−++

−

Q
n

L
n

K
n q

spill
qst

sh
qtqst

l
shed
lst
k
D
kst

U
kst WWWLrr

( ) 000 =+−−+ ∑
Φ∈

mstmtnstnt
m

nm
M
n

b δδδδ

: , , , (3)
tkPP ktkt ∀∀≤ ,,

max (4)

tskrrP Dkst
U
kstkt ∀∀∀≥−+ ,,,0 (5)

tskPrrP kt
D

kst
U
kstkt ∀∀∀≤−+ ,,,

max (6)

( ) ( ) tmnCb nmmtntnm ∀Λ∈∀≤− ,,,max00 δδ (7)
( ) ( ) tsmnCb nmmstnstnm ∀∀Λ∈∀≤− ,,,,maxδδ (8)

tkRR uk
U
kt ∀∀≤≤ ,,0

max (9)

tkRR dk
D
kt ∀∀≤≤ ,,0

max (10)

tskRr Ukt
U
kst ∀∀∀≤ ,,, (11)

tskRr Dkt
D

kst ∀∀∀≤ ,,, (12)

tslLL l
shed
lst ∀∀∀≤ ,,, (13)

tsqWW qst
spill

qst ∀∀∀≤ ,,, (14)
tkPP ktkt ∀∀≥ ,,

min (15)

tskrr Dkst
U
kst ∀∀∀≥ ,,,0, (16)

tsqWtqW spillqst
sh

qt ∀∀∀≥∀∀≥ ,,,0;,,0 (17)
tslLshedlst ∀∀∀≥ ,,,0 (18)

0
ntδ free nstn δ;, ∀∀ free tsn ∀∀∀ ,, (19)

Where equation (1) is the objective function representing
the total cost of operation which is to be minimized

subject to

the constraints given by equations (2) to (19), and are the
set and index of generator units respectively. , , and , are the production cost, upward/downward reserve cost
and the cost for increasing/decreasing the generation in
balancing stage for unit at time period . The set of all time
periods is indicated by , equation (2) is a power balance
equality constraint for which the day ahead prices are the dual
variables represented by where , both used as the index
of nodes, , , , represents the power generation,
scheduled wind power, load demand and the susceptance
respectively, and are used to indicate the set and index of
wind generators respectively. , , , are up/down
reserves and increasing/decreasing power levels in balancing
stage respectively, , indicate the value of lost
load, load shed in scenario at time , and phase angles of the
node in day ahead stage respectively. , are the
actual wind power generated and the wind power spilled
because of the line flow constraints in scenario at time .
Equation (3) is the power balance constraint equation in
balancing stage in which price is a dual variable.
Equations (4) to (18) are inequality constraints in which , , , , , are the limits of
generation, line flows, up/down reserves and the load present at
a load point respectively. Φ , Φ , Φ are the sets which
relate the set of nodes to generators, load points, and wind
generators where as Φ gives the set of adjacent lines. Π
represents the probability of wind generation scenario .

D. Assumptions
• Wind energy is considered as the freely available

energy hence no cost is associated with it
• Inelastic loads are considered
• Since DC power flow is used network losses and

reactive power flows are neglected
• Fuel cost of the units is assumed to be a piece wise

linear curve

III. RESULTS
IEEE 24 bus reliability test system [14] is considered in

which there are 12 generating units present, which supply a
load of 2850 MW through 34 transmission lines. Cost data of
the system is taken from [15], according to [2] wind farms are

2

connected at buses 7 and 8 having a capacity of 23.6% of the
total demand i.e. 750 MW, thus the two wind farms have a
total of 300 wind turbines of 2.5 MW each in which, 7th and 8th
nodes have 100 and 200 wind turbines respectively [2]. There
is a double line circuit exist between the nodes 15 and 21
carrying a total power of 448.6 MW, for analyzing the effect of
congestion on the system prices a single line circuit is
considered in place of the double line hence reducing the
capacity of line flow to 400 MW. Fig. 1 shows the price
variations in day ahead market stage giving the comparison
between the normal and the congestion cases, where horizontal

Fig. 1. Price variations in day ahead stage comparing the normal and

congestion cases

Fig. 2. Price variations in low wind scenario comparing the normal and

congestion cases

axis represents the system nodes and the vertical axis represent
the price in $/MWh. In normal condition of operation the
prices at all nodes are nearly equal but in congestion case, price
varies randomly between the nodes 15 and 21 showing a
maximum value of 24.917 $/MWh at 15th bus and a minimum
value of 5.47 $/MWh at bus 21. Fig. 2 and Fig. 3 shows the
price variations for low wind and high wind scenarios, it can be
seen that in low wind energy scenario the price is almost
constant in normal case but the value is at 20.93 $/MWh
which is higher than the day ahead average price (13.32
$/MWh). In congestion case the cost variation is similar to the
previous case but the variation of price is reduced between the

Fig. 3. Price variations in high wind scenario comparing the normal and

congestion cases

nodes 5 to 15 indicating the effect of scheduled wind energy in
the day ahead stage. The average price for both the normal and
congestion cases is reduced drastically in high wind energy
scenario because of the availability of wind generation to its
maximum capacity (26.3% of total load). The price variations
also increased showing a minimum value at buses 7 and 8,
since the wind farms are connected at 7th and 8th buses the price
varied randomly between the adjacent nodes due to line flow
constraints.

In Fig. 4 and Fig. 5 the effect of varying load on nodal
prices is shown in normal and congestion cases respectively,
where the horizontal axis represents the percentage of the
nominal load of the system and the vertical axis represents the
cost in $/MWh, in normal case as the load increased the cost of
the nodal prices are increased. Except the nodes 7 and 8 where
wind farms are connected, remaining all nodes have a similar
price pattern as shown in Fig. 4, but for the case of congestion
node 15 has a maximum cost where as the cost at node 21 is
constant for all the loading conditions as in Fig. 5.

0
5

10

15

20

25

30

1 3 5 7 9 11 13 15 17 19 21 23

P
ri

ce
(

$/
M

W
h

)

Nodes

Normal Congestion

0
5
10
15
20
25
30
1 3 5 7 9 11 13 15 17 19 21 23
P
ri
ce
(
$/
M
W
h
)
Nodes
Normal Congestion
0
2
4
6

8

10
12

14

16

18

20
1 3 5 7 9 11 13 15 17 19 21 23
P
ri
ce
(
$/
M
W
h
)
Nodes
Normal Congestion
3

Fig. 4. Price variations in selected nodes at different loadings under normal

conditions

The above system is solved using the model developed in
section II which is implemented in General Algebraic
Modeling System (GAMS) as a linear programming problem.
The solver used is the CPLEX 12.1.0 under GAMS, the
CPLEX’s state of the art dual simplex algorithm is used in
solving the model.

Fig. 5. Price variations in selected nodes at different loadings in a congested

case

IV. CONCLUSION
This paper gives the variations of electricity prices in the

power system network considered by taking into account the
various parameters such as wind generation, load fluctuation,
and congestion. Though the wind generation decreases the cost
of operation of the system, it increases the variation of price

among the nodes. The same analysis can be extended by
considering start up and shut down cost of units and also the
cost of wind generation to get more reliable price variations.

REFERENCES
[1] [online]. Available: http://www.gwec.net/global-figure
[2] J. M. Morales, Antonio J. Conejo, Kai Liu, and Jin Zhong,

“Pricing electricity in pools with wind producers,” IEEE
Trans. Power Syst., vol. 27, no. 3, pp. 1366-1376, Aug.
2012.

[3] [online]. Available: http://www.ewea.org
[4] J. M. Morales, A. J. Conejo, and J. P. Ruiz, “Simulating

the impact of wind production on locational marginal
prices,” IEEE Trans. Power Syst., vol. 26, no. 2, pp. 820 –
828, May 2011.

[5] Maria Dicorato, Giuseppe Forte, Mariagiovanna Pisani
and Michele Trovato, “Planning and operating combined
wind – storage system in electricity market,” IEEE Trans.
Sust. Energ., vol. 3, no. 2, pp. 209 – 217, April 2012.

[6] Cedric De Jonghe, Benjamin F. Hobbs and Ronnie
Belmans, “Optimal generation mix with short term
demand response and wind penetration,” IEEE Trans.
Power Syst., vol. 27, no. 2, pp. 830 – 839, May 2012.

[7] Ting Dai and Wei Qiao, “Trading wind power in a
competitive electricity market using stochastic
programming and game theory,” IEEE Trans. Sust. Energ.,
vol. 4, no. 3, pp. 805 – 815, July 2013.

[8] Dexiang Zhan, Haijun Zhang, Zhaoming Lu, Xiangming
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[9] Wenmin Ma, Haijun Zhang, Wei Zheng and Xiangming
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dense femtocell networks,” Proc. IEEE WPMC, pp. 599 –
603, Sept. 2012.

[10] Wenmin Ma, Wei Zheng, Haijun Zhang, Xiangming Wen,
Zhaoming Lu and Deli Liu, “Pricing based resource
allocation in downlink multi – cell OFDMA networks,”
Proc. IEEE ICCNT, pp. 260 – 263, Aug. 2012.

[11] Yanyao Shen, Chunxiao Jiang, Tony Q. S. Quek, Haijun
Zhang and Yong Ren, “Pricing equilibrium for data
redistribution market in wireless networks with matching
methodology,” Proc. IEEE ICC, pp. 3051 – 3056, June
2015.

[12] J. M. Morales, A. J. Conejo, Henrik Madsen, Pierre
Pinson, and Marco Zugno, Decision making under
uncertainty in electricity, operational problems. New
York: Springer 2014.

[13] J. M. Morales, A. J. Conejo, Henrik Madsen, Pierre
Pinson, and Marco Zugno, Integrating renewables in
electricity markets, operational problems. New York:
Springer 2014.

0
5
10
15
20
25

60 70 80 90 100 110 120

P
ri
ce
(
$/
M
W
h
)

Load (%)

Node 1 Node 7

Node 15 Node 21

0
5
10
15
20
25
30

35

40

45

60 70 80 90 100 110 120
P
ri
ce
(
$/
M
W
h
)
Load (%)
Node 1 Node 7
Node 15 Node 21
4

[14] Reliability Test System Task Force, “The IEEE reliability
test system – 1996,” IEEE Trans. Power Syst., vol. 14, no.
3, pp. 1010-1020, Aug. 1999.

[15] [online]. Available: http://pierrepinson.com

APPENDIX
In this model, cost for increasing/decreasing the generation in
balancing stage is taken as the production cost. The other
parameters used in the model are given in Table I.

TABLE I. COST PARAMETERS

Units
(k)

($/MWh) ($/MW) ($/MW)

1 13.32 15 14

2 13.32 15 14

3 20.70 10 9

4 20.93 8 7

5 26.11 7 5

6 10.52 16 14

7 10.52 16 14

8 5.47 0 0

9 5.47 0 0

10 0 0 0

11 10.52 17 16

12 10.89 16 14

5

1

Abstract—Wind generation is playing an increasingly

significant role in many electricity industries around the world.
It has very different operational characteristics from existing
generation and the integration challenge is to facilitate wind in
achieving its maximum energy, environmental and wider
societal value. Maximizing energy value is a particular
challenge for electricity arrangements as penetrations increase.
This paper summarises some of the key design features of the
Australian National Electricity Market (NEM) relevant to wind
integration, assesses its performance to date in facilitating
appropriate wind deployment, and outlines possible changes to
support much higher wind penetrations in the future. It suggests
that the NEM has reasonably effectively and efficiently managed
regionally significant wind penetrations to date. Wind is now
having significant impacts on market outcomes in high
penetration regions. In particular, periods of high wind output
are associated with lower wholesale market prices and wind is
receiving lower revenue ($/MWh) than other generation types.
This reflects the lower energy value of a generation source
relying on a non-storage energy source. Projected significant
increases in wind penetration due to government renewable
energy targets for 2020 will continue to test the adequacy of
market arrangements and likely require ongoing market design
changes. These changes need to be seen in the wider context of
NEM challenges in transitioning towards a low-carbon future.

Index Terms— Australia, Electricity market, Wind
integration, market design

I. INTRODUCTION
Ind generation is playing an increasingly significant
role in many electricity industries around the world and

Australia is no exception. Over 2GW of wind generation has
now been deployed and it is expected that current government
policy will drive considerable further investment over the
coming decade.

In its broadest sense, the challenge for wind integration
within electricity industries is to facilitate wind energy in
achieving its maximum energy, environmental and wider

I. F. MacGill is with the Centre for Energy and Environmental Markets

and School of Electrical Engineering and Telecommunications at the
University of New South Wales, Sydney, Australia (email:
i.macgill@unsw.edu.au).

societal value. Maximizing energy value is a particular
challenge as wind penetrations increase and has to be
addressed in the context of the electricity industry as a whole.
Wind’s energy value depends in part on the investment and
operational costs of particular wind farms. However, its value
is also determined by the impact it has on the benefits and
costs of other power system participants. These costs and
benefits have temporal and locational variability and
uncertainty that emerge from the coordinated behaviour of
all loads and generation on a shared network

Electricity industries, of course, face many challenges
other than wind energy integration, which should therefore
be considered in a broader context that addresses issues
including the security and efficiency of delivering energy
services to end users, and appropriately supporting other
opportunities for meeting our growing energy security and
greenhouse challenges.

Wind energy, however, represents the first highly non-
conventional generation to reach significant penetrations in
large power systems, and is therefore now testing the
adequacy of decision-making arrangements in traditional
monopoly and restructured industries alike around the world.

Australia’s National Electricity Market (NEM) provides
an interesting context for considering the challenges and
options of facilitating wind energy integration in
restructured electricity industries. The NEM has a large
geographical scope, a rather different mix of generation from
many other jurisdictions, and its own particular electricity
market and associated renewable energy policy support
framework. The last decade has also seen considerable
wind energy development, some resulting integration
challenges and significant consequent changes to NEM
arrangements.

This paper aims to summarise some of the key design
features of the NEM relevant to wind integration, its
performance to date in facilitating appropriate wind
deployment and possible changes to support much higher
wind penetrations in the future. It first briefly summarises the
current design of the NEM and its general performance to
date. Section III assesses how well the current NEM
arrangements might be expected to facilitate wind integration,

Impacts and best practices of large-scale
wind power integration into electricity
markets – some Australian perspectives

I. F. MacGill, Member, IEEE

W

978-1-4673-2729-9/12/$31.00 ©2012 IEEE

2

particular challenges that have emerged and possible changes
to the NEM design. Section IV assesses how high wind
penetrations in some regions of the NEM have already
impacted on market outcomes both in terms of market prices,
and also the revenues of market participants. It also briefly
outlines some current investment challenges for wind. Section
V then summarises some of the key lessons that the NEM
holds for electricity market design to facilitate appropriate
wind integration, and briefly flags some possible wider
lessons for broader energy related policies including those to
support greater renewable energy.

II. NEM DESIGN AND PERFORMANCE TO DATE
The Australian NEM design is unusual amongst

restructured electricity industries around the world [1]. Its
centre-piece is a set of compulsory regional gross-pool spot
energy and Frequency Control Ancillary Services (FCAS)
markets that solve a security-constrained dispatch every five
minutes for five interconnected regions. All generators greater
than 30MW in size (except for intermittent sources as will be
discussed later) must participate in the NEM’s scheduling and
cost allocation procedures. Both the system and market fall
under the direction of the Australian Energy Market Operator
(AEMO). Notably, the NEM has no formal (commercially
accountable) day-ahead market or capacity market.

It has formal objectives of open access, ‘causer pays’ cost
allocations and equal treatment, although these are
difficult to deliver in practice within an electricity industry.
Still, the general principle is that market participants pay,
and/or are paid, according to their contribution to overall
industry costs and benefits.

The wholesale spot market prices electricity every five
minutes across five regions. FCAS markets determine
frequency control ancillary services costs for regulation and
contingencies, again at five minute intervals for the five
regions. The NEM is therefore infused with uncertainty—
generators can rebid within five minutes of dispatch and are
highly motivated to respond to changing market conditions.
A range of derivative markets that are not formally provided
or supervised by NEM governance arrangements, offer
opportunities to manage the price risk associated with these
wholesale spot markets.

The NEM appears to have achieved reasonable success to
date in matching commercial market signals with the
underlying economics of the electricity industry, within an
effective security regime. Wholesale prices have been
generally low by international standards although a key factor
here is the low costs of coal-fired generation in Australia, and
the NEM reliability target has been met to date [2]. It has also
successfully integrated wind generation approaching 2GW
with over half of this wind located in the single, relatively
small (peak demand of approximately 3.5GW) region of
South Australia (SA) that now has a wind energy penetration
of around 20%, and only limited interconnection with the rest
of the NEM.

III. ELECTRICITY MARKET DESIGN TO FACILITATE WIND
INTEGRATION

A. Key electricity market challenges with the
large scale deployment of wind energy in the
NEM

Challenges with high wind penetrations within electricity
markets need to be seen within the context of wider electricity
market objectives. Wind is proving one of the most assured
and economically attractive options for reducing the
environmental impacts and dependence on (often imported)
fossil fuels in electricity industries around the world. By
comparison, other low-carbon generation options such as
Carbon Capture and Storage (CCS) and nuclear power have
proven more difficult to deploy than many expected [3].

The challenges are, in the most general sense, the
challenges of deploying a relatively novel generation source
with rather different operational characteristics from existing
options, into an industry providing an essential public good
and posing complex operational challenges due to the nature
of electricity itself, and the existing industry. It might be
expected that similar challenges will emerge with other
‘disruptive’ technologies and this is, indeed, being seen with
the growing deployment of solar technologies such as PV
which raise some similar challenges.

Experience to date within the NEM and by comparison
other electricity industries around the world would suggest
that the NEM has some advantageous arrangements for
effectively and efficiently facilitating the integration of
significant levels of wind energy [1]:

Supply/demand balance for regulation, contingencies
and energy is managed through a gross pool rather than
primarily bilaterally. This may be advantageous for variable
and somewhat unpredictable generation which can find it
difficult to contract bilaterally forward on fixed volumes
without finding itself having significant exposure to net
balancing market outcomes. In the NEM, the spot market
solves supply/demand balance for all generation and load
every five minutes. .

The NEM provides transparent regional prices for all
market participants that reflect a considerable aspect of the
underlying locational, temporal and uncertainty value of
electricity as it evolves over time. These price signals
have significant implications for wind-farm investment
as energy value represents a significant proportion of
project revenue. In particular, the additional cashflow
delivered to wind farms through the 20% renewable energy
target does not shield wind farms from these signals in the
way that feed-in tariff support arrangements may. Wind farms
are therefore located with consideration of expected
regional pool prices (that reflect potential inter-
regional transmission constraints) and intraregional loss
factors, and the predicted match between their generation and
periods of generally higher pool prices. .

The NEM FCAS arrangements provide a highly
transparent approach for pricing regulation and

3

contingency ancillary services which wind farms may have
particular needs to call upon. There is considerable value in
co-optimising energy and FCAS dispatch for intermittent
generation.

The freedom of scheduled participants to rebid to the 5
minute dispatch boundary lets them revise offers with
improving forecasting information down to near real-time and
provides strong incentives for them to enhance their
short-term operational flexibility. The use of shorter ‘gate
closures’ also supports wind energy integration.

However, many challenges remain and new ones are
emerging. Particular issues in the NEM include:
– Providing the most useful forecasts of potential future wind

generation to assist all market participants. A key issue is
forecasting possible extreme weather events that potentially
threaten system security and therefore, given the NEM
design, have potentially very significant commercial
implications.

– The degree and nature of formal wind participation in
market scheduling and cost allocations given its operational
characteristics and capabilities. There are particular issues
with the provision and payment of ancillary services,
however, there are likely to be growing challenges in
efficiently and securely managing supply-demand balance
over the 5 minute to hours ahead time frame given
potentially significant wind variability possible over such
time frames and the limitations of conventional dispatchable
plant in responding to such variability. .

– Transmission investment given that sites with good wind
resources are often located in remote regions without strong
network connection. Efficient use of existing network assets
also would seem to require further attention in order to more
efficiently manage congestion. .

B. Possible market design changes to facilitate
wind

There are many emerging challenges and opportunities for
electricity markets beyond wind generation including other
intermittent and, furthermore, highly distributed renewables
such as PV, the growing integration between gas and
electricity markets and the need to facilitate far greater
demand-side participation. These all need to be addressed in a
coherent and comprehensive manner which, as far as possible,
provides a technology and participant neutral basis for
achieving both direct electricity industry objectives, but also
wider societal objectives for the energy sector.

The last five years has seen a number of changes to
NEM arrangements and rules to better facilitate higher
wind penetrations including improved transparency,
development of centralized Wind Energy Forecasting and
changes to require wind generation to participate more
formally in scheduling and security processes through a new
semi-scheduled category of market participant. This latest
development reflects the growing challenges of having
significant intermittent generation within the NEM
remaining outside some of the market’s important
scheduling and security processes.

Particular areas receiving greater attention from the policy
and rule making process at present include network
investment and congestion and procurement of short-term
reserves to meet security requirements [2]. More generally,
wind energy will be facilitated through efforts to improve
demand-side participation in the electricity industry where
controllable loads can help manage ongoing supply-demand
balance, as well as improvements to gas market arrangements
that support greater use of highly flexible and hence
complementary gas-fired generation.

More generally again, it is important that policy and rule
makers revisit the governance arrangements in place for the
electricity market design but also wider issues including
industry structure and related markets such as those providing
targeted policy support for renewables. Effective action on
climate change will require ongoing changes at a speed and
scale that may be beyond the present framework.

IV. HIGH WIND PENETRATION IMPACTS ON NEM OUTCOMES
Whilst the NEM still has a relatively low wind energy

penetration of less than 3%, a number of regions have
significant penetrations that provide some basis for assessing
the impacts of high wind penetrations [4]. Considerable
caution is, however, required given the many complex,
uncertain and interacting drivers of market outcomes.
Wholesale spot market prices and participant dispatch have
very high transparency within the NEM, however derivative
market prices are more important for many market participants
in terms of driving investment and managing revenue risks
associated with the spot market. Derivative prices and
participant positions have far lower transparency than that
seen for the wholesale market. There are also eight FCAS
prices of potential relevance as well. All these prices vary by
time and location, and the exposure of market participants to
these varying prices depends upon their location and time
changing generation.

Another key factor is that the NEM is an energy-only
market and has no capacity markets as such. Instead, the
market design permits very high prices in the short-term (two
orders of magnitude greater than typical prices) at times of
tight supply-demand balance. This provides a major incentive
for generators to ensure they are available at such times. The
risks associated with such price variability can be managed, at
least in part, through derivative markets. The challenges wind
energy poses in the capacity markets of some electricity
industries might reflect the challenges of these capacity
markets more generally – the electricity industry’s need for
capacity depends on ongoing and potentially rapidly changing
market conditions. A strength of the energy only market is that
it rewards generation capacity available at the time it is
required.

A. Large-scale wind integration impacts on NEM
prices

Analysis of wholesale spot price outcomes over recent
years in high wind penetration regions of the NEM highlights

4

that periods of high wind generation see typically lower prices
than periods of low generation for all levels of demand (the
most significant price driver) as shown in Fig. 1.

It is not clear what the overall impact of wind generation is
on prices overall because of the many other drivers of price,
and significant year on year variability that has been seen due
to these. For example, SA annual volume weighted prices
have varied from A$32/MWh to over A$100/MWh over the
last decade. Reasons for this variation include extreme
weather (particularly summer heat waves) as well as
generation investment other than wind [2]. However, it is
certainly apparent that periods of high wind generation in SA
are associated with lower prices and, indeed, a growing
number of negative price events [4].

These spot market outcomes highlight the importance of
derivative market strategies for securing NEM participant
returns. For a range of reasons, however, (notably the
dominant market position of AGL in SA), derivative markets
in SA exhibit relatively low liquidity by comparison with
some other regions.

Fig. 1. Wholesale spot prices in the SA and VIC regions of the NEM as a
function of demand for periods of top quartile and bottom quartile wind

generation at the time of that demand [5].

At present, turnover in the FCAS markets in the NEM is
typically less than 0.5% of spot market turnover, reflecting at
least in part the short time period over which these
ancillary service markets operate given five minute
spot market operation. ‘Causer pays’ principles for
ancillary services in the NEM are challenging to implement
in practice due to the shared nature of ongoing net supply

and demand imbalances within the industry. Current and
potentially greater wind penetrations are not seen as likely to
significantly increase contingency requirements, however,
some work does suggest that high wind penetrations might
drive significant increases in regulation service requirements
and hence costs [6]. For wind farms, FCAS costs of currently
A$0.40/MWh are low compared to typical spot market prices
that are around one hundred times higher. However, causer
pay requirements might see these increase significantly
(perhaps ten fold) under higher penetrations

B. High wind penetration impacts on generation
participants

Significant wind capacity investment will, as with any
other generation technology investment, shift market
outcomes and hence potentially impact on the revenue of all
market participants. In a restructured electricity industry, such
impacts are, of course, an intended outcome. However, the
‘external’ policy support for wind generation is certainly
argued by some incumbent market participants to represent a
market distortion.

The formal objective of the Australian NEM does not
explicitly include any environmental, or for that matter social,
goals. This was a design choice. Given that Australia’s energy
policy objectives do include such objectives, external policies
to change NEM outcomes in support of these objectives can
actually be seen as an obligation rather than an imposition on
the NEM [3].

As noted previously derivative markets play the key role in
determining overall revenue for most market participants and
the relationship between these prices and spot market prices is
complex and uncertain. Nevertheless, it is interesting to note
the spot price outcomes for wind and other generation seen in
three NEM regions over recent years as wind penetrations
have increased as shown in Figure 2.

Fig. 2. A comparison of volume weighted average annual prices received by

wind and non-wind generation in three regions of the NEM over 2009-10.

In SA and Victoria with significant wind penetrations,
wind generation has on average been earning considerably
less spot market revenue ($/MWh) than the other generation
types. In SA certainly, this revenue difference has generally
grown as wind penetrations have as well [4].

This has increasingly important implications for wind farm
owners and operators, and potential investors within those
regions. It reflects the reality that generation without inherent
energy storage has lower value than conventional generation
with storable primary energy sources such as coal, gas and

5

hydro. This earned price varies significantly between wind
farms depending on how their particular wind regime matches
underlying price patterns (typically higher prices in summer
and winter afternoons and evenings) and the output of other
wind farms (high wind output correlation with other wind
generation means greater adverse merit order effects and
hence typically lower prices at times of high output).

The participation of wind energy in the NEM’s
associated derivative markets is made more challenging by
the volume uncertainty associated with their operation. Wind
farms might choose not to participate in these markets.
However, there is also anecdotal evidence of volume firming
derivatives being offered by some participants with flexible
gas-fired plant for wind farms participating in the derivative
markets. Although the volume risk poses significant
challenges, there are good reasons for wind generation to
participate and it has some potentially attractive capabilities
by comparison with other generation.

For example, SA is seeing a growing number of negative
price events [2]. Depending on their contracting arrangements,
a number of windfarms are now reducing output when these
negative prices fall below the renewable energy certificate
price that they earn on their generation. Unlike coal-fired plant
which can not reduce output below some minimum operating
level (typically 40-50% of rated output), wind farms can
reduce output rapidly and hence potentially profit significantly
from such events when contracted.

C. Wind energy forecasting
Since 2008 the NEM has had a centralized Australian Wind

Energy Forecasting System (AWEFS) to support
security-driven and commercial decision making. AWEFS is
designed to produce wind forecasts that can be integrated
into AEMO’s forecasting processes, from the five minute
dispatch process to the two year medium term projected
assessment of system adequacy. It provides wind energy
forecasts at individual windfarm, regional and system-wide
aggregations including some measures of expected
uncertainties

It is challenging to assess the value that the system
provides in security and commercial terms. AEMO reports on
forecast accuracy metrics, however, issues include both which
measures of accuracy are the most relevant (for example,
forecasting of extreme weather events may have far higher
security and commercial value than forecast accuracy during
times of normal market operation) and what value these
forecasts actually provide. This is an important area for future
work.

D. Wind generation investment
The deployment of some 2GW of wind generation in the

NEM over the last decade highlights the success of NEM and
wider policy arrangements to facilitate wind farm investment.
The Federal Government’s 20% renewable energy target for
2020 is widely projected to drive considerable wind
generation investment over the coming few years [2].
However, there are some current challenges and uncertainties

that are adversely impacting wind farm development. In large
part these would seem to result from the poor design of the
renewable energy target which has seen unexpectedly high
deployment of small-scale PV systems and even solar hot
water systems over the past few years provide ‘deemed’
renewable energy generation that exceeds the scheme targets
for the next several years.

However, there are growing concerns regarding
transmission arrangements, particularly given the time lines
required for major network development. Likely most
importantly, there are also emerging structural challenges
within the NEM that are influencing investment. Through
both horizontal and vertical integration over the past decade,
three large and vertically integrated utilities now own and
operate 30% of NEM generation and supply over 80% of
small retail customers. They are also responsible around 60%
of the generation investment seen since 2007. These three
firms are now the key players in whether and how the
renewable target might be achieved.

V. CONCLUSIONS
The NEM would appear to have reasonably effectively and

efficiently managed regionally significant, although NEM-
wide still relatively modest, wind penetrations. Wind
generation would now seem to be having some significant
impacts on market outcomes in several market regions. In
particular, wind generation in regions with high wind
penetrations is receiving reduced market revenues (A$/MWh)
than other generation. This is providing an increasing
incentive for wind-farm developers to look for project
opportunities in other States that may not feature as attractive
wind resource or site availability, but which have a lower
wind penetration. This is, of course, a better industry outcome
in terms of managing the integration costs and other
challenges associated with high penetrations of renewable
energy sources that do not have inherent energy storage.

Despite these achievements, projected significant increases
in wind penetration due to federal government renewable
energy targets for 2020 will continue to test the adequacy of
market arrangements and likely require ongoing market design
changes. These changes need to be seen in the wider context
of NEM challenges in transitioning towards a low-carbon and
hence more sustainable electricity industry future.

A larger market and wider policy design challenge would
actually appear to be the low-carbon energy technology policy
support that will be required to deploy disruptive low carbon
technologies. Designers of the market-oriented policy support
measures such as the renewable energy target scheme in
Australia might well learn from electricity market design on
the importance of
– high transparency and disclosure by market participants
– robustness against unexpected developments and market

participant behaviours (including during the design process)
as seen with the close integration of commercial and
security arrangements within the NEM, and

– governance arrangements including formal separation of

6

powers and interfaces between policy making rule making,
operation and enforcement, as well as formal rules for
changing the rules in response to changing market outcomes
as seen with Australian NEM rule change process.

VI. ACKNOWLEDGMENT
The author gratefully acknowledges the many and varied

contributions of colleagues to the work that has been
undertaken at the University of NSW Centre for Energy nad
Environmental Markets (CEEM) on the challenges and
opportunities of renewable energy integration. Particular
thanks are owed to Professor Hugh Outhred and Dr Nicholas
Cutler. Numerous research students have also contributed to
this work including Nicholas Boerema, Sam Forrest and
Sebastian Oliva. This work is supported in part by Australian
Research Council and Australian Solar Institute funding.

VII. REFERENCES
[1] I.F. MacGill, “Electricity market design for facilitating the integration of

wind energy: Experience and prospects with the Australian National
Electricity Market,” Energy Policy 38 (2010) p. 3180–3191.

[2] Australian Energy Regulator (AER), “State of the Energy Market 2011,”
Available at www.aer.gov.au.

[3] International Energy Agency (IEA), “World Energy Outlook” (2011),
available at www.iea.org.

[4] N. Cutler, N. Boerema, I.F. MacGill and H.R. Outhred, “High
penetration wind generation impacts on spot prices in the Australian
national electricity market, Energy Policy 39 (2011) p. 5939-5949.

[5] S. Forrest, “Quantifying the Impact of Intermittent Wind Generation in
[6] the Australian National Electricity Market,” Engineering Honours

Thesis, University of NSW, June 2011.
[7] Roam Consulting, “Impact of the LRET on the costs of FCAS, NCAS

and Transmission augmentation,” Report to the Australian Energy
Market Commission, September 2011, available at www.aemc.gov.au.

VIII. BIOGRAPHIES

Iain MacGill is an Associate Professor in the
School of Electrical Engineering and
Telecommunications at the University of New South
Wales, Sydney, Australia, and Joint Director for the
University’s interdisciplinary Centre for Energy and
Environmental Markets. His teaching and research
interests include electricity industry restructuring
and sustainable energy technologies, with a
particular focus on distributed resources and energy
policy.

862 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 4, OCTOBER 2012

Integration of Wind Generation Into the
ERCOT Market

Henry L. Durrwachter, Senior Member, IEEE, and Sherry K. Looney

Abstract—This paper examines the market rules implemented
within the Electric Reliability Council of Texas (ERCOT) zonal
and nodal market designs related to the integration of wind gen-
eration. Currently, there is almost 10000 MW of wind generation
installed and operating in the ERCOT market. Total ERCOT
summer peak load is in excess of 65000 MW and wind generation
penetration has exceeded 25% on several instances. This paper
discusses the various rules and market mechanisms put in place
to effectively deal with a high penetration of variable wind gener-
ation while maintaining adequate system reliability and creating
proper market price signals for wind generation. Specifically, the
paper includes treatment of wind generation in both the zonal
and nodal markets, including day-ahead markets and real-time
operations and describes the actual market outcomes which have
been realized through several years of increasing penetration of
wind generation on the ERCOT system.

IndexTerms—Ancillaryservicemarkets,bilateralmarkets,elec-
tricity markets, market design, market operation, variable gener-
ation, wind generation.

I. INTRODUCTION

A COMPETITIVE retail electricity market became realityin the Electric Reliability Council of Texas (ERCOT) on
January 1, 2002 [1]. Since the initial implementation of that
zonal market, significant amounts of variable wind generation
resourceshaveaddedtotheportfolioof resources in theERCOT
system as shown in Fig. 1.
The impetus for theadditionof thesenewwindgeneration re-

sourceswasprovidedbyaRenewablePortfolioStandard (RPS)
implemented by the Texas Legislature in 1999 that initially re-
quired that2000megawatts (MW)ofnewrenewablegeneration
capacity be added in Texas by January 1, 2009 [2]. That RPS
was subsequently changed to 5000 MW by January 1, 2015.
Today, there is almost 10000 MW of wind generation installed
and operating in the ERCOT market.
The integration of increasing quantities of wind generation

resources requires increasingly complex analysis and impacts
the design of many operational systems. Some of the most sig-
nificant concerns associatedwith the integration of wind gener-
ation in the ERCOT system have been transmission adequacy
and congestion, reactive and primary frequency response, and
adequacy of ancillary services plans.

Manuscript received September 16, 2011; revised May 11, 2012; accepted
May 28, 2012. Date of publication July 25, 2012; date of current version
September 14, 2012.
The authors are with the Department of Regulatory, Luminant Energy Com-

pany LLC, Dallas, TX 75201 USA (e-mail: hdurrwachter@luminant.com).
Color versions of oneormore of thefigures in this paper are available online

at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TSTE.2012.2202697

Fig. 1. Installed wind generation capacity in ERCOT.

ERCOTand thePublicUtilityCommissionofTexas (PUCT)
completed an exhaustive transmission study to develop the
Competitive Renewable Energy Zones (CREZs) [3]. The
CREZs were created to determine the likely location of addi-
tional wind generation and to provide a means of determining
the amount of additional transmission needed to deliver wind
power to ERCOT loads. However, the additional transmission
capacity resulting from the CREZ construction will not be
completed until the 2013 to 2014 time frame.
The effects of inadequate transmission capacity have been

addressed in the short term by changes in market system
rules to provide equitable dispatch, i.e., curtailment, of wind
generation resources to control local and interzonal congestion
whilemaintainingminimumreliability standardsestablishedby
ERCOT operating as the Independent System Operator (ISO).
As wind generation capacity increased during the ERCOT
zonal market operation, rules and operational changes were
required to manage the increasing amount of wind curtailments
needed to manage congestion and maintain system reliability.
Provisions for reactivepower support andprimary frequency

response from wind generation resources have been addressed
through ERCOT market participant and regulatory processes.
The reactive requirements for wind generation resources were
initially proposed through zonal Protocol Revision Request
(PRR) 830, which was approved by the ERCOT Board and
subsequently appealed to the PUCT by a group of wind gen-
erator owners. The proposal sets a minimum standard for
reactive voltage support from wind generators which can be
accomplished through dynamic ratings and static VAR devices.
Upon final dispositionof the appeal by the PUCT, the approved
requirements will also be incorporated into the ERCOT nodal

1949-3029/$31.00 © 2012 IEEE

DURRWACHTER AND LOONEY: INTEGRATION OF WIND GENERATION INTO THE ERCOT MARKET 863

Fig. 2. Monthly energy from wind as a percentage of total ERCOT energy
production.

protocols via a Nodal Protocol Revision Request (NPRR). Pro-
tocol revisions also added a requirement for wind generators
which connected after January 1, 2010 to have an adjustable
dead band and provide primary frequency response similar to
the droop characteristic of five percent used by conventional
electric generators.
On December 1, 2010, the ERCOT market changed from a

zonal market to a nodal market design [4]. This paper provides
a brief review of the current status of the wind generation in
ERCOT. This paper also examines the market rules put into
place, first in the ERCOT zonal market and later in the ERCOT
nodalmarket, toallowtheeffective integrationof largeamounts
of variable wind generation resources in both day-ahead and
real-timemarkets.Andfinally, this paper examines themethod-
ology fordeterminingancillary service requirements associated
with increasing quantities of wind generation.

II. STATUS OF WIND GENERATION CAPACITY IN ERCOT

At the end of June, 2011, ERCOT had approximately
9452 MW of wind generation capacity [5]. The monthly
amounts of wind energy production as a percentage of total
monthly energy production in ERCOT for the years 2010 and
2011 are shown in Fig. 2.
Furthermore, the total amount of energy in megawatt hours

(MWh) produced annually by wind generation in ERCOT con-
tinues to increase as shown in Fig. 3.

III. ZONAL MARKET OPERATION OF WIND
GENERATION RESOURCES

Wind generation resources (WGRs) were initially incorpo-
rated into the ERCOT system through a zonal market struc-
ture. In the beginning, when the quantities of wind generation
were low,WGRswere treatedasnoncontrollablegenerationand
price-takers in themarket.As thewindgenerationquantities in-
creased in theWest congestionzone,market rules changeswere
approvedby themarketparticipants toaddressoperatingandre-
liability concerns.
At first, areas of local transmission congestion were ad-

dressed with preassigned, prorated capacity rights to equitably
manage transmission capacity among competing WGRs. As
additionalWGRcapacitywasadded to thesystem, transmission
congestion occurred at commercially significant constraints

Fig. 3. Cumulative energy from wind in ERCOT.

(CSCs) on the 345-kV interconnections between the ERCOT
West congestion zone and each of the other market congestions
zones, (i.e., North, South, and Houston).
In the ERCOT zonal market, balancing energy was deployed

from an up and down bid energy stack during each 15-min set-
tlement interval in order to manage the projected generation
load for the next 15 min. When energy flows on the CSCs be-
tween zones were below the operating limits, the balancing en-
ergywasdeployedsystem-wideandall zonalpriceswereequal.
However, when any one of the CSCs became limiting, the bal-
ancing energy was deployed among zones and the zonal prices
diverged, i.e., prices in “generation pockets” went down and
prices in “load pockets” went up. As ERCOT began encoun-
tering significant amounts of congestion from the West Zone
(causedbythe large influxofwindgeneration in thatzone),gen-
eration had to be decreased to manage the CSC limits.
Initially, WGRs were allowed to produce at full capability

with wind variability. The ERCOT Zonal Market employed a
15-min interval for balancing generation to load. The sched-
uling, pricing, and dispatch (SPD) software was executed for
minimumcostdispatchevery15minfor thenext interval’s fore-
casted load. Each qualified scheduling entity (QSE) submitted
up balancing energy services (UBES) and down balancing en-
ergy services (DBES) offers for those generation units in the
QSE’sportfolio.TheUBESandDBESofferswereonaconsol-
idatedportfolio basis so thatwhen theQSEreceivedaUBESor
DBES deployment for each congestion zone for each interval,
the QSE could optimally generate from any unit to fulfill the
deployment. ERCOT had traditionally used a zonal minimum
down-balancing requirement applied to the conventional gen-
eration portfolio of each qualified scheduling entity (QSE) as a
method for ensuring the ability to manage decreasing load sce-
narios, i.e., when generation on the system should decrease for
powerbalance.TheWGRswereinitiallyexemptfromthisdown
balancing requirement.
As the need to curtail WGRs increased, ERCOT initially

managed this need with out of merit energy (OOME) instruc-
tions to the individual WGRs. This required numerous manual
interventions by the ERCOT operators. Due to widespread
curtailment of wind generation during 2008, West Zone prices
were frequently set at negative values due to the quantity of
DBES required to manage the congestion. The negative prices
were a reflection of the effect of the federal Production Tax
Credit (PTC) which resulted in a negative break-even price

864 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 4, OCTOBER 2012

Fig. 4. Number of congested intervals per quarter.

Fig. 5. West Zone real-time price distribution during congested hours.

to generators. In January, 2009, ERCOT market rules were
revised to require WGRs to set their low sustainable limit
(LSL) to 10% of their installed capacity and begin offering the
minimum down-balancing requirement based on the difference
between the high sustainable limit (HSL) and LSL [6]. This re-
sulted in a more operationally systematic solution to interzonal
congestion.
Fig. 4 shows thenumberof settlement intervalsperquarter in

whichtheWestZonewindproductionwascurtailedduetozonal
congestion resulting in negative West Zone real-time Market
Clearing Price of Energy (MCPE) for the years 2007 through
2010. With few exceptions, the curtailed intervals resulted in
negativeWestZoneprices, as shownonFig. 5.Fig. 6 shows the
MCPEs which resulted from operating the zonal market during
theperiodof2007 through2010whenwindgenerationcapacity
increasedsignificantly.Thenumberofcongested intervalsactu-
ally decreased from 2008 to 2009 due to transmission upgrades
which increased the transfer limits between the West and North
congestion zones. However, the large number of intervals with
negative pricing and significant price separation between the
West Zone and all othermarket zones persisted through the end
of the zonal market operation on November 30, 2010.

IV. INTEGRATION OF WGRs INTO THE NODAL
MARKET STRUCTURE

Upon implementation of the ERCOT Nodal market on De-
cember 1, 2010, transmission congestion was managed by dis-
patching each WGR based on its Energy Offer Curve. All gen-
eration resourceswithinERCOT,whenonline andavailable for
energydispatch, are required to have either anOutputSchedule
or anEnergyOfferCurve for the specificunit.Thesecuritycon-
strained economic dispatch (SCED) algorithm in the ERCOT

Fig. 6. Average annual zonal real time energy price.

Nodal market optimally dispatches each unit’s energy in inter-
valsof5minor less tomatchaggregate systemloadwhileman-
aging congestion limits. The SCED algorithm relies on the net-
work topologyprovidedbyadetailednetworkmodel that accu-
ratelydepicts thenear real-time transmission capacity available
throughout the system, allowingERCOTtodispatch theWGRs
more efficiently. However, the negative pricing for wind gener-
ation during certain periods will continue to persist until addi-
tional transmission capacity is realized through the completion
of the CREZ projects.
In the initialdevelopmentof theNodalmarket rules, itwasas-

sumedthat thehighsustainable limit (HSL)of theWGRswould
be set to their actual output level based on telemetry so that
SCEDwouldhave thebest estimateof the actual production for
the next SCED execution. However, as the need for WGR cur-
tailment became apparent in the zonal market, setting the HSL
equal to the actual energy production from the wind generation
resource was not indicative of the actual production potential
due to the reduction in actual energy production as a result of
curtailment. Thus, the HSL was limited due to the curtailment.
When not curtailed, the WGR is required to continue to

telemeter the actual production as its HSL. Initially, when
the dispatch level (referred to as the Base Point in the Nodal
market protocols) received from SCED was less than the actual
wind generation production by more than 2 MW, the WGR
was considered to be curtailed because SCED had calculated a
lower base point than full output due to congestion limitations
on the dispatch. However, determining systematically exactly
when the WGR was curtailed was problematic because too
many variables were changing in real time. Therefore, ERCOT
made a change in their system to telemeter a curtailment in-
dicator or “flag” to the control system of the QSE responsible
for responding to the ERCOT dispatch instruction to the WGR.
When the WGR is curtailed by SCED to manage congestion,
the HSL telemetered from the WGR is set to a calculated
wind generation resource production potential (WGRPP).
The WGRPP is calculated by the WGR based on wind speed
at the plant site using empirical data correlation. When the
curtailment ends, the WGR is required to return to telemetering
the HSL equal to the actual wind generation (after a delay to
allow for the effects of the curtailment to end) so that HSL

DURRWACHTER AND LOONEY: INTEGRATION OF WIND GENERATION INTO THE ERCOT MARKET 865

Fig. 7. Actual wind production and estimates of curtailment.

telemetered value represents the actual physical uncurtailed
WGR generation.
Fig. 7 shows actual wind production and estimates of local,

zonal curtailment for 2008 through November 2010. The data
shown for 2011 shows only wind production. Data on curtail-
ments under the nodal market design in 2011 are not currently
available.

V. METHODOLOGY FOR DETERMINING ANCILLARY SERVICE
REQUIREMENTS WITH INCREASING QUANTITIES

OF WIND GENERATION

ERCOT contracted with GE Energy to produce an Analysis
of Wind Generation Impact on ERCOT Ancillary Services Re-
quirements in 2008, referred to hereafter as the 2008 GE Wind
Study [7]. Prior to 2008, ERCOT had used a methodology for
determining the quantity of up regulation service (URS) and
down regulation service (DRS) by blocks of hours for each day
of the month. The methodology required ERCOT to calculate
the average and standard deviation for the URS and DRS
deployed for each hour in the previous month and for the same
month of the previous year. For each of these months, ERCOT
then calculated the amount of regulation service required by
hour to provide an adequate supply of regulation capability
98.8% of the time, or effectively 2.5 standard deviations [8].
A key component for successful integration of large quanti-

ties of wind generation into ERCOT was and continues to be

provision of adequate quantities of ancillary services to com-
pensate for large, unpredictable wind ramping events. Every
year, ERCOT has made incremental improvements to its an-
cillary service requirements methodologies to address the chal-
lenges of increasing wind penetration in the market. In 2008,
theblockhour concept of regulation requirementswas replaced
by individual hourly requirements. ERCOT also started per-
forming a back-cast of the regulation exhaustion rate to deter-
mine how much additional regulation would be needed in the
previous month to avoid exceeding a 1.2% exhaustion rate for
any hour. In 2009, ERCOT began using tables from the 2008
GEWindStudy toestimate the increase in regulationneededfor
increasing wind generation capacity during the year. In 2010,
ERCOT added the provision for an additional regulation incre-
mental requirement if the average 1-min Control Performance
Standard (CPS1) score was less than 90 or 100 for the previous
month. All of these successive changes were made to continue
to optimize the regulation requirements on the system.
Forpurposesofcalculationofancillary service requirements,

ERCOT treats wind generation as “negative load.” Ancillary
service responsibilities resulting from uncertainty in load fore-
castingalsoconsiderseparately theuncertaintyof thewindfore-
cast itself.Conventionalgenerationcapacity(i.e.,generatingca-
pacity other than wind generation) is dispatched to meet the net
load which is defined as firm customer load less actual wind
generation. In2011,ERCOTfurther increased thegranularityof

866 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 4, OCTOBER 2012

Fig. 8. Hourly URS requirements for the month of April.

Fig. 9. Hourly DRS requirements for the month of April.

the calculationof regulation requirements by adding criteria re-
quirement that theregulationforeachhourmustbeat leastequal
to the greatest 5-min net load change for the same hour of any
day of the previous month. This progression of improvements
to the regulation requirement calculations shows the proactive
response ERCOT has made to optimize the procurement of an-
cillary services as thewindgenerationhas steadily increasedon
the system.
Wind generation in West Texas is anti-correlated or out-of-

phase with the daily load curve (i.e., the maximum output of
windgeneration inWestTexasgenerally occurswhencustomer
loads are the lowest). In ERCOT’s experience, load and wind
forecasterrorsarevirtually independentandnet loadforecasting
accuracy decreases with increasing wind penetration.
One would expect that the requirement for regulation ser-

vice on the ERCOT system would increase significantly with
increasing wind penetration. However, with the optimization
of the requirement methodology, the increase in quantities of
regulation purchased has actually been minimal. For the month
of April, the increase in wind generation was 79% from 2007
to 2008, 40% from 2008 to 2009, and 28% from 2009 to 2010.
Figs. 8 and 9 show the hourly URS and DRS requirements for
the month of April, 2007 through 2010. The URS and DRS
requirements increased in some hours and decreased in other
hours year to year, but overall, did not significantly change
even though the amount of installed wind generating capacity
in ERCOT significantly increased over the same time period.

Fig. 10. Wind output, regulation, and RRS deployments, January 28, 2010.

Based on meteorological analysis, the 2008 GE Study pre-
dicted there could be a 2000- to 3000-MW decrease in wind
generationovera30-minperiodunder thehighwindgeneration
capacity scenario. This type of large change in wind generation
requiresadequateresponseofallancillaryservice typestomain-
tain reliability standards and grid stability. Fig. 10 shows the
actual wind output, regulation and responsive reserve service
(RRS), i.e., spinningreserve,deployments forJanuary28,2010.
On that day, ERCOT experienced an actual decrease in wind
generation of 2500 MW in approximately one hour. As shown
on Fig. 10, at 0930 and 1400 on January 28, 2010, URS was
fully deployed and RRS was deployed to maintain frequency.
Since Nonspin Reserve Service (NSRS) in ERCOT is pro-

cured for load uncertainty and wind is considered to be “nega-
tive load,” large changes in wind generation (often referred to
as “ramping events”) must be considered in the NSRS require-
mentcriteria.ERCOTincorporated the recommendationsof the
2008GEStudy into itsmethodologyfordeterminationofNSRS
requirements byfirst calculating anet loadvalue by subtracting
the actual wind generation from the actual load. The historical
net load is then compared to the forecasts of customer load and
wind generation output to determine the historical accuracy ob-
served in forecasting. ERCOT then subtracts the up regulation
service requirement from the 95th percentile of the net load un-
certainty to determine the amount of NSRS to purchase during
each hour of the day for the upcoming month. For on-peak
hours, i.e., hours ending 7 through 22, ERCOT sets a floor on
the NSRS requirement equal to the capacity of the largest con-
ventional generating unit. By appropriately distributing the ad-
ditional reservescoincidentwith the risk factorsassociatedwith
specific timeperiods,bothseasonallyandona timeofdaybasis,
the quantities of ancillary service requirements are optimized.
Improvements in wind generation forecasting will help

optimize system control performance and ancillary service
procurements.ERCOTcurrently utilizes a state-of-the-artwind
forecasting model that applies artificial intelligence techniques
to “learn” from its mistakes to continually improve both its
hour-ahead and day-ahead wind production forecasts. The
mean absolute percent error in the day-ahead and hour-ahead
forecasts is currently approximately 10% and 7%–8%, respec-
tively. ERCOT has found it difficult to correlate the actual
generation of WGRs with the forecast of WGR production due
to the significant durations of curtailments that are continuing

DURRWACHTER AND LOONEY: INTEGRATION OF WIND GENERATION INTO THE ERCOT MARKET 867

to be experienced. For example, during periods when the WGR
limits production due to curtailments, the correlation of actual
wind togenerationcannotbe included in theempirical analysis.
In addition, ERCOT has recently implemented a tool de-

veloped in conjunction with its wind forecasting contractor
to alert the ERCOT system operators to predict impending
large ramping events [9]. Improvement in short-term wind
forecasting will increase the effectiveness of this tool. With the
recent implementation of a curtailment indicator flag so that
curtailment periods are more accurately accounted for, wind
generators will now have information necessary to allow them
to more accurately analyze operational data and improve their
wind generation forecasts.

VI. CONCLUSION

Since 2001, ERCOT has successfully integrated almost
10000MWofwindgeneration resources into a relatively small
system (68000 MW peak load) through continuous modifica-
tion and improvement of its market rules [10]. That success
has been achieved by continual review and revision of market
rules by stakeholders and regulators alike. New tools and
operational procedures have been developed and implemented
to allow ERCOT to more efficiently and more reliably manage
an ever-increasing penetration of variable wind generation in
its system. However, as more wind generation is added to the
ERCOT system, additional challenges lie ahead.

REFERENCES

[1] Public Utility Regulatory Act Title II, Public Utilities Code, Chapter
39, Restructuring of Electric Utility Industry, Subchapter A—General
Provisions, Section 39.001(b).

[2] Public Utility Regulatory Act Title II, Public Utilities Code, Chapter
39, Restructuring of Electric Utility Industry, Subchapter Z—Miscel-
laneous Provisions, Section 39.904(a).

[3] Competitive Renewable Energy Zones (CREZ) Transmission Op-
timization Study Public Utility Commission of Texas Docket No.
33672, Apr. 2, 2008, .

[4] ERCOT Market Notice General, Remaining effective dates of certain
Nodal Protocols and Operating Guides Sections and retirement dates
of Certain Zonal Protocols and Zonal Operating Guides in preparation
for fullNodalMarketOperationsonOperatingDayDecember1,2010,
October 20, 2010.

[5] ERCOT System Planning Report , Jul. 2011.
[6] ERCOT Zonal Protocols Section 4.10.4, Resource Low Sustainable

Limit as aPercent of High Sustainable Limit Measure [Online].Avail-
able: www.ercot.com

[7] Public Utility Commission of Texas Docket No. 33672 Mar. 28, 2008,
Analysis of Wind Generation Impact on ERCOT Ancillary Services
Requirements, GE Energy.

[8] 2010–2011ERCOTMethodologies forDeterminingAncillaryService
Requirements ERCOT Board Approved 11/16/2010.

[9] ERCOT Press Release Mar. 25, 2010.
[10] ERCOT Press Release Aug. 3, 2011.

Henry L. Durrwachter (M’73–SM’89) received
the B.S.E.E. degree in electrical engineering from
the University of Texas at Arlington in 1972.
He is currently Director of ERCOT Market

Services at Luminant Energy Company LLC. He
has served on the Board of Directors of the Texas
Renewable Energy Industries Association (TREIA),
the Dallas Electric Club, and currently serves as
President of the Board of Directors of the Utility
Wind Integration Group (UWIG). He is a Registered
Professional Engineer in the State of Texas and has

over 39 years of experience in the electric power industry.
Mr.Durrwachter is amemberof the IEEEPowerEngineeringSociety (PES).

SherryK.Looney received theB.S.C.H.E.degree in
chemical engineering from the University of Texas,
Austin, in 1980.
She is currently a Sr. Project Manager in Regu-

latory Affairs at Luminant Energy Company LLC.
She also serves as the Chair of the Qualified Sched-
ulingEntitiesManagersWorkingGroupaspartof the
ERCOTmarketparticipantorganization.Sheandhas
30years experience inoil andgasproduction,project
engineering, gas pipeline operation, and power mar-
keting operations.

Risk Modeling in Strategic Behavior and Equilibrium
of Electricity Markets with Wind Power

Kang Xiaoning, Wang Xian, Zhang Shaohua,
Key Laboratory of Power Station Automation Technology, Department of Automation,

Shanghai University, Shanghai 200072, China
E-mail: angie_1288@126.com

Abstract—High wind power penetration in power systems will
significantly increase risks faced by the conventional generators
in the deregulated electricity markets. This will further affect
these generators’ risk preferences and strategic behaviors. A
supply function equilibrium model of electricity markets with
wind power is developed taking into account the conventional
strategic generators’ risk preferences. The impacts of generators’
risk preferences on the strategic behaviors and market
equilibrium are theoretically examined in detail. Numerical
examples are presented to verify the validity of the theoretical
analysis. It is shown that through strategic bidding, a generator’s
output and profit risk will be lowered with increasing its risk
aversion. In addition, a generator’s expected profit and its risk
will increase with increasing its rivals’ risk aversion.

Keywords-electricity market; wind power; risk preference;
supply function equilibrium

I. INTRODUCTION
The worldwide restructuring and deregulation of electric

power industries has been accompanied by extensive research
on strategic behaviors and market power analysis of
oligopolistic electricity markets. Equilibrium models using
game-theoretic behavioral assumptions are broadly employed
to examine strategic interactions among participants in
electricity markets [1], [2]. In recent years, wind power, due to
its significant role in mitigating environment pollution, has
been widely used and rapidly developed around the world [3],
[4]. Wind power has features of relatively strong randomness,
volatility and intermittence [5]. Thus large-scale wind power
penetration in power systems will inevitably increase risks
faced by conventional generators who participate in market
competition. This will further affect these generators’ risk
preferences and strategic behaviors in electricity markets. In
this context, integration of risks in equilibrium models of
electricity markets with wind power is identified as an
important issue.

Until now, on the one hand, a considerable amount of
research has been conducted on risk modeling in single
generator or purchaser’s optimization problem in the
deregulated power market. For example, in order to model the
generator or purchaser’s risk preference, the mean-variance
utility theory is used in [6]-[12] while in [13]-[15] the
conditional value at risk (CVaR) theory is employed. On the
other hand, much research work has also been conducted
regarding equilibrium models of electricity markets. Among

the most extensively used models are the Cournot model
[16]-[20] and the linear supply function equilibrium (LSFE)
model [21]-[26]. It can be noted that to date, in most of the
equilibrium models of electricity markets, wind power is not
taken into account. In addition, the conventional generators’
risk preferences are overlooked, or all generators are assumed
to be risk neutral in the presence of risks.

Given this background, a LSFE model of electricity
markets with wind power is presented in this paper. The
mean-variance utility theory is employed to model the
conventional generators’ risk preferences. The impacts of
generators’ risk preferences on the strategic behaviors and
market equilibrium are theoretically examined in detail.
Numerical examples are presented to verify the validity of the
theoretical analysis.

II. THEORETICAL MODEL

A. Assumptions
Suppose that in a power market, there are n strategic

conventional generators and a certain number of wind power
units. The wind power units are assumed to be price-takers.
The market demand at time period t is expressed by the
following linear inverse demand function:

p a bD= − (1)
where, p is the market price at time t; a and b are constant
coefficients taking values greater than 0; D is the market
demand at time t.

The conventional generators have the following quadratic
cost functions:

2( ) 0.5i i i i i iC Q Q Qα β= + ，i=1,2,…,n (2)
where，Qi is generator i’s output; αi and βi are cost parameters
taking values greater than 0.

The output of wind power units in time t, Qw, is assumed
to follow a probability distribution with a mean value of μw and
a standard deviation of σw. Since the market demand D
satisfies:

1
n

i w
i

D Q Q
=

= +∑ (3)
the inverse demand function in (1) can be reformulated as
follows:

This project is supported by National Natural Science Foundation of China
(No.70871074) and “11th Five-Year Plan” 211 Construction Project of Shanghai
University.

978-1-4244-6255-1/11/$26.00 ©2011 IEEE

1
( )

n

i
i

p a b Q Q

ω

=

= − +∑ (4)

The conventional generators compete by submitting their
bids in the form of linear supply functions as follows:

i i iQ x d p= + ，i=1,2,…,n (5)
where, xi and di are the intercept and slope of supply function
respectively; p is the expected market price. In this paper,
the parameter xi is chosen as generator i’s strategic variable,
and di is assumed to be the same as the slope of generator i’s
marginal cost, i.e. di=1/βi.

B. Equilibrium Model
The profit of generator i is equal to the payment for its

production quantities at the market prices, minus its generation
costs, that is:

( )i i i ip Q C Qπ = ⋅ − (6)
The mean and variance of generator i’s profit can be

calculated as follows:
2E[ ] 0.5i i i i i ip Q Q Qπ α β= ⋅ − − (7)

2 2 2 2Var[ ] Var[ ]i i i wQ p Q bπ σ= ⋅ = (8)
Using the mean-variance utility theory, generator i’s

decision problem can be formulated as the following
utility-maximization problem:

2 2

2 2

M ax (1 )E[ ] Var[ ]

(1 ) ( ) 0.5
i

i i i i ix

i i i i

i

i i

J r r

r p Q Q r b Q ω

π π

α β

σ

= − −

⎡ ⎤= − − − −⎣ ⎦
(9)

subject to
1
( )
n

j
j

p a b Q ωμ
=

= − +∑ (10)
, 1, 2, ,j j jQ x d p j n= + = (11)

where，Ji is generator i’s utility function；ri is generator i’s risk
preference factor. ri=0 means that generator i is risk neutral,
0

The equilibrium model taking into account generators’ risk
preferences can be formulated by gathering n generators’
decision problems expressed by (9)-(11). By combining each
generator’s first-order optimality (KKT) conditions, the
equilibrium solution of the model can be obtained by solving
these conditions.

From (9)-(11), the KKT conditions of generator i’s
decision problem can be derived as follows:

2 2
1

(1 )[ ( ) ]

2 ( 1) 0

i i i i i i

i
n

i i i j
j

r Q d p Q d

r b d Q b dω

α β

σ λ
=

− + − −

− − + =∑
(12)

2 2(1 )[( ) ] 2 0i i i i i ir p Q r b Q bωα β σ λ− − − − − = (13)

1
0

n
j
j

a b Q pωμ
=

⎡ ⎤
− + − =⎢ ⎥

⎣ ⎦
∑ (14)

, 1, 2, ,j j jQ x d p j n= + = (15)
where, λ is the Lagrange multiplier associated with (10).

From (12) and (13), the equilibrium output of generator
i(i=1,2,…,n) can be obtained by eliminating λ:

(1 )( )( )i

i i
i

i

r p bd G
Q

F

α− − −

= (16)

where, 2 22 ( ) (1 )i i i i iF rb bd G G rωσ β= − − − (17)

1

1
n

j
j

G

b d

=

= +∑ (18)
Substituting (16) into (14), the equilibrium of expected

market price can be derived as follows:

1
1
1

n

j j

j j
n

j
j j

H
a b b

F
p

H
b

F
ω

α
μ

=
=

− +
=

+
∑

∑
(19)

Where, (1 )( )j j jH r bd G= − − ，j=1,2,…,n (20)
Generator i’s equilibrium output Qi (i=1,2,…,n) can be

obtained by applying (19) to (16). Using (15), generator i’s
strategic variable xi can be easily calculated as:

i i ix Q d p= − ，i=1,2,…,n (21)

C. Impacts of Generator’s Risk Preference on Equilibrium
In this section, the impacts of generators’ risk preferences

on the equilibrium results, such as generators’ strategic
variables, expected profits and the expected market price, are
examined by theoretical analysis.

From (17)-(20), the first-order partial derivative of the
expected market price with respect to generator i’s risk
preference can be derived as follows：

3 2 2

2
1

2 ( )
( )

(1 )

i
in

ji
i

j j

b G bdp
p

Hr
b F

F

ωσ α

=

−∂
= ⋅ −

∂
+ ⋅∑

(22)

From (16)-(20), the first-order partial derivative of
generator i’s equilibrium output with respect to its risk
preference can be expressed as follows.

2 2 2

1,2

1

2 ( )
(1 )( )

(1 )

n
ji i

in
j j j ii j

i
j j

HQ b G bd
b p

Hr F
b F

F

ωσ α

= ≠

=

∂ −
= ⋅ + −

∂
+

∑
∑

(23)

Using (21), the first-order partial derivative of generator i’s
strategic variable with respect to its risk preference is:

i i
i
i i i

x Q p
d

r r r
∂ ∂ ∂

= −
∂ ∂ ∂

(24)

From (18), it can be found that
1

1 0
n

j

i
j

G b d bd
=

= + > >∑ ,
this implies that 0ibd G− < . Given that 0 1ir≤ < , it can be observed from (17) and (21) that Fj<0 and Hj<0, j=1,2,…,n. Due to the fact that in general, Qi≥0, thus it can be seen from (16) that ip α≥ . Therefore, from (22) and (23), it can be concluded that 0ip r∂ ∂ ≥ and 0i iQ r∂ ∂ ≤ . As such, it can be followed from (24) that 0i ix r∂ ∂ ≤ .

The above theoretical results show that generators’ risk
preferences have important influences on their strategic
behaviors and the market equilibrium. A generator’s output
will decrease with increasing its risk aversion and this is
achieved by reducing its strategic variable. As a result, the
expected market price will increase with increasing generators’
risk aversion.

In the following, the impacts of each generator’s risk
preference on its expected profit and variance are further
examined. From (7), the first-order partial derivative of
generator i’s expected profit with respect to its risk preference
can be derived as follows.

E[ ]
[ ( )]i ii i i i

i i i

Qp
Q p Q

r

r r
π

α β
∂ ∂∂

= ⋅ + − +
∂ ∂ ∂

(25)

From (12) and (13), the following equation can be obtained
by eliminating λ:

2 2
1,

2
( )

1 1

i i i
i i i n

i
j

j j i

r b Q Q b
p Q

r b d

ωσα β

= ≠

− + = +
− +∑

(26)

From (22) and (23), we have:

1,
1
n
j

j j i ji

i i
H
b

FQ p
r r b

= ≠

+
∂ ∂

= − ⋅
∂ ∂

∑
(27)

Substituting (26) and (27) into (25) gives:
E[ ]

(1 )i i
i i

p
Q K

r r
π∂ ∂

= ⋅ −
∂ ∂

(28)

where,

2
1,
1

2
1

1,
1,

2

1
1 1

12
1

1 (1 )

i
n
i
j
j j i
n

kn
j k

n
j j i j

k j
k k j

r b
K

r b d

b dr b
r b d d b

ω
ω
σ
σ
= ≠
−
=
= ≠
= ≠

⎡ ⎤
⎢ ⎥
⎢ ⎥= +
⎢ ⎥− +⎢ ⎥
⎣ ⎦
⎡ ⎤⎛ ⎞
⎢ ⎥+⎜ ⎟
⎢ ⎥⎜ ⎟⋅ + +⎢ ⎥⎜ ⎟− +⎢ ⎥⎜ ⎟
⎢ ⎥⎝ ⎠⎣ ⎦

∑
∑
∑
∑

(29)

Given that 0 1ir≤ < , it can be followed that 1lim 1i i

r
i

r
r→

= ∞
−

,

and
1

i
i

r
r−

is an increasing function of ri. As such, it can be

observed from (29) that K is also an increasing function of ri.

In addition, there must exists a 0r ( 2
1

2 1b ωσ
=

+
) such that

22
1

1
i

i

r b
r

ωσ ≥
−

when 0ir r≥ . That is, K>1 holds when 0ir r≥ . Thus

it can be concluded from (28) that E[ ] 0i irπ∂ ∂ ≤ holds
when 0ir r≥ .

For the case when 00 ir r≤ < , consider first that ri=0 and

rj=0(j≠i), the expression (29) can be rewritten as:

1,
1,

1, 1

(1 )
1

1
1 1
n

k jn
k k j

n n
j j i

j k
j j i k

b d d b
K

b d b d

= ≠
= ≠

= ≠ =

⎡ ⎤
+⎢ ⎥

⎢ ⎥= ⋅ +
⎢ ⎥

+ +⎢ ⎥
⎣ ⎦

∑
∑

∑ ∑
(30)

Due to the fact that

1,

1, 1,

1
(1 )
1
n

k jn n
k k j

jn
j j i j j i

k
k

b d d b
b d

b d
= ≠

= ≠ = ≠

=

+
<

+

∑
∑ ∑

∑
(31)

thus from (30), K<1 holds for ri=0 and rj=0(j≠i). From (29) it follows that K is a decreasing function of rj(j≠i). Therefore, K<1 holds for ri=0 and 0jr ≥ (j≠i), which means that

E[ ] 0i irπ∂ ∂ ≥ when ri=0 and 0jr ≥ (j≠i). K will increase
with increasing ri. Since K is also affected by other generator’s
risk preference rj(j≠i), it is possible that K becomes greater
than 1 (which leads to E[ ] 0i irπ∂ ∂ ≤ ) when ri increases to a
value smaller than r0.

Therefore, each generator’s expected profit will generally
increase with increasing its risk aversion when its risk aversion
is relatively low. For relatively high degrees of a generator’s
risk aversion, the generator’s expected profit will decrease
with increasing its risk aversion.

From (8), the first-order partial deviation of the variance of
generator i’s profit with respect to its risk preference is:

2 2[ ] 2i iw i
i i

Var Q
b Q

r r
π

σ
∂ ∂

= ⋅
∂ ∂

(32)

Since 0i iQ r∂ ∂ ≤ , it is obvious that Var[ ] 0i irπ∂ ∂ ≤ ,
which means that the variance of generator’s profit will
decrease with increasing its risk aversion.

These results can be explained to some extent according to
the generator’s utility-maximization objective in (9). When the
degree of a generator’s risk aversion is relatively low, the
generator’s optimization objective is mainly to maximize its
expected profit. As the degree of a generator’s risk aversion
goes up, the weight of profit risk in the generator’s
optimization objective will increase and the generator will
sacrifice part of its expected profit to reduce its profit risk.

III. NUMERICAL EXAMPLES
Consider a power market, the coefficients in the market

demand function in a certain time period (1h) are assumed to
be: a=$80/MWh，b=$1.0/(MW)2 h. In this time period, the
output of wind power units is assumed to follow a normal
distribution with a mean of 10MW and a variance of 9.

We assume there are two symmetric conventional
generators in the power market. The cost parameters of
generator i(i=1,2) are: αi=$10.0/MWh, βi=$1.0/(MW)

2h.
Impacts of the two generators’ risk preference on generator 1’s
strategic variable, equilibrium output and the expected market
price are illustrated in Fig. 1, 2 and 3, respectively.

Figure 1. Impact of risk preference on generator 1’s strategic variable

Figure 2. Impact of risk preference on generator 1’s output

Figure 3. Impact of risk preference on the expected market price

From Fig. 1, it can be found that generator 1’s strategic
variable not only decreases with increasing its own risk
aversion but also decreases with increasing its rivals’ risk
aversion. However, the impact of its own risk aversion is much
greater. In addition, the more risk neutral a generator is, the
less impact its rivals’ risk preferences will have on its strategic
variable. From Fig. 2, it can be observed that generator 1’s
equilibrium output will decrease with increasing its risk
aversion, and will increase with increasing its rivals’ risk
aversion. However, the impact of its own risk aversion is much

greater. From Fig. 3, it can be seen that the expected market
price will increase with increasing the degree of generators’
risk aversion.

The above results indicate that as a generator’s risk
aversion increases, the generator will decrease its output by
strategic bidding. This will lead to an increase in its rival’s
output. However, the decrease in its own output is greater than
the increase in its rival’s output, leading to an increase in the
expected market price.

The impact of generators’ risk preferences on generator 1’s
expected profit is depicted in Fig.4. It is shown that generator
1’s expected profit will generally increase with increasing its
risk aversion when its risk aversion is relatively low. For
relatively high degrees of generator 1’s risk aversion, its
expected profit will decrease with increasing its risk aversion.
This result is consistent with the theoretical analysis. In
addition, generator 1’s expected profit increases with
increasing its rivals’ risk aversion.

The impact of the generators’ risk preferences on the
standard deviation of generator 1’s profit is given in Fig.5. The
standard deviation of generator 1’s profit decreases with
increasing its risk aversion, but slightly increases with
increasing generator 2’s risk aversion. This means that
increasing generator’s risk aversion can effectively reduce its
profit risk, while an increase in its rivals’ risk aversion will
have an adverse effect on its profit risk.

Figure 4. Impact of risk preference on generator 1’s expected profit

Figure 5. Impact of risk preference on the standard deviation of generator

1’s profit

IV. CONCLUSIONS
In this paper, a supply function equilibrium model of

electricity markets with wind power is developed taking into
account the conventional strategic generators’ risk preference.
The impacts of generator’s risk preference on the equilibrium
results are examined by theoretical analysis and numerical
simulation. It is shown that as the degree of a generator’s risk
aversion increases, the generator will decrease its output by
strategic bidding. This will lead to an increase in its rival’s
output. However, the decrease in its own output is greater than
the increase in its rival’s output, leading to an increase in the
expected market price. Furthermore, it is also demonstrated
that each generator’s expected profit will generally increase
with increasing its risk aversion when its risk aversion is
relatively low. For relatively high degrees of a generator’s risk
aversion, its expected profit will decrease with increasing its
risk aversion. Each generator’s expected profit will increase
with increasing its rivals’ risk aversion. In addition, increase in
the degree of a generator’s risk aversion can effectively reduce
its profit risk, but increasing its rivals’ risk aversion will have
an adverse effect on its profit risk.

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Selling Wind Power in Electricity Markets: The status today, the
opportunities tomorrow

Eilyan Y. Bitar & Kameshwar Poolla

Abstract— California has set a target of 33% penetration
from all renewable sources by 2020. Wind energy must play
a key role in realizing such aggressive targets. At these deep
penetration levels, integration of utility scale wind production
into the electricity grid poses serious engineering and market
challenges. These are due to the uncertainty, intermittency, and
uncontrollability of wind power. Wind is random.

Today, wind energy is assimilated into the grid by legislative
mandates, feed-in tariffs, favorable penalty pricing, guaranteed
grid access, and/or construction subsidies. The variability in
production is absorbed by scheduling operating reserves. For
example, in California, the Participating Intermittent Resource
Program (PIRP) legislation compels the system operator to
accept all produced wind power subject to certain contractual
constraints. This amounts to a system take-all-wind scenario in
which wind power is treated as a negative load. The burden
of reserve costs is socialized among the load serving entities
(LSE).

This extra-market approach works at today’s modest pene-
tration levels, but will become untenable as wind penetration
increases for both economic and environmental reasons. We
discuss the consequences on wind integration in the near
term as wind power producers are forced to participate in
competitive electricity markets alongside conventional dispatch-
able generation. We explore aggregation, firming strategies,
and fair reserve cost allocation. In the long term, we argue
that new market mechanisms are required to deal with wind
power variability. These include intra-day markets to leverage
on improved forecast accuracy on shorter horizons, bilateral
contracts with interruptible loads such as electric vehicles, and
most radically, the possibility of selling random power through
price differentiated quality of supply.

I. THE VARIABILITY CHALLENGE

Wind power is inherently variable [12]. It is non-
dispatchable, highly intermittent, and difficult to forecast on
horizons beyond five minutes. At levels of deep renewable
penetration, these generation variability characteristics will
pose formidable challenges to the preservation of instanta-
neous balance between supply and demand, while simulta-
neously respecting system security constraints.

In contrast to variable renewable generators, the majority
of conventional thermal generators in use today are funda-
mentally dispatchable and, to a large extent, predictable.
Nonetheless, the power system architecture and attending
operations have been designed to explicitly deal with the

Supported in part by the NSF under Grants EECS-0925337, ECCS-
1129001. Thanks to Pramod Khargonekar, Pravin Varaiya, and Ram Ra-
jagopal for many helpful discussions.

Corresponding author: E.Y. Bitar is with the Department of Electrical
and Computer Engineering, Cornell University eyb5@cornell.edu

K. Poolla is with the Department of Electrical Engineering and Computer
Science, U.C. Berkeley poolla@eecs.berkeley.edu

variability emanating from natural fluctuations in load and
unplanned contingency events such as branch and/or gen-
eration outages. Generally speaking, imbalances arising be-
tween generation and load are compensated for by reserve
generation capacity procured by the independent system
operator (ISO) through ancillary services (AS) markets. The
various phenomena responsible for system imbalances occur
on differing time scales and thus require the procurement
of reserve resources with a variety of response capabilities
[11], [13]. The subsequent cost of the procured reserve
capacity is then socialized among the participating load
serving entities (LSE) based on their relative contribution to
the total demand. To a large extent, the current approach to
compensating variability amounts to a paradigm in which
generation is tailored to follow load. Given the relative
success of the status quo, it is tempting to presume that the
added variability of renewable generation can be similarly
compensated for with existing reserve mechanisms. In fact,
this is the approach taken by many balancing authorities
within the United States: all wind and solar power pro-
duction is taken by the system operator and the attendant
variability is compensated for with existing reserve margins.
As we will see in the following section, this approach to
renewable energy integration will become untenable at deep
penetration levels.

II. DEALING WITH VARIABILITY TODAY

Today, wind and solar energy are assimilated into the
grid through legislative mandates, feed-in tariffs, lenient
penalty pricing, guaranteed grid access, and/or construc-
tion subsidies. Specifically, in California, the Participating
Intermittent Resource Program (PIRP) legislation compels
the independent system operator (ISO) to accept all produced
wind power subject to certain contractual constraints. This
amounts to a system take-all-wind scenario in which wind
power is treated as a negative load and the subsequent
increase in the variability of net-load is absorbed by a port-
folio of reserve generation capacity, whose cost is allocated
among the load serving entities (LSE). This socialization
of added reserve costs among the LSEs can be interpreted
as an implicit subsidy for variability costs to participating
wind power producers. Accordingly, there are ongoing public
policy and operational procedure debates regarding the fair
allocation of the costs of these increased reserves [15].

Even at today’s modest levels of penetration, the added
variability due to wind results in systemic operational prob-
lems. For example, on February 26, 2008, the Electric

2012 American Control Conference
Fairmont Queen Elizabeth, Montréal, Canada
June 27-June 29, 2012

978-1-4577-1096-4/12/$26.00 ©2012 AACC 3144

Reliability Council of Texas (ERCOT) had to declare an
emergency load curtailment plan due in part to an inaccurate
forecast of wind power production [6]. The impact of inter-
mittency and inaccurate forecasting on reserve margins will
only become more pronounced as wind energy penetration
increases [9], [8], [7]. In order to quantify this statement,
several wind integration studies have computed detailed
estimates of the increase in reserve requirements needed to
compensate the added variability due to wind under a system-
take-all wind regime. For example, the 2010 EWITS report
[7] by NREL projects that regulating reserve requirements
will increase by 1500 MW (on average) under a 20% percent
wind energy penetration scenario in the PJM interconnection.
Such an increase in reserve requirements is unacceptable. It
is too expensive. Ergo, it will rapidly become infeasible to
continue the implicit subsidization of the variability costs
among the load serving entities. Moreover, it severely mit-
igates the net greenhouse gas benefit of renewable energy,
as regulating reserves are normally supplied by fast-acting,
fossil fuel based thermal generators such as natural gas
turbines. Clearly, the current strategy cannot scale.

III. THE OPPORTUNITIES TOMORROW

As renewable energy penetration increases, how must the
assimilation of variable power evolve, so as to minimize
integration costs, while maximizing the net environmental
benefit? Clearly, strategies that mitigate additional reserve
requirements will be an essential means to this end. Such
strategies will fundamentally fall into three (overlapping)
categories: (a) direct reduction of variability in generation,
(b) new market mechanisms to absorb variability, and (c)
demand-side solutions that use flexibility to adapt to vari-
ability in generation.

A. Generation-side Firming Solutions

In the near term, it is likely that wind and solar power
producers will be faced with increased exposure to market
signals that incentivize reduction in output variability – a
stark contrast to the California Participating Intermittent
Resource Program (PIRP). For example, in the United King-
dom, large wind power producers are forced to participate
in conventional wholesale electricity markets where they are
subject to ex-post financial penalties for deviations from
contracts offered ex-ante in forward markets [1], [3] – thus
eliminating the implicit subsidy for variability costs. The
implementation of imbalance penalty mechanisms represents
an initial departure from the system-take-all-wind approach.
In response to the financial risk emanating from uncertainty
in wind power production, a rational wind power producer
will be forced to curtail its projected output, thus decreasing
the amount of variability that has to be compensated for with
reserve generation by the system operator. However, such
a removal of the implicit subsidy for variability cost may
result in significant profit loss to the wind power producer.
Consequently, it will become necessary for the wind power
producer to develop and evaluate strategies that aid in the
mitigation of generation variability. We refer to this as

firming of variable renewable power. Potential approaches
to firming include: Improved Forecasting [20], [21], [22],
Storage [25], [24], Renewable Resource Aggregation [19],
Local Generation [3].

A fundamental question arises in this setting: What is the
marginal value of a given firming strategy? For example,
what is the subsequent increase in expected profit to a
renewable power producer per MWh of co-located storage
capacity [2]? What is the sensitivity of expected profit to
forecast uncertainty? Such a monetization of the aforemen-
tioned firming strategies will play a central role in shaping
investment decisions.

B. Market Mechanism Solutions
Firming mechanisms alone, may offer substantial near-

term benefits. However, if we are to transition to a power
system with >30% of the energy supply coming from
variable renewable resources, we will have to fundamentally
rethink the way the electricity grid is operated. In the long-
term, we argue that new market systems and instruments to
explicitly address the difficulties with variable generation are
required. For example, wind power forecast accuracy tends
to steadily decrease with the shortening of the prediction
horizon below five hours. Hence, the creation of intra-day en-
ergy and ancillary service markets (spanning the day-ahead
and real-time markets) [18] will introduce additional trading
opportunities to leverage on improved forecast accuracy on
shorter horizons. In this manner, renewable power producers
will have the opportunity to incrementally offer their variable
energy in a sequence of intra-day markets – allowing for a
more effective balance of risk and return.

More radically, we envision that market systems will
evolve to allow for price differentiated quality of supply.
Traditionally, the electric grid has been operated such that
generation is tailored to counteract the variability in load.
Load is largely treated as inelastic. However, there exists
significant flexibility inherent to load that is currently not
utilized. The power requirements of many commercial and
residential loads are such that a fraction of instantaneous
power demand at any given moment is inherently deferrable
in time (subject to certain deadlines on delivery). Examples
include thermal systems such as refrigerators, water heaters,
HVAC systems, and, assuming mass adoption of plug-in
electric vehicles, batteries. Given the inherent deferrability
of such loads, certain customers may be willing to accept
varying degrees of interruptible power supply in exchange
for a lower price, all without experiencing significant loss
of utility. Clearly, there is an opportunity and need to design
novel market systems that provide flexible consumers the op-
tion to purchase multiple quantities of energy – with varying
degrees of reliability – from variable generators. Initial work,
along these lines, on efficient pricing of interruptible power
service contracts can be found in Tan et al. [14]. Further,
in addition to participating in such novel market systems,
flexible load devices – if intelligently controlled in aggregate
– may be capable of providing various ancillary services
(e.g., regulation, load-following) to the system operator at
a much lower cost than conventional reserve generation, [4].

3145

As the penetration of variable renewable generation con-
tinues to increase, we must necessarily transition to a modus
operandi in which load is elevated to the equivalent status of
dispatchable generation. In this way, load can be tailored to
absorb variability in supply.

C. Coordinated Demand-side Solutions

On the demand side, the primary instrument to deal with
variability of renewable generation is coordinated aggrega-
tion of networked resources at the distribution side including
renewable and micro-generation, electricity storage, smart
appliances, and responsive loads. Coordinated Aggregation
[16], [17] is substantially more powerful than traditional
demand response (DR) or naive aggregation. It (a) involves
the intelligent control of deferrable loads and available
storage to match the variable generation, (b) requires Smart
Grid communication and computation infrastructure, and (c)
creates bulk power and ancillary service market opportunities
that are much more substantial that those offered by the peak-
shaving capacity markets. Existing DR research has focused
on the limited market opportunities of (a) shifting load from
peak [26], [27], [28], (b) contingency reserves, [29], [32],
[30], and (c) decentralized frequency response [31], [34],
[33]. Simple aggregation appeals to the fact that the sum
of random individual demands or renewable resources will
frequently have a smaller variance than the sum of the
individual variances. The value of coordinated aggregation
is derived from the improved forecastability it offers in ex
ante markets. This, in turn, will require less capacity reserve
requirements for load following. There are two approaches
to coordination that have been studied in the literature:
(a) direct-load control [29], where resources are centrally
managed, and (b) indirect load-control [35], where resources
respond to generation conditions through the proxy of real-
time prices. We submit that direct-load control is the more
attractive option as it offers greater flexibility and would face
fewer adoption obstacles as customers are not exposed to
real-time price volatility.

A central challenge in using coordinated aggregation to
absorb variability from renewable generation is that of re-
warding resource participation. This has two components:
(a) the avoided cost benefits of requiring smaller operating
reserves must be equitably monetized, and (b) resources
must be rewarded for participation. Harvesting revenue from
absorbing variability will require mandating new ancillary
service markets. This expensive proposition faces regula-
tory hurdles, fair pricing mechanisms, and acceptance by
renewable generators. Incentivizing participating resources
is difficult because the rewards are so small. Options that
we will discuss include lottery mechanisms and up-front
adoption rebates.

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