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Wind energy is a valuable supplement to conven-
tional energy sources, as wind power technology has
become mature. However, the maximum penetration of
wind power in electricity networks is limited by the
intermittent nature of wind energy. Fluctuations in wind
power production also makes it difficult for owners of
wind power plants to compete in the emerging electricity
markets. Energy storage devices with the ability to store
large amounts of energy for several hours or more, such
as flow cells and fuel cell systems [1], could provide the
necessary flexibility for smoothing of wind power, and
thus increase the possibilities for market operation.
Moreover, for potential wind farm sites remote from a
strong electrical connection point, energy storage could
provide an alternative to grid reinforcements.
There is a growing research interest in using energy
storage to increase the value of intermittent energy
sources in electricity markets [2,3,4]. However, impor-
tant issues such as the impact of market mechanisms,
transmission line constraints and forecasting accuracy ofwind power must be further explored to fully determine
the advantages and limitations of energy storage for this
purpose. Therefore, a method for the scheduling and
operation of such a distributed resource in a market sys-
tem has been developed and implemented in a computer
model. This paper aims to describe the proposed method,
and to show an application of the method on a case-
study, where the impact of energy storage sizing and
wind forecasting accuracy on system operation and eco-
nomics are emphasized. A list of symbols is provided in
the appendix.
The distributed resource is presented in Figure 1, and
consists of a wind power plant and an energy storage
device. The owner of the resource is assumed either to
have a demand for electricity,
, or to have contracts
with the local electricity consumers represented by an
aggregated load demand. The system is connected to the
main electricity network by a transmission line with lim-
ited capacity. Reactive power flow is neglected in the
simulations performed here, in order to keep the focus
on the scheduling and flow of real power.
The system components and the electricity market
model are presented below.
Wind power plant with local energy storage
connected to a scarcely populated grid. The direction of the
arrows refers to positive values of the variables.
The power output of the wind power plant is calcu-lated from the power curve in Figure 2. It is assumed that
the wind power plant consists of identical wind turbines,
wind power plant
Pw
transmission
line
external grid
local load
Pe
Pl
energy
storage
Ps
controllable load
Pcl
Magnus KorpsNTNU
Trondheim, Norway
Ragne HildrumStatkraft SF
Oslo, Norway
Arne T. HolenNTNU
Trondheim, Norway
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and that the wind conditions are the same for all tur-
bines.
The wind generator input/output characteristics used
in the model.
The energy storage device is defined by its energy
capacity, charging efficiency, discharging efficiency,
charging power capacity and discharging power capac-ity. The storage equations are as follows:
(1)
(2)
(3)
where
and
are the efficiencies of charging and
discharging respectively.
Charging and discharging occurs for negative and
positive values of
, respectively. The round-trip effi-
ciency of the storage device is .
The transmission line will act as a power source or
sink, depending on the balance between local load and
generation. The power transported on the line is the
power exchanged with the marked system, and is calcu-
lated from the power balance:
(4)
(5)
Power export corresponds to positive values for
which is measured at the load side of the transmission
line. The expression for active power losses is:
(6)
The maximum allowable power exchange is equal to
the transmission line capacity, while the minimum value
is given by.
(7)
If the net power production exceeds the line capacity,
the excess power is consumed by a controllable load,
, which is used only for this purpose.
In the Nordic spot market, daily bids for sale and pur-
chase of energy in the spot market are provided to thepower pool 12 hours before the actual day. After the spot
price has been settled, the final schedule is worked out.
During the operation, if a participant does not deliver the
specified amount at the spot market, then the discrep-
ancy must be settled on the regulating power market,
which normally results in a reduced income [6].
Market operation is simplified considerably in the
model. Since the marginal cost of power produced from
a wind power plant is zero, it is presumed that wind
energy always can be sold on the market. Each day at
12.00, the owner of the distributed resource performs the
scheduling of the hourly power exchange with the mar-
ket, . The hourly income from the spot market partic-
ipation is:
(8)
Power flow in the transmission line causes losses
which are bought for spot price:
(9)
The regulating market is simplified by using average
values. The prices for sale and purchase of electricity
traded on the regulating market are assumed to be pro-portional to the spot price:
(10)
where the deviation between actual and scheduled
power exchange, , is traded on the regu-
lating market. In the Norwegian regulating market, a dis-
crepancy between the actual and planned production
could in fact lead to higher revenue, depending on the
overall power balance in the market. This could for
instance happen in the cases when the actual powerexchange is higher than scheduled at the same time as
there is a power deficit in the market. However, it is pre-
sumed that in average, deviations from the production
plan are disadvantageous, since they increase the uncer-
tainty of the overall power balance.
The operation strategy consists of three separate
parts: 1) forecasting of wind velocity 2) scheduling of
the power exchange with the market and 3) on-line oper-
ation of the storage. In the present model, the forecastsof load and spot price are assumed to have 100% accu-
racy. A flowchart of the method is shown in Figure 3,
0 5 10 15 20 25 30wind velocity, v [m/s]
Pw[MW]
Pw
S t 1+( )S t( )
1
d------Ps t( ) t Ps 0>( )
S t( ) cPs t( ) t Ps 0( )
1 crp+( ) SP Pde vt Pde v 0
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and the various steps of the algorithm are described
below.
Flowchart of the operation strategy for a wind power
plant with energy storage. The indexes for day and hour are and .
A simple algorithm for computer-generated wind
velocity forecasts has been developed. The forecasted
average wind velocity for a specified time period is cal-
culated, based on the actual wind data for the same
period. The algorithm includes the following steps:
Read wind data for t=1..tend and the coefficient
of variation for mean wind velocity prediction
Calculate
Draw a random numberfrom the normal distribu-
tion with mean and standard deviation
Return the predicted wind velocity for
t=1..tend
As an example, for a wind series with mean value 8.1
m/s and standard deviation of 4.4 m/s, the RMSE value
of prediction error is found to be 2.57 m/s using the pro-
posed method with V = 0.
The operation scheduling of the system is performed
at the specified hour
each day. The objective is to
find the scheduling plan for the next day which maxi-mizes the expected profit. Since the wind velocity fore-
cast is uncertain, and a penalty is given for trading in the
regulating market, one should consider all possible com-
binations of wind velocities in the complete optimization
problem. However, at this stage of the modelling work,
the forecasted values are treated as deterministic vari-
ables in order to reduce the computational effort to a rea-
sonable size. Trading losses due to deviations between
actual and scheduled generation are consequently omit-ted in the optimization problem.
Given the spot price, load demand and short term
forecast of wind velocity, the optimization task is to
determine the hourly trading of electricity in the spot
market which maximizes the expected profit over the
scheduling period. Mathematically, the scheduling of the
distributed resource can be formulated as:
(11)
where
(12)
subject to the system operating constraints (1)-(7) and
the initial storage level. The terms
and
are
defined in equations (8) and (9). Since there are nor-
mally large uncertainties both in the short-term and long-
term prediction of wind velocity, the optimization hori-
zon
is chosen to be 24 hours. It is therefore benefi-
cial from an economic point of view to discharge the
storage completely at the end of each day. If there were
negligible errors in wind velocity predictions, the opti-
mization horizon should be increased. Then, it could be
favorable to store energy at the end of the day, for
instance if there were a risk for long periods with no
wind.
The optimization problem is solved using a dynamic
programming algorithm, which requires discretisation of
the storage level. The optimization routine returns the
expected path for t = 1..tend, which gives the opti-
mal scheduling of power exchange from equa-
tions (1) and (4). The controllable load
is used as
dump load, and therefore its value differs from zero only
when the storage is completely filled at the same time as
the net local production exceeds the transmission line
capacity. Alternatively, one or more wind turbines could
be shut down or downregulated in order to not overload
the transmission line. The energy loss due to downregu-
lation of wind power output will be equal to
.
The operation scheduling is performed 12 hours in
advance, which means that the storage level is unknown
at the start of the optimization period. If the wind fore-
casts were 100% correct, the estimated value
from the previous optimization should be used. How-
ever, because of uncertainties in the wind forecasts, the
hourly storage levels will deviate from the estimated val-ues. To get a new estimate of the initial storage level of
day the following equation is employed:
Read system info
i = 1 , t = 1
t = tsch
?
OPERATION SCHEDULING
Estimate Si+1(0).
Find optimal Pei+1(t) for t= 1..t
end
ON-LINE OPERATION
Measure v(t), Pl(t)
Operate storage such that Pe(t)=P
e(t)
i = iend
?
TERMINATE
Read vi+1(t) , Pli+1(t) , SPi+1(t)
for t = 1..tend
Update
i , t
Y
N
N
Y
^
^
^
^
v
v v V
v t( ) x=
max F f Pe t( )( )t 1=
ten d
=
Pe t( )( ) fs pot Pe t( )( ) flos s Pe t( )( )+=
S t( )
Pe t( )
S ten d( )
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(13)
where is the storage level correction based on themeasured level at the scheduling hour
and an
improved forecast of the wind forecast for the remaining
hours of the day.
A straightforward operation strategy is used. The
energy storage is operated in order to follow the hourly
scheduling plan for power exchange with the market.
Consequently, it is presumed that the amount of electric-
ity produced by the wind power plant and consumed by
the load are continuously measured.
A case-study is used to test the proposed operation
strategy of the distributed resource. The system data for
the base case are listed in Table I. Time series for wind
velocity are computed using a synthesis algorithmdescribed in [5], and time series for load demand are
computed using the daily load curve in Figure 4. The
mean load for a certain day is obtained from a normal
distribution where is the daily mean load
and is standard deviation of the daily mean load. The
hourly values is obtained by multiplying with the corre-
sponding value of the curve in Figure 4.
Typical daily load curve for Norwegian households.
Electricity prices are shown in Figure 5, and are
assumed to be the equal for all days. The type of energy
storage is not specified, but could for instance be a
regenerative fuel cell or a redox flow cell. Such storagesystems are still under development, and the future spe-
cific costs are uncertain. Therefore, the spot prices used
in the simulations are chosen to be higher than present
values observed in the nordic power market. As a com-
parison, the actual average spot price in year 1996 and
year 2000 were 254 NOK/MWh and 103 NOK/MWh
respectively [7](1$ = 9 NOK in november 2001). Also,
the variations in simulated spot price during the day are
chosen to be higher than the relatively low variations
observed in the market today.
The simulated average price for purchase of electric-
ity in the regulating market is 17% higher than the spotprice, and the average price for sales is 12% lower than
the spot price. These values are partly based on [6],
assuming a relatively high penetration of wind power in
the market.
System data for the base case.
Simulated hourly spot price (middle) and price for
purchase (upper) and sale (lower) of electricity in the
regulating market.
In order to study the hourly system operation, a 48
hour simulation run of the base case is presented. Fore-
casted and actual values of hourly wind power produc-
tion are shown in Figure 6.
Actual,
, and forecasted, , values of wind
power production.
Figure 7 displays the scheduled and actual power
exchange with the market. The system manages to fol-
low the production plan most of the time except for
some hours at the start and at the end of the simulation
period. This discrepancy can be explained from Figure 8,
where the estimated and actual storage level are plotted.
At the start and the end of the period, the actual storage
level is empty for a longer period than expected. For
those hours, the storage cannot compensate if the windpower production is lower than predicted. This undesir-
able situation can be avoided by setting the minimum
Si 0( ) S i 1 ten d( ) S+=
N Pl l,( ) Pl
l
5 10 15 200.8
0.9
1
1.1
1.2
time [hours]
load[pu]
[MW] [MW-1] [MW] [MW] [MWh]
4.0 0.04 10 0.75 6 100
[MW] [MW] [m/s]
RMSE
[m/s]
2.6 0.52 8.2 2.0 2.6
Pema x ct Pw
ma x s Psma x Sma x
Pl l v
5 10 15 2010
20
30
40
50
time [hours]
electricityprice[$/MWh]
30 40 50 60 700
2
4
6
8
10
time [hours]
realpower[MW]
Pw
Pw
Pw
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allowable storage level, , larger than zero in the opti-
mization routine. Moreover, the actual power exchange
also deviates from the scheduling plan for . The rea-
son for this discrepancy is that the power capacity of the
storage is too low compared to the wind power produc-
tion in that hour.
Actual,
, and scheduled, , power exchange with
the market. Positive values mean export of power.
Actual, and estimated, , storage level for a
storage device with 75% round-trip efficiency.
It is important to obtain a good estimate of the initial
storage level used in the optimization routine. If the
actual storage level is higher than the estimate, the stor-
age can reach its maximum value too early by followingthe scheduling plan. Likewise, if the storage level is
lower than the estimate, the storage can be discharged
too early. The latter is observed in Figure 8, where the
estimated storage level at the start of day two () is
higher than the actual value. This causes a full discharge
of the storage at the end of the period one hour earlier
than estimated, and the system becomes less flexible.
A simple Monte-Carlo simulation technique has been
employed in order to study the impact of storage design
and wind forecasting error on the performance and eco-
nomics of the system. The parameter values in Table I
are used in the base case, and , and the RMSE
of wind speed prediction are varied in the simulations.
The stochastic variables are wind velocity and load
demand
. It should be noted that the modelling method
of wind speed and load described above does not take
into account seasonal variations. However, the error
caused by this simplification is considered to be small
for Norwegian conditions, since there is a close matchbetween the seasonal electricity demand and wind
energy in Norway [8].
The impact of storage sizing on the performance and
economics of the system. The base case parameters are used,
except for and .
Results from simulation runs with different storage
parameters and are presented in Table II. The
relative deviation from scheduled power varies
from 3% for the largest storage system to 11% for the
smallest storage system. Thus, unpredictable variations
in wind power production are smoothed by the storage
most of the time. The ratio is a measure of the
probability of line overload, since the controllable load
is only used when the net local production exceeds the
line capacity. The relative usage of the controllable load
is low for all storage designs, although there is a clear
correlation with . A two-fold increase in energy
capacity results in a four-fold reduction in the electricity
consumed by the controllable load. Moreover, an inter-esting effect is observed when comparing for
storage configurations with different power capacities,
but equal energy capacity. The usage of the controllable
load actually increases slightly for increasing power
capacity, although the opposite could be expected,
because the ability of the storage to consume excess
power also increases. However, with a higher power
capacity, it is possible to store more energy during off-
peak periods. Consequently, the storage will be com-
pletely filled more often. This is undesirable, but can be
avoided by adding a limitation on the power capacity
used in the optimization routine.Furthermore, Table II shows that the revenue
increases with increasing power and energy capacity of
30 40 50 60 70
0
1
2
3
4
time [hours]
realpower[MW]
Pe
Pe
Pe
30 40 50 60 700
10
20
30
40
50
time [hours]
stora
gelevel[MWh]
SS
S
Psma x
[MW]
[MWh]
yearly
revenue
[1000 $]
4 50 0.114 0.045 314
4 100 0.097 0.014 339
4 150 0.098 0.007 346
6 50 0.078 0.046 318
6 100 0.046 0.016 3436 150 0.043 0.008 350
8 50 0.068 0.046 320
8 100 0.033 0.016 345
8 150 0.029 0.008 352
Psma x Pde v
P
e
----------- Pcl
Pw-------
Psma x
Psm ax
Pde v Pe
Pcl Pw
Pcl Pw
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the storage, as expected. On the other hand, the storage
device is then likely to be more expensive, which is par-
ticularly true for fuel cell systems. Thus, finding an
appropriate size of the storage is not only critical for the
system operation but is also of great economic impor-
tance, due to potential high investment costs. Figure 9
displays the duration curves of charging, discharging andthe energy reserve, which provides information about
the utilization of the storage device. It is evident from
the charging and discharging curves that an energy stor-
age with separate charging and discharging devices (for
instance an electrolyser and a fuel cell) will have an
undesirable low utilization of the total installed capacity.
However, the difference between the curves implies that
storage designs with different charging and discharging
capacities should be investigated further. The usage of
the total energy capacity is also relatively low, as can be
seen from the duration curve for storage level in Figure
9. This is beneficial from a operation point of view, sincea full storage increases the risk for transmission line
overload. In the case of no transmission constraints, the
energy capacity could be considerably lower. Moreover,
the duration curve also shows that the storage is empty
for some times. As this reduces the flexibility of the stor-
age, one should consider to set the minimum allowable
storage level in the scheduling routine higher than zero.
Duration curves for charge (circles), discharge
(crosses) and the energy reserve (solid) of the storage device.
The base case parameters are used for the simulation.
The economic value of accurate wind forecasts is
illustrated in Figure 10. As expected, the revenue is
highest for perfect forecasting, since in that case all the
energy can be traded in the spot market. As the forecast-
ing error increases, it becomes more difficult to follow
the scheduled production plan. Hence, more energy must
be traded in the balancing market, and the revenue is
reduced, according to the price curves in Figure 5. This
is particularly true for the persistence method, with
RMSE equal to 4.84 m/s. The benefit of accurate wind
forecasts depends strongly on the price differencebetween spot price and regulating power prices. In this
study, the difference is relatively large, which means that
the effect of forecasting accuracy can be smaller in prac-
tice.
Yearly revenue as a function of forecast error ofwind velocity. The base case parameters are used, except for
RMSE.
The simulation results show that with a properly sized
energy storage, it is possible for owners of wind power
plants to take advantage of hourly price variations in the
spot market. Furthermore, results obtained from the sim-
ulations should ultimately be used as a part of an eco-
nomic assessment where also investment costs are
considered. It is also interesting to compare energy stor-
age with grid reinforcements in cases where the wind
power potential exceeds the capacity of the existing
transmission line. Taking into account the reduced costs
for providing the load with electricity from the distrib-
uted resource, the yearly revenue for the base case is 1.1
Mill.$. For comparison, simulations of the system with anew parallel transmission line instead of energy storage
gives a revenue of 1.0 Mill.$. Consequently, with a
period of analysis of 20 years and 7% interest rate, the
difference in investment costs of the energy storage and
the new line cannot be higher than about 1 Mill.$, if the
storage should be an economic viable alternative. With
present cost estimates [4], it is likely that electrochemi-
cal energy storage such as the hydrogen-bromine fuel
cell system will be an expensive alternative to grid rein-
forcements. On the other hand, for areas where grid
expansions lead to unwanted interference with the local
environment, energy storage should be considered as areasonable way to increase the penetration of wind
power. Another alternative is to reduce the power output
from the wind power plant in periods with high wind and
low load by either shutting down units or downregulat-
ing the output. For the system studied here, such a strat-
egy would give a yearly revenue of 0.9 Mill.$. The
energy loss due to the downregulation is 16%.
The chosen operation strategy of the energy storage is
simple, namely to follow the specified production plan.
Other, more sophisticated methods could be employed if
the spot price and regulating prices were represented as
stochastic variables, and if a more detailed model of theelectricity market were used. The optimal power
exchange with the market could then be updated each
0 20 40 60 80 1000
20
40
60
80
100
% of total time
%ofmaximumvalue
charge
discharge
energy reserve
200
250
300
350
RMSE of wind velocity prediction [m/s]
revenue[1000$]
4.840.0 2.57 3.16
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hour, by using principles of stochastic programming.
Moreover, in some cases it will be valuable to have an
energy reserve in the storage at the end of the day, for
instance if high spot prices and low wind speeds are pre-
dicted for the next days. This approach is analogous to
the so-called water value method used in hydro power
planning [9], and will be investigated further.It should also be noticed that the proposed method is
not limited to wind power, but could also be useful for
the analysis of other intermittent energy resources such
as solar, wave and small-scale hydro.
A method for the scheduling and operation of a wind
power plant with energy storage in a market system has
been presented. The method is suitable for any type of
electrical energy storage and is also useful for other
intermittent energy resources than wind. By implement-
ing the method in a computer simulation model, valuableinformation about the impact of energy storage sizing on
system operation and economics can be obtained. Simu-
lation results of a case study show that with a properly
sized energy storage, owners of wind power plants can
take advantage of variations in the spot price of electric-
ity, by thus increasing the value of wind power in elec-
tricity markets. However, with available technology and
existing price estimates, energy storage devices such as
reversible fuel cells are likely to be an more expensive
alternative than grid expansions for the siting of wind
farms in weak networks, although reducing the environ-
mental impact.A simplified representation of the electricity market is
used in the model. In order to explore further possibili-
ties for energy storage in connection with stochastic gen-
eration, further work with a detailed market description
will be carried out.
List of symbols:
[1] J. N. Baker and A. Collinson, Electrical energy stor-
age at the turn of the millennium, Power Engineer-
ing Journal, vol 13, no.3, pp 107-112, June 1999
[2] A. Cruden and G. J. W. Dudgeon, Opportunities for
Energy Storage operating with Renewable Energy
Systems, Proc. EESAT 98, Electric Energy Storage
Applications & Technologies, September 2000
[3] W. A. Amos, Economic Assessment of Wind
Energy Coupled with a Reversible Hydrogen Fuel
Cell, National Renewable Energy Laboratory,
Golden, CO, Milestone Type P report, February2000.
[4] M. Korps, R. Hildrum and A. T. Holen, Hydrogen
energy storage for grid-connected wind farms, 6th
IASTED International Conference, Power and
Energy Systems, pp 590-594, July 2001, ISBN 0-
88986-291-5
[5] D. Infield et al, Engineering tools for wind diesel
systems - Volume 6, EFI Technical Report No.
A4330, Trondheim, September 1995
[6] L. H. Nielsen and P. E. Morthorst, System integra-
tion of wind power on liberalised electricity market
conditions. Medium term aspects (in Danish), Ris-R-1055(DA), april 1998, ISBN 87-550-2396-7
[7] Homepages of Nordpool - The Nordic Power
Exchange, http://www.nordpool.no
[8] J. O. G. Tande and K-O. Vogstad, Operation impli-
cations of wind power in a hydro based power sys-
tem, EWEC99, 1999 European Wind Energy
conference, pp 425-428, August 1999
[9] O. B. Fosso et al, "Generation Scheduling in a de-
regulated system. The Norwegian case", IEEE Trans-
actions on power systems, Vol. 14, No.1, 1999
: load demand [MW]
: output of wind power plant [MW]
: power exchange with market [MW]
: power output of energy storage [MW]
: controllable load [MW]
: Deviation between actual and scheduled
power exchange [MW]
: energy storage level [MWh]
s : round-trip efficiency of energy storage
c : charging efficiency of energy storage
d : discharging efficiency of energy storage
: wind velocity [m/s]
: estimated value of variable
: mean value of variable
: hourly revenue [$/h]
x
x
: Expected revenue over the scheduling
period [$]
: transmission losses coefficient [MW-1]
: spot price of electricity [$/MWh]
: relative difference between spot price and
sales price in the regulating market
: relative difference between spot price and
purchase price in the regulating market
: standard deviation of random variables : coefficient of variation of random vari-
ables
: Weibull shape parameter
RMSE : Root-mean-squared error of wind fore-
cast [m/s]
: index for time
: time step [hours] : index for day
14th PSCC, Sevilla, 24-28 June 2002 Session 31, Paper 6, Page 7