Capacity Worth of Energy Storage System in Renewable Power Generation Plant

doi:10.4236/eng.2013.59B001

Paper Menu >>

Journal Menu >>

Engineering, 2013, 5, 1-5

http://dx.doi.org/10.4236/eng.2013.59B001 Published Online September 2013 (http://www.scirp.org/journal/eng)

Capacity Worth of Energy Storage System in Renewable

Power Gener a tion Pl an t

Jinbin Li1,2, Yao Yao1,2

1Hubei Electric Power Research Institute, Wuhan, China

2Key Laboratory of High-Voltage Field-Test Technique of SGCC, Wuhan, China

Email: [email protected]

Received May 2013

ABSTRACT

With the advance in renewable generation technologies, the cost of renewable energy becomes increasingly competitive

when compared to fossil fuel-based generation resources. It is economically beneficial to integrate large amounts of

renewable capacity in power systems. Unlike traditional generation facilities, however, using renewable resources for

generation presents technical challenges in producing continuous power. In this report, an Energy Storage System (ESS)

is integrated to smooth the variations in renewable power production and ensure the output power more controllable.

Since it requires capital investment for the storage devices, it is important to obtain reasonable estimate of the storage

capacities. This project is therefore for mulated as an optimization problem in determining the two dominating factors of

the capital cost for the ESS: the power capacity and the energy capacity. The objective is to make the renewable power

more reliable and simultaneously maximize the economic benefits that can be obtained from the scheme. To make the

results more convincing, analyses in this report start with wind generation, for wind has greater variability and unpre-

dictability than other renewable sources. Selection of ESS type is narrowed down to battery energy storage system

(BESS) in the scheme. However, the methods presented here are suitable for any type of energy storage methods and

are also useful for intermittent renewable energy resources other than wind.

Keywords: Wind Power; Energy Storage System; Power Capacity; Energy Capacity

1. Introduction

Economic growth and prosperity since the industrial rev-

olution have, in large part, been due to the utilization of

fossil fuels. The consumption of fossil fuels has nearly

doubled every 20 years since 1900 [1]. However, the

long-term large combustion of fossil fuels has caused

undesirable environment impacts. The burning process

emits large amount of carbon dioxide and other gases

which cause the “greenhouse effect” and pollution [2].

And, even if we set aside worries about the environment

problems fossil fuels have brought to us, it must be ac-

knowledged that continuous and economically-priced

fossil fuels are fast coming to an end.

One obvious wa y to solve the worldwide energy crisis

problem is to find new primary sources. Amongst the

alternatives, wind energy is very promising. Many coun-

tries have set goal for high penetration levels of wind

generations. The annual growth rate of global installed

wind power capacity has exceeded 26% since 1990s [3].

However, one down side of wind power is its variabil-

ity and relative unpredictability. The output of wind

power system depends highly on the local natural envi-

ronment which is hardly controllable. Hence this form of

electricity generation would introduce more uncertainty

into the power grid. This presents a challenge when inte-

grating large scale wind power into electrical power

networks. To counter this, a usual scheme is to use ener-

gy storage devices as an energy buffer. It leads to a re-

duction in the amount of spinning and standby power

reserve needed to mitigate system frequency and voltage

excursions due to the variations in the wind power output

[4].

2. Power in Wind and Wind Turbine Model

2.1. Power in the Wind

The power in the wind is closely related to the wind

speed, the relationship between them is presented as fol-

lows [5]:

Pw = 0.5ρA v3 (1)

where Pw is the power in the wind (W); ρ is the air den-

sity (kg/m3); A is the cross-sectional area through which

the wind passes (m2); and v is wind speed normal to A

(m/s).

One expression that is often used to characterize the

impact of height on wind speed is the following [5]:

J. B. LI, Y. YAO

v1/v2 = (H1/H2)α (2)

The friction coefficient α is a function of the terrain

over which the wind blows. Usually, for rough approxi-

mation in somewhat open terrain, the rule-of-thumb is to

take a value of 1/7 for α [5].

In research activities, the most commonly used func-

tion for characterizing the statistics of wind speed is the

Rayleigh probability density fun c tion, given by [5]:

( )

2exp[() ]

fv c

= −

(3 )

where c is called the scale parameter.

2.2. Idealized Wind Turbine Power Curve

To figure out how much energy the wind turbine can

obtain by harnessing the wind, the performance characte-

ristics of wind turbine must be known. The most impor-

tant technical information for a specific wind turbine is

its power curve, which shows the relationship between

turbine generator’s electrical output and wind speed. An

idealized power cure is shown in Figure 1.

The relationship between the output wind power and

the wind speed can be described as follows [5],

00or

PC R

R RF

vv vv

PAvC vvv

Pv vv

≤≤ ≥





= ≤≤



≤≤





(4)

where vc is the cut-in wind speed; vR is the rated wind

speed; vF is the furling wind speed; PR is the rated pow er

of the wind turbine generator. Cp is the rotor efficiency,

which is the faction of the wind’s power that is extracted

by the turbine blades.

3. Approach to Determine the Capacity

of ESS

Utilizing Equations (3) and (4), the probability distribu-

tion of the wind power outputted from the wind turbine

generator is thus available. Its profile is illustrated in

Figure 1. Ide alized wind tur bi ne power curve.

Figure 2. The probability increases sharply when the

power reaches the rated value. This is du e to the fact that

the output power is limited to the rated power. Therefore,

the probability increases sharply when one sums up all

the probabilities that the wind speed ranges from vR to vF.

The average of the generated power can be calculated as

( )

()

PP dP

∫

(5)

In this project, it is proposed that the average power 

�

will be set as the constant delivered power dispatched

from the wind turbine station over the studied period. It

will caus e no net change in the stored energy in the ESS.

The stored energy in the ESS will return to the same

energy level at the end of the period. Generally, the av-

erage power in a short-term, say 24 hrs ahead, can be

estimated according to the local historical wind speed

data or forecasting techniques. Thus, the intention of the

constant delivered power is to ensure no net change in

the stored energy in the ESS at the end of the subsequen t

24 hrs.

The power capacity of the BESS can always be found

as long as the probability distribution of the wind power

is known and the average power is calculated. The power

capacity can be equal to the larger of either the maximum

charge power or the maximum discharge power, as

shown on Figure 2.

3.1. Method to Determine the Reduced Power

Capacity of ESS

Since the power capacity of the BESS is determined by

the larger of the maximum charge and discharge power,

therefore in certain situation, the power capacity may be

equal to the maximum discharge power. Compared to the

much higher probability at the rated power level, the

probabilities of the low generated wind power level are

much lower. While their contributions to the total gener-

ated energy are small, they have brought a notable in-

crease in the power capacity of the BESS.

Figure 2. Probability distr ibution of gene rat ed wind power.

W ind Speed

Delivered Power

Cut in Wind Speed

Shedding the wind

Rated Win d Sp eed

R ated Power

Furling Wind Speed

Generated Wi nd Power Pw

Probabilit y of Pw

Max Discharge Power

Constant Delivered

Power

Max Char ge Power

J. B. LI, Y. YAO

It is now proposed that contributions from the low

generated wind power components are delivered to a

capacitor. This is because compared to the rated power,

the energy contained in these components can be ex-

pected to be small. Furthermore, these components will

cause frequent charge-discharge, it will not be desirable

for the battery to deal with these charges [6,7]. Therefore,

the author considers a capacitor a more suitable storage

medium for these low power components. For those

components of higher generated wind power levels, they

are still to be dealt with by the BESS. The power net-

work model is as shown in Figure 3.

By doing so, the lower limit of the BESS’s discharging

power would increase, and the average generated power

from the BESS will also decrease most possibly sligh tly.

Consequently the discharging capacity will decrease and

the specified power capacity of the BESS may be re-

duced simultaneously. This decrease in the BESS power

capacity will reduce the capital investment for the BESS,

and thus may lead to an overall reduction in the cost for

this wind power plant. This intention is illustrated in

Figure 4, the darkened area refers to those low generated

power instances which have been delivered to the capa-

citor storage.

The benefit of this method is a decrease in the amor-

tized cost for the BESS’s power capacity. Also, it can be

expected that the energy capacity, which is another do-

minating factor of the capital cost for the BESS, will vary.

To maximize the overall economic benefit that can be

Figure 3. Power network model (BESS + Capacitor).

Figure 4. Reduce the power capacity by delivering the low

power components to the capacitor.

obtained from this scheme, both the changes need to be

taken into account to examine the economic effect and

determine the optimal level of the low power compo-

nents that should be assigned to the capacitor.

An analysis based on an actual site measurement ra-

ther than relying on Rayleigh assumptions is provided to

examine the economic effects of this scheme.

3.2. Analysis Based on Actua l Site Measurement

Data used in the following case study was recorded in Le

Mars (Latitude 42.78, Longitude −96.2) at a reference

height of 10m on Nov 1st 2000 [8-10]. Figure 5 shows

the probability distribution of the wind speed during the

studie d pe riod.

To analyze the output power which can be harnessed

from the wind resource, a typical idealized wind pow-

er-wind speed curve is assumed for the wind turbine. The

parameters are set as vc = 3 m/s, vR = 12 m/s, vF = 25 m/s,

PR = 2000 kW, and the blade’s radius 38m. The wind

turbine is mounted with its hub at 50 m above the ground

surface. Utilizing E qu a tion s (2) and (4), the probability

distribution of the output power from the wind turbine is

thus calculated, as shown in Figure 6.

Figure 5. Probability distribution of the wind speed in Le

Mars.

Figure 6. Probabili ty distribution of the gene rated power in

Le Mars.

510 15 20 25

0.02

0.04

0.06

0.08

0.1

0.12

W ind Speed at the Height of 10m (knots)

Probabili ty of the Wind Speed

500 1,000 1,500 2,00080

5x 10

-3

Generated Wind Power (kW)

Probabili ty of Pw

J. B. LI, Y. YAO

The average generated power is evaluated to be 1370.5

kW. Thus, the BESS power capacity is 1370.5 – 80 =

1290.5 kW (the probability of the generated power rang-

ing from 0 to 80 kW is zero), according to the maximum

discharge power.

Next, suppose the intention is to progressively deliver

those contributions when the generated power level is

low to the capacitor. The reason for doing so is to ob-

serve how by delivering the low power components to

the capacitor will affect the power capacity of the storage

element. The power level below which the contributions

are delivered to the capacitor is denoted as

. Figure 7

shows how the power capacity of the BESS

varies

with

When

increases successively, owing to the slight

reduction in the average of the generated power, on one

hand, the discharging capacity of the BESS decreases, on

the other hand, the charging capacity increases simulta-

neously. It is reasonable to expect that at a certain

value, the maximum charging capacity will be equal to

the maximum discharging capacity, and beyond this val-

ue, the maximum charging capacity will exceed the

maximum discharging capacity and the latter starts to

dictate the BESS power capacity. This break point is

shown in Figure 7.

After the low generated power components are as-

signed to the capacitor, the change in the energy capacity

should be taken into account. Figure 8 shows how the

energy capacity Ce varies with

. The depth of the dis-

charge is assumed to be 20% [11].

Assuming the wind power farm has N identical wind

turbines, the life-time of the battery is T (year).

($/kW) and

($/kWh) represent the coefficien ts of the

amortized capital cost for the power capacity and energy

capacity of the BESS, respectively. Coefficient

($/kWh/year) is used to represent the maintenance cost

for the BESS each kWh of the energy capacity each year.

Since the cost for the capacitor is really negligible when

compared to the cost fo r the b at tery, the cap it al in vestmen t

Figure 7. Power capacity Cp of the BESS vs. Pc.

for the capacitor is not taken into account. Then the total

cost for the storage system can be described as:

( )

()

ec ec

FiNCP i

C PiTC Pi

βγ



=⋅⋅





+⋅+⋅⋅ 

(6)

For a predetermined battery type,

, and

will be known. Thus, the total cost for the BESS of this

method

( )

can be evaluated, and the optimal power

level

will be found.

In this case study, the coefficients in (6) are selected in

the following way. Suppose the wind farm has N = 10

identical wind turbines. The amortized power capacity

cost of the BESS is such that

= 203$/kW. The amor-

tized energy capacity cost of BESS

= 116$/kWh.

The battery life is 2 years, and the maintenance cost for

the BESS per year is

= 29$/kWh [12]. Figure 9

shows a plot of

( )

based on these data.

According to the above figure, the capital cost for the

BESS has a gentle decrease at the beginning. In this case,

the best solution is to set

373 kW

, and then the

capital cost for the BESS is about $1.2513 × 107. Com-

pared to the capital cost of $1.2981 × 107 for the BESS

before assigning those low generated power components

to the capacitor, the method has resulted in a benefit of

about 4%.

Figure 8. Energy capacity Ce of the BESS vs. Pc.

Figure 9. Capital cost for the BESS F vs. Pc.

200 400 600 800 1000 1200 1400 1600 1800 2000

6 00

8 00

1000

1200

1400

1600

Pc ( kW)

Power Capacity of the BESS ( kW)

Cp vs. Pc

Break Poin t

200 400 600 800 1000 1200 1400 1600 1800 2000

5000

6000

7000

8000

9000

10000

11000 C e vs. Pc

Pc ( kW)

Energy Capacity of the BESS (kW h)

200 400600 80010001200 14001600 1800200080

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.1 x 10

Pc ( kW)

Cap ital Cost f or the BESS ( S)

F vs. Pc

J. B. LI, Y. YAO

Using a similar process, other wind power plants cou ld

be analyzed. The optimal

level can be found to mi-

nimize the capital cost for the BESS.

4. Conclusion

The methodology is proposed to assign those low gener-

ated power contributions to a capacitor. For those com-

ponents of higher generated wind power levels, they are

to be dealt with by the BESS to provide a constant deli-

vered power for the grid. By doing so, the power capaci-

ty of the BESS would drop, and thus the capital cost for

the BESS might probably decrease. The result from the

overall design demonstrated that utilizing this method

could create a considerable economic benefit.

5. Acknowledgeme nts

The author wish to express his deepest gratitude and ap-

preciation to his supervisor, Prof. Choi San Shing, for all

his invaluable suggestion and patient guidance through-

out the research period.

REFERENCES

[1] H. Lund, “Renewable Energy Systems,” Elsevier Inc.,

San Diego, California, 2010.

[2] S. C. W. Krauter, “Solar Electric Power Generation—

Photovolataic Energy Systems,” Springer Inc., Rio de Ja-

neiro, Brazil, Oct 2005.

[3] M. S. Lu, C. L. Chang, W. J. Lee and L. Wang, “Com-

bining the Wind Power Generation System with Energy

Storage Equipments,” IEEE, 2008.

[4] PEP (Professional Engineering Publishing), “Renewable

Energy Storage—Its Role in Renewables and Future

Electricity Markets,” John Wiley & Sons Inc., 11 Jun

2008.

[5] G. M. Masters, “Renewable and Efficient Electric Power

Systems,” John Wiley & Sons Inc., Hoboken, New Jersey,

2004. http://dx.doi.org/10.1002/0471668826

[6] T. R. Crompton, “Battery of Reference Book,” Butter-

worth International Edition, 1990, ISBN: 0-408-00791-5.

[7] D. L. Yao, “Battery Energy Storage System for a Wind

Power Generation Scheme,” Master Dissertation, Na-

nyang Technological University, Singapore, 2008.

[8] “Wind Data Source: The lowa Department of Transporta-

tion (DOT),” URL.

http://mesonet.agron.iastate.edu/request/awos/1min.php

[9] “National Renewable Energy Laboratory (NREL),” URL.

http://www.nrel.gov/

[10] National Renewable Energy Laboratory, “National Solar

Radiation Database 1991-2005 Update: User’s Manual,”

Technical Report, NREL/TP-581-41364, April 2007.

[11] UNiROSS Industrial, “The Rechargeable Specialists,”

URL.

http://www.uniross.com/UK/industrial/Cycle%20life/010

102

[12] S. S. Choi, X. Y. Wang and D. M. Vilathgamuwa, “On

the Design of Power Buffer and its Applications,” IEEE,

DRPT2008, 6-9 April 2008, Nanjing, China.