IEEE PEDS 2011, Singapore, 5 - 8 December 2011

Design and Dynamic Power Management of Energy Storage System for Wind Plant

Duong Tran, Student Member, IEEE, Haihua Zhou, and Ashwin M. Khambadkone, Senior Member, IEEE 4 Engineering Drive 3, ECE, National University of Singapore, Singapore 117576

Abstract-To integrate wind energy into power grid, energy storage system (ESS) is needed to smooth out fluctuations of wind power output. As power and energy requirements for energy

storage in wind application are high, ESS cost is high. In this paper, selection of storage technologies and sizing of storage units in ESS to achieve high power, high energy capacity and low cost are presented. A Dynamic Power Manager is introduced for management of power dynamics and constraints of energy storages inside ESS, while improving controllability of wind plant at pee. Simulation result shows that the Dynamic Power Manager is effective to manage the ESS and improve power at pee with RMS ripple down from 17.97 % to 6.42 %.

Index Terms-Dynamic Power Management, energy storage system, micro-grid

NOMENCL ATURE

DOD Depth-of-Discharge

DPM Dynamic Power Manager

ESS Energy Storage System

HV DC Hight Voltage Direct Current

LVRT Low Voltage Ride Through

PCC Point of Common Coupling

PCU Power Conditioning Unit

RMS Root Mean Square

SOC State-of-Charge

I. INTRODUCTION

In recent few years, wind energy has been penetrating

significantly into power grids in many countries like US, China

and Europe. Wind energy has a major drawback of variable

output [1]. Energy storage, therefore, is needed to alleviate

this problem of wind energy [2-4], i.e. to smooth out power

variations of wind plant. Energy storage is also needed in wind

plant:

1) to compensate power and energy mismatches between

scheduled and actual output of wind plant;

2) to provide power and energy reserve for wind plant to

operate during faults, e.g. when one wind turbine fails,

wind plant can operate with N - 1 turbines; or during

grid disturbance, wind plant can ride through the low­

voltage condition (LVRT);

3) to enhance stability of local power network in wind

plant.

This paper investigates feasibility of a medium-scale ESS

dedicated to wind plant with the target to improve controlla­

bility of output of wind plant at Point-of-Common-Coupling

[ Energy Storage System 1 Fig. !. Energy Storage System dedicated to wind plant

Energy Storage System -----------,

DC bus 1 --.. ----------.... ------�--- I

PCU . Power Conditioning Unit

1 1 1 1 1 1 1

___ I

Fig. 2. Structure of Energy Storage System (ESS)

(PCC). The general structure of the approach is shown in Fig.

1.

To meet high power demand, large-scale energy storage

technologies such as pumped hydro and compressed air are

usually selected. However, they are limited due to geographic

conditions required. Pumped hydro energy storage needs to be

close to both a river and a mountain. Compressed air energy

storage needs an abandoned mine to operate. Therefore, the

ESS herein considers energy storage technologies of normal

batteries, flow batteries, flywheel, ultra-capacitor etc. with a

certain number of storage units to meet power demand from

wind plant. The structure of ESS is shown in Fig. 2.

To design a high power, high energy capacity and low cost

ESS, the paper provides some guidelines for selection and

sizing of energy storages. Configuration of ESS inside a wind

plant is also presented. To operate ESS, a Dynamic Power

Manager (DPM) is proposed. The DPM internally allocates

dynamic power to energy storages such that energy storages'

constraints are complied and energy efficiency is improved.

978-1-4577-0001-9/11/$26.00 ©2011 IEEE 351

The DPM also manages ESS to provide power compensation

such that power output of wind plant at PCC is controllable.

II. DESIGN OF ESS DEDICATED TO A WIND PL ANT

The most popular energy storages that can be used in

the ESS for wind application, except pumped hydro and

compressed air, are shown in Table. I. Among them, uItra­

capacitor has high power density but low energy density, and

is suitable for power applications below IMW. Flywheel and

Li-ion battery are the two energy storages that have advantages

in both power density and energy density. However, due to

its high cost, Li-ion battery is just mainly used in power

applications such as automotive storage. The well-known lead­

acid battery has low cost, high energy density but low power

density. Flow battery has high energy density but on the

other side, poor power density. More detailed information on

electrical energy storage technologies and their potential for

wind energy applications can be found in [2, 5].

TABLE I POWER AND ENERGY DENSITIES OF ENERGY STORAGES

Type of Power Energy Power level energy density density (MW) storage PDi(Wlkg) EDi(Whlkg) Ultra-capacitor 20000 30 <1

Li-ion battery 300-800 150-250 <1

Lead-acid battery 200-400 25-30 0.001-10 Flywheel 150-3000 5-80 0.1-10 Flow battery 5-40 90-400 0.01-100

There are many ways to connect wind turbines to the power

grid. As [6] indicates, topologies shown in Fig. 3(a)(b) have

20% and 47% world-market share respectively. Configuration

of a wind plant, therefore, will be mainly discussed based on

these two topologies. There are two types of common bus in

wind plant:

• common DC bus: For applications like off-shore wind

farm, wind power has to be transmitted via undersea

cables. Due to cost and efficiency factors, Hight Voltage

Direct Current (HV DC) technology is used. Because most

of energy storages are DC type, internal bus of ESS

should be DC. Therefore, ESS can be directly connected

to the DC bus, as shown in Fig. 3(a). This also helps

alleviate the issue of lifetime limit of DC electrolytic

capacitor in traditional AC-DC-AC converter.

• common AC bus: One example of the structures is shown

in Fig. 3(b). In this case, the ESS is connected to the

common AC bus via a DCI AC converter.

In order to increase controllability of wind plant at PCC,

ESS must provide:

• static energy balancing: compensate difference between

scheduled power and actual wind power;

• dynamic power response: provide power with sufficient

quality to internal bus such that output power at PCC

meets requirements of grid power quality.

turbin�

l ................. _ ........... ..... _ .... ...... .... _ ... ............ � .. Fig. 3. Two popular configurations of wind plant and location of ESS

The power and energy requirements of ESS can also be

determined by objectives of a grid-connected ESS e.g. pri­

mary control, spinning reserve. Several factors can be taken

into consideration, including: power rating, discharge time,

response time, deployment time, lifetime, round-trip efficiency,

and so on [2, 3, 7-9].

With specified power and energy ratings, sizing of storages

then can be determined based on minimum cost for 20-year

operation. The optimal sizing is to find types and sizes of the

energy storages to minimize cost function

j min L WiEDiCiRi (1)

i=1

subject to:

• power constraints: 2:,;=1 WiPOi1]i ?: P, 0 :::; WiPDi < Pub,j, where j is number of types of energy storages

used; Wj, PDi, 1]i are weight, power density and efficiency

of ith energy storage respectively; P is required power;

Pub,i is upper bound power of storage i; • energy constraints: 2:,;=1 WiEOi1]ikfoc ?: E, 0 :::;

WiEOi < Eub,i where EOi and kfoC are energy density

and SOC coefficient for ith energy storage respectively;

E is required energy; Eub,i is upper bound energy ; Cj, Ri are cost and number of replacement within 20 years

of ith energy storage respectively.

352

TABLE II SPECIFICATIONS OF ENERGY STORAGES

Energy

Storage Ultra-capacitor Li-ion battery Lead-acid battery

Flywheel (lOMW) Flow battery

"State-of-Charge bDepth-of-Discharge

Time Cost

(hours) Ci(jkW) 10/3600 300-450

4 1950-2900 4 1740-2580

0.25 3695-4313 4 1545-3100

The values of parameters mentioned above are listed in Table. I

and Table. II.

Example: For a wind farm consisting of 6 GE-1.5MW

wind turbines, to facilitate reserve for the wind farm during

fault, power rating is chosen as peak power of one wind

farm (1.5MW) and energy rating is chosen as amount of

energy needed for 1 hour of fault (l.5MWh). These ratings are

sufficient for grid operator to update wind power scheduling.

Moreover, the chosen ratings can allow the hourly wind power

prediction error up to 16.7% while the error can be as low as

10% [10].

Considering ultra-capacitor, lead acid battery, Lithium-ion

battery, flywheel and flow battery are used in ESS, minimum

cost using the above-mentioned methodology is calculated.

The result as illustrated in Fig. 4 shows that combination of

lead-acid battery and flywheel achieves minimum cost. On

the other side, ultra-capacitor and Lithium-ion battery are not

present in the set of cheapest energy storage mix.

•••••••••••••••• 2.35

o 0.5 1.5 Cost

Fig. 4. Energy storage mix with lowest costs

III. DYNAMIC POWER MANAGEMENT FOR ESS

As introduced, DPM has to allocate dynamic power to

energy storages in ESS. Time scale of DPM is in seconds.

Diagram of DPM is shown in Fig. 5.

To allocate dynamic power to energy storages in ESS, DPM

has to take into account characteristics of all the storages. They

include State-Of-Charges, limits of rate-of-charge and rate-of­

discharge, power dynamics and DOD limits of the storages. If

Replacement in Efficiency SOC" factor

20-year Ri 'f/i%DODb kSOC I

None 99% 0.9 1-2 96% 0.3 4-6 75% 0.5

Maintenance 93% 1 Unknown 80% 1

constraints of energy storages are violated, the storages will

be partially or fully damaged.

Fig. 5. Diagram of Dynamic Power Manager

An optimizer is used to decide how to allocate dynamic

power in order to reduce power fluctuation at PCC of wind

plant, improve ESS efficiency while complying with the

constraints. The optimizer uses information from wind power

predictor and grid scheduler for the decision. In this paper,

Genetic Algorithm is used for the optimizer .

The DPM has to allocate dynamic power {pd to energy

storages such that constraints are complied and energy effi­

ciency is improved. The DPM then has to minimize the cost

function of power losses:

N

min L Ploss,k k=l

N is number of energy storages, subject to constraints:

(2)

• Limit of deviation between actual power and scheduled

power at PCe:

IP�wind + PESS - Pplant I :S b.P (3)

where P�wind is predicted total power generated from

wind turbines, PESS is total power delivered from ESS,

Pplant is scheduled power for wind plant from grid

scheduler, and b.P is limit of power variation at PCe.

PESS = L Pbus,k k

(4)

353

where Pbus,k is power delivered from energy storage kth

to internal DC bus of ESS considering conversion

efficiency,

for Pk > 0 for Pk < 0 (5)

Pk is power delivered at terminal of energy storage kth,

�(pk) is power conversion efficiency of Power Condition­

ing Unit of energy storage kth for the power Pk.

• Limits of Rate-of-Charge and Rate-of-Discharge:

IPk I :::; UBRoc,k for Pk < 0

Pk :::; UBRoO,k for Pk > 0

• Depth-of-Discharge limits:

LBooo,k :::; DoDk :::; UBooo,k

(6)

(7)

(8)

where UB and LB are upper bound and lower bound;

• Dynamic constraints:

(9)

!k (dpk) is function describing dynamic constraint of kth

energy storage;

• Time constraint: tcomp :::; T (10)

tcomp and T are computing time and allowed time for

computation. As computing time is limited in seconds,

local optimum or quasi optimum as optimization results

are accepted.

IV. SIMUL ATION RESULT

The DPM has been verified in simulation for an ESS

including a high energy capacity energy storage subsystem

(SS1) and a high power energy storage subsystem (SS2). The

former storage suffers poor power performance while the latter

suffers low energy capacity. Specification of ESS is presented

in Appendix. Simulation time is 10 minutes. The wind data

with short term variations is collected from Ref. [11]. The

DPM uses predicted wind power from wind predictor with an

inaccuracy of 10%. Fig. 6 shows the wind data and predicted

wind data in 10 minutes.

Fig. 7 shows power output of wind plant at PCC in 10

minutes with DPM and without DPM. When there is no DPM,

power reference to ESS is calculated based on average wind

power of previous minute, and power distribution to energy

storages is divided half-half. Tab. III shows the results of the

2 cases. Power ripple at PCC is reduced from RMS 17.97%

to RMS 6.42%, and from P-P 441 kW to P-P 372.6 kW. In

addition, the improvement is obtained with less ESS energy

exchange and less ESS energy loss, 41,762.9 kJ compared with

48,544.3 kJ.

Fig. 8 shows the power delivered from ESS, and power

losses over energy storage subsystem 1 (SS 1) and subsystem

2 (SS2) with DPM and without DPM. As shown, with DPM,

1600

§' 1400 e. 1200 Q; >: 1000 0 a.

800 600 0 100

1600

� 1400 Q; 1200

Wind power data in 10 minutes

200 300 (a)

400 500 600

� c: o >: 0 1000 a. 200 .�

800

Time(s) (b)

Q) o

Fig. 6. Wind power data in to minutes (a) Actual wind power (b) Predicted wind power and deviation

� ::.­Q) �

c.. 1400 ...... .... ..... .. " WithDPM ............ ................... . ..... Hipple;. RMS

.:;; .6.42%

p-p '" 372.6 1200 �0----�10�0�--� 2�0�0-----3LOO�--- 4� 0�0-----5�0-0----�

600 (a)

2200 � 2000

Q) 1800 � 1600 '

. c..

. ..

..

. ..

. ' . . . . . . · · W.fhi:iiit OPM

. . . . . . . . • • I . . • • - .

1400 . . . . . . . . . ... ... ... . . .. . . . . ... · · · · · · · · · · · · ·. · · · · · · · Rippie: RMS = 17,97% P-P '" 441 1200 �0----�10�0----�2�070----�3LOO�---4� 0�0

�� -5LOO���600

Time(s) (b)

Fig. 7. Power output of wind plant at pee in to minutes (a) with DPM (b) without DPM

ESS power changes faster than without DPM to compensate

wind power variations. Besides, SS I loss with DPM is higher

than that without DPM. However, SS2 loss with DPM is less

than that without DPM, and the total energy loss with DPM

is less.

TABLE III SIMULATION RESULT OF 2 CASES WITH DPM AND WITHOUT DPM

Parameter With DPM Without DPM RMS power ripple 6.42 % l7.97% P-P power ripple 372.6 kW 441 kW Energy loss 41,762.9 kJ 48,544.3 kJ Energy exchange 333,343 kJ 338,684 kJ

354

800

� 600

6400 Q; � 200 0..

o

• lrnJr1L--> Power from ESS

. ' . � . . . . .

••.••.•.••• l . • . . . . . . . ��i.VjAfA�t:{J

-- WithDPM .... -- Without DPM

-200,"-0------'1'- 00------'20- 0------'30-0-- -4-'-0-0-- -5-'- 0 -0-----'600

500 600

[�� :�. .

.

.

..

. ... P��.::::�S:��< � .• ••.. • •.

� 20 ..... "' : ' " . " : .

... . .

.. .. .

. . . . . . ' : .

.

. .

. . . . . . . .

00 100 200 300 400 500 600

Time (5)

Fig. 8. Power delivered from ESS, and power losses over energy storage subsystem I (SS I) and subsystem 2 (SS2)

Fig. 9 shows the charge/discharge rates of SS I and SS2

inside ESS. C-rate is used as unit of charge/discharge rate: IC

is equal to the capacity of the energy storage in one hour. As

shown, power allocated to SS I has slower variations compared

to power allocated to SS2, Moreover, both charge/discharge

rates of SS I and SS2 are within their limits.

Charge/D

ischarge Rate of SS1 (C-rate)

£ T � , r .

..

�

...

cha

l

r

�

ge

.

/D

A:.

i�SC

.

",,� •• at�e.

OfSS2. (C�rat

.

e

.

)

.

• : ..........

.

......

:

.

i:�� o 100 200 300 400 500 600

Time (s)

Fig. 9. Charge/Discharge rates of SSI and SS2

Simulation results have shown that DPM is effective to al­

locate dynamic power to energy storages such that constraints

of energy are complied, energy efficiency is improved, and

power output of wind plant at PCC is controllable.

V. CONCLUSION

The paper has presented a design of high power energy

storage system dedicated to wind plant. A Dynamic Power

Manager has been introduced to manage energy storage system

to provide high-quality power that wind plant requires. The

Dynamic Power Manager has also optimally distributed power

amongst energy storages inside energy storage system. Simu­

lation results have shown that with Dynamic Power Manager,

power output of wind plant at PCC is controllable, power

quality at PCC and energy efficiency of energy storage system

are improved.

ApPENDIX

Time step of simulation and computation time for Genetic

Algorithm are T = lsec. Deviation limit is b.P = 2kW.

TABLE IV SPECIFICATION OF ENERGY STORAGE SYSTEM

Parameters SS1 SS2 Capacity 600 kWh 200 kWh Max charge rate lC 2C Max discharge rate lC 2C Dynamics limit 0.2 Cis 0.5C/s Max DOD 70 % 80 % Efficiency 90 % 85 %

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