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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