- Open Access
- Total Downloads : 79
- Authors : Le Tien Phong, Ngo Duc Minh
- Paper ID : IJERTV5IS080247
- Volume & Issue : Volume 05, Issue 08 (August 2016)
- Published (First Online): 24-08-2016
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
Determine Battery Capacity Basing on the Distribution of Power Flows in Hybrid Photovoltaic and Wind Generations Operating in Half-Isolation Mode
Le Tien Phong
Electrical Faculty
Thai Nguyen University of Technology Thai Nguyen city, Viet Nam
Ngo Duc Minh
Electrical Faculty
Thai Nguyen University of Technology Thai Nguyen city, Viet Nam
AbstractHybrid models exploiting photovoltaic and wind generations are combined with energy storage to overcome disadvantages of each other. This paper mentions the distribution of power flows through power converters to make operating plans for each element. It can be done by using known data collected or predicted and applied in half-isolation mode for whole system. This paper also proposes a new program to determine the optimal capacity for DC storage using battery. The operation of whole system is simulated in Matlab software accounting for random characteristic of input data. Experimental
grid problem and the available value for lack of power Plp is not limited.
Wg
PVg
Wg
Electric grid
results represented the ability of controlling power flows in a photovoltaic system.
PVg
Energy
storage
KeywordsBattery, distribution of power flows, energy management system, optimal battery capacity, photovoltaic generation, power converter, smart grid, wind generation.
-
INTRODUCTION
Photovoltaic generation (PVg) and wind generation (Wg) are potential energy types to replace traditional sources. Because of advance in producing, PVg has enhanced its efficiency and reduced cost price whereas Wg has some silent types to install at any place in residental zone and used as distributed source.
In smart grid, power converters can help us operate hybrid generations flexibly in isolation or half-isolation mode to have some support from grid. One case of operating in half-isolation
Fig. 1. Connect islands in the smart grid.
This paper proposes a new algorithm to determine C basing on collected or predicted input data (solar irradiance, wind speed), load diagram in periodic time T and execute the distribution of power flows at each time for all elements in the system operated in half-isolation mode and limited Plp.
-
SYSTEM STRUCTURE AND DISTRIBUTE POWER FLOWS IN WHOLE SYSTEM
-
System structure
A system operates in half-isolation mode often has DC parallel structure as Fig. 2 [7-9].
mode, Fort Collins – Colorado State (America) is executing a special energy policy consume energy less than generate by using the following ways: photovoltaic systems are located on roofs and gardens, wind turbine, thermal and electrical energy storage, etc, and saving energy programs. Separating the grid into many small islands represented in Fig. 1 will become a good growth trend to keep our existence on the earth [1]. In this system, battery storage is used to overcome random characteristic of input data (solar irradiance, wind speed, etc). Two criterions, enough capacity to absorb all energy from
Sun
PVg
Battery
DC/DC1h
Converter
DC/DC2h
Converter
EMS
DC
load
DC/AC2h
Converter
APS
~
AC
load
source or enough to provide for load power, can be often applied to determine battery capacity C for an individual generation but cant be applied for hybrid generations [2].
Wind ~
Wg
AC/DC1h
Converter
DC/DC1h
Converter DCbus
ACbus
References [3-6] are introduced an approximative method to calculate the distribution of power flows in hybrid systems for all periodic time T (day, month, year) and execute an economic problem to compare cost price between purchasing and selling electricity but it only can be applied in completely connected
Fig. 2. System structure.
Where:
APS (Auxiliary Power Source), such as grid, diesel generators, etc, provides power in a limited range because of
DC load
DC load
AC load
AC load
PVg
DC/DC1h
Battery
the ability of grid, other systems or rated power of generators.
DC/DC1h and AC/DC1h converters are used to control the operation point for PVg and Wg.
AC/DC1h
Wg DC/DC1h
DC
bus
DC/AC2h
AC
bus
APS
DC/DC2h and DC/AC2h are are bidirectional converters used to control charging or discharging mode for battery and generate power to ACbus and connect to APS.
EMS (Energy Management System) collects all
PVg
-
Scenarios 1
DC load
DC load
AC load
AC load
Battery
DC/DC2h
information about load diagram, solar irradiance, wind speed and instant battery capacity to calculate power flows having to operate for all elements in periodic time T [10-12].
DC/DC1h
AC/DC1h
Wg DC/DC1h
DC
bus
DC/AC2h
AC
bus
APS
To limit power from APS, battery has to have enough large capacity to adapt technical requirements but not allow abundance because of high price and low working life.
-
-
Distribute power flows in whole system
PVg
Battery
-
Scenarios 2
DC load
AC load
DC load
AC load
DC/DC2h
Power flows in whole system are executed by the following criterions [3-6]:
-
Power from generations is prior to provide load.
DC/DC1h
AC/DC1h
Wg DC/DC1h
DC
bus
DC/AC2h
AC
bus
APS
-
Battery operates in discharging mode when generations dont provide enough power for load or in charging mode when load cant absorb power from generations
-
-
Scenarios 3
DC load
DC load
AC load
AC load
Battery
whereas instant capacity Cins is at admittable limitation (Cmin=0.2C Cins C).
PVg
DC/DC1h
DC/DC2h
-
APS isnt used to charge battery or aborb power from battery. The value of lack of power from APS is always less than admittable value Plpcp (PlpPlpcp).
AC/DC1h
Wg DC/DC1h
DC
bus
DC/AC2h
AC
bus
APS
Because of above criterions, power flows in whole system can be clasified into the following scenarios:
Scenarios 1: generations provide enough power for load by themselves. In this case, load doesnt use power from battery and APS
PVg
Battery
DC/DC1h
-
-
Scenarios 4
DC load
AC load
DC load
AC load
Scenarios 2: Abundunt power from generations after providing for load power is used to charge battery
AC/DC1h
Wg DC/DC1h
DC
bus
DC/AC2h
AC
bus
APS
Scenarios 3: Generations dont provide enough power for load. Battery operates in discharging mode.
Scenarios 4 and 5: Generations dont provide enough power for load. Load consumes power from both battery and APS.
AC load
AC load
PVg
Battery
DC/DC1h
AC/DC1h
-
Scenarios 5
DC load
DC load
AC
APS
Scenarios 6 and 7: Generations dont provide enough power for load. Load consumes power from only APS.
Wg DC/DC1h
DC
bus
DC/AC2h
bus
Power flows in whole system are represented clearly in Fig.
-
Where, DC/DC1h, DC/DC2h, AC/DC1h, DC/AC2h are efficiencies for converters.
PVg
DC/DC1h
e. Scenarios 6
DC load
AC load
AC load
Battery
AC/DC1h
Wg DC/DC1h
DC
bus
DC/AC2h
AC
bus
APS
g. Scenarios 7
Fig. 3. Power flows in whole system.
-
-
DETERMINE BATTERY CAPACITY
-
Proposed algorithm
Value of lack of power in scenarios 6 and 7 can be determined by Kirhoff law 1:
In preliminary calculation, the value of power from generations can be received by a simple model basing on solar irradiance S, start-up wind speed vcut-in, cut-out speed vcut-out,
Plp (i) Pload(i) PG (i)
rated speed vr, rated power PrPVg and PrW at standard conditions.
-
For PVg
Using half-fold diagram to calculate exact power at each instant time [13] or estimate power approximately [14], [15]:
Basing on preliminary calculation, battery capacity C can
be determined by a proposed algorithm in Fig. 4. New ideals for this algorithm is that only applies in half-isolation mode limiting received power from the grid and battery capacity step
C will be increased immediately if current capacity doesnt adapt to requirements.
P P . S
PVg rPVg 1000
For Wg [3], [4]
Collect data about irradiance, wind, load
Collect data about irradiance, wind, load
0 when v vcutin or v vcutout
Start
Calculate PPVg(i), PWg(i), PG(i)
Calculate PPVg(i), PWg(i), PG(i)
3
P P . v vcutin when v v v
Wg rW v v
cutin r
r cutin
Choose intial value C
Choose intial value C
PrWg
when vr v vcutout
At ith time, total power PG(i) generates from generations:
PG (i) PPVg (i) PWg(i)
C=C+C N
i=1
i=1
i=i+1
i=i+1
PG(i)=Pload(i) Y
N
Basing on data about DC and AC load, total equivalent load at ith time:
Plp(i)Plpcp
Y
Y
Scenarios 2:
PG(i)>Pload(i)
Scenarios 1
Scenarios 1
P (i) P
(i) PloadAC(i)
Battery can N
Scenarios 3: Battery can discharge
to Cp(i)
Scenarios 3: Battery can discharge
to Cp(i)
load
loadDC
DC / AC2h
charge to Cn(i)
Calculate Plp(i)
Calculate Plp(i)
For battery, battery capacity can charge to Cn(i) or discharge to Cp(i) at ith time can be determined by time step t [3-6], [16]:
Scenarios (4 ÷7)
Scenarios (4 ÷7)
Cins(i+1)
=Cins(i)
Cins(i+1)
=Cins(i)
Y
Cn(i)>C
C (i)
Pload PG (i).t
N Y
Cp(i)<Cmin
p Cins(i
1).(1
)
DCDC2h
VDCbus
Cins(i+1)=Cn(i) N
C (i)
Pload PG (i).t
C (i+1)=C
Cins(i+1)=Cp(i)
n
Where:
Cins(i
1).(1
)
VDCbus
. DCDC2h
ins
min
Y
i<=24
S
Cins(i-1) is instant battery capacity at (i-1)th time.
Value of lack of power consuming from APS in scenarios 4 and 5 can be determined by Kirhoff law 1:
Stop
Fig. 4. Proposed algorithm to determine battery capacity.
-
-
Simulate the problem determining battery capacity and
Plp (i) Pload(i) PG (i) Pb (i)
distributing power flows
Periodic time T=24 [h], time step t=1 [h], battery capacity
Discharging capacity from battery to DCbus can be determined by:
step C=10 [Ah], admittable value of power Plpcp = 1600 [W].
Generations: Rated power of Wg is 5000W and PVg is 1360W. Generating diagrams of PVg and Wg are represented
Pb (i) (Cins(i) Cins(i 1)).VDCbus
in Fig. 5.
5000
4500
4000
3500
Power [W]
Power [W]
3000
2500
2000
1500
1000
500
0
Fig. 8 represents time ranges for charging and discharging battery, where positive power presents charging power and negative power presents discharging power.
PVg
PVg
Wg
Wg
Charging capacity
Discharging capacity
Charging capacity
Discharging capacity
200
150
Capacity of Battery [Ah]
Capacity of Battery [Ah]
100
50
0
]
]
0 4 8 12Time [h 16 20 24
Fig. 5. Generating diagrams of PVg and Wg.
DC and AC load diagrams are represented in Fig. 6.
4000
3500
-50
-100
-150
0 4 8 12 16 20 24
Time [h]
Fig. 8. Charging and discharging battery.
3000
Power [W]
Power [W]
2500
2000
DC load
AC load
DC load
AC load
1500
1000
500
0
0 4 8 12 16 20 24
Time [h]
Fig. 6. DC and AC load diagrams.
Apply proposed algorithm in Fig. 4 for above data, suitable battery capacity resulting from program is 200 [Ah]. Use some local values of above suitable battery capacity to recalculate maximum value of Plp and abundant power Pabundant of generations. Results in TABLE. I show that 200 Ah is the most value for above input data.
Connect to computer, display
Connect to computer, display
TABLE I. RECALCULATE PLPMAX AND PABUNDANT FOR SOME LOCAL VALUES OF SUITABLE BATTERY CAPACITY
In Fig. 8, the battery operates in discharging mode in time range (0÷1), (2÷3), (6÷7), (9÷12), (17÷18)h and charging mode
in time range (1÷2), (3÷6), (9÷10), (14÷15), (16÷17), (23÷24).
-
-
EXPERIMENTAL DISTRIBUTION OF POWER FLOWS
-
Experimental model
Modules of PVg located in Thai Nguyen university of technology by Phoenix Solar Pte, Singapore are parameters shown in TABLE II. Parameters of battery are shown in TABLE III.
TABLE II. PARAMETERS OF PVg
Type
Kyocera KC85/Japan
Rated power [Wp]
1360 Wp
TABLE III. PARAMETERS OF BATTERY
Type
Lead-acid, Dong Nai N200
Nominal voltage
12 V
Capacity
200Ah
Structure model for PVg is represented in Fig. 9, where DC/DC converter is flyback type.
C [Ah] 180 190 200 210
Plpmax[W] 1582.6
Sun
Iref
I + e
Pulse
EMS
Pabundant [W] 117,1 67,5 0 0
out –
generator Control
signal
Power generating from generations and battery, load power, lack of power Plp diagrams corresponding C=200Ah are represented in Fig. 7.
Charge battery Battery
PVg
Battery
DC/DC1h
converter
DC
load
DCbus
5000
Power [W]
Power [W]
4000
3000
Load
PVg+Wg Plp
-
Control structure
Control signal
Control signal
Iout
2000
1000
*
*
C1
C1
D
C2
D
C2
PVg Battery,
*
*
load
0
0 4 8 12 16 20 24
Time [h]
Fig. 7. Power generating from generations and battery, load power, lack
of power diagrams.
-
DC/DC converter type flyback Fig. 9. Structure model for PVg.
Experimental model for PVg is represented in Fig. 10. Where, load block has 4 compact lights (4x33W) and each light can be switch on or off invidually.
er
er
Collect data and controller
Results of TRACKING PV SYSTEM PROGRAM in 27
minutes are represented in Fig. 12. Where, the red curve represents input power and the green curve represents output power of the converter.
PVg
DC
load
Battery
Control cabinet
Comp
Control cabinet
Comp
ut
Fig. 10. Experimental mode.
-
-
Experimental results
Reference current Iref is installed to controller by buttons on board or computer. Maximum power point tracking in control program is only actived when output current of converter Iout is less than Iref. Operation parameters can be observed online on display panel of control board and program TRACKING PV SYSTEM PROGRAM in computer using Visual Studio software version 10. At the end of observation process, the program will create a file collected data about the operation process.
Experimental time: from 8p5 to 8h43 on 11 September 2014, battery voltage is 12,8V. Operation scenarios are shown in TABLE IV.
Operation time
Operation scenarios
From 8p5 to 8p1
Iref =16A, PloadDC=0
From 8p1 to 8p7
Iref =10A, PloadDC=0
From 8p7 to 8p3
Iref =10A, PloadDC=33W
From 8p3 to 8p7
Iref =10A, PloadDC=0
From 8p7 to 8h43
Iref =17A, PloadDC=0
Operation time
Operation scenarios
From 8p5 to 8p1
Iref =16A, PloadDC=0
From 8p1 to 8p7
Iref =10A, PloadDC=0
From 8p7 to 8p3
Iref =10A, PloadDC=33W
From 8p3 to 8p7
Iref =10A, PloadDC=0
From 8p7 to 8h43
Iref =17A, PloadDC=0
TABLE IV. EXPERIMENTAL SCENARIOS
Fig. 12. Results of observation program.
Results from control program are shown:
-
Converter only exploited power from PVg in time ranges (8p5÷8p1), (8p1÷8p7), (8p3÷8p7) and (8p7÷8h43) to charge battery. After changing Iref, input and output power curves of the converter changed pulse to track energy requiments.
-
From 8p7 to 8p3, Iref was hold in constant by 10A whereas added a 33W light load at 8p7. Immediately after adding load, EMS hadnt changed pulse so input and output power curve of PVg was built-up corresponding 33W. After transient time, EMS changed pulse control to take output current converter to Iref and power charging battery curve dropped corresponding 33W. It showed that it has amount of power from PVg shared partial power into light, partial power into battery.
Power flows controlled in all operation modes of experimental model are represented in Fig. 13.
Iref, current and voltage output converter diagrams are repr ted in Fig. 11.
Sun
PVg
Power flow from PVg DC/DC
Converter
DC
load
esen
17
Battery
DCbus
Iref [A]
Output current of converter [A]
Iref [A]
Output current of converter [A]
Output voltage of converter [V]
Output voltage of converter [V]
16
15
14
Add DC load
13
12
Sun
-
Charging battery and not adding load
Power flow from PVg DC/DC
converter
PVg
DC
load
11 Battery
10
DC bus
9
0 3 6 9 12 15 18 21 24 27
Time [minute]
-
Both charging battery and adding load
-
Fig. 13. Distribution of power flows in experimental model corresponding to
scenarios.
Fig. 11. Iref, current and voltage output converter diagrams corresponding
scenarios.
-
-
CONCLUSION
Operating hybrid generations in half-isolation mode to receive support from grid but not depending much on the grid can provide power for load in site and take some benefits such as reducing transmition power loss and pressure on the national power system. This is also the future model for all power systems.
Experimental model showed that converters can help us control power flows between all elements flexibly and adapt to many different requirements.
Proposed algorithm determining battery capacity basing on distribution of power flows in hybrid generations in half- isolation mode can help system adapt to load more actively. The program basing on proposed algorithm collects multiple sets of data to evaluate the best value for battery or optimal battery capacity.
When input data (wind speed, solar irradiance) can be predicted, above program will help operator calculate distribution of power flows for each element or make operation plan for whole system.
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Le Tien Phong, received the M.Sc. degree in 2010 in Electrical Engineering from HaNoi University of Technology and Science and working in Thai Nguyen University of Technolgy now. His research interests include renewable energy in power system, control electrical energy conversions, control of power flow in power system.
Ngo Duc Minh, received the PhD. degree in Automation from Ha Noi University of Technology and Science in 2010 and working in Thai Nguyen University of Technolgy now. His research interests include active rectifier, active filter, FACTS BESS, D-STATCOM, Control of power system, distribution grid, renewable energy in power system.