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Decentralized Battery Energy Management for Stand-Alone PV-Battery Systems
Umarin Sangpanich (PhD.)Faculty of Engineering at Sriracha
Kasetsart University (Sriracha campus)
19 May 2016
OutlineA key of stand-alone renewable energy systems… “Battery management”
A traditional battery control methodDecentralized battery storage control methods
Decentralized Battery Energy Management (DBEM) methodOptimization of a stand-alone PV-Battery system with the DBEM methodCase study
Results and Discussions
ConclusionsPrototypePublication
2
A key of stand-alone renewable energy systems… “Battery management”
A traditional battery control method is a single charge controller by grouping all batteries into parallel strings.
3
Drawbacks: Batteries in the same string must have the same capacity
and initial State of Charge (SOC) .If a battery has a higher initial SOC, it would be
charged faster and have gassing while other batteries in the string are still undercharged, leading to the reduction of battery lifetime.
Due to limitation of charging power levels, therefore, the energy produced will be wasted and the wasted energy will increase in larger battery energy storage systems.
DC bus
AC wind turbines
Dispatched load
Photovoltaic array
Charge
controller
Control and
Metering system
AC/DC
converter
Inverter
Charging process situation
4
One large group
PV
ou
tpu
t p
ow
er
Time
Waste energy
Waste energy
Minimum current limit of batteries
Cloudy dayWaste energy
5
Decentralized Photovoltaic (PV) and battery system with multilevel inverter
InverterPV array DC-DC
S1 S2 S3
Load
S4 S5 S6
Decentralized battery storage control system
A key of stand-alone renewable energy systems… “Battery management”
Source: R. Kaiser, 2007
Source : M. I. Desconzi etc., 2010
-Better performance-Longer lifetime of battery storage systemsHowever, larger systems will be more expensive.
Decentralized Battery Energy Management (DBEM) method
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Load
PV array
DBEM system
Inverter
Control center
Charger
Different group sizes
Multi-switch groups
Objectives:To minimize ….
loss of power supplycost of energy wasted electrical energy
To prolong battery life
**The system should be practical and economical for operation.***
Charging process situations
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Large group
Medium group
Small group Small group
PV
ou
tpu
t p
ow
er
Time
Large/Medium groups
Medium group
Small group Small group
PV
ou
tpu
t p
ow
er
Time
Decentralized Battery Energy Management (DBEM) method
Cloudy daySunny day
Charging and discharging processes of the DBEM method
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Ppv>Pload
Ppv, Pload
Yes Imin,rated ≤ Ipv,remain ≤ Imax,rated
Charge a battery
group having higher
voltage by checking
rated current
No
Charge a battery
group having the
lowest voltage
Yes
Discharge a
battery group
having the highest
voltage (V1)
Discharge the
battery group until
V1=V2 if Ppv<Pload
Discharge the
battery groups
until V1=V2=V3 if
Ppv<Pload
No
Check Vbatt & Irated in each group
Charging process
Discharging process
Ppv is PV power.Pload is load power.Vbatt is battery voltage. Ipv,remained is PV current remained from supplying load. Imin,rated and Imax,rated are minimum and maximum rated battery current of each battery group, respectively.
Optimization of a stand-alone PV-Battery system with the DBEM method
9
Load demand
Operation of a PV-Battery
system: calculate LPSP, LCOE
Maximum number of
generation reached
Yes
SPEA2 operation
Solar radiation &
Air temperature data
New populations
No
Final population: Npv & Nbatt Optimal solution:
minLPSP, minLCOE
Random initial populations:
number of PVs, batteriesObjectives: To minimize Levelized Cost of Energy (LCOE)To minimize Loss of Power Supply Probability (LPSP), 0 LPSP1.
Optimization method: Strength Pareto Evolutionary Algorithm 2 (SPEA2)
Case study
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A load profile in a Thai rural area
Components Initial capital cost Replacement cost O&M cost Lifetime
PV module including Tax
- Installation cost and other components
15,000 Baht per module
20% of total PV system cost 15,000 Baht per module 0.5% of ICC 25 years
Battery, 550 Ah 12,500 Baht per cell 12,500 Baht per cell 3% of ICC 1000 cycles
Bi-directional converter and system control 22,550 Baht per kW 22,550 Baht per kW 3% of ICC 15 years
Charge controller 190 Baht per Ampere 190 Baht per Ampere 3% of ICC 15 years
0
5
10
15
20
25
0
100
200
300
4000
0.2
0.4
0.6
0.8
1
1.2
1.4
HourDay
Sola
r ra
dia
tion (
kW
/m2)
0
0.2
0.4
0.6
0.8
1
Average hourly solar radiation in Thailand
Estimated costs and lifetime of system components
Results and Discussions
11
LPSP and LCOE of stand-alone PV-Battery systems
The fractions of the three groups were set as 1/6, 1/3 and 1/2 to be small, medium and large groups, respectively.
Results and Discussions
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ParticularBattery groups
1 group 3 groups
Loss of power supply probability, LPSP 0%1%
( 87 hrs)32.11%
(2,813 hrs)0%
1% ( 87 hrs)
PV capacities (kWp) 35 31 20.5 20.5 13Battery capacities (kWh) 356.4 237.6 316.8 316.8 237.6Annual PV energy (MWh/year) 67.62 59.89 43.5 43.5 25.12Annual total waste energy (MWh/year) 25.33 17.6 1.40 1.21 0.32
Annual overall energy efficiency of systems 62.5% - - 97.22% -LCOE (Baht/kWh) 10.86 9.80 9.64 9.35 6.81
Comparisons between one and three battery groups
The 3-groups system has lower PV and battery capacities and lower annual total wasted energy, leading to higher overall energy efficiency and lower LCOE.
The 3-groups system has higher reliability than the 1-group system.
Results and Discussions
13
00.10.20.30.40.50.60.70.80.91
0
5
10
15
20
25
30
35
02
AM
, 3
0-S
ep
07
AM
, 3
0-S
ep
12
PM
, 30-S
ep
04
PM
, 30-S
ep
09
PM
, 30-S
ep
02
AM
, 1
-Oct
07
AM
, 1
-Oct
12
PM
, 1-O
ct
04
PM
, 1-O
ct
09
PM
, 1-O
ct
02
AM
, 2
-Oct
07
AM
, 2
-Oct
12
PM
, 2-O
ct
04
PM
, 2-O
ct
09
PM
, 2-O
ct
02
AM
, 3
-Oct
07
AM
, 3
-Oct
12
PM
, 3-O
ct
04
PM
, 3-O
ct
09
PM
, 3-O
ct
Sta
te o
f C
har
ge,
SO
C
Po
wer
(k
W)
Time, Date
PV power Load power SOC
The SOC and PV power supply for load demand, of the system using one battery group
Cloudy days in rainy season
Results and Discussions
14
0
0.2
0.4
0.6
0.8
1
1.2
0
5
10
15
20
25
30
35
02 A
M, 3
0-S
ep
07 A
M, 3
0-S
ep
12 P
M,
30-S
ep
04 P
M,
30-S
ep
09 P
M,
30-S
ep
02 A
M, 1
-Oct
07 A
M, 1
-Oct
12 P
M,
1-O
ct
04 P
M,
1-O
ct
09 P
M,
1-O
ct
02 A
M, 2
-Oct
07 A
M, 2
-Oct
12 P
M,
2-O
ct
04 P
M,
2-O
ct
09 P
M,
2-O
ct
02 A
M, 3
-Oct
07 A
M, 3
-Oct
12 P
M,
3-O
ct
04 P
M,
3-O
ct
09 P
M,
3-O
ct
Sta
te o
f C
har
ge,
SO
C
Po
wer
(k
W)
Time/Date
PV power Load power SOC of a small group
SOC of a medium group SOC of a large group
The SOC and PV power supply for load demand, of the system using three battery groups
Cloudy days in rainy season
Conclusions
• The DBEM method is proposed for minimizing loss of power supply, cost of energy and wasted electrical energy.
• From SPEA2 optimization and energy simulation, – the PV system using the DBEM method has higher reliability and energy efficiency than the system using one
battery group,– while decreasing the number of PV and battery modules leading to lower LCOE and waste energy.
• The DBEM method can be applied for renewable energy systems such as wind turbines.• A DBEM prototype, of which controller and circuit are uncomplicated, has being designed and
built.
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16
Load
PV array
DBEM system
Inverter
Control center
Charger
Prototype System
Specification:• For a PV system of 3 kWp• Using a micro processor to be a
center controller between a charger and an inverter
• Data monitoring via Ethernet • Upload data via USB• Sleep mode for saving energy
Publications
• U. Sangpanich, “Optimization of Photovoltaic Systems Using Batteries for Peak Demand to Improve Rural Electrification, CIGRÉ Canada Conference 2015 Proceeding, Canada, August 31-September 2, 2015
• U. Sangpanich, “A Novel Method of Decentralized Battery Energy Management for Stand-Alone PV-Battery Systems,” in The 6th IEEE PES Asia-Pacific Power and Energy Engineering Conference (IEEE PES APPEEC 2014), 2014.
17
Thank you very much for your attention.
18
DC bus
AC wind turbines
Dispatched load
Photovoltaic array
Charge
controller
Control and
Metering system
AC/DC
converter
Inverter
AC wind turbines
AC/DC
converter
