This document summarizes a presentation on modeling and simulating a solar PV and battery-based DC microgrid system. It discusses the advantages of DC microgrids over AC microgrids. It also outlines several real-world DC microgrid implementations worldwide and describes the designed microgrid system. Three scenarios are simulated to test the energy management control algorithm in maintaining the DC bus voltage and regulating the battery state of charge. The results demonstrate the controls working as expected. Future work is proposed to expand the system and design protections.

1. Modeling and Simulation of a
Solar PV and Battery Based DC
Microgrid System.
International Conference on Electrical, Electronics and
Optimization Techniques (ICEEOT) - 2016
Presenter: Mr. M.H.F Ahamed
1

2. DC Vs AC Microgrids.
 No need to consider about synchronization with
utility grid and reactive power.
Steadily increasing fraction of power is consumed by
DC loads.
More resilient in the face of natural disasters.
Minimum losses at the interfaces.
Any change in the frequency in the utility grid does
not effect the operating frequency of ac loads in the
DC grid
2

3. Usage of DC Microgrids Worldwide
3
Intel Corp.
400V DC
UCSD
380VDC
Duke energy
380VDC
Sycracuce
University
380VDC
US Validus
550VDC
Japan NTT
GROUP
380VDC
Korea
300/380VDC
China Telecom
230/380VDC
Sweden Netpower
350/380VDC
France Telecom
350/380VDC
Taiwan IT
Manufacture
380VDC
New Zealand
Telecom
220VDC
Uttar Pradesh
rural
electrification
24V Solar

4. Designed DC Microgrid System.
4
Designed system represents a isolated rural electrification system.
Grid Tied
Rectifier
Solar PV
Utility Grid
Distribution
Transformer Battery Bank
DC Loads
Micro Grid
Central switch
Buck
Converter
Battery
Converter
3 km
Distribution
line
48 V DC Bus

5.  Critical for Microgrids based on distributed sources.
 Objectives of Energy Management and Control are:
o Control of solar PV: Provides a reference voltage (Vmpp) for the
unidirectional DC-DC buck converter.
o Control of the DC-DC buck converter at the PV output: Forces the
solar array to operate at a voltage to harness the maximum power.
o Battery charging and discharging circuit control: Maintains the DC
link voltage at a desired value.
o Control of grid tied rectifier: Supply power to the loads when solar
power is not available.
5
Energy Management and Control.

6. Battery Dynamics and Energy Management System
Control Algorithm[1].
6
Start
Measure SOC
Pload, PPV
0.3<SOC<0.9SOC<0.3SOC>0.9
PPV>Pload
Add an additional
load or halt
PPV>Pload
Load shedding
PPV<Pload
Charging Discharging
Return
N
N
YY
N
N
Y N
Y
Y
[1] R. Zamora and A. Srivastava, "Energy Management and Control algorithms for Integration of Energy storage Within Microgrid", in IEEE 23rd
International Symposium on Industrial Electronics (ISIE), 2014, pp. 1805 – 1810

7. Solar Maximum Power Point Tracking
(MPPT).
7
Solar PV array and boost converter in PSCAD
Maximum power point tracking model in
PSCAD
Vpv (V)
Ppv (kW)
Solar PV Characteristics Curve.
 Algorithm used for mppt is incremental
conductance (IC).

8. Simulation Results.
8
 Solar irradiance data is an actual variation measured at a
particular place in Sri Lanka.
 Three scenarios are considered for the simulations.
 During all three scenarios the main objectives are to keep the
bus voltage at 48VDC and battery state of charge (SOC)
within safe limits.
0 16 32 48 64 7272
0
200
400
600
800
PSCAD simulation time (s)
Irradiance(W/m
2
)
Variation of solar irradiance

9. Scenarios Considered for the
Simulation.
9
 Scenario (01) Battery reaches its maximum charging limit
and then the system switches to halt mode when there is no
additional load available.
 Scenario (02) Scenario 01 is extended by connecting an
additional dynamic load to the system during the charging
mode of the battery so that the battery SOC is regulated
within its maximum limit.
 Scenario (03) Battery SOC reaches its minimum limit at a
particular point of operation. So in order to keep the DC link
voltage at 48 V load shedding is done.

10. 10
Scenario (01)
0 16 32 48 64 7272
0
16
32
48
6060
Time(s)
(V)
0 16 32 48 64 72
0
16
32
48
60
(V)
0 16 32 48 64 72
50
60
70
80
90
100
SOC(%)
0 16 32 48 64 72
0
0.5
1
1.5
(kW)
0 16 32 48 64 72
0
1
2
33
(kW)
0 16 32 48 64 72
0
15
30
4040
(V)
Vmpp
Vpv

11. 11
Scenario (02)
0 16 32 48 64 7272
0
16
32
48
6060
Time(s)
(V)
0 16 32 48 64 72
0
0.5
1
1.5
1.7
(kW)
0 16 32 48 64 72
0
16
32
48
60
(V)
0 16 32 48 64 72
0
1
2
3
(kW)
0 16 32 48 64 72
50
60
70
80
90
100100
SOC(%)
0 16 32 48 64 72
0
10
20
30
40
(V)
Vmpp
Vpv

12. 12
Scenario (03)
0 16 32 48 64 72
0
16
32
48
6060
Time (s)
(V)
0 16 32 48 64 72
0
0.5
1
1.5
2
(kW)
0 16 32 48 64 72
0
1
2
3
(kW)
0 16 32 48 64 72
0
10
20
30
4040
(V)
Vmpp
Vpv
0 16 32 48 64 72
0
16
32
48
60
(V)
0 16 32 48 64 72
10
30
50
70
90
100100
SOC(%)

13. Conclusions and Future Work.
13
Simulation results shows that energy management and
controls work as expected.
The designed system can be used to test various power
system scenarios and including transient and protection.
System can be further expanded by incorporating wind
turbines, super capacitors and diesel engines.
Our future work is to design a suitable protection
system for the designed microgrid.

14. Thank you!
14