Contribution to the achievement of SDGs
by Furukawa Electric Group

Development of DC Microgrid Control Method
Masahiro Rikiso*1, Nobuhiko Sakai* 1, Asuka Abe* 1, Noritaka Murohfushi* 1,
Sumio Kachi* 1, Kengo Nakao*2, Tadataka Wakabishi* 1

Looking ahead on the future energy infrastructure such as the decarbonizationand others that contributes to the Sustainable Development Goals (SDGs),
Direct Current Microgrids (DC-MGs) have been attracting an interest as a mean to contribute to
the expansion of renewable energies, storage batteries and Electric Vehicles (EVs). In order toimplement the DC-MG in the society, it is important to establish an economical and stable
power control technology. In this paper, we will introduce the hierarchical decentralized control,
which is characterized by the introduction of Droop control and virtual inertia control to the primary control.

ABSTRACTINTRODUCTION
1.1. INTRODUCTION
In the process of realizing a carbon-neutral society(decarbonized society) by 2050, it is expected that
renewable energies will become the main power sources
and the spread and expansion of Electric Vehicles (EVs)will steadily progress. Since renewable energies, storage
batteries, EVs and etc. are all Direct Current (DC) devices,
it is thought that areas where the features of DC can beutilized will surely emerge in the existing Alternating
Current (AC) power grid.
Since the widespread adoption of renewable energy
sources with unstable power generation capacity and
EVs as a new power load may induce the instability in theexisting power grid, DC-MGs are attracting interest as amean of realizing the future power and energy infrastructure.
An MG is a system that controls the energy supply anddemand in a specific region by combining small-scale

and a variety of decentralized power sources and it is atechnology that supports the maintenance of power quality comparable to that of the power grid during an autonomous operation and the introduction and expansion ofrenewable energy sources such as Photo Voltaic (PV),wind power, etc 3).
In the power system applying the DC technology, theDC power can be used without converting to DC, so thatthe energy loss can be suppressed and the power can beconverted by the simple voltage control without a frequency and phase control.
The DC-MG (Figure 1) is a fusion of this DC technology
and the MG, and it is important to establish an economical and stable power control technology in order toimplement the DC- MG in the society. This paper will
introduce the hierarchical decentralized control, whichfeatures the introduction of Droop control and the virtualinertial control techniques for the primary control.

AC
AC

DC

DC

DC

DC

DC

DC

DC

DC

DC

DC

DC

DC

PV

BatteryEV

PV

Battery

DC

DC

EV

Figure 1 Figure 1 Outline diagram of DC-MG.

Incubator Department, Future Infrastructure Technology Center,R&D Division*2
Strategic Planning Department, R&D Division*1

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Development of DC Microgrid Control Method

Secondary Control

EMS

AC lineACDC

DC lineDC

Primary Control
DC

DC

DC

DC

DC

DC

DC

DC

DC

DC

DC

PV

BatteryEV

PV

BatteryEV

Figure 2 DC-MG hierarchical decentralized control.

2.

FEATURES OF OUR CONTROL METHOD

2.1 Hierarchical Decentralized Control
Figure 2 shows the hierarchical decentralized control consisting of the primary control and the secondary control ina DC-MG where the power devices of grid power supply,PV, storage batteries and EV charger/discharger are connected to the DC line. The primary control is a decentralized control installed in the power converters within eachdevice and the secondary control is a centralized control
installed in the Energy Management System (EMS).
In the primary control, each power equipment plays arole in controlling the DC line voltage stabilization and thepower conversion in a decentralized manner based on
the EMS commands. When the EV rapid charger/discharger connected to the DC line operates, a sudden
load fluctuation occurs so that the conventional centralized control method cannot follow the voltage control,
resulting in unstable operation of DC-MG. On the other
hand, in the decentralized control method, multiple powerdevices perform the voltage control in a decentralizedmanner, so that it can also follow sudden load changes.
In the secondary control, the EMS performs optimization calculations (derivation of target functions) based on
the evaluation index (minimization of operating costs,etc.), and plays a role in sending communication commands (target functions) to each electric devices such as
storage batteries and others. Target functions that reflect
the evaluation index are used to achieve an efficient
DC-MG operation.
The hierarchical decentralized control, which combines
the primary control and the secondary control, can beapplied to countermeasures against sudden changes in
the power supply and demand, effective utilization of
resources, etc., because it can achieve both the control
stability and the control efficiency, which are the characteristics of each control.
2.2 Droop Control
The hierarchical decentralized control in this paper is built
on the basis of Droop control. The Droop control can beclassified into the Droop voltage control and the Droop
current control according to the implementation details of
the control.

The Droop voltage control is a voltage control in which
the target voltage Vout varies linearly according to theobserved current Iobsv. Let the proportionality coefficientbe -R, the target function is determined by the following
1equation.
(1)Controlling the power converter looks equivalent to a
voltage source Vo + series resistance R, as shown in
Figure 3.
I obsv

R

Vo

V out

Figure 3 Droop voltage control.

In the Droop voltage control, by adjusting Vo and R, thevoltage of DC line can be stabilized while the load sharing
for the power conversion among devices is implemented
according to the amount of power required for the DC
line. In addition, if R is set to 0, Constant Voltage (CV)control is achieved, so that the Droop voltage control is
an extension of so-called CV control.
Similarly, the Droop current control is the one in which
the target current I out varies linearly according to theobserved voltage Vobsv. Let the proportionality coefficientbe -1/R, the target function is determined by the following
equation.

2(2)Controlling the power converter looks equivalent to a
voltage source Io + series resistance R, as shown in
Figure 4.
I out

Io

R

V obsv

Figure 4 Droop current control.

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Development of DC Microgrid Control Method

In the Droop current control, by adjusting Io and R, theDC line voltage can be also stabilized while the load sharing for the power conversion among devices is implemented according to the amount of power required forthe DC line. In addition, if R is set to ∞, constant current(CC) control is achieved, so that the Droop current control
is an extension of so-called CC control.

3.

Each power converter implements feedback control sothat the operating points (Pobsv, Vobsv) can follow the targetfunction. As a specific method, the Droop current control(Droop P control) and the Droop voltage control (Droop V
control) shown in Figure 6 are applied according to theoperating condition. This improves the robustness
against observation and control errors and the control
stability against power disturbances.

THE DEVELOPMENT OF A HIERACHICALDECENTRALIZED CONTROL

3.1 Algorithm Design
3.1.1 Primary control (Decentralized control)
As a primary control, we invented a target function tracking control that is an extension of the Droop control
described above. The target function is defined as a combination of polygonal line in a two-dimensional spaceconsisting of Voltage (V) and Power (P) as shown in
Figure 5. For each power converter, a target function isset according to its equipment characteristics. Some target functions are defined statically, while others aredefined dynamically so that they are updated according to
the equipment information (e.g., SoC (state of charge) of
storage battery observed locally by the power converter.
V(Pobsv, Vobsv)

P

Figure 5 Target function.

Minimize OPEX =V

Droop P control

ΔP

Operating point★

ΔV
Droop V control
P

Figure 6 Target function tracking control; Droop P control andDroop V control.

3.1.2 Secondary control (centralized control)
As a secondary control, we designed an algorithm that
calculates the target function to be set for each power
converter by performing a mathematical optimization calculation based on an evaluation index (e.g., minimizationof operating costs) while the EMS takes into account thepower status of each device and the power supply anddemand forecasts. In the mathematical optimization calculation, a constrained linear programming problem issolved as shown in the following equation.
The EMS executes the mathematical optimization calculation by triggering a periodic processing timer and an
event such as an alarm, etc. The flow of EMS operation isshown in Figure 7.

Usage power charge [¥/kWh]＊Power consumption [kWh]+Basic power charge [¥/kW]＊Contract power [kW](3)

Subject to Inﬂow power [kW] = outﬂow power [kW] for each bus line at each time...< simultaneous same volume constraint >Lower limit SoC [%] ≤ SoC ≤ upper limit SoC [kW] at each battery node at each time…< SoC constraint>

Output lower limit [kW] ≤ output [kW] ≤ output upper limit for each node at each time...< output constraint >Reference SoC [%] ≤ Final SoC [%] for each battery node...< ﬁnal SoC constraint >Output [kW] ≤ contract power [kW] for utility node at each time...< contract power constraint >Output [kW] ≤ predicted output value [kW] for each suppressionable node at each time…< output suppression constraint >Output [kW] = predicted output power [kW] for each unsuppressionable node at each time…
< power supply and demand predicted value constraint >Output [kW] = output plan value [kW] for each scheduled node at each time…< planned operation constraint >Power status acquisition
Predicted power demand acquisition
Mathematical optimization
Calculation of control target function
Distribution of control target function
Figure 7 Flow diagram of a secondary control by the EMS.

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Development of DC Microgrid Control Method

[i1]
[v2]
[i2]
[v1]

×
×
[i1]
[i2]

+
-

[i3]
[v4]
[i4]
[v3]

+
+
-

+

+

Vref_pu

×
÷

×
×

[i3]

PI(s)

-

+
-

Voltage

400

Type
Battery
Utility
PV
EV
Load

350
300
250

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60

Power
Figure 8 An example of the target function during normal time.
450

Voltage

400

Type
Battery
Utility
PV
EV
Load

350
300
250

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60

Power
Figure 9 An example of the target function duringcommunication interruption.
450
400

Type
Battery
PV
EV
Load

350
300
250

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60

Power
Figure 10 An example of the target function during power
failure.

×+++

Iref_pu

[i]
I_pu

+

-

PI(s)

dV_pu

+
+

Vref_pu

Vnom

1

1
Vnom_pu

×
R_pu

×
÷

+
-

×
÷

450

>

[v2]

I_pu

[V]
[v2]

<

[i2]

[i]
+
+
-

+

[v]
v_pu

×

[V]
[v3]

[i3]
[i4]

[v4]
[v3]
[i4]

V(i=0)_pu

+
-

[i]
[v2]
[v1]
[i2]
[i1]

×
÷

get function set from the EMS can be followed well. As aresult, the effectiveness of the hierarchical decentralized
control algorithm could be confirmed even in an actual
equipment environment.

Voltage

The EMS defines a robust normal-time target functionthat allows each power converter to maintain the control
stability against disturbances, while aiming to follow thecontrol target value obtained from the mathematical optimization calculation. In addition, the target function at thetime of communication interruption, which transitions
when the power converter detects a communicationinterruption to the EMS, and the target function at thetime of power failure, which transitions when the power
converter detects a power failure, are simultaneously
defined and distributed to the power converter as failsafe target functions. Figure 8 to Figure 10 show examples of these target functions.
3.2 The Simulation Verification
In order to verify the effectiveness of the algorithm
designed in Section 3.1, the primary control and the secondary control were developed as the MATLAB / Simulink
simulator and the Python simulator, respectively.
As shown in Figure 11, we implemented a control blockin the MATLAB / Simulink simulator where each power
converter follows the target function in an autonomousand decentralized manner. In the Python simulator, amathematical optimization module was developed as an
optimization solver using Google OR-Tools. Also, a tool toevaluate the efficiency of hierarchical control was developed as a target function design / evaluation module.
Figure 12 shows an example of the output. The power
balance at every time section, and the effectiveness ofthe hierarchical decentralized control algorithm was confirmed in the simulation environment.
3.3 Actual Equipment Verification
A Dual Active Bridge (DAB) converter shown in Figure 13
was prototyped as a 300 W class bidirectional insulated
DC / DC converter that supports the primary control of
the hierarchical decentralized control, and a system
assuming the actual grid operation was constructed andthe operation verification was performed. Figure 14
shows a schematic view of DC grid.
Figure 15 shows the verification results of control traceability for the target function. It can be seen that the tar-

V(i=0)_pu++>

Vref_pu

+

PI(s)

-

[v]
v_pu
[v3]

I_pu

×

＜

×
R_pu

Figure 11 An example of the Droop control block implementation (MATLAB/Simulink).

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Development of DC Microgrid Control Method

260 mm

TIME＝12：08
SOC＝63.5％
SOC_ref＝63.2％

440
420
380

Utility
Battery
PV
Load

360
340
320

200 mmVoltage400
Primary side inductor

Secondary side inductor

Transformer

300
-40

-20

0

Power

20

40

60
Primary side
terminal block

Figure 12 An example output from the target function designand evaluation module (Python).

DC

DC

#1

Power grid simulated
power supply

DC

DC

Figure 13 DAB Converter.

DC

#2

#3

DC

Battery

Secondary side
terminal block

Battery

DC

DC

DC

#4

EV simulated loadDC

#5

PV simulated DC
power supply

Figure 14 DC grid configuration.

Voltage / V

8070

DC / DC2 target function

60

DC / DC3 target function

50

Measuring point (DC / DC #2)
Measuring point (DC / DC #3)

40
30
20
10
0

-300 -250 -200 -150 -100 -50

050 100 150 200 250 300

Power / W
Figure 15 An example of experimental result.

4.

EXTENSION OF THE HIERACHICALDECENTRALIZED CONTROL:
DC VIRTUAL INERTIA CONTROL

4.1 Algorithm Design
In order to further improve the control stability of the hierarchical decentralized control, we developed “DC virtualinertia control”, designed by expanding the Droop control. The Droop control was realized by introducing a virtual resistance to the CV control and the CC control, but
the DC virtual inertia control can be realized by introducing a virtual capacitor in addition to the virtual resistance4)-9). The virtual capacitor generates an inertial force
and suppresses fluctuations when sudden voltage changes occur. Because of this effect, the stability of DC-MG
can be improved. Similar to the Droop control, the DC virtual inertia control can be roughly classified into the DC
virtual inertia voltage (DC-VIV) control and the DC virtualinertia current (DC-VIC) control, depending on the detailsof control implementations.
Figure 16 shows the equation and the equivalent circuit

4of DC-VIV control, and Figure 17 shows that of DC-VIC
control.
(4)

Vo

R

I obsvC

V out

Figure 16 DC-VIV control.

5
(5)
I out

Io

R

C

V obsv

Figure 17 DC-VIC control.

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Development of DC Microgrid Control Method

To verify the operation of DC virtual inertial controls in thesimulator and the actual equipment, we designed a digital
control equation, by using bilinear transformation(Equation 6). For example, applying Equation 6 (bi-linear
transformation) to Equation 5 (DC virtual inertia current
control) gives the following digital control equation.

6

(6)

7

The following digital control equation is obtained.
(7)4.2 Simulation Results
The DC virtual inertial control was introduced by implementing Equation 7(digital control equation) in the Python
simulator developed in Section 3.2. The evaluation resultsof the voltage stability against power disturbances are
Output : Voltage,Inertia : 0395

Output : Voltage,Inertia : 1000

395

390

390

385

385

Voltage / V

Voltage / V

shown in Figure 18. Without DC virtual inertia control, thevoltage drops from 390 V to around 365 V when a power
disturbance occurs, but with DC virtual inertia control, thevoltage does not drop below 370 V. The time until theconvergence of the fluctuation is also shorter after the
introduction of DC virtual inertia control.
4.3 Experimental Results
We improved the firmware of DAB converter developed in
Section 3.3, implemented Equation 7 (digital control
equation). Then we verified the operation. Figure 19
shows the state of the voltage fluctuation when a step-like
power disturbance is applied. Table 1 shows the results ofcomparing the values around 20 seconds, where the voltage fluctuation is particularly large in Figure 19.
From Figure 19 and Table 1, it was confirmed that theDC virtual inertia control suppressed the voltage fluctuation during the transient.

380
375
370

380
375
370

365
0.00

0.01

0.02

0.03

0.04

0.05

365

0.00

0.01

0.02

0.03

0.04

0.05

Time / sec

Time / sec

Voltage / V

Figure 18 Voltage fluctuation during power disturbances for Droop control (left) and DC virtual inertia control(right) (simulation results).
70
65
60
55
50
45
40
35

Secondary side voltage V

70
65
60
55
50
45
40
35

Secondary side voltage V

Conventional Droop
Virtual inertia control

Voltage / V

0

19.00

5

19.25

19.50

10

15
Time / sec

19.75

20.00
Time / sec

20

20.25

20.50

25

20.75

21.00

Figure 19 Voltage fluctuation when a step-like power disturbance is applied (evaluation of actual equipment).
Table 1

Experimental results.
Overshoot quantity

Overshoot rate

Droop control

71.6 - 55.5 = 16.1 V

16.1 / 55.5 = 0.29

DC virtual inertia control

59.0 - 55.5 = 3.5 V

3.5 / 55.5 = 0.063

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Development of DC Microgrid Control Method

5.

CONCLUSION

In this paper, as a part of the development of DC-MG
control method, the Droop control and the virtual inertia
control are introduced to the decentralized control of primary control, and the results of algorithm design, simulation verification, and actual verification of the hierarchicaldecentralized control combined with the centralized control of the secondary control are introduced.
These efforts are expected to lead to the social implementation of a DC-MG that can contribute to the realization of a carbon-neutral society and future urban development. We will continue to build a highly reliable system, toconduct the system demonstration assuming an operational environment, and to take on the challenge of solving social issues.

REFERENCES
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Monitoring Unit (BMU),” Furukawa Electric Jihou 131 (2013),
pp.16-20 In Japanese.
3) Takayuki Tanabe [Meidensha Corporation]: “Explanation ofTerms, Theme 3: Microgrid," IEEJ Electric Power & Energy ,2020/8/19.
4) Google Developers, “https: // developers. google. com/ optimization”.
5) Jin, Z. et al.,: “Admittance-type RC-mode Droop Control toIntroduce Virtual Inertia in DC Microgrids”, Proceedings of 2017
IEEE Energy Conversion Congress and Exposition (ECCE),
2017, pp.4107-4112.
6) Runfan Zhang. et al.,: “Distributed Control with Vir tual
Capacitance for the Voltage Restorations, State of Charge
Balancing and Load Allocations of Heterogeneous Energy
Storages in a DC Datacenter Microgrid”, IEEE Transactions on
Power Electronics, 2018, pp.1296-1308.
7) Satabdy Jena, et al.,: “A hybrid RC-droop control strategy for power-sharing and voltage restoration in islanded DC microgrids”,
IEEE, 2019, pp.1-6.
8) Wu, W, et al.,: “A Vir tual Iner tia Control Strategy for DC
Microgrids Analogized with Virtual Synchronous Machines”,
IEEE Transactions on Industrial Electronics, 2017, pp.6006-6016.
9) Jin, Z. et al.,: “An Alternative Realization of Droop Control and
Virtual Impedance for Paralleled Converters in DC Microgrid”
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