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Analysis and distributed control of power flow in DC microgrids to improve system efficiency

01 Sep 2016-pp 1-6

TL;DR: In this paper, a general dc microgrid is modeled based on a cluster of general dc nodes, which includes constant power renewables generation, droop-controlled voltage source and different kinds of load.

AbstractDC Microgrid attains popularity in integrating renewable energy sources and batteries. It also has the potential to achieve higher efficiency than ac power grid with optimized power flow. In this paper, a general dc microgrid is modeled based on a cluster of general dc nodes, which includes constant power renewables generation, droop-controlled voltage source and different kinds of load. Then the dc power flow is solved for optimization. A voltage restoration method based on consensus algorithm is used to restore the voltage deviation from droop characteristic. An enhanced current regulator is adopted to guarantee the accurate load sharing even under the influence of sensor drift and line resistance. A tie line power flow control method is proposed to regulate the tie line power directly and increase the system efficiency at light load condition. All the considered methods only need the local information and the information from its nearest neighbor thus the system expendability is guaranteed. Simulation and experiment results are provided to validate the proposed methods.

Summary (2 min read)

INTRODUCTION

  • DC microgrid gains more and more attention because of the ease to integrate different renewable sources and energy storage, no frequency issue and the possibility to achieve higher efficiency [1] .
  • Though the droop principle is used in many applications, how to analyze and optimize power flow in a dc grid that consists of multiple power nodes is seldom analyzed.
  • All of them rely on dedicated communication links, which reduces system reliability and expendability.
  • What is more, though these methods improve the bus voltage regulation and proportional load sharing, the outcomes and benefits of these improvement is still unclear.
  • It enables the bus voltage restoration and load sharing with communications between only adjacent nodes.

II. POWER FLOW ANALYSIS FOR A GENERAL DC GRID

  • To get a generalized power flow solution for optimization, a general dc grid needs modeling.
  • All the nodes are connected to a common dc bus through power converters.
  • The injected power from this source to the dc bus changes with different bus voltage.
  • Nowadays, most loads like consumer electronics, LEDs, microwaves, washing machines are all constant power style.
  • Its elements yij is the line admittance between node i and node j.

III. POWER FLOW CONTROL AND OPTIMIZATION

  • In order to maximize the system efficiency, the system loss needs to be analyzed and minimized.
  • The proportional load sharing can minimize the conversion loss by distributing the load.
  • For both control method, a consensus based voltage restoration is applied to compensate the steady state error.
  • The local controller receives the pu current from previous node and compare it with the local pu current.
  • The tie line current control part is used.

IV. SIMULATION EXPERIMENT VERIFICATION

  • A three-node system is constructed to simulate and validate the effectiveness of the proposed control strategy.
  • So without compensation, the load sharing will be inaccurate.
  • In (d), both the voltage restoration and current sharing control are enabled.
  • The authors can observe the source current of node 1 and node 3 are constant and equal to their individual fixed load.
  • When the tie line current control is enabled, after some transient time, the tie line current becomes zero.

V. CONCLUSION

  • Based on it, the power flow optimization can be realized.
  • This paper focuses on the secondary control to improve system efficiency, which prefers even load sharing at heavy load and uneven load sharing at light load.
  • A voltage restoration method based on consensus communication is used to restore the voltage deviation from droop characteristic.
  • A tie line power flow control method is proposed to regulate tie line current and increase the system efficiency at light load.
  • All the considered methods only need the local measurement and the information from its nearest neighbor thus system expendability is guaranteed.

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Aalborg Universitet
Analysis and distributed control of power flow in DC microgrids to improve system
efficiency
Chen, Fang; Burgos, Rolando; Boroyevich, Dushan; Diaz, Enrique Rodriguez; Meng, Lexuan;
Quintero, Juan Carlos Vasquez; Guerrero, Josep M.
Published in:
Proceedings of IEEE 2016 4th International Symposium on Environment Friendly Energies and Applications
(EFEA)
DOI (link to publication from Publisher):
10.1109/EFEA.2016.7748774
Publication date:
2016
Document Version
Early version, also known as pre-print
Link to publication from Aalborg University
Citation for published version (APA):
Chen, F., Burgos, R., Boroyevich, D., Diaz, E. R., Meng, L., Quintero, J. C. V., & Guerrero, J. M. (2016).
Analysis and distributed control of power flow in DC microgrids to improve system efficiency. In Proceedings of
IEEE 2016 4th International Symposium on Environment Friendly Energies and Applications (EFEA) IEEE
Press. https://doi.org/10.1109/EFEA.2016.7748774
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Power Flow Analysis and Control for DC Microgrid
to Improve System Efficiency
Fang Chen, Rolando Burgos, Dushan Boroyevich
Center for Power Electronics Systems (CPES)
Virginia Polytechnic Institute & State University
Blacksburg, VA, US
fangchen@vt.edu
Enrique Rodriguez-Diaz, Juan C. Vasquez,
Josep M. Guerrero
Department of Energy Technology
Aalborg University, Aalborg, Denmark
www.microgrids.et.aau.dk
AbstractDC Microgrid attains popularity in integrating
renewable energy sources and batteries. It also has the potential to
achieve higher efficiency than ac power grid under the condition
of optimized power flow. In this paper, a general dc microgrid is
modeled based on a cluster of general dc nodes, which includes
constant power renewables generation, droop-controlled voltage
source and different kinds of load. Then the dc power flow is
solved for optimization. A voltage restoration method based on
consensus communication is used to restore the voltage deviation
from droop characteristic. An enhanced current regulator is
adopted to guarantee the accurate load sharing considering the
impact from sensor error and line resistance. A tie line power flow
control method is proposed to regulate the tie line power and
increase the system efficiency at light load. All the considered
methods only need the local information and the information from
its nearest neighbor thus the system expendability is guaranteed.
Simulation and experiment results are provided to validate the
proposed methods.
Keywordsdc microgrid; power flow; efficiency
I. INTRODUCTION
DC microgrid gains more and more attention because of the
ease to integrate different renewable sources and energy storage,
no frequency issue and the possibility to achieve higher
efficiency [1]. To coordinate multiple sources in a dc grid that
are paralleled to a common bus, droop control is broadly used
[2][4]. By introducing a virtual output resistance to each
source, the circulating current is suppressed and load sharing
among sources is realized.
Though the droop principle is used in many applications,
how to analyze and optimize power flow in a dc grid that
consists of multiple power nodes is seldom analyzed. In [5], the
steady-state performance and sensitivity in a dc microgrid are
analyzed. But it only considered the droop-controlled voltage
source and constant current load. In [6], the droop voltage range
design and the cable’s impact on system performance are
analyzed quantitatively, but the analyzed system is small and
only has a limited number of sources. In [7][10], different
secondary control methods were proposed to restore the dc bus
voltage deviation from droop control and ensure a proportional
load sharing. However, all of them rely on dedicated
communication links, which reduces system reliability and
expendability. What is more, though these methods improve the
bus voltage regulation and proportional load sharing, the
outcomes and benefits of these improvement is still unclear. In
[11], a hierarchical control structure was used to optimize the
efficiency of a dc microgrid. It was reported that, to improve the
overall system efficiency, uneven load sharing is better at light
load condition while proportional load sharing is better at heavy
load. But the proposed algorithm is very complicate and was
only demonstrated for a two-source system where source
efficiency curves are already known.
To save the dedicated communication link in distributed
control, consensus based control method was introduced to the
field of microgrid from multi-agent systems in [12][17]. It
enables the bus voltage restoration and load sharing with
communications between only adjacent nodes. It also enables an
opportunity to achieve other optimization without dedicated
communication.
In this paper, the analytical solution for the power flow in a
general dc grid is derived, which reveals the impact from cable
resistance and voltage sensor error. Then a secondary level
voltage restoration based on consensus communication is
applied. Two power flow control methods are considered and
compared for the sake of efficiency. All the control methods are
based on the nearest nodes communication in which one node
only talks to its nearest neighbors.
II. POWER FLOW ANALYSIS FOR A GENERAL DC GRID
To get a generalized power flow solution for optimization, a
general dc grid needs modeling. Consider a general dc microgrid
as shown in Fig. 1. It can include many dc nodes which have
different structure and are geographically distributed. In the
figure, five nodes are drawn as an example. Every dc node has
its different local power generation (e.g. solar and wind), energy
storage and loads. The nodes can even be another dc or ac
system. All the nodes are connected to a common dc bus through
power converters.
For the modeling, the sources and loads are categorized into
following types.
1) constant power source (CPS), which is usually the
distributed renewable energy generation (photovoltaic and
wind) working at maximum power point tracking (MPPT)
mode. It can be either one source or a combination of multiple
CPSs. This kind of sources inject a constant power no matter the
dc bus voltage.
www.microgrids.et.aau.dk

2) droop controlled voltage regulators (VR), which buffers
the intermittent power from renewable sources and regulates the
dc bus voltage. Battery is a good candidate for this purpose
because of its bidirectional power capability. In some case, it can
be also a bidirectional ac-dc converter interfaced to the ac utility.
The injected power from this source to the dc bus changes with
different bus voltage.
3) constant power load (CPL) and constant resistive load
(CRL). Nowadays, most loads like consumer electronics, LEDs,
microwaves, washing machines are all constant power style. But
some old lighting bulbs and oven can be still resistive.
Consider the span of a real distribution system, the distance
between different nodes cannot be omitted.
Similar to the power flow analysis in ac system, we define
the system to include N power nodes and the corresponding
admittance matrix Y
N
. Its elements y
ij
is the line admittance
between node i and node j. The self admittance y
ii
is defined as
the sum of all branch admittance connecting to node i.
Obviously, the admittance matrix is symmetric and usually
sparse.
For certain node i, suppose the total generation of CPS is
P
CPSi
and total CPL consumption is P
CPLi
. The VR follows the
droop output characteristic in (1) to share the load, where v
i
is
the bus voltage of node i and
*
i
v
is the voltage set point of the
droop for node i. r
d
is the droop resistance and i
o
is the output
current of VR.
*
i i d o
v v r i
(1)
Then the injected power from VR is
*
ii
VRi i o i
d
vv
P v i v
r

(2)
The power consumed on CRL is
2
i
CRLi
CRLi
v
P
R
(3)
The power injected into node i is in (4). If there is no
connection between node i and j, then y
ij
equals to zero.
1,
N
INi i j i ij
j j i
P v v v y


(4)
Due to power balance, for every node i, (5) needs to be
satisfied.
0
CPSi VRi CPLi CRLi INi
P P P P P
(5)
If we put it in a matrix form, then the system power
description can be expressed in (6).
In this equation, the node voltage v
1
to v
n
are unknowns. The
P
CPSi
and P
CPLi
are usually unadjustable. But the droop voltage
set point
and droop resistance r
di
are controllable. They can
be finely programmed to control the power flow between
different nodes and enable the optimized power flow. Since the
droop resistance is usually programmed inverse proportional to
the source rating. In the following section, the voltage set point
is used as the control variable to achieve different optimization
targets.
III. POWER FLOW CONTROL AND OPTIMIZATION
In order to maximize the system efficiency, the system loss
needs to be analyzed and minimized. In practical, the system
loss are mainly from the power conversion loss generated by
power converters and transmission loss consumed on power
cables. In this section, two different power control methods are
Node 4
Node 5
Node 3
DC
DC
Solar
Load
AC
DC
Wind
Load
DC
DC
Battery
Load
DC
DC
DC Grid
Load
DC
AC
AC Grid
Load
Node 1
Node 2
y
12
y
23
y
34
y
45
v
2
v
1
v
3
v
4
v
5
y
13
dc power
distribution
Fig. 1. A general dc microgrid.
_________________________________________________________________________________________________________
*
1 1 1
11
*
2 2 2
22
*
1 11 1 1 12 1
2 21 22 2
0 0 0
/
0 0 0
/
0 0 0
0 0 0
/
0 0 0 1/ 1/
0 0 0 1/ 1/
0 0 0
0 0 0
CPS CPL
d
CPS CPL
d
CPSn CPLn n
n dn
d CRL n
d CRL
n
P P v
vr
P P v
vr
P P v
vr
v y r R y y
v y y r R
v











1
2 2 2
12
0
1/ 1/
n
n n nn dn CRLn n
v
yv
y y y r R v
(6)

discussed by using only the communication between nearest
nodes. One is accurate load sharing control among different
nodes according to their power rating. When the load is heavy,
the conversion loss dominates. The proportional load sharing
can minimize the conversion loss by distributing the load.
However, when the load is light, instead of distributing the load,
it is better to let local source provides the local power
consumption. It has two benefits. Firstly, according to [11], at
light load condition, unevenly distributing the load at light load
condition achieves lower overall conversion loss. Secondly,
since the loads are fed by their local sources, the transmission
loss is eliminated. In order to directly control the power flow on
the tie lines, it requires additional measurement to measure the
current on the tie lines. But the cost is considered reasonable to
achieve the direct power flow control target. In this case, under
normal load condition, the tie line current can be controlled to
be zero. The transmission loss on the lines is completely
eliminated. At heavy load condition, the control can switch to
the proportional sharing mode to guarantee the system
availability.
Because of the droop characteristic of VRs, the steady state
bus voltage will be lower than the nominal voltage which can
deteriorate the system performance and lower the system
efficiency. For both control method, a consensus based voltage
restoration is applied to compensate the steady state error.
Again, this only requires the communication between adjacent
nodes.
The controller structure for each node is shown in Fig. 2.
This paper focuses on the secondary level control, which
consists of three paralleled parts: 1) consensus based voltage
restoration. 2) proportional current control and 3) tie line current
control. It is worth mentioning that the communication can be
bidirectional or unidirectional. In this paper, to show the
simpleness of the proposed method, only unidirectional
communication is used. It means the information can only be
passed from node i-1 to node i, and then to node i+1, but not in
the reversed direction.
The function of the voltage restoration controller is to
generate a compensation voltage offset to cancel the voltage
drift caused by the primary droop control. In order to generate
this restoration signal, each node needs to estimate the bus
voltage by comparing the local voltage estimation
i
v
with the
estimation
1i
v
received from the previous node. The error is
passed through a gain of K
v
and an integrator. The result is added
to the current local voltage measurement result v
i
to update the
local estimation
i
v
. The estimation of bus voltage is then
compared with the voltage set point
*
i
v
. The error is
compensated by a PI controller. This part will restore the voltage
deviation caused by droop characteristic and cable voltage drop.
The local bus voltage estimation is also passed to the next node
for its calculation.
The proportional current control part and tie line current
control part are for different load condition. Though the droop
resistance is designed for the proportional load sharing between
different sources without communication, the ratio is never
accurate in real case because of sensor discrepancy and cable
resistance. The proportional current control part is designed for
this. A new term called pu current is defined. It is the
percentage of local current compared to its rated current. So
when the local VR current is 0 then the pu current is also 0.
When the local current is maximized at its rating, the pu current
is 1. By such definition, different sources can compare their
output current directly without any transformation. In this part,
the local controller receives the pu current from previous node
and compare it with the local pu current. Based on the
difference, a second adjustment part for local voltage reference
is generated. Because this is a closed loop compensation with an
integrator, the steady state error is eliminated. Accurate load
distribution is achieved for heavy load condition.
At light load condition, it is not preferred to have even load
sharing. In this case, the tie line current control part is used. It
directly senses the current of transmission line which is
connected to the local node and compare it with the reference.
In this case, we want to stop transmitting power from other
nodes, then the current reference is zero. In fact, this reference
can be set to other desired non-zero values to achieve other
optimization target.
It is worth mentioning that the proportional load sharing and
tie line current control can be contradictory with each other, so
it is preferred to choose one for a certain load condition. But the
K
i
+
11
,
pu
ii
vi

1
pu
i
i
+
-
+
i
v
pu
i
i
K
v
1i
v
i
v
-
+
+
+
*
i
v
+
-
PI
1i
v
2i
v
1,ii
i
K
p
+
+
modified voltage
set point for node i
,
pu
ii
vi
-
+
r
i
i
i
voltage
controller
Primary droop control
Consensus based voltage restoration
Proportional current control
Tie line current control
Secondary control
From tertiary control
from the previous node
from the local tie line
current measurement
to the next node
heavy load
light load
mode
selection
Fig. 2. Consensus based optimized power flow controller.

voltage restoration can work with both since it adjusts all the
node voltage in the same manner.
IV. SIMULATION AND EXPERIMENT VERIFICATION
A three-node system is constructed to simulate and validate
the effectiveness of the proposed control strategy. The system
structure and load profile is shown in Fig. 3. Three VR nodes are
connected through two segments of tie lines. Each tie line has
different resistance. There is also sensor discrepancy for
different VRs, i.e. VR
1
has accurate sensing while VR
2
and VR
3
have 0.2% and 0.1% drift. So without compensation, the load
sharing will be inaccurate. The load at each node is expressed as
load 1 to load 3. In the simulation, loads at node 1 and 3 are fixed
at 0.3 pu and 0.7 pu. Load at node 2 steps up at 10 second and
20 second to demonstrate the performance. The communication
is only between the nearest nodes and unidirectional. Node 1
sends its pu current and voltage estimation to node 2. The same
from node 2 to node 3. Each node also senses its local tie line
current for direct power flow control. To make the conclusion
more general, the nominal bus voltage is 1 p.u.. The current
rating for each source is also 1 p.u..
Fig. 3 (b)-(e) show the simulation results. In (b), no
secondary control is enabled. The system can still work with the
primary droop control. But the load is not evenly shared among
sources. Also, the bus voltage drops as the load current increases
at 10 second and 20 second. In (c), the voltage restoration loop
is enabled. The current sharing is the same as in (a). But the
voltage is lifted up. At the heaviest load condition, the load node
voltage is 0.997 p.u., which is much higher than the value of
0.983 p.u. in (b). In (d), both the voltage restoration and current
sharing control are enabled. In this case, the three source current
are always the same, even with the realistic line resistance and
sensor drift. The effectiveness of the current sharing control is
proved. In (e), the tie line current control is enabled. We can
observe the source current of node 1 and node 3 are constant and
equal to their individual fixed load. Current from source 2 tightly
follows the load step at node 2. The current flow through tie lines
are zero.
The proposal was also verified by hardware experiment. Fig.
4 shows a picture of the experiment setup. Three three-phase ac-
dc converters are placed in a cabinet to mimic the operation of
three distributed sources. The converters are connected through
adjustable cable emulator. So the resistance of each cable can be
accurately controlled. A dSpace control system is used to fulfill
the converter control and higher level optimization. It also works
as the monitoring system to observe the interested waveforms in
real time. It makes it convenient to start and stop each control
function so the effect of each control loop can be identified.
Constant resistive and constant power loads are connected along
the bus to typify the distributed load.
node 1 node 2 node 3
tie line tie line
Source 1 Source 2 Source 3
Load 1 Load 2 Load 3
communicatio n
v
s1
v
s2
v
s3
v
L2
Time (s)
voltage
current
i
s1
i
s3
i
s2
(a) (b)
voltage
current
Time (s)
Time (s)
voltage
current
i
s1
i
s3
i
s2
v
s1
v
s3
v
s2
v
L2
v
s1
v
s2
v
s3
i
s2
i
s3
i
s1
Time (s)
voltage
current
v
s1
v
s2
v
s3
v
L2
i
s1
i
s3
i
s2
(c) (d) (e)
Fig. 3. System structure and simulation results.
(a) simulated three-source three-load dc distribution system. (b) voltage and current without secondary control
(c) with voltage restoration (d) with both voltage restoration and current sharing control (e) with tie line current control



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    [...]

Journal ArticleDOI
TL;DR: A secondary level control technique for dc microgrids, which achieves accurate power sharing through a distributed strategy while performing dc bus voltage restoration in a decentralized fashion and is robust to some communication failure scenarios and moderate communication delays.
Abstract: This article proposes a secondary level control technique for dc microgrids, which achieves accurate power sharing through a distributed strategy while performing dc bus voltage restoration in a decentralized fashion. In order to attain proper power sharing, each power converter exchanges its output power information with neighboring converters through a low-bandwidth network at defined time intervals. A consensus-based algorithm is employed to process this information and modify the converter's droop coefficient, compensating droop mismatches and cable resistances and enabling power sharing. Restoration of the average dc bus voltage is realized locally with each converter compensating its own output voltage drop through an integrator. A comprehensive design procedure and performance and stability analysis, including communication loss and substantial time delays, are also provided. The strategy has shown to be robust to some communication failure scenarios and moderate communication delays. The proposed method is evaluated through simulation in the software PLECS and it is experimentally validated in a 4.5-kW dc microgrid setup.

10 citations


References
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Journal ArticleDOI
TL;DR: A distinctive feature of this work is to address consensus problems for networks with directed information flow by establishing a direct connection between the algebraic connectivity of the network and the performance of a linear consensus protocol.
Abstract: In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3) undirected networks with communication time-delays and fixed topology. We introduce two consensus protocols for networks with and without time-delays and provide a convergence analysis in all three cases. We establish a direct connection between the algebraic connectivity (or Fiedler eigenvalue) of the network and the performance (or negotiation speed) of a linear consensus protocol. This required the generalization of the notion of algebraic connectivity of undirected graphs to digraphs. It turns out that balanced digraphs play a key role in addressing average-consensus problems. We introduce disagreement functions for convergence analysis of consensus protocols. A disagreement function is a Lyapunov function for the disagreement network dynamics. We proposed a simple disagreement function that is a common Lyapunov function for the disagreement dynamics of a directed network with switching topology. A distinctive feature of this work is to address consensus problems for networks with directed information flow. We provide analytical tools that rely on algebraic graph theory, matrix theory, and control theory. Simulations are provided that demonstrate the effectiveness of our theoretical results.

10,379 citations


"Analysis and distributed control of..." refers methods in this paper

  • ...To save the dedicated communication link in distributed control, consensus based control method was introduced to the field of microgrid from multi-agent systems in [12]–[17]....

    [...]

Journal ArticleDOI
05 Mar 2007
TL;DR: A theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees is provided.
Abstract: This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis framework is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small-world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions. Simulation results are presented that demonstrate the role of small-world effects on the speed of consensus algorithms and cooperative control of multivehicle formations

8,696 citations


"Analysis and distributed control of..." refers methods in this paper

  • ...To save the dedicated communication link in distributed control, consensus based control method was introduced to the field of microgrid from multi-agent systems in [12]–[15]....

    [...]

Journal ArticleDOI
TL;DR: In this article, a lowbandwidth communication (LBC)-based improved droop control method is proposed to improve the performance of the dc microgrid operation, which does not require a centralized secondary controller.
Abstract: Droop control is the basic control method for load current sharing in dc microgrid applications. The conventional dc droop control method is realized by linearly reducing the dc output voltage as the output current increases. This method has two limitations. First, with the consideration of line resistance in a droop-controlled dc microgrid, since the output voltage of each converter cannot be exactly the same, the output current sharing accuracy is degraded. Second, the dc-bus voltage deviation increases with the load due to the droop action. In this paper, in order to improve the performance of the dc microgrid operation, a low-bandwidth communication (LBC)-based improved droop control method is proposed. In contrast with the conventional approach, the control system does not require a centralized secondary controller. Instead, it uses local controllers and the LBC network to exchange information between converter units. The droop controller is employed to achieve independent operation, and the average voltage and current controllers are used in each converter to simultaneously enhance the current sharing accuracy and restore the dc bus voltage. All of the controllers are realized locally, and the LBC system is only used for changing the values of the dc voltage and current. Hence, a decentralized control scheme is accomplished. The simulation test based on MATLAB/Simulink and the experimental validation based on a 2 × 2.2 kW prototype were implemented to demonstrate the proposed approach.

728 citations


"Analysis and distributed control of..." refers background in this paper

  • ...In [7]– [10], different secondary control methods were proposed to restore the dc bus voltage deviation from droop control and ensure a proportional load sharing....

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Journal ArticleDOI
TL;DR: In this paper, a decentralized controller for DC microgrid is proposed to achieve high reliability, low-voltage regulation, and equal load sharing, utilizing low-bandwidth communication.
Abstract: DC microgrids are gaining popularity due to high efficiency, high reliability, and easy interconnection of renewable sources as compared to the ac system. Control objectives of dc microgrid are: 1) to ensure equal load sharing (in per unit) among sources; and 2) to maintain low-voltage regulation of the system. Conventional droop controllers are not effective in achieving both the aforementioned objectives simultaneously. Reasons for this are identified to be the error in nominal voltages and load distribution. Though centralized controller achieves these objectives, it requires high-speed communication and offers less reliability due to single point of failure. To address these limitations, this paper proposes a new decentralized controller for dc microgrid. Key advantages are high reliability, low-voltage regulation, and equal load sharing, utilizing low-bandwidth communication. To evaluate the dynamic performance, mathematical model of the scheme is derived. Stability of the system is evaluated by eigenvalue analysis. The effectiveness of the scheme is verified through a detailed simulation study. To confirm the viability of the scheme, experimental studies are carried out on a laboratory prototype developed for this purpose. Controller area network protocol is utilized to achieve communication between the sources.

657 citations

Journal ArticleDOI
TL;DR: This paper explains the control structure required for the converters to permit the use of DBS, and explains a procedure for implementing a system-wide control law through independent control of the source/storage interface converters.
Abstract: A dc nanogrid is a hybrid renewable system since renewable sources supply the average load demand, while storage and nonrenewable generation maintain the power balance in the presence of the stochastic renewable sources. The system is power electronic based, with converters being used to interface both the sources and loads to the system. The nanogrid is controlled using dc-bus signaling (DBS), a distributed control strategy in which the control nodes, the source/storage interface converters, induce voltage-level changes to communicate with the other control nodes. This paper explains the control structure required for the converters to permit the use of DBS, and explains a procedure for implementing a system-wide control law through independent control of the source/storage interface converters. Experimental results are presented to demonstrate the operation of this novel control strategy

495 citations


"Analysis and distributed control of..." refers background in this paper

  • ...To coordinate multiple sources in a dc grid that are paralleled to a common bus, droop control is broadly used [2], [3]....

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Frequently Asked Questions (1)
Q1. What are the contributions in "Power flow analysis and control for dc microgrid to improve system efficiency" ?

In this paper, a general dc microgrid is modeled based on a cluster of general dc nodes, which includes constant power renewables generation, droop-controlled voltage source and different kinds of load.