practical evaluation of droop and consensus control of
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Abstract—Electric spring (ES) was originally proposed as
a distributed demand-side management (DSM) technology
for stabilizing power distribution network in the presence
of intermittent power generation without using
communication. This paper explores the practical use of
consensus control for a cluster of electric springs (ESs)
through a WiFi communication layer for new functions not
previously realized in practice. This approach can be
considered as a form of DSM for smart grid technology. A
novel consensus control is introduced to enable distributed
ES circuits to provide local voltage and system frequency
regulations in a microgrid with shared responsibility of
active and reactive power compensation. The practical
implementation details of consensus control for a cluster of
ESs are addressed. New plug-and-play functions of ESs are
practically demonstrated for the first time under consensus
control. Practical results indicate that droop control
(without communication) and consensus control (with
communication) are complementary. Under normal
condition when the communication network is available,
distributed ESs can perform with shared power
compensation efforts based on consensus control. If the
communication network fails, ESs can revert to perform
under droop control.
Index Terms—Consensus control, droop control, demand-side
management, distributed control, electric spring, microgrid.
I. INTRODUCTION
NSTANTANEOUS balance of electric “power generation”
and “power demand” is a fundamental requirement for power
system stability. If the percentage of renewable power
generation is negligible, utility companies can adopt the
traditional control paradigm of “power generation following
power demand”. Controlling power generation/supply is a type
of “supply-side management” (SSM) with which the utility
companies generate electric power to meet the load demand. In
the emerging power grids with increasing penetration of
Manuscript received November 23, 2017; revised February 19, April 16 and
August 14, 2018; accepted September 20, 2018. This work was supported in part by the Hong Kong Research Grant Council under the theme-based project
T23-701/14-N.
J. Chen, S. Yan, T. Yang and S.C. Tan are with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong (e-mail:
distributed renewable energy generation of intermittent nature
(known or unknown to the utility companies), the total power
generation/supply becomes difficult to predict and control in
real time. This situation will be worsen when the penetration of
intermittent renewable energy becomes substantial. Under such
situation, the power generation on the supply side is changing
dynamically with the wind and solar power profiles. Any
mismatch of power generation and demand could lead to
fluctuations in the mains voltage and frequency. Generally,
mismatch of active power leads to frequency variations while
mismatch of reactive power results in voltage variations.
Therefore, utility companies would have difficulty in ensuring
that the local ac mains voltage can meet the typically +/-5%
tolerance, because tap changing of local transformers is too
slow to cope with fast transients. For a weak power grid or a
microgrid using small generators with low inertia, the
frequency fluctuation could become a serious issue and a threat
of power system collapse.
An alternative control paradigm of “power demand
following power generation” has been suggested for future
power grid with a high percentage of intermittent renewable
energy generation [1]. If power consumption of some loads can
vary adaptively to follow the fluctuating renewable power
generation profile, the power demand and power generation can
be balanced in real time [2]. Modulating the load consumption
adaptively to achieve power balance in a power grid is a type of
“Demand-Side Management” (DSM). In practice, SSM and
DSM are two approaches that can play a part in achieving
power balance. They are not mutually exclusive and can be
complementary. For example, consensus control of distributed
generators has been proposed to maintain voltage stability [13].
Fig.1 (a) shows a simplified schematic of using distributed
generators for providing regulated mains voltage at the point of
common coupling (PCC). Curtailment of wind and solar power
is one example of SSM [3]. However, using SSM itself is
insufficient in maintaining local voltage stability particularly
when the load is remotely connected to the local transformer
through a long distribution line (e.g. in rural areas) as shown in
Fig.1 (b). If DSM can be applied on the remote load in Fig.1(b),
the local load voltage can also be regulated. This important
jiechen@eee.hku.hk; yanshuo@connect.hku.hk; yang2014@connect.hku.hk;
sctan@eee.hku.hk). S. Y. R. Hui is with the Department of Electrical and Electronic Engineering,
The University of Hong Kong, Hong Kong, and also with the Department of
Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. (e-mail: ronhui@eee.hku.hk; r.hui@imperial.ac.uk).
Practical Evaluation of Droop and Consensus
Control of Distributed Electric Springs for Both
Voltage and Frequency Regulation in Microgrid
Jie Chen, Student Member, IEEE, Shuo Yan, Member, IEEE, Tianbo Yang, Student Member, IEEE,
S.C. Tan, Senior Member, IEEE, S.Y. (Ron) Hui, Fellow, IEEE
I
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point has recently been highlighted by a power company in [23].
This paper focuses on the control and use of distributed ESs
as a DSM method for achieving instantaneous power balance
and hence mitigation of fluctuations in both frequency and
voltage in a microgrid. Several forms of ES technologies have
been evaluated by various research groups [2],[4]-[9] as a new
form of distributed DSM for maintaining voltage and/or
frequency stability in smart grid fed with intermittent renewable
energy sources. ESs are power electronics circuits designed to
be distributed over the distribution power grid for providing
local active and reactive power compensation for regulating the
local mains voltage and/or system frequency [10]. The original
versions of ES were designed to work independently under
droop control [11] and without the requirement of wired or
wireless communication. Being able to work independently
without communication and internet control, ESs can provide
local support for the power grid even when major central
control structures fail. However, under normal conditions,
coordination of ES operations could offer new features not
previously contemplated. This project aims at exploring the
practical implementation issues of a distributed control for
coordinating a cluster of ESs for regulating both ac mains
voltage and frequency in a mircogrid.
Among various distributed control methods, consensus
control has gained substantial attention recently. It is an
algorithm developed for ensuring a global agreement regarding
a common quantity of interest through limited information
exchange between neighbours [12]. Agents in this kind of
system are typically equipped with distributed computing and
communication devices. Without any global information, each
agent has access to its local measurement and neighbours’
information only. For energy related applications, consensus
control has been applied to coordinate the operation of
distributed generators [13], speed regulation of induction
motors [14], energy storage systems [15] and sharing of
distributed reactive power compensation [16]. Consensus
control of distributed generators (DGs) is a type of SSM
because the power inverters involved are connected to the
“renewable energy sources” such as solar panels or wind
generators that supply electrical power. In this paper, we focus
on both droop and consensus control of distributed ESs, which
is a type of DSM because the ESs are controlled to modulate
the power consumption of their associated “noncritical loads”.
While droop control does not require communication and
consensus control require communication, they are also not
exclusive and are in fact complementary. It will be shown in
this paper that consensus control of distributed ESs provide
extra features such a good sharing of responsibility in the
process of voltage and frequency regulation, while droop
control can be activated when the communication layer for
consensus control fails.
Simulation studies of consensus control on ESs have been
reported in several previous works. The first simulation study
of using consensus control of ES is reported in [17]. A
hierarchical control scheme is proposed to reduce the grid
voltage steady state error. However, the use of simplified model
of ES only allows the implementation of reactive power
compensation. Moreover, the reactive power sharing ability of
ES with consensus control is not realized. In [18], a leader-
following consensus control is developed to coordinate
distributed ESs to support the chosen critical buses voltage. The
work is further improved to form a two-level voltage
management scheme in [19]. The proposed control shows good
reactive power sharing for voltage regulation performance.
However, the method is restricted to reactive power
compensation for voltage regulation only and does not cover
system frequency fluctuation issue. Also, the control system
could be at risk and would fail upon the loss of the leader of
consensus control.
The common drawback of previous works using simplified
models of ES in system level simulation is the insufficient
consideration of utilizing the active power capability of ES in
tackling frequency fluctuation. Meanwhile, practical
implementation of consensus control of ES involving both
active and reactive power compensation for regulating both ac
mains voltage and frequency in microgrid has not been
previously reported. The new contributions of this paper (over
existing works of distributed control of ES) include the
following novel aspects:
1) This paper is the first report on the practical design and
implementation of “consensus control of ES” for “BOTH grid
voltage AND frequency regulation”. The experimental setup
consists of (i) a physical layer comprising the hardware setup
of several ESs distributed in a small-scale power grid
environment, (ii) a WiFi communication network and (iii) a
control layer.
2) The proposed consensus control of ES has a good
voltage/frequency regulation and active/reactive power sharing
performance. For the first time, the plug-and-play capability of
ES is practically demonstrated to show the control robustness
against hardware failure.
3) An experimental comparison study between droop
control and consensus control is included to verify the
advantages of consensus scheme. Previously, a cluster of ESs
have been demonstrated to work independently through droop
control, but good sharing of reactive power compensation is not
guaranteed [11]. The use of consensus control allows new
functions such as improved sharing of compensation efforts
among ESs in the cluster. Hence, the utilization of consensus
control adds new functions to the coordinated control of
distributed ESs, while droop control can stay as the last resort
of control when the communication network fails.
G
G
feeder
DGs PCC
local load
(a)Local load voltage control
G
G
feeder
DGs PCC
distribution line
remoteload
(b)Remote load voltage control
voltage control pointload voltage point
Fig. 1. Schematic of a distribution line fed by distributed generators with (a)
a load closely connected to the PCC and (b) a load remotely connected to the
PCC through a long distribution line.
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II. DISTRIBUTED ES IN MICROGRID
A. Microgrid setup
Consider an islanded microgrid comprising a weak ac power
source and a cluster of loads as shown in Fig.2. The ac power
source is programmed to emulate a weak grid fed with
intermittent renewable energy sources. A pre-recorded mains
voltage profile is stored in the programmable power source to
create a time-varying voltage fluctuation along the distribution
line. To emulate the frequency response of a weak grid with
small inertia, the AC power supply is programmed to behave
like a small-capacity spinning machine. Mismatch between
mechanical power and electrical power may cause the
frequency fluctuation. The cluster of loads consist of a mixture
of smart loads and critical loads. Each smart load consists of an
ES connected in series with a noncritical load [2]. The
noncritical load is one that can tolerate certain voltage variation
without causing consumer inconvenience. An example is an
electric water heater. The critical load requires a well-regulated
mains voltage and frequency within a tight tolerance.
The challenge in this study is how to coordinate the control
of the active power and reactive power consumption of a cluster
of distributed smart loads to regulate the voltage and frequency
(that is subject to the disturbance of the intermittent renewable
power generation). Distributed ESs operating under droop
control enjoy the underlying advantage of being independent of
wireless communication and internet control. This feature is
important because the droop control enables them to continue
to work even if the wireless communication network and
internet of the power company fail. The distributed feature of
ESs makes the system robust against potential hardware failure
of ES. It is therefore beneficial to retain the droop control for
abnormal situations while exploring new functions of
distributed control under normal conditions.
The schematic of the second version of ES is illustrated in
Fig.3. Implemented by a half bridge power inverter, this ES
provides a sinusoidal voltage (PWM voltage of the inverter
filtered by an LC filter) in series with a noncritical load. A
bypass switch will disconnect the ES out of the system in case
of hardware failure. The amplitude and phase of ES voltage (Ves)
directly affects the power fed into the noncritical load. A proper
power variation range of noncritical load is still necessary for
the safe operation of the electric devices. The energy storage
(such as batteries) in the DC link makes the bidirectional power
flow (±Pes, ±Qes) possible. To regulate the local voltage and
system frequency, the ES can operate in eight different modes
to modulate the total active and reactive power dynamically
[20]. Output voltage of batteries clamps the dc link voltage at a
stable value.
B. Analysis of Active and Reactive Power of the Smart Load
The key issue in AC microgrid control is the decoupled
control of active and reactive power for regulating grid
frequency and voltage respectively. Consider the case when the
noncritical load is a resistive load, the vector diagram of ES
voltage and current is shown in Fig.4 to demonstrate the
adjustment mechanism of active and reactive power of smart
load. Vs is the mains voltage of AC bus. A decoupled single-
phase d-q framework is developed by choosing the noncritical
load current (Inc) as the reference d vector. θ is the angle
between ES voltage and non-critical load current. The total
active power of smart load (Psl) consists of noncritical load
active power (Pnc) and ES active power (Pes). All reactive power
of smart load (Qsl) is contributed by the ES (Qes). Active and
reactive power of smart load can be calculated as: 2
_nc nc es d
sl nc es
nc
V V VP P P
R
+= + = (1)
_nc es q
esslnc
V VQ Q
R= = (2)
where Rnc is the resistance of noncritical load.
As shown in (1) and (2), the active power of smart load Psl is
directly controlled by the d-component of ES voltage (Ves_d),
while reactive power Qsl is mainly decided by q-component of
ES voltage (Ves_q).
C. Basic Control Scheme of Single ES
The primary level control of ES is devoted to regulating the
local voltage and frequency by tracking the reference points
generated by upper level control. Fig.5 shows the detailed
primary control diagram of ES_i, where voltage and frequency
control loops are highlighted in different colours. V s,nom
i and
ω nom
i are correspondingly the nominal voltage and frequency
points assigned by upper level control. V s
i is the local voltage
and ωi is the measured frequency of ES_i.
Fig. 2. Microgrid with a cluster of smart loads.
DistributionLine
DistributionLine
DistributionLine
RenewableSource
SmartLoad
SmartLoad
SmartLoad
CriticalLoad
Fig. 3. Schematic of an ES.
Battery
Battery
ncV
esV
ncI
NoncriticalLoad
sVesP esQ
LC Filter
BypassSwitch
Fig. 4. Vector diagram of ES in case of resistive load.
d
q
ncI
ncV
sV
esV es_qV
es_dV
θ
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As discussed in Section II-B, a synchronized rotating d-q
reference frame is developed with load current I nc
i chosen as the
reference vector in order to separate the control of active and
reactive power. The subscripts d and q represent the d and q
components of the corresponding variables in Fig.5. The
frequency fluctuation can be mitigated by controlling the d
component of the ES voltage (V es_d
i ) while the local voltage can
be regulated by modulating the q component of ES voltage
(V es_q
i ).
Both the voltage and frequency regulators have a dual-loop
structure. The inner loops are ES-voltage tracking loops and the
outer loops are the system voltage/frequency control loops. In
the outer loops, the voltage and frequency error signals are fed
into PI controllers to generate references for the inner ES-
voltage loops. (V es_d
i )* and (V es_q
i )* are the reference signals for
d and q components of ES voltage respectively. The inner ES-
voltage control loops then track the references and generate the
modulation signals u es_d
i and u es_q
i .
Most of the previous works focus on the primary level (droop)
control, which is independent, decentralized and
communication-free. Consider a cluster of ESs with only
primary level control located in various places, ESs are
designed to regulate local voltage and the system frequency
towards a common reference value. However, since the control
inputs (especially the local voltage values along the
distribution line) of the ESs and their system dynamics are
different, the operating states of ESs will be different. To
preserve the power sharing ability of ESs, a distributed
secondary control is introduced. The consensus-based
secondary control is implemented inside ES local controller. By
collecting both local and neighbour’s information, consensus
control then generates a compensation value to update the
nominal set point V s,nom
i and ω nom
i for the primary level control
of the ith ES (ES_i). A dynamic voltage observer is also
developed to regulate the global average voltage.
III. PROPOSED DISTRIBUTED SECONDARY CONSENSUS
CONTROL
As the primary level control of ES mainly deals with the local
voltage and frequency regulation problems, the power sharing
ability of distributed ESs has not been considered carefully in
practical implementation. A distributed secondary consensus
control is proposed to perform accurate proportional power
sharing control during the voltage and frequency regulation
processes. The nominal set point for the primary level control
V s,nom
i and ω nom
i will be dynamically updated in each secondary
control cycle. The basic control objectives of both secondary
control and primary control can be summarized as:
(1) Global average voltage regulation and accurate proportional
reactive power sharing for all ESs 1,2,..., N , i.e.,
{1,2,..., }i j N
1 ,( )
lim 0
N s
i i s ref
t
V tV
N
=
→− =
(3)
,max ,max
( )( )lim 0
esesji
es esti j
Q tQ t
Q Q→− = (4)
(2) Frequency regulation and accurate weighted active power
sharing for all ESs 1,2,..., N , i.e., {1,2,..., }i j N
lim ( ) 0ref
it
t →
− = (5)
,max ,max
( )( )lim 0
esesji
es esti j
P tP t
P P→− = (6)
Remark 1: P es
i,max and Q es
i,max are correspondingly the maximum
active and reactive power output of ES_i. The boundary of
power compensation is usually pre-determined by the electrical
power ratings of the inverters [13]. However, the maximum
power output of ES is also affected by the noncritical load
impedance, as discussed in [21]. Consider the case when
noncritical load R nc
i is a pure resistance, P es
i,max and Q es
i,max can be
derived as: , 2
,max
( )=
4
s refes
i
nc
VP
R (7)
, 2
,max
( )=
2
s refes
i
nc
VQ
R (8)
Assuming that the rated power of implemented inverters is large
enough, then P es
i,max and Q es
i,max are determined by (7) and (8) only.
The flow chart structure of the proposed consensus control is
illustrated in Fig.6 and a detailed block diagram is shown in
Fig.7. Each ES transfers a set of data ,max ,max
[ , , ]es es
s i i
i es es
i i
P QV
P Q to its
neighbours. �̅�𝑖𝑠 is ES_i’s estimation of the global average
Cri
tica
l
LoadNoncritical
Load
fLi
fCi
Li Ri
R inc
MainBus
VDC
R ic
ω i
nom
ω iω Δ iPI Frequency
Controller
PLLd/q
θ PLL
Ii
nc
PI Voltage
Controller
Q Axis
Controller
D Axis
Controller
SPWM
Synchro
Voltage Regulator
Frequency Regulator
Smart Load
( )Vies_q *
( )Vies_d *
Δ ViVi
s,nom
Vies
VDC
Vi
sVies_q
uies_d
uies_q
Fig. 5. Primary control loops of single ES.
Neighbor ES j
,V i
s
,P i,max
es
P i
es
Qi,max
es
Qi
es
,V j
s
,P j,max
es
P j
es
Qj,max
es
Qj
es
ES i
Consensus Control
Initialize
Pi,max
esV
s,ref ref, , Q i,max
es
,
V is
Qi
esPi
esI i
nc, , , [ES i data]
[ES j data]
Send/Receive data
Send [ES i data]Receive [ES j data]
Measure
Updateupdate
update Q
i P
i,
V is (9)
(13),(22)
Drive reference for
primary control
V i
s,nomωi
nom
,
Primary Control
Voltage observer
Replace measured
voltage by estimated
average voltage V is
V i
s,nom
ωi
nom
Frequency regulator
Voltage regulator
Inner ES-voltage
controller
SPWM
Modulation
signal
ES i
Fig. 6. Flow chart structure of the proposed consensus control.
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voltage. P es
i /P es
i,max and Q es
i /Q es
i,max are per-unit terms which
represent the active and reactive output compensation
percentage of ES_i, respectively.
As shown in Fig.6, the proposed secondary control starts with
initialization of the system reference voltage Vs,ref , reference
frequency ωref and maximum ES power capacity. After
measuring the local data, ES_i will send the consensus
information to its neighbours and receive neighbours’ data.
Voltage observer then updates the estimated global average
voltage according to (9). For power sharing purposes,
consensus algorithm generates the active and reactive power
error signals (δ P
i and δ Q
i ) according to (13) and (22). The
original system reference points will be compensated by these
two error signals, as shown in Fig.7. The shifted nominal points
V s,nom
i and ω nom
i are then transferred to primary level control as
references. For the primary level control, the measured local
voltage will be replaced by the estimated global average voltage
in order to achieve the global voltage regulation (will be
described in the following section).
A. Global voltage regulation and reactive power sharing
based on a new distributed voltage observer
Reactive power sharing control is required to prevent
overloading of individual ES. A distributed voltage observer
method is introduced into the ES controller for the first time.
Instead of controlling the local voltage directly (as in a
traditional droop control), each ES will have an estimated value
(�̅�𝑖𝑠) of the global average voltage and compare this estimated
value with the voltage reference. The estimated value is updated
through a dynamic consensus algorithm and all local estimated
values will converge to the global average voltage.
1) Voltage Observer
The voltage observer module is based on the dynamic
consensus algorithm as shown in Fig.8. Each ES receives the
neighbours’ estimated information ( )s
j iV j N . The voltage
observer will update its own estimate �̅�𝑖𝑠 by combining the
local measured voltage V s
i and neighbours’ information as
follow:
0( ) ( )+ ( ( ) ( ))
i
ts s s s
i i ij j i
j N
V t V t a V V d
= − (9)
Let 1 2[ , ,..., ]s s s s
NV V V V= ,1 2[ , ,..., ]s s s s
NV V V V= and
( )ij N NL l = (the Laplacian matrix of communication network),
the global dynamic of voltage observer can be formulated as
�̇̅�𝑠 = �̇�𝑠 − 𝐿�̅�𝑠 (10)
In the frequency domain, (10) can be written as 1( )s s −= +s s
NV I L V (11)
where
N N NI R . s
V and sV are the Laplace transforms of
voltage estimation vector �̇̅�𝑠 and voltage measurement vector sV , respectively. If the proposed communication graph is
undirected and connected, then L is irreducible and -1( )s s +
NI L is stable. A special left eigenvector
[1/ ,1/ ,...,1/ ]N N N = corresponds to the zero eigenvalue of
matrix L. Based on the proof in [12], all estimation values will
converge to the average value of global voltage,
1 ( )lim (t)= lim
N s
k ks
it t
V tV
N
=
→ →
(12)
2) Reactive power sharing and voltage regulation
A cooperative reactive power control loop can be
implemented in the voltage regulator. In each ES, the local
controller processes local per-unit reactive power and
neighbours’ per-unit reactive power to find the error signal δ Q
i :
,max ,max
=c ( )i
es esjQ Q i
i ij es esj N j i
Q Qa
Q Q
− (13)
where cQ is the coupling parameter between voltage and
reactive power regulator. The error signal is added to the
original voltage reference as a compensation to generate the
new nominal reference point V s,nom
i , ,=s nom s ref Q
i iV V + (14)
Fig. 7. Detailed block diagram of the proposed consensus control scheme.
DC
Sour
ce
SPWM LC Filter
Smart Load
Crit
ical
LoadNoncritical
Load
Mic
rogr
id
Communication Network
N2
1Electric Spring
Link
i
ES i information
,[V is
P i
es
P i,max
es
/ ,Qi
esQ
i,max
es
/ ]
Proposed Consensus Control
ES j information
,[V js
P j
es
P j,max
es
/ ,Qj
esQ
j,max
es
/ ]
=
i
P es es es es
i ij j j,max i i,max
j N
δ a (P (t) / P - P (t) / P )
i
Q es es es es
i ij j j,max i i,max
j N
δ = a (Q (t) / Q - Q (t) / Q )
ts s ss
i j ii ij0
V = V + a (V (t) -V (t))dt
Vs,ref
cQ
cP
iv
i
ref
s
iV
i
Inner Voltage Control Loop
PWM
,s nom
iVs
iV
s
i
Voltage Regulator
nom
iω
Frequency Regulator
Fig. 8. Voltage observer: dynamic consensus algorithm.
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As shown in Fig.7, the voltage estimation s
iV is compared
with the reference V s,nom
i and the error signal is then fed to a PI
controller to generate the ES q-component voltage reference (
V es_q
i )*
_ * , ,( ) = ( ) ( )s
es q V s nom s V s nomii P i i I iV K V V K V V− + − (15)
where K V
P and K V
I are the proportional and integrator gain of
voltage regulator, respectively. By differentiating (15)
(�̇�𝑖𝑒𝑠_𝑞
)∗
= 𝐾𝑃𝑉 (�̇�𝑖
𝑄− �̅�𝑖
𝑠̇ ) + 𝐾𝐼𝑉(𝑉𝑠,𝑟𝑒𝑓+𝛿𝑖
𝑄 − �̅�𝑖𝑠 ) (16)
In steady operation, it can be assumed that (�̇�𝑖𝑒𝑠_𝑞
)∗
= �̇�𝑖
𝑄=
�̅�𝑖𝑠̇ = 0 since the control inputs are the root-mean-square
values with DC components. Let , [1,1,...,1]s ref TV=s,refV ,
1 1,max 2 2,max ,max[ / , / ,..., / ]es es es es es es
N NQ Q Q Q Q Q=q and reorganize (16)
to describe the global steady state of the system:
0sL V− − =s,refV q (17)
Since [1/ ,1/ ,...,1/ ]N N N = is a left eigenvector of L
corresponding to zero eigenvalue. Multiplying the equation (17)
by λ on the left yields
,
1- - 0
sNs s ref i
i
VV V
N
== =s,ref
V (18)
As mentioned before, the estimated voltage value will reach a
consensus on the average measured voltage. By integrating (12)
into (18),
,
1
s
s ref N i
i
VV
N== (19)
The control objective of global voltage regulation is then
achieved. Combining (19) and (17) yields:
0L =q (20)
If the communication graph is connected and undirected,
then [1,1,...,1]=q is a stable equilibrium of system (20) [12].
Equivalently, the solution of (20) can be expressed as:
{1,2,..., }i j N
,max ,max
0
esesji
es es
i j
Q Q− = (21)
The proposed control can successfully carry out (i) global
average voltage regulation and (ii) proportional reactive power
sharing.
B. Frequency regulation and active power sharing
In this experiment, the programmable AC power supply is
controlled to emulate the frequency characteristic of a small
generator with low inertia. As defined by the swing equation,
any imbalance between mechanical power input and electrical
power output could cause the generator frequency to vary.
Typically, the primary frequency control operates at a timescale
up to tens of seconds. Then a governor is used to adjust the
mechanical power of the generator and restore its frequency to
the nominal value [22]. A cluster of ESs can monitor the
frequency deviation and reduce power imbalance by
manipulating the active power consumption on the load side.
The fast dynamic of ES will enable a much faster system
frequency recovery process in a time scale of several seconds.
A frequency control loop is introduced in the consensus control.
As seen in Fig.7, the mismatch of the active power is
expressed as an error signal δ P
i :
,max ,max
=c ( )i
es esjP P i
i ij es esj N j i
P Pa
P P
− (22)
where cP is the coupling parameter between frequency and
active power regulator. This signal is added to the frequency
reference to generate the new nominal reference point ω nom
i .
Similar to the voltage regulator, the error between the measured
frequency and nominal reference will be fed to a PI controller
to generate the ES d-component voltage reference (V es_d
i )*,
which has a differential form of:
(�̇�𝑖𝑒𝑠_𝑑
)∗
= 𝐾𝑃𝑓
(�̇�𝑖
𝑃− 𝜔𝑖̇ ) + 𝐾𝐼
𝑓(𝜔𝑟𝑒𝑓+𝛿𝑖𝑃 − 𝜔𝑖 ) (23)
where K f
P and K f
I are the proportional and integrator gain of
frequency regulator.
Let [1,1,...,1]ref T=refω ,
1 2[ , ,..., ]T
N =ω and
1 1,max 2 2,max ,max[ / , / ,..., / ]es es es es es es
N NP P P P P P=p , the global system
dynamic in steady state can be calculated as:
0L− − =refω p ω (24)
It is reasonable to assume that the measured frequency is
unique, i.e., 1 2= =...= N . Multiplying the equation (24) by λ
on the left yields ref
1 2ω = = =...= N (25)
By integrating (25) and (24) yields: {1,2,..., }i j N
,max ,max
0
esesji
es es
i j
PP
P P− = (26)
The control objectives of frequency regulation and proportional
active power sharing can therefore be derived.
IV. PRACTICAL IMPLEMENTATION AND EVALUATION OF
CONSENSUS CONTROL FOR A CLUSTER OF ELECTRIC SPRINGS
A. Hardware Setup for A Cluster of Distributed ESs in a
Microgrid
1) General overview of hardware setup
Fig.9 shows the schematic of the hardware setup of
consensus control for a cluster of distributed ESs in a microgrid.
It consists of three layers: 1) physical layer, 2) cyber layer and
3) control layer. The physical layer describes the electrical
connections among different ES agents. The communication
links between different agents are displayed in the cyber layer
intuitively. The control layer involves local controller of each
ES.
A practical case study of an 110V AC microgrid with five
distributed ESs is used to demonstrate the effectiveness of the
proposed consensus control. The distribution line impedances
are non-uniform. The distribution line-1 has a larger impedance
than the others to represent a longer distance between the power
source and loads (Fig.9). Lead acid battery cells with Model No.
LC-R127R2NA (and ratings of 12V, 7.2 Ah/20 hr) are used in
the experiment. System parameters are listed in TABLE I.
2) Communication layer topology: convergence requirement
and eigenvalues influence
A circular communication topology is applied in the cyber
layer to meet the convergence requirement of consensus
TPEL
7
algorithm. Graph 𝐺 = (𝑽, 𝑬) is a graph with nodes set 𝑽 =[1,2, … , 𝑁] and edges 𝑬 ⊆ 𝑽 × 𝑽. In our case, the nodes are the
distributed ESs and the edges represent the communication
links between different agents. According to the results in
reference [12], the graph G should be a connected undirected
graph in order to guarantee the consensus convergence. A graph
is undirected if the information flow between agents is
bidirectional. A graph is connected when there is a path
between every pair of vertices. The path may consist of several
communication links. In other words, there are no unreachable
agents. In case of circular communication topology, the
communication graph G is a connected undirected graph.
Therefore, the convergence requirement is achieved.
The eigenvalues of graph G will directly affect the time
response of consensus algorithm. As defined in graph theory,
the adjacency matrix A = (𝑎𝑖𝑗)𝑁×𝑁 has non-negative elements.
𝑎𝑖𝑗 = 1 if and only if there is communication link between node
i and j. Graph Laplacian matrix L is defined as 𝐿 = 𝐷 − 𝐴 ,
where D is the degree matrix of graph G. L is a symmetric
matrix with real eigenvalues 1 20= 2n , where
is the max degree of graph G. If the undirected graph is
connected, the zero eigenvalue is isolated. The second-smallest
eigenvalue 𝜆2 is called algebraic connectivity, which is a
measure of consensus convergence rate [12]. A larger 𝜆2 leads
to a faster convergence rate. At this stage of our study, the
communication topology is fixed because the number of
hardware units of ESs is relatively small. In future research for
large-scale system, the communication network will be an
important research topic.
B. Practical Implementation of the Consensus Control and
Communication Links
1) Detailed implementation of the communication-based
consensus control
A photograph of the experimental setup is shown in Fig.10.
Fig.11 shows the system structure of the practical
implementation. The dSPACE control interface has mux ADC
inputs and multiple PWM output channels. It is used to sense
and control five ESs in a single platform (i.e the physical layer).
For the control layer, five virtual control blocks are established
in the dSPACE. Each controller will collect local sensors’ data
and send real-time PWM signal to ES hardware.
To express the distributed feature of consensus control, the
cyber layer is implemented inside dSPACE. WiFi (WLAN
IEEE 802.11) communication modules are used as
communication links between adjacent ESs. Local
measurement data of ES_i will be processed by the WiFi blocks.
The output of WiFi blocks is the consensus information
received by ES_j.
2) WLAN communication link characteristics
In the tests, the distributed ESs are located in a small
microgrid. The WiFi modules allow a wireless communication
distance between adjacent ESs to be up to 150 meters typically.
The time delay of this kind of short distance communication is
very small (< 500ns typically). Compared with the ES’s
switching period (0.05 ms), this communication time delay is
negligible. (In the laboratory tests, the locations of the 5 ESs are
much closer in practice.) As shown in Fig.12, the WiFi module
in dSPACE consists of three parts: transmitter, receiver and
communication channel. The channel parameters are listed in
TABLE II.
AC Power Source
Noncritical Load 1
Critical Load 1
Critical Load 2
Critical Load 3
Critical Load 4
Critical Load 5
5
4 3
2
1
ES 1
Physical Layer
Cyber Layer
To Control Layer
Neighbors Information
Bus 1 Bus 2 Bus 3 Bus 4 Bus 5Distribution Line 1 Distribution Line 2 Distribution Line 3 Distribution Line 4 Distribution Line 5
Smart Load1
Vs1 Vs2 Vs3 Vs4 Vs5
DC
Link
1
Noncritical Load 2
ES 2
DC
Link
2
Smart Load2
Noncritical Load 3
ES 3
DC
Link
3
Smart Load3 Smart Load4
Noncritical Load 4
ES 4
DC
Link
4
Noncritical Load 5
ES 5
DC
Link
5
Smart Load5
Fig. 9. Hardware setup of a cluster of distributed ESs in a microgrid. Fig. 10. Hardware setup devices.
DSPACE
Controller
LoadSmart
Load
Distribution
Line
TABLE I PARAMETERS OF THE EXPERIMENTAL SYSTEM
Digital Controller
dSPACE DS1006 Board
dSPACE DAC/ADC board DS2002 MUX AD
Microgrid
Distribution line 1 resistance 0.4 Ω Distribution line 1 inductance 5 mH
Distribution line 2-5 resistance 0.2 Ω
Distribution line 2-5 inductance 1.2 mH Non-critical load 1-2 resistance 40 Ω
Non-critical load 1-2 rated power 302.5 W
Non-critical load 3-5 resistance 60 Ω
Non-critical load 3-5 rated power 201.7 W
Critical load 1-5 resistance 180 Ω
Critical load 1-5 rated power 67.2 W Power Source voltage (RMS) 120 V
ESs
Switching frequency 20 kHz
DC link voltage 300 V Output voltage range(peak) 0-110 Vac
DC link capacitor 4500 uF
Filter inductor 500 uH Filter capacitor 13.2 uF
PI Controller
Voltage controller proportional gain (K V
P ) 4
Voltage controller integral gain (K V
I ) 2 Frequency controller proportional gain (K
f
P) 20
Frequency controller integral gain (K f
I ) 20
TABLE II COMMUNICATION LINK CHARACTERISTICS
Communication channel
Communication distance < 150 m
Signal-to-noise ratio (Es/N0) 20 dB
Time delay < 500 ns
TPEL
8
C. Performance Assessment of the Proposed Consensus
Control
The proposed distributed consensus control has been
practically evaluated in the 110V micro grid with a cluster of
five ESs for system performance assessment including voltage
regulation, frequency regulation, active and reactive power
sharing.
1) Voltage regulation and accurate reactive power sharing
Fig.13 shows the variations of the local mains voltage at the
locations of the five ESs. Initially (time t from 0 - 30s), the ESs
are not activated. Fig.13(a) shows the wide variation of the
voltage along the line. Some of the local mains voltages fall out
of the acceptable range (105 - 115V). The five ESs are activated
with the proposed control at t = 30s. As a result, the five local
mains voltages are restored to the desired value 110V 2V as
shown in Fig.13(a). Fig.13(b) shows the per-unit reactive power
compensation states of the ESs. After the ESs are activated at t
= 30s, good reactive power sharing is reached at around t = 50s.
At t = 60s, a step change of generator voltage of +3 V is injected.
Fig.13(a) and (b) confirm that good voltage regulation and
sharing of reactive power compensation can be practically
achieved. The variations of the ESs’ estimated global average
voltage values are recorded in Fig.13(c). It can be seen that they
converge to the global voltage, which is tuned at 110V.
2) Frequency regulation and accurate active power sharing
The use of distributed ESs is highly effective in improving
the frequency response of a weak grid. The frequency of the
microgrid implemented with ESs recovers much faster than that
without ES. Step load change tests are then carried out to
evaluate the frequency control and active power sharing. A
critical load of 100Ω is connected at t = 20s. Without using ESs,
the frequency control of this generator will take over 30s for the
frequency to be restored and the peak frequency deviation is
0.009 p.u (Fig.14(a)). The large deviation is caused by a small
inertia of the generator. Fig.14(b) shows the frequency profile
under the same step load change when the ESs are activated
under consensus control. It takes only about 3s for the
frequency to be restored and the peak frequency deviation is
reduced to 0.0048 p.u. Fig.14(c) shows the variations of the
active power of the five ESs. [Note: Negative active power in
Fig.14(c) means that the smart load is reducing active power.]
Two important observations should be noted. First, the ESs
react to the sudden increase in critical load power at t = 20s by
providing active power through a reduction of noncritical load
power and through their battery sources. This fast reaction
reduces quickly the imbalance between power supply and
demand and consequently the system frequency recovery time.
Second, the consensus control ensures good active power
sharing among the ESs in this power balancing mechanism.
DSPACE
ES1
Controller1
SensorPWM
Controller2 Controller3 Controller4 Controller5
ES2
SensorPWM
ES3
SensorPWM
ES4
SensorPWM
ES5
SensorPWM
LocalData
LocalData
LocalData
LocalData
LocalData
Cyber Layer
Control Layer
Fig. 11. Detailed implementation structure of consensus control.
MODULE
Data InTransmitter Channel Receiver
Data Out
DSPACE
ES LOCATION
ES1 ES2
ES3
ES4ES5
Fig. 12. dSPACE WiFi module and ES locations.
0 10 20 30 40 50 60 70 80 90
VsR
MS
(V)
Main Voltage1 RMS
Main Voltage2 RMS
Main Vlotage4 RMS
Main Voltage3 RMS
Main Voltage5 RMS
Time(s)
STEP
ES OFF ES ON
98
106
102
110
114
(a)
0
0.4
0.2
0.6
0.8
1.0
ES1 Reactive Power
ES2 Reactive Power
ES4 Reactive Power
ES3 Reactive Power
ES5 Reactive Power
Rea
ctiv
e P
ow
er(
p.u
.)
ES OFF ES ON
0 10 20 30 40 50 60 70 80 90
Time(s)
STEP
(b)
Est
imat
ed V
olt
age(
V)
ES1 Estimated Voltage
ES2 Estimated Voltage
ES4 Estimated Voltage
ES3 Estimated Voltage
ES5 Estimated Voltage
0 10 20 30 40 50 60 70 80 90
Time(s)
ES OFF ES ON
STEP
98
106
102
110
114
(c)
Fig. 13. Practical performance of voltage regulation and reactive power
sharing. (a) Mains voltages along the distribution line. (b) Per-unit reactive power output. (c) Estimated global average voltages.
TPEL
9
3) Coordinated voltage and frequency regulation with step
load change
Tests on the coordinated voltage and frequency regulation
performance have been conducted. The system begins with ESs
working in reactive power compensation mode. A critical load
of 100Ω is plugged in and out at t = 40s and t = 80s, respectively.
The practical measurements of (a) the local voltages of the ESs,
(b) the reactive power compensation efforts of the ESs, (c) the
system frequency and (d) the active power of the ESs are
included in Fig.15. It can be seen that the consensus control has
enabled this cluster of ESs to successfully achieve simultaneous
local voltage and system frequency regulations in this
microgrid setup. In the frequency response period, the local
voltages are still regulated close to 110V with a shared steady-
state reactive power output ranging from 0.32 p.u. to 0.4 p.u.
D. Plug-and-play Test
The plug-and-play capability of the proposed control has also
been tested for the first time. Initially, ES1 to ES4 are working.
ES5 is plugged in at t = 40s and two communication links ES1-
ES5 and ES4-ES5 are established. Then ES4 is plugged out at t
= 80s, and the communication links of ES3-ES4 and ES4-ES5
are disconnected. Fig.16 shows the (a) the mains voltages at the
locations of the five ESs, (b) the active power and (c) reactive
power of the five ESs. It can be seen from Fig.16(a) that the
mains voltages can be regulated after the plug-in and plug-out
processes. Fig.16(b) and Fig.16(c) show that the ESs can share
their responsibilities of active and reactive compensations well.
These results confirm plug-in and plug-out capability of the ESs
with consensus control.
0 10 20 30 40 50 60
Time(s)
Fre
quen
cy(p
.u.)
0.992
1.000
0.9960.009
t=30.0sΔ
(a)
Time(s)
Fre
qu
enc
y(p
.u.)
0 1 2 3 4 5 6 7 8 9
0.994
0.998
0.996
1.000
1.002
0.0048
t=3.0sΔ
(b)
0 1 2 3 4 5 6 7 8 9-0.8
-0.6
-0.2
-0.4
0
Act
ive
Pow
er(
p.u
.)
Time(s)
ES1 Active Power
ES2 Active Power
ES4 Active Power
ES3 Active Power
ES5 Active Power
(c)
Fig. 14. Practical performance of frequency regulation and active power sharing. (a) Frequency of a.c. generator (i.e grid frequency). (b) Frequency
response with ESs. (c) Per-unit active power output of a group of ESs.
0 20 40 60 80 100 120
106
110
108
112
114
VsR
MS
(V)
Time(s)
V1 RMS V2 RMS
V4 RMSV3 RMS
V5 RMS
LOAD IN LOAD OUT
(a)
0 20 40 60 80 100 120
0.28
0.36
0.32
0.4
0.44
Time(s)
Rea
ctiv
e P
ow
er(
p.u
.)
ES1 Reactive Power
ES2 Reactive Power
ES4 Reactive Power
ES3 Reactive Power
ES5 Reactive Power
(b)
Fre
quen
cy(p
.u.)
0.996
1.000
0.998
1.002
1.004
0 20 40 60 80 100 120
Time(s)
LOAD IN LOAD OUT
(c)
-0.4
-0.3
-0.1
-0.2
0
0.1
ES1 Active PowerES2 Active Power
ES4 Active PowerES3 Active Power
ES5 Active Power
Act
ive
Po
we
r(p
.u.)
0 20 40 60 80 100 120
Time(s) (d)
Fig. 15. Practical performance of coordinated frequency and voltage
regulation with step load change. (a) Voltage response at the locations of a group of ESs. (b) Per-unit reactive power output of a group of ESs. (c)
Frequency response of the grid. (d) Per-unit active power output of a group of
ESs.
TPEL
10
E. Practical Comparison between Droop Control and
Consensus Control
Droop control has been successfully used previously to allow
a group of ESs without communication links to work in a
coordinated manner [11]. Droop control is important in a sense
that it offers the last defense in the control of ESs when the
communication network fails. However, under normal situation,
consensus control can provide much improved performance.
This section presents a practical comparison of the droop
control and consensus control of 5 ESs distributed in a
distribution line with a nominal mains voltage of 110V.
The first set of comparative tests are conducted under 3
consecutive stages: (1) with the five ESs disabled, (2) with the
five ESs activated under the droop control and (3) with the five
ESs activated under consensus control. Each stage lasts for 30
seconds. Fig.17(a) and Fig.17(b) show the measured mains
voltages and reactive power of the distributed ESs, respectively.
When the ES are turned off in the first stage, the five local
voltages decrease with their distance from the power source
because of the voltage drop along the distribution line. During
the first stage, the reactive powers of the ES are zero as shown
in Fig.17(b). When the droop control is activated in the second
stage, the mains voltage values at the locations of the distributed
ESs are regulated close to the nominal value of 110V, but the
ESs do not share their reactive power compensation equally.
After the consensus control is turned on, the measured mains
voltages along the distribution line are regulated close to the
nominal value, while the ESs have good sharing of reactive
power compensation.
106
110
108
112
114
V1 RMSV2 RMS
V4 RMSV3 RMS
V5 RMS
VsR
MS
(V)
0 20 40 60 80 100 120
Time(s)
ES5 PLUG IN
ES4PLUG OUT
(a)
-0.6
-0.4
0
-0.2
Act
ive
Po
we
r(p
.u.)
ES1 Active Power
ES2 Active Power
ES4 Active Power
ES3 Active Power
ES5 Active Power
0 20 40 60 80 100 120
Time(s) (b)
0 20 40 60 80 100 120
0
-0.1
0.1
0.5
0.4
0.3
0.2
Time(s)
Rec
tiv
e P
ow
er(p
.u.)
ES1 Reactive Power
ES2 Reactive Power
ES4 Reactive Power
ES3 Reactive Power
ES5 Reactive Power
(c)
Fig. 16. Practical plug-and-play capability study. (a) Voltage response. (b)
Per-unit active power output. (c) Per-unit reactive power output.
0 10 20 30 40 50 60 70 80 90
VsR
MS
(V)
Time(s)
V1 RMS
V2 RMS
V4 RMS
V3 RMS
V5 RMS98
106
102
110
114 ES OFF ES ON:Droop Control
ES ON:Consensus Control
(a)
0
60
30
Qes
(Var
)
ES1 Reactive Power
ES2 Reactive Power
ES4 Reactive Power
ES3 Reactive Power
ES5 Reactive Power
0 10 20 30 40 50 60 70 80 90
Time(s)
ES OFF ES ON:Droop Control
ES ON:Consensus Control
(b)
Fig. 17. Practical comparison between droop control and consensus control:
One steady operation state. (a) Mains voltage comparison. (b) Reactive power
comparison.
(a)
Qes
(Var
)
ES1 Reactive Power
ES2 Reactive Power
ES4 Reactive Power
ES3 Reactive Power
ES5 Reactive Power
-150
-100
0
-50
50
100
150
100 300 400 500 7006002000
Time(s)
ES OFFES ON:
Droop ControlES ON:
Consensus Control
(b)
Fig. 18. Practical measurements for comparison between droop control and consensus control: Long time operation with fluctuating source. (a)Mains
voltage comparison. (b)Reactive power comparison.
100 300 400 500 7006002000
Time(s)
VsR
MS
(V)
V1 RMSV2 RMS
V4 RMSV3 RMS
V5 RMS
100
95
90
85
105
115
110
120ES OFF
ES ON:Droop Control
ES ON:Consensus Control
TPEL
11
The second set of comparative tests has been conducted with
the power source programmed as a renewable energy source of
intermittent nature. The voltage source is programmed to
fluctuate for 250 seconds. TABLE.III shows the voltage
fluctuation profile. The voltage source will change output
voltage continuously from start-time to end-time in each time
interval. Again, the 3 consecutive stages are adopted, but each
stage lasts for 250 seconds. Fig.18(a) shows the measured
mains voltage along the distributed line where the ESs and their
noncritical loads are connected. The corresponding reactive
power provided by the five ESs are included in Fig.18(b). When
ESs are turned off, the voltages are fluctuating from 90 V to 113
V. The upper and lower boundaries of the nominal voltage
(0.95-1.05 p.u.) are marked by two dotted lines in Fig.18(a). In
the first stage without using ESs, the mains voltages go below
the lower boundary. When droop control is used in the second
stage, the mains voltages are well regulated within the tolerance
range, but the sharing of the reactive power compensation
among the five ESs are not good. In the last stage when
consensus control is adopted, good mains voltage regulation
along the distribution line and sharing of reactive power
compensation among the ESs can be achieved simultaneously.
V. CONCLUSION
ES was originally proposed as a DSM method to provide
local voltage regulation of the distribution network using droop
control without the need for communication network. While
this feature is highly useful during abnormal conditions, this
paper shows that an extra layer of consensus control can offer
new functions not previously realized under the normal
conditions. This paper presents three novel contributions.
Firstly, it is the first practical design and implementation of
consensus control of a group of distributed ESs in a microgrid
environment for sharing both active and reactive power
compensation efforts in mitigating voltage and frequency
fluctuations. Secondly, the study investigates the coexistence of
droop control and consensus control for this group of ESs.
These two control methods are found to be complementary and
not exclusive. Thirdly, new plug-and-play features of ESs are
practically demonstrated for the first time.
A circular communication link in the cyber layer has been
used to implement the consensus control for practical
demonstration. The hardware tests confirm the practical use of
consensus control for a group of ESs in mains voltage and
system frequency regulation in a microgrid environment. The
plug-and-play and the distributed features of the ESs indicate
that a large group of ESs can in principle increase the robustness
of the microgrid because any failure of a small number of ESs
will not affect the overall regulations of the mains voltage and
system frequency. Practical results of two sets of comparative
studies on droop control and consensus control are included to
demonstrate their different features. These experimental results
show that droop control and consensus control are
complementary and not exclusive. Consensus control can be
used under normal conditions when the communication
network is available to provide good sharing of power
compensation efforts among the distributed ESs. Such ESs can
revert to droop control if the communication network fails
and/or become unavailable.
ACKNOWLEDGMENT
This project was supported by the Hong Kong Research
Grant Council under the Theme-based Research Project T23-
701/14-N.
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TABLE III VOLTAGE FLUCTUATION PROFILE
[Start time, end time] (s) Voltage Change from the
start time value (V)
[0,50] +10
[50,90] +4 [90,130] -16
[130,170] +7
[170,210] -7 [210,250] +10
TPEL
12
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Jie Chen (S’17) received the B.Eng. in electronic information and electrical engineering from the
Shanghai Jiao Tong University, Shanghai, China in
2015. He is currently a Ph.D. candidate in Power Electronics Research Group, Department of Electrical
and Electronic Engineering, The University of Hong
Kong, Hong Kong. His research interests include smart grid technologies, electric springs and
consensus control.
Yan Shuo (S’13-M’16) received his B. Eng. at The
University of South China in 2007, M. Eng. at Southeast University in 2010, and Ph. D. at The
University of Hong Kong in 2016. He is currently a
Postdoctoral Research Fellow in the Department of Electrical and Electronic Engineering at The
University of Hong Kong. His current research
interests include power electronic technologies, smart grid, renewable energy, and Microgrids.
Tianbo Yang (S’15) received the B.Eng. and M.Phil.
degrees in automation and control engineering from
the Harbin Institute of Technology, China, in 2012
and 2014, respectively. He is currently pursuing the Ph.D. degree at the Department of Electrical and
Electronic Engineering, the University of Hong Kong.
His research interest includes power electronic technologies in smart grid and energy storage system.
Siew-Chong Tan (M’06–SM’11) received the B.Eng. (Hons.) and M.Eng. degrees in electrical and
computer engineering from the National University of
Singapore, Singapore, in 2000 and 2002, respectively, and the Ph.D. degree in electronic and information
engineering from the Hong Kong Polytechnic
University, Hong Kong, in 2005. He is currently a Professor in Department of
Electrical and Electronic Engineering, The University
of Hong Kong, Hong Kong. Prof. Tan was a Visiting Scholar at Grainger Center for Electric Machinery and
Electromechanics, University of Illinois at Urbana-Champaign, Champaign,
from September to October 2009, and an Invited Academic Visitor of Huazhong University of Science and Technology, Wuhan, China, in December
2011. His research interests are focused in the areas of power electronics and
control, LED lightings, smart grids, and clean energy technologies. Prof. Tan serves as an Associate Editor of the IEEE Transactions on Power
Electronics. He is a coauthor of the book Sliding Mode Control of Switching
Power Converters: Techniques and Implementation (Boca Raton: CRC, 2011).
S. Y. (Ron) Hui (M’87-SM’94-F’03) received his
B.Sc. (Eng. Hons.) from the University of Birmingham in 1984 and the D.I.C. and Ph.D. degrees
from Imperial College London, London, U.K., in
1987. He has previously held academic positions at the
University of Nottingham (1987–1990), University of Technology Sydney (1991–1992), University of
Sydney (1992–1996), City University of Hong Kong
(1996–2011). Since July 2011, he has been holding the Chair Professorship at the University of Hong Kong. Since July 2010, he
has concurrently been holding the Chair Professor-ship at Imperial College
London. He has published more than 300 technical papers, including more than 180 refereed journal publications and book chapters. Over 50 of his patents have
been adopted by industry.
Dr. Hui is a Fellow of the IET. He has been an Associate Editor of the IEEE TRANSACTIONS ON POWER ELECTRONICS since 1997 and an Associate Editor
of the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS since 2007. He has
been appointed twice as an IEEE Distinguished Lecturer by the IEEE Power Electronics Society in 2004 and 2006. He served as one of the 18
Administrative Committee members of the IEEE Power Electronics Society
and was the Chair-man of its Constitution and Bylaws Committee from 2002-2010. He received the Excellent Teaching Award at CityU in 1998 and the Earth
Champion Award in 2008. He won an IEEE Best Paper Award from the IEEE
IAS Committee on Production and Applications of Light in 2002, and two IEEE Power Electronics Transactions Prize Paper Awards for his publications on
Wireless Charging Platform Technology in 2009 and on LED system theory in
2010. His inventions on wireless charging platform technology underpin key dimensions of Qi, the world’s first wireless power standard, with freedom of
positioning and localized charging features for wireless charging of consumer
electronics. In November 2010, he received the IEEE Rudolf Chope R&D Award from the IEEE Industrial Electronics Society, the IET Achievement
Medal (The Crompton Medal), and was elected to the Fellowship of the
Australian Academy of Technological Sciences and Engineering.
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