energies
Article
Application of Liquid Hydrogen with SMES for
Efficient Use of Renewable Energy in the
Energy Internet
Xin Wang 1 , Jun Yang 1, *, Lei Chen 1 and Jifeng He 2
1
2
*
School of Electrical Engineering, Wuhan University, Wuhan 430072, China;
[email protected] (X.W.); [email protected] (L.C.)
State Grid Hubei Electric Power Economic and Technology Research Institute, Wuhan 430077, China;
[email protected]
Correspondence: [email protected]; Tel.: +86-13995638969
Academic Editor: Hailong Li
Received: 6 January 2017; Accepted: 2 February 2017; Published: 8 February 2017
Abstract: Considering that generally frequency instability problems occur due to abrupt variations in
load demand growth and power variations generated by different renewable energy sources (RESs),
the application of superconducting magnetic energy storage (SMES) may become crucial due to its
rapid response features. In this paper, liquid hydrogen with SMES (LIQHYSMES) is proposed to play
a role in the future energy internet in terms of its combination of the SMES and the liquid hydrogen
storage unit, which can help to overcome the capacity limit and high investment cost disadvantages
of SMES. The generalized predictive control (GPC) algorithm is presented to be appreciatively used
to eliminate the frequency deviations of the isolated micro energy grid including the LIQHYSMES
and RESs. A benchmark micro energy grid with distributed generators (DGs), electrical vehicle
(EV) stations, smart loads and a LIQHYSMES unit is modeled in the Matlab/Simulink environment.
The simulation results show that the proposed GPC strategy can reschedule the active power output of
each component to maintain the stability of the grid. In addition, in order to improve the performance
of the SMES, a detailed optimization design of the superconducting coil is conducted, and the
optimized SMES unit can offer better technical advantages in damping the frequency fluctuations.
Keywords: superconducting magnetic energy storage (SMSE); load frequency control; generalized
predictive control (GPC); energy internet
1. Introduction
The increasing number of renewable energy sources (RESs) and distributed generators (DGs) has
become a serious challenge for the stability and reliability of the electric power system, because of
the fluctuation of power supply needed to meet the demand [1,2]. With the concerns related to this
and other problems, e.g., conventional energy cost, greenhouse gas emissions, security of traditional
power systems [3], the concept of the energy internet is proposed [4], which is composed of numerous
micro-energy grids and supports the flexible access of various RESs [5]. Therefore, energy storage
technology is crucial for the energy internet to suppress power fluctuations and achieve the efficient
operation of RESs by decoupling the electricity generation from demand [6,7].
Superconducting magnetic energy storage (SMES) units offer quick responses to power
fluctuations and the ability to deliver large amounts of power instantaneously, while their limited
storage capacity is a weak point for long term operation [8]. Liquid hydrogen (LH2 ) storage units
have the characteristics of large storage capacity [9] and economic efficiency that can make up for the
Energies 2017, 10, 185; doi:10.3390/en10020185
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Energies 2017, 10, 185
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disadvantages of SMES, but their response is too slow to be used as the single storage mode to support
the RESs in the energy internet.
Some studies have focused on the use of LH2 as the cooling medium for SMES [10–12] for a long
period, and the concept of liquid hydrogen with SMES (LIQHYSMES) that combines the SMES with
LH2 storage units is proposed for further study [13]. The simulation and analysis of the buffering
behavior of the LIQHYSMES plant model was carried out in [14] and it seems to be capable of handling
even very strong variations of the imbalance between supply and demand. Also, different SMES
structure designs for the 10 GJ range are compared in terms of size and ramping losses in [15], and
the cost targets for different power levels and supply periods are addressed. It can be concluded
from these publications that the application of LIQHYSMES are quite feasible and suitable for the
energy internet.
The load frequency control (LFC) has been widely used in conventional electric power systems,
and the micro-grid can maintain the stability of frequency by the optimal control, proportional integral
(PI) control and other methods [16–18]. In the micro energy grid including LIQHYSMES units proposed
here, the changes of the state of the system are fairly rapid so a controller with robust performance
over a wide range of operating conditions is strongly needed for LFC in an isolated micro energy
grid. The LFC of the micro-grid including the SMES was studied in [19], however the impacts of
the parameters were not taken into account. The GPC algorithm can also be used to control isolated
micro-grids with electric vehicles [20].
In this paper, a LIQHYSMES unit to be used as the energy storage system with RESs in the energy
internet to solve the frequency instability problem is proposed. Based on the presentation of the
LIQHYSMES characteristics, the benefits and applications to the energy internet are analyzed. Then a
new coordinated LFC controller based on the GPC algorithm is proposed for the equivalent model of
the micro energy grid with LIQHYSMES. Meanwhile, the optimization design of the superconducting
coil parameters, including initial current, inductance and initial energy storage capacity is carried out
in this paper.
The rest of this paper is organized as follows: Section 2 introduces the structure of the
LIQHYSMES. In Section 3, the equivalent models of the components in the micro energy grid for
LFC are constructed. Then, the coordinated LFC controller based on GPC is proposed in Section 4.
In Section 5, the superconducting coil is optimized; the effectiveness and robustness of the proposed
coordinated controller is demonstrated by numerical simulations on an isolated micro energy grid
with LIQHYSMES in Section 6. Finally, conclusions are drawn in Section 7.
2. Liquid Hydrogen with Superconducting Magnetic Energy Storage (SMES)
The core of the energy internet in the future is the electric power system, combined with the
natural gas network, transportation network and thermal network to form a comprehensive network.
As shown in Figure 1, the LIQHYSMES can play an important role as the energy router in connecting,
scheduling and controlling the networks concertedly in the future energy internet.
Figure 1 shows the structure of the hybrid energy storage device, which consists of three major
parts: the electrochemical energy conversion (EEC), the LIQHYSMES storage unit (LSU) and the power
conversion & control Unit (PCC). When a power fluctuation occurs as a result of the RES connected to
the electric power system, it’s prone to cause a power imbalance and affect the stability of the electric
power system.
To suppress the fluctuation rapidly, the PCC will control the SMES unit to charge or discharge
depending on the supply and demand imbalance of the system, in which condition the energy is stored
and released by means of electric energy. As shown in Figure 2, the SMES system has a DC magnetic
coil that is connected to the AC grid through a power conversion system. Meanwhile, PCC will control
the LH2 storage unit to work on the conversion of electric energy to achieve the slow suppression of
the fluctuation. The surplus electric energy generated by the RESs can be converted into chemical
energy by the electrolyser. Then, the gaseous hydrogen produced previously is liquefied into LH2
LIQHYSMES. In Section 3, the equivalent models of the components in the micro energy grid for LFC
are constructed. Then, the coordinated LFC controller based on GPC is proposed in Section 4. In
Section 5, the superconducting coil is optimized; the effectiveness and robustness of the proposed
coordinated controller is demonstrated by numerical simulations on an isolated micro energy grid
with LIQHYSMES in Section 6. Finally, conclusions are drawn in Section 7.
Energies 2017, 10, 185
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2. Liquid Hydrogen with Superconducting Magnetic Energy Storage (SMES)
for storage.
Onofthe
when a power
shortage
occurs
in the
electric
powercombined
system, the
liquid
The core
thecontrary,
energy internet
in the future
is the
electric
power
system,
with
the
hydrogen
stored
in
the
liquid
hydrogen
tank
is
vaporized
and
supplied
to
the
gas
turbine
(GT),
fuel
natural gas network, transportation network and thermal network to form a comprehensive network.
cells
(FC),
and
combined
heat & power (CHP) to supply electricity and heat to the electric
power
As shown
Figure
Energiesin
2017,
10, 1851, the LIQHYSMES can play an important role as the energy router in connecting,
3 of 20
system
and
the
thermal
network,
respectively.
scheduling and controlling the networks concertedly in the future energy internet.
Figure 1. The liquid hydroge n with supe rconducting magne tic e ne rgy storage (LIQHYSMES) unit
use d in the e ne rgy inte rnet.
ECC: Electrochemical Energy Conversion
H2-FC
Powered
Figure 1 shows the structure of the hybrid
energy storage device, which consists
of three major
Electrolyser
Electric
parts: the electrochemical energy conversion (EEC), the LIQHYSMES storage unit
(LSU)
and the
Vehicles
Renewable
power Energy
conversion & control Unit (PCC). When a power fluctuation occurs as a result of the RES
Gas Turbine(GT)
Sources
connected
to the electric power system, it’s prone to cause
power imbalance andThermal
affect the stability
Fuel a
Cells(FC)
PCC:
Network
Combined Heat & Power(CHP)
of the electric power Power
system.
To suppress the
fluctuation rapidly, the PCC will control the SMES unit to charge or discharge
Conversion
Gas the energy is
& Control
depending on the supply
and demand imbalance of the system, in which condition
Network
Unit
Electric
stored and released by means of electric energy. As
shown in Figure 2, the SMES system has a DC
H2-Liquefier
power
magnetic
coil that is connected to the AC grid through a power conversion system. Meanwhile, PCC
system
Gaseous hydrogen flow
will control the LH2 storage unit to work on the conversion of electric energy to achieve the slow
Liquied hydrogen flow
SMES
suppression of the fluctuation. The surplus electric energy
generated by the RESs can be converted
Electric flow
LSU:LIQHYSMES
into chemical energy by the electrolyser.
Then, the gaseous
hydrogen produced
previously is
LH2-Tank
storage unit
Heat flow
liquefied into LH2 for storage. On the contrary, when a power shortage occurs in the electric power
system, the liquid hydrogen stored in the liquid hydrogen tank is vaporized and supplied to the gas
Figure
The fuel
liquid
hydrogen
superconducting
magnetic
energy
storage
(LIQHYSMES)
turbine1.(GT),
cells
(FC), andwith
combined
heat & power
(CHP) to
supply
electricity
and heat tounit
the
used
in the
energy
internet.
electric
power
system
and the thermal network, respectively.
3 phase AC
system bus
Dump Register
DC Breaker
Y
Y
12-plus
bridge
converter
Lsc
Ed
Transformer
Bypass SCR
Y
Δ
Superconducting
Coil
12-plus
bridge
converter
Figure 2. The sche matic diagram of supe rconducting magne tic e ne rgy storage (SMES) connectedto
Figure 2. The schematic diagram of superconducting magnetic energy storage (SMES) connected to
e le ctric AC grid.
electric AC grid.
The schematic of a LIQHYSMES device is shown in Figure 3. It includes a multi-stage
compressor,
two-stage
heat exchangers
(HEX),
liquid in
nitrogen
refrigerants'
preThe
schematic
of a LIQHYSMES
device
is shown
Figureor3.multi-component
It includes a multi-stage
compressor,
cooling
(PREC),
expansion
turbines
and
gas
recycling
(EXP-REC),
Joule-Thomson
expansion
valves
two-stage heat exchangers (HEX), liquid nitrogen or multi-component refrigerants' pre-cooling (PREC),
(JTV),turbines
a LH2 storage
tankrecycling
and liquid
nitrogen shielding.
SMES based
on coated
conductors
on
expansion
and gas
(EXP-REC),
Joule-Thomson
expansion
valves
(JTV), a(based
LH2 storage
high temperature superconductors, mostly YBaCuO and magnesium diboride (MgB2)
tank and liquid nitrogen shielding. SMES based on coated conductors (based on high temperature
superconducting wires [21,22]) is utilized here, which could be operated in the LH2 bath for sharing
superconductors,
mostly YBaCuO and magnesium diboride (MgB ) superconducting wires [21,22]) is
cooling system. In the charging process 1–4 shown in Figure 3, the2gaseous hydrogen obtained after
utilized
here,
which
could
be operated
in the
LH2 bathheat
for exchanger
sharing cooling
system.
the of
charging
electrolysis is passed
through
a multistage
compressor,
s and JTV1,
so thatInmost
the
process
1–4 shown
in Figure
3, the
gaseous
obtained
after
through a
gaseous
hydrogen
is liquefied
and
stored inhydrogen
the LH2 tank
at 10 bar
andelectrolysis
30 K. Besides,isa passed
small amount
multistage
compressor,
heat
exchangers
JTV1,
mostmedium
of the gaseous
hydrogen
of unliquefied
gaseous
hydrogen
is fed and
to the
JTV2so
as that
a cooling
for the SMES
coil at is
1.2liquefied
bar
and 20inK,
and
supplied
to
the
HEX
and
re-compressed
for
another
expansion
cycle.
and stored
the
LHsubsequently
tank
at
10
bar
and
30
K.
Besides,
a
small
amount
of
unliquefied
gaseous
hydrogen
2
Energies 2017, 10, 185
4 of 20
Energies
10, 185as a cooling medium for the SMES coil at 1.2 bar and 20 K, and subsequently supplied
4 of 20
is
fed to2017,
the JTV2
to the HEX and re-compressed for another expansion cycle. From the discharge process 5 to process 8,
From
discharge process 5 to process 8, the LH2 stored in the LH2 tank is sent to JTV2. Then the
the
LHthe
2 stored in the LH2 tank is sent to JTV2. Then the LH2 is converted into gaseous hydrogen
LH
2
is
converted
into stages
gaseous
the two HEX stages for use in GT, FC or CHP.
through the two HEX
forhydrogen
use in GT,through
FC or CHP.
LIQHYSMES features
the combined
combined use
use of
of LH
LH22 storage
LIQHYSMES
features the
storage and
and SMES
SMESto
tostabilize
stabilize power
power fluctuations
fluctuations
in
the
electric
power
system
with
RES.
The
combined
use
of
the
SMES
and
liquid
hydrogen
can help
help
in the electric power system with RES. The combined use of the SMES and liquid hydrogen can
to
expand
storage
capacity
substantially.
On
the
other
hand,
the
liquid
hydrogen
is
used
as
cooling
to expand storage capacity substantially. On the other hand, the liquid hydrogen is used as cooling
medium for
for the
the SMES
SMES and
plant with
with itit to
to enhance
enhance the
the refrigeration
efficiency
medium
and shares
shares the
the refrigeration
refrigeration plant
refrigeration efficiency
and
reduce
the
investment
cost.
and reduce the investment cost.
Gas turbine(GT)
Fuel cells(FC)
Combined heat &
power(CHP)
8
Grid
including
RESs
Electrolyser
1
Compressor
HEX&PREC
2
7
HEX&EXP-REC
JTV2 JTV1
5
3
4
SMES
SMES
Liquid
Nitrogen
Liquid
Hydrogen
(30K)
Liquid
Hydrogen
（20K）
6
Liquid
Hydrogen
（20K）
Figure
matic of
of the
the LIQHYSMES.
LIQHYSMES.
Figure 3.
3. Sche
Schematic
The use of LIQHYSMES is not subject to strict geographical restrictions, and can be applied to a
The use of LIQHYSMES is not subject to strict geographical restrictions, and can be applied to a
variety of voltage levels in the electric power system, which are of important significance for
variety of voltage levels in the electric power system, which are of important significance for achieving
achieving large-scale use of RES.
large-scale use of RES.
3. The
The Micro
Micro Energy
Energy Grid
Grid Including
Including LIQHYSMES
LIQHYSMES
3.
The micro
micro energy
energy grid
grid concept
concept is
is consistent
with the
thenotion
notion of
of the
the future
power system,
The
consistent with
future electric
electric power
system,
the
characteristics
of
which
will
be
profoundly
different
from
those
of
the
systems
existing
today.
It
the characteristics of which will be profoundly different from those of the systems existing
today.
represents
in
It
representsthe
thefurther
furtherdevelopment
developmentofofmicrogrids.
microgrids.InInaamicrogrid
microgridthe
theenergy
energyisis only
only transmitted
transmitted in
the
form
of
electricity.
However,
in
the
micro
energy
grid
with
LIQHYSMES
shown
in
Figure
1
the
the form of electricity. However, in the micro energy grid with LIQHYSMES shown in Figure 1 the
energy can
can be
be converted
converted into
into electricity,
electricity,chemical
chemicalenergy,
energy,thermal
thermalenergy
energyand
andother
otherforms.
forms.The
The smart
smart
energy
loads (SL)
(SL) become
systems,as
as well
well as
as vehicle-to-grid
vehicle-to-grid(V2G)
(V2G) systems,
systems,
loads
become controllable,
controllable, and
and energy-storage
energy-storage systems,
further
contribute
to
active
controllable
loads.
Renewable
energy
sources
will
thus
be
used
more
and
further contribute to active controllable loads. Renewable energy sources will thus be used more and
more
in
homes,
buildings,
and
factories
[23].
more in homes, buildings, and factories [23].
The configuration
configurationarchitecture
architectureofofthe
themicro
micro
energy
grid
is presented
in Figure
is composed
The
energy
grid
is presented
in Figure
4. It4.isIt composed
of
of a micro turbine (MT), DGs, an electrical vehicle station, smart loads and a LIQHYSMES unit. The
a micro turbine (MT), DGs, an electrical vehicle station, smart loads and a LIQHYSMES unit. The micro
micro energy grid is managed by a distribution management sys tem (DMS). Phasor measurement
energy grid is managed by a distribution management system (DMS). Phasor measurement units
units (PMUs) are installed in this micro energy grid to measure the real-time information of the
components. A large number of data from PMUs can be handled by cloud computing in the DMS
[24–30]. The micro energy grid is capable to work in either the grid-connected or isolated mode,
transforming from one into the other by controlling the circuit breaker 1. In the grid-connected mode,
the deviation of the frequency resulting from abrupt variation s in load demand growth and
Energies 2017, 10, 185
5 of 20
(PMUs) are installed in this micro energy grid to measure the real-time information of the components.
A large number of data from PMUs can be handled by cloud computing in the DMS [24–30]. The micro
energy grid is capable to work in either the grid-connected or isolated mode, transforming from one
into the other by controlling the circuit breaker 1. In the grid-connected mode, the deviation of the
Energiesresulting
2017, 10, 185from abrupt variations in load demand growth and generated power variations
5 of 20
frequency
from different RESs can be eliminated rapidly by the electric power system to maintain stable operation.
generated power variations from different RESs can be eliminated rapidly by the electric power
The isolated micro energy grid on the other hand needs to control the components coordinately to
system to maintain stable operation. The isolated micro energy grid on the other hand needs to
maintain the stability. With the LIQHYSMES, the system inertia could be increased, thus improving
control the components coordinately to maintain the stability. With the LIQHYSMES, the system
the frequency
stability
of the isolated
micro energy
internet. Here
the equivalent
model
forenergy
LFC of the
inertia could
be increased,
thus improving
the frequency
stability
of the isolated
micro
isolated
micro
energy
grid
is
constructed.
internet. Here the equivalent model for LFC of the isolated micro energy grid is constructed.
Micro energy grid
PMU
LV
AC
DC
AC
DC
Breaker 2
Micro-turbine
PMU
MV
Grid
Load 1
PMU
Breaker 1 Transformer
10.5/0.4kV
Breaker 3
PMU
Load 2
Breaker 4
DMS
Wind power
system
PCC
PMU
PMU
PMU
SMES with
liquid hydrogen
PMU
Photovoltaic
system
Load 3
Electrical vehicles
Figure 4. Sche matic of a micro e ne rgy grid including the LIQHYSMES.
Figure 4. Schematic of a micro energy grid including the LIQHYSMES.
3.1. Model of LIQHSMES
3.1. Model of LIQHSMES
During the LFC, the voltage and current of the superconducting coil vary with the frequency
During
the
voltageamounts
and current
of the
superconducting
coilofvary
with the
frequency
deviationthe
to LFC,
supply
different
of power
to maintain
the stability
the system.
When
a
disturbance
disappears,
the current
of the
coil should
be restored
to the
initial value
deviation
to supply
different
amounts
of superconducting
power to maintain
the stability
of the
system.
When a
preparing
for the subsequent
disturbances.
disturbance
disappears,
the current
of the superconducting coil should be restored to the initial value
The
deviation
of
the
superconducting
preparing for the subsequent disturbances. coil (SC) voltage is given by:
The deviation of the superconducting coil (SC)
voltage is given by:
K SMES
Ed 
∆E =
1  sTDC
KSMES
f
d
The deviation of the SC current is expressed
as:sTDC
1+
(1)
∆f
Ed
sLSC
I d as:

The deviation of the SC current is expressed
∆E
sLSC
(1)
(2)
d as follows:
The power supplying by the SMES can∆I
bedobtained
=
PSMES  Ed  ( I SC 0  I d )
(2)
(3)
The power supplying by the SMES can be obtained as follows:
Therefore, the model of the SMES in LFC can be represented as in Figure 5. Herein, the GPC
algorithm proposed to be used in LFC is based on the controlled auto-regressive integrated moving∆PSMES = ∆Ed · ( ISC0 + ∆Id )
(3)
average (CARIMA) model. It can identify and linearize
the model of the system online, so a feedback
of the deviation of the SC current is added to eliminate the error caused by linearization and achieve
Therefore, the model of the SMES in LFC can be represented as in Figure 5. Herein, the GPC
rapid recovery of SC current meanwhile.
algorithm proposed to be used in LFC is based on the controlled auto-regressive integrated
moving-average (CARIMA) model. It can identify and linearize the model of the system online,
so a feedback of the deviation of the SC current is added to eliminate the error caused by linearization
and achieve rapid recovery of SC current meanwhile.
Energies 2017, 10, 185
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Energies 2017, 10, 185
Energies 2017, 10, 185
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K id
LFC signal
Energies 2017,
10, 185
I SC 0


u SMES
K SMES
LFC signal


u SMES
LFC signal
K SMES
u SMES
K
E id
D
1
1sTDC
K id
1
1sTDC


E D
1
sLSC
E D
1
1sTDC
K SMES
1
sLSC
1
sLSC
I D

6 of 20

I SC 0
I D
I D
 SMES

 I SC 0
π


π
PSMES
 SMES
  SMES
 SMES
PSMES
PSMES
π
 ncy
Figure 5. The transfe r function mode l of the SMES unit for load fre que
SMEScontrol (LFC).
  SMES
Figuresupplied
Thetransfe
transfer
function
modell of
the
load
frequency
control
(LFC).
The power
by rthe
LH2 storage
and unit
SMES
the
micro
energy
grid
are only
Figure
5.5.The
function
mode
ofunit
the SMES
SMES
unitfor
forto
load
fre
que ncy
control
(LFC).
Figure
5. The
r functionof
mode
l ofother,
the SMES
unit for
load fre
quetwo
ncy control
controlled by PCC
and
aretransfe
independent
each
which
means
the
can be(LFC).
regarded as a
common
parallel
system
in
When
the
negative,
the
controlled
2
The
powersupplied
supplied
by
the
2 storage
unitSMES
anddeviation
SMES
to is
the
microgrid
energy
gridcontrolled
areLH
only
The
power
bythe
theLFC.
LH2LH
storage
unitfrequency
and
to
the micro
energy
are
only
The power supplied by the
LH2 storage unit and SMES to the micro energy grid are only
storage
unit
provides
power
to
compensate
the
power
shortage
by
using
the
FC,
GT
or
CHP.
When
controlled
by
PCC
and
are
independent
of
each
other,
which
means
the
two
can
be
regarded
as
a
by
PCC
and
are
independent
of
each
other,
which
means
the
two
can
be
regarded
as
a
common
parallel
controlled by PCC and are independent of each other, which means the two can be regarded as a
the
frequency
deviation
is
positive,
the
LH
2
storage
unit
utilizes
the
electrolyser
for
consumption
of
common
parallel
system
in frequency
the
LFC.
When
deviation
negative,
controlled
system
in the
LFC.
When
the
deviation
isfrequency
negative,deviation
the controlled
LH2 storage
unit provides
common
parallel
system
in the
LFC.
Whenthe
thefrequency
isisnegative,
thethe
controlled
LH2 LH2
excess
power.
The
ofpower
the to
LH
2 storage
for
LFC
is
shown
inusing
Figure
6.
Here,
the
is used
storage
provides
power
compensate
the
power
shortage
by
using
the
FC,
GT
orFC
CHP.
When
power
tounit
compensate
the
shortage
byunit
using
the
FC,
GT
or CHP.
When
the
frequency
deviation
storage
unit model
provides
power
to
compensate
the
power
shortage
by
the
FC,
GT
or
CHP.
When
asisthe
the
device
to deviation
convert
the
LH
2
into
electric
energy.
The
FC
constant
time
is
set
as
same
as
the of
frequency
is
positive,
the
LH
2
storage
unit
utilizes
the
electrolyser
for
consumption
the frequency
deviation
is
positive,
the
LH
2
storage
unit
utilizes
the
electrolyser
for
consumption
of
positive,
the
LH
storage
unit
utilizes
the
electrolyser
for
consumption
of
excess
power.
The
model
2
excess
power.
The
model
of
the
LH
2
storage
unit
for
LFC
is
shown
in
Figure
6.
Here,
the
FC
is
used
electrolyser
constant
toof
simplify
model
used
this
paper.
excess
power.
The
model
the is
LH
2the
storage
unit
forin
is
shown
in used
Figure
Here,
the to
FCconvert
is used
of
the LH
storage
unit
for
LFC
shown
in Figure
6.LFC
Here,
the
FC is
as 6.
the
device
2time
the device
toenergy.
convert
the
2 into
electric
energy.
The
FC
time
is is
setset
astime
same
as the
as the
to convert
theThe
LHLH
2 into
electric
energy.
FCconstant
constant
time
as same
as the
the
LHas2device
into
electric
FC
constant
time
is setThe
as same
as the electrolyser
constant
to
electrolyser
time
constant
to simplify
the
modelused
used in
in this
this paper.
electrolyser
time
constant
to
simplify
the
model
paper.
simplify the model used in this paper.
Liquefier
Electrolyser

1
Electrolyser
0
LFC signal
u LH 2 LFC signal
0
0
0
0
0
0
LFC signal
u
LH 2
u LH 2
0
0
TEEC
s 1
Electrolyser
1
1 s 1
TEEC
TEEC s 1
1
TcLiquefier
s 1
 LH 2
 LH2PLH 2
1
PLH 2


1
Tc s 1 LH 2
Tc s 1 
LH 2
 LH 2
Fuel Cell
Liquefier
1Liquefier
TcLiquefier
s 1 1
Tc s 1
1
LH 2 P
LH 2
Liquefier
Fuel Cell
1
 LH 2
 LH 2
PLH 2
P
 LH 2 LH 2
TFCFuel
s11Cell
PLH 2
TFC s 
1
1  LH 2

TFC s 1 LH 2
unit
Figure
6. The
transfe
r function mode
l of the
LH2 storage
LH
2for
Figure
6. The
transfer
model
unit
forLFC.
LFC.
Figure
6. The
transfefunction
r function
modeof
l ofthe
theLH
LH22 storage
storage unit
for
LFC.
Tc s 1
3.2. Models
OtherofComponents
Figure
6. The transfe r function mode l of the LH2 storage unit for LFC.
3.2. of
Models
Other
Components
3.2. Models
of Other
Components
Figure 7Figure
shows
the model
of a micro-turbine
for LFC,
which
simulates
the
dynamicprocess
process
7 shows
the model
a micro-turbine
LFC,
which
simulatesthe
thedynamic
dynamic
of of
Figure 7ofshows
the model
of a of
micro-turbine
for for
LFC,
which
simulates
process of
the
3.2. Models
Other Components
the micro-turbine
power
following
signal.
Themodel
model includes
includes the
the micro-turbine
outputoutput
power
following
the the
LFCLFC
signal.
The
the governor,
governor,fuel
fuel
micro-turbine output power following the LFC signal. The model includes the governor, fuel system
system
gas turbine
of
theof
micro-turbine.
equivalent
modelsofsimulates
ofthe
thefuel
fuel system
system
and
systemFigure
and
gas
turbine
of the
micro-turbine.
TheThe
equivalent
models
andthe
theturbine
turbine of
7and
shows
model
a micro-turbine
for LFC,
which
the dynamic
process
and gas turbine
of thethe
micro-turbine.
The equivalent
models
of the fuel system
and the turbine
are
are represented
by
the first-order
inertia
units.
arethe
represented
by theoutput
first-order
inertia
units.
micro-turbine
power
following
the LFC signal. The model includes the governor, fuel
represented by the first-order inertia units.
system and gas turbine of the micro-turbine. The equivalent models of the fuel system and the turbine
f
are represented by thefirst-order
inertia units.
f
LFCsignal
f
LFC signal

u MT

1
R

1
R
1
R
Micro turbine
Governor
Governor
Micro turbine
X MT
1
1
1 T f s 1X MT
TGovernor
s 1
 MT  MT
 MT  MT
P
MT
1Tt s 1
PMT
Micro
turbine
  MT    MT
LFC signal
Tt s 1


f
MT MT



X



1
MT
MT
1
MT
uFigure
mode l of the micro turbine for LFC. PMT
MT 7. The transfe r function
T f s 1
Tt s 1
Figure
7. The
transfe
r function
mode
l ofof
the
for
Figure
7. The
transfer
function
model
themicro
microturbine
for
LFC.
 LFC.
turbine
 MT
MT
Since there are different numbers of EVs in each EV station,
the modelling
of EVs could be
u MT
handled by using equivalent EVs with different inverter capacities. The equivalent EV model which
Since there are different
numbers
offunction
EVs in mode
each l EV
station,
the modelling
of EVs could be
Figure
The
transfe
of the
micro
turbine
forof
LFC.
Since
there
numbers
ofr EVs
in each
EVcan
station,
the modelling
EVsonly
could
be handled
can be
usedare
fordifferent
LFC is 7.
shown
in Figure
8 [31].
The EV
be charged
and discharged
within
the
handled by using equivalent EVs with different inverter capacities. The equivalent EV model which
by using
equivalent
EVs with if
different
inverter
which
can only
be used
range
of  e . However,
the energy
of thecapacities.
EV exceedsThe
the equivalent
upper limit EV
(i.e.,model
Emax), the
EV can
can be used
for
LFCare
is shown
in Figure
8 [31].
The EV
can beEV
charged
and
only
within
the
Since
there
different
numbers
of EVs
in each
station,
thedischarged
modelling
of EVs
could
for LFC
is shown
in Figure
8 [31].
The EV can
be charged
and discharged
only
within the
range
of ±µbe
e.
handled
by
using
equivalent
EVs
with
different
inverter
capacities.
The
equivalent
EV
model
which


range
of
.
However,
if
the
energy
of
the
EV
exceeds
the
upper
limit
(i.e.,
E
max), the
EV
can
only
e
However, if the energy of the EV exceeds the upper limit (i.e., Emax ), the EV can only be discharged
can be used for LFC is shown in Figure 8 [31]. The EV can be charged and discharged only within the
range of  e . However, if the energy of the EV exceeds the upper limit (i.e., Emax), the EV can only
Energies 2017, 10, 185
Energies 2017, 10, 185
7 of 20
Energies 2017, 10, 185
7 of 20
7 of 20
be discharged within the range of (0~ e ). Also, if the energy of the EV is smaller than the lower limit
withinEnergies
the range
of185(0~µe ). Also, if the energy of the EV is smaller than the lower limit (i.e.,
E ),
7limit
of 20 min
be discharged
within
the range
of (0~ within
). Also,
the energy
of the
than
the lower
e
(i.e.,
Emin),2017,
the 10,
EV
can only
be charged
theifrange
of (  
~0).EV
the time
constant
of EV.
T isissmaller
e the time
e
the EV can only be charged within the range of (−µe ~0). Te is
constant of EV.
(i.e., Emin), the EV can only be charged within the range of (  e ~0). Te is the time constant of EV.
be discharged within the range of (0~ e ). Also, if the energy of the EV is smaller than the lower limit
e
e
K 0
K 0
LFC signal
(i.e., Emin), the EV
can only be charged within the range of
(
~0).
time constant of EV.
Te isthe
e
  e

LFC signal
uE
uE
LFC signal
uE
1
Tes+1
1
Tes+1
e
1
Tes+1
Emax
Emax

0 
e
0 
ee
- e
- 0e 
e
K
- e
 K

Emax


K
K 0
K K
0 0
K 0
Emin
K 0
Emin
Emin
- e e
0
- e 
0e
-
e
-
- e
e0
 K
-
 Ke

K 0
K K0 0
K 0
K 0
e
 e
 e
e
PE
PE
PE
 e

K energy model
Total

Total
energy model
Figure 8. The transfe r function mode l of the e le ctric ve hicle for LFC.
Figure
8. The transfer function model of the electric vehicle for LFC.
Figure 8. The transfe r function mode l of the Total
e le energy
ctric model
ve hicle for LFC.
In the micro energy internet, the loads data computing center can calculate the total supplied
Figure 8. The transfe
r function
mode
l of the e le ctric
ve hicle
for
LFC.
In the
micro
energy
loads
data
computing
center
can
calculate
the total
In depending
the
micro
energy
internet, the
the
loads
data
computing
canthe
calculate
the
total
supplied
power
on theinternet,
frequency
deviation.
Subsequently,
itcenter
adjusts
amount
of smart
loadssupplied
in
power
depending
onon
the
frequency
deviation.
Subsequently,
it
adjusts
amount
of smart
loads
in
power
the
frequency
deviation.
Subsequently,
it adjusts
thethe
amount
of smart
loads
in
need
of depending
being open
or
closed,
although
the output
power of each
smart
load
is uncontrollable.
Smart
In the micro energy internet, the loads data computing center can calculate the total supplied
need
of
being
open
orclosed,
closed,
although
the
output
power
of each
smart
loadload
isinuncontrollable.
SmartSmart
have
the
advantage
of rapid
response
for LFC
and the
model
issmart
shown
Figure
9.
needloads
ofpower
being
open
or
although
the
output
power
of
each
is
uncontrollable.
depending on the frequency deviation. Subsequently, it adjusts the amount of smart loads in
loads
have
the
advantage
rapidresponse
response for
and
model
is shown
in Figure
9. 9.
loads
have
the
advantage
ofofrapid
forLFC
LFC
andthe
the
model
isload
shown
in Figure
need of
being
open or closed,
although the output
power
of
each
smart
is uncontrollable.
Smart
LFC signal
loads have the advantage of rapid response for LFC and the model is shown in Figure 9.
LFC signal
uLFC
SL
uSL signal
uSL
Loads Data Computing Center
Loads Data Computing Center
Loads Data Computing Center
 SL
 SL P
SL
 SL
 SL PSL
 SL
PSL
Figure 9. The transfe r function mode l of the smart load
 SLfor LFC.
Figure
The transfe
transfer
functionmode
model
smart
load
LFC.
Figure 9. The
r function
l ofof
thethe
smart
load
forfor
LFC.
Because the fluctuation of wind power and photovoltaic (PV) power output is relatively large,
Figure 9. The transfe r function mode l of the smart load for LFC.
fluctuation
of wind
power sources
and photovoltaic
power
is relatively
large,
they Because
can be allthe
equalized
to the
disturbance
in the LFC(PV)
model
[32].output
The power
disturbances
Because the fluctuation of wind power and photovoltaic (PV) power output is relatively large,
they
can
be
all
equalized
to
the
disturbance
sources
in
the
LFC
model
[32].
The
power
disturbances
of
wind
have
similar
responses
to
PV
systems
in
the
LFC
model,
so
it
is
only
considered
here.
the fluctuation of wind power and photovoltaic (PV) power output is relatively large,
they can beBecause
all equalized
to the disturbance sources in the LFC model [32]. The power disturbances of
of wind
have
responses
todisturbance
PV systems
in the
LFC
model,
so it
is[32].
onlyThe
considered
here.
Based
on
theequalized
LFC
response
models
of the
above-mentioned
components,
a model
of micro
they
can
besimilar
all
to the
sources
in the
LFC model
power
disturbances
wind
have
similar
responses
to PV models
systems
inthe
the
LFC
model,controller
so itcomponents,
is only
considered
here.
Based
on
the
LFC
response
of
above-mentioned
a
model
of micro
energy
network
including
LIQHYSMES
with
load
frequency
for
LFC
is
constructed
as
of wind have similar responses to PV systems in the LFC model, so it is only considered here.
Based
on
the
LFC
response
models
of
the
above-mentioned
components,
a
model
of
micro
energy
network
including
LIQHYSMES
with
load
frequency
controller
for
LFC
is
constructed
asenergy

P

P
shown Based
in Figure
10. LFC Lresponse
is the load
disturbance,
of the
windofpower
on the
models
of the above-mentioned
components,
a model
micro
W is the fluctuation
network
including
LIQHYSMES
with
load
frequency
controller
for
LFC
is
constructed
as
shown
in

P

P
shown
in
Figure
10.
is
the
load
disturbance,
is
the
fluctuation
of
the
wind
power
energy network
LIQHYSMES
with
frequency
controller for LFC is constructed as
generation,
and H t including
is theLinertia
constant of
theload
micro
energy
internet.
W
Figure
10.
∆P
is
the
load
disturbance,
∆P
is
the
fluctuation
of
the
wind
power
generation,
and
H
PL is the
PW isinternet.
shown in
Figure
loadWdisturbance,
the fluctuation of the wind power t is
L and
H t 10.
generation,
is theinertia
constant
of the micro energy
the inertia
constant
micro energy internet.
generation,
andofHthe
t is the inertia constant of the micro energy internet.

1 

1 
1
R
1
R
u
Load
MT
1
u
R
PDG
Load
MT
u2 u 
1
PDG
PLoad
EV MT
PE
L
u 2
PDG P
EV
PE
L
1
+

u
+


u

P
3 2
LFC
SL
PL 2 H s
SLEV
PE +
Controller
1t
+
+

u
+

+

P
3
LFC
f
SL
PW
+
SL
2 H t1s

P
+
Controller uu3
+++ +
SMES
PSL
4
LFC
f
PW
+
SMES
SL
2 Ht s
Controller u
PSMES
++ +
Wind power f
4
PW
SMES

P
generation
PSMES
LH 2
u5u4
Wind power
LH2
SMES
PLH 2
generation
Wind power
u5
LH2
PLH 2
generation
umode
5
Figure 10. The control
l of the micro
e
ne
rgy
grid
including
LIQHYSMES .
LH2
Figure 10. The control mode l of the micro e ne rgy grid including LIQHYSMES .
Figure 10. The
control
mode l of the
4. Generalized Predictive
Control
Algorithm
formicro
LFC e ne rgy grid including LIQHYSMES .
Figure 10. The control model of the micro energy grid including LIQHYSMES.
4. Generalized Predictive Control Algorithm for LFC
The
principle
of the GPCControl
algorithm
can be summarized
as three parts: predictive model, rolling
4. Generalized Predictive
Algorithm
for LFC
The
principle
of
the
GPC
algorithm
can
be
summarized
as three
predictive
rolling
optimization
and
feedback
compensation.
In
the
GPC
algorithm,
theparts:
predictive
modelmodel,
is described
4. Generalized Predictive Control Algorithm for LFC
The principle
of the
GPC
can
be
as
three
parts:
predictive
model,
rolling
optimization
andmodel,
feedback
compensation.
In unstable
thesummarized
GPC system
algorithm,
the
predictive
is described
by
the CARIMA
which
isalgorithm
suitable for
and
is easier
to
be model
recognized
online.
optimization
and
feedback
compensation.
In
the
GPC
algorithm,
the
predictive
model
is
described
The
the GPC
algorithm
summarized
as three
parts:
predictive
model,
by
theprinciple
CARIMA of
model,
which
is suitablecan
for be
unstable
system and
is easier
to be
recognized
online.rolling
by the CARIMA
model, which
is suitable for
unstable
and isthe
easier
to be recognized
online.
optimization
and feedback
compensation.
In the
GPCsystem
algorithm,
predictive
model is
described
by the CARIMA model, which is suitable for unstable system and is easier to be recognized online.
The LFC signals shown in Figure 10 are the multiple inputs and the frequency deviation is the single
Energies 2017, 10, 185
8 of 20
output of the predictive model. Also, the unmeasured disturbance caused by load disturbance or
the fluctuation of wind power generation and measurable noise are taken into account in the model.
The predictive model can be described as follows:
A ( z −1 ) ∆ f ( t ) =
5
∑ Bi (z−1 )∆ui + D(z−1 )w(t) + ξ (t)/∆
(4)
i =1
where t is the discrete sampling time point of control, ∆ui is the LFC signal for each component in
Figure 10, z−1 is the backward shift operator. ∆ = 1 − z−1 is the difference operator, which represents
the effect of random noise. ξ (t) is a n-dimensional zero mean white noise sequence. A(z−1 ), B(z−1 ),
D (z−1 ) are the polynomial matrixes of z−1 :

−1
−1
−2
−n

 A(z ) = In×n + a1 z + a2 z + . . . + ana z a
−
1
−
1
−
2
−
Bi (z ) = bi0 + bi1 z + bi2 z + . . . + binb z nb

 D ( z −1 ) = d + d z −1 + d z −2 + . . . + d z − n d
0
2
1
nd
(5)
where a1 , a2 . . . , bi1 , bi2 . . . and d1 , d2 . . . are the polynomial coefficients, n a , nb , nd are the orders of
the polynomial, respectively. n a is the prediction time domain, nb is the control time domain, where
the first term can be 0, denoting the number of time delays of the response, and nd is the interference
time domain.
In order to track the set reference value w(t + j) of the predicted output, we can calculate the
control vectors using optimization techniques based on the following objective function:
na
J=
5
∧
nb
∑ ||∆ f (t + j|t) − w(t + j)||2Q + ∑ ( ∑ ||∆ui (t + j − 1)||2R )
j =1
(6)
i =1 j =1
∧
where Q and R are the positive definite weighting matrixes, ∆ f (t + j|t) is an optimal j-step prediction
of the frequency deviation at time t. The reference value w(t + j) of the j-step frequency deviation
is set as constant 0. The control vectors can be obtained by many algorithms to solve this quadratic
programming problem, such as sequential minimal optimization (SMO) [33–37]. Herein, the function
’quadprog’ provided by Matlab is used and the first row of the vectors ∆u(t|t) is carried out as the
LFC signals to eliminate the frequency deviation at the sampling time point t.
In the GPC algorithm, the recursive least squares method is used to identify the parameters
of the predictive model for the LFC in the micro energy internet, which means that the polynomial
matrixes A(z−1 ), B(z−1 ), C (z−1 ) vary with the sampling time. Then the optimal control sequence can
be calculated. This online identification and the control sequence correction mechanism constitute the
GPC algorithm feedback correction.
5. The Optimization Design of the Superconducting Coil
The SC parameters, e.g., initial current ISC0 , coil inductance LSC and initial stored energy Esc0 ,
are optimized to improve the control effect of the LFC model for the micro energy internet. In order to
improve the stability, the following objective function can be used:
Min J1 =
Z t
sim
0
|∆ f |dt
(7)
where tsim is the total simulation time.
∆ f (s) = (∆PMT (s) + ∆PEV (s) + ∆PSL (s) + ∆PSMES (s) + ∆PLH2 (s) − ∆D ) ·
1
2Ht s
(8)
∆D is the system uncertainty model which represents several operating conditions of unpredictable
wind power and loads variation.
Energies 2017, 10, 185
9 of 20
The response of SMES is expressed as:
Energies 2017, 10,
P185
SMES ( s )
=(
I
sLSC R(s)
R(s)
+ SC0 )(
)
sLSC (1 + sTDC ) + 1
s
sLSC (1 + sTDC ) + 1
9 of 20
(9)
The response of SMES is expressed as:
The input signal R(s) is the load frequency control signal. Also the responses of other components
I
sL R( s)
R( s )
can be expressed as the form of
SMES
PSMES
( s)  (so that the response
 SC 0 )(of theSCfrequency
) deviation can be(9)obtained.
sLSC (1  sTDC )  1
s sLSC (1  sTDC )  1
Then, the numerical inversion of Laplace transform is employed for the time domain response of the
The input signal R( s) is the load frequency control signal. Also the responses of other
frequency deviation.
components
be expressed
as the form
of SMES
Moreover,
thecan
initial
stored energy
is given
by: so that the response of the frequency deviation
can be obtained. Then, the numerical inversion of Laplace transform is employed for the time domain
response of the frequency deviation.
1
= byL:SC ISC0 2
Moreover, the initial stored energy E
issc0
given
2
(10)
1
(10)
To optimize the Esc0 , the optimal LSCEscand
ILSC0
is2 obtained by taking it into consideration.
0 
SC I SC 0
2
The above two parts are weighted linearly, so the optimization problem can be formulated as follows:
To optimize the Esc 0 , the optimal LSC and I SC 0 is obtained by taking it into consideration. The
above two parts are weighted linearly,
so the
optimization
can be formulated as follows:
Min
J=
W J + Wproblem
E
1 1
Subject to:
Subject to:
(11)
2 SC0
Min J  W1 J1  W2 ESC 0
(11)
0.001 ≤ LSC ≤ 10H
0.001  LSC  10H
1.5 ≤ ISC0 ≤ 4kA
1.5  I SC 0  4kA
where the weighting factors are set as W1 = 1, W2 = 0.01 in this paper. Then the particle swarm
where the weighting factors are set as W1  1 , W2  0.01 in this paper. Then the particle swarm
optimization
(PSO) is applied to solve the problem. Figure 11 shows the flowchart of PSO [38].
optimization (PSO) is applied to solve the problem. Figure 11 shows the flowchart of PSO [38].
Start
Set initial parameters
N=50,G=200
Lsc,Isc0
Update all searched
parameters(Lsc,Isc0)
in each particle
Check if the searched
parameters satisfy the
constraints
Yes
Calculate Pbest in
each particle
No
Correct the
particles
Check if the searched
parameters satisfy the
constraints again
Yes
Select Jbest from 50 particles,
then check:
If Jold < Jbest, then Jold=Jold
Jold > Jbest, then Jold=Jbest
G=G+1
No
Check the stopping criterion:
If G=200
Yes
End
Figure 11. Flowchart of PSO.
No
Generate new
particles
Energies 2017, 10, 185
10 of 20
Figure 11. Flowchart of PSO.
6. Simulation Study
Energies 2017, 10, 185
10 of 20
Simulations are carried out based on the above-mentioned model of micro energy grid, and the
model parameters are shown in Table 1. Some of the values are chosen referring to [20]. Suppose that
6. Simulation
Study
the
micro energy
grid is in steady isolated state at the beginning of the simulation. The wind power
fromSimulations
an offshore wind
farm in
Denmark
is shown
in Figure 12, where
thegrid,
windand
power
PW micro
 0 means
are carried
out
based on
the above-mentioned
modelof
energy
the
is
equalparameters
to the average
powerin
during
period.
model
are shown
Table the
1. Some
of the values are chosen referring to [20]. Suppose that
the micro energy grid is in steady isolated state at the beginning of the simulation. The wind power
1. Parame
te rs ofinthe
micro12,
e ne
rgy grid.
from an offshore wind farm inTable
Denmark
is shown
Figure
where
∆PW = 0 means the wind power
is equal to the average
power
during
the
period.
Grid Component
Parameters
Values
Unit
T
0.03
Table 1. Parameters ofDC
the micro energy grid.
K SMES
1
Grid Component
SMES
K id
Parameters
L
TDC SC
KSMESI SC 0
Kid SMES
LSC
T
ISC0 EEC
SMES
δSMES Tc
LH2 storage unit
T
TEEC FC
Tc  LH 2
TFC T
δLH2 f
LH2 storage unit
T
MT
t
Tf
R
Tt
R  MT
δMT  MT
µ MT T
MT
Values1
0.03 5
s
/
Unit /
s H
1 1.5
1 0.15
5
1
1.5
0.15 50
/ kA
/pu·MW/s
H
s
kA
pu·MW/ss
1 1
500.006
1
0.1
0.006
8
0.1
2.5
8
s
s
spu·MW/s
s
s
pu·MW/s
s
s
Hz/pu·
MW
s
EV
EV
Te 
e
δe
e
µe
EmaxEmax
EminEmin
2.5 0.01
0.010.04
0.04
1
1 0.05
0.05
0.025
0.025
0.950.95
0.800.80
pu·
MW/s
Hz/pu
·MW
pu·MW/s
pu·MW
pu·MW
s
spu·MW/s
pu·MW/s
pu·MW
pu·MW
pu·
MWh
pu·MWh
pu·MWh
pu·MWh
SL
SL
δSL  SL
0.1 0.1
pu·MW
pu·MW
Gird
rtia
GirdIne
Inertia
Ht H t
7.117.11
e
s
s
0.03
0.02
0.01
 Pw/pu.
0
-0.01
-0.02
-0.03
-0.04
-0.05
-0.06
0
10
20
30
Time/s
40
50
60
Figure12.
12.The
Thepowe
power
fluctuation of
of wind
wind powe
power
generation.
Figure
r fluctuation
r ge
ne ration.
As shown in Figure 13, it can be concluded that the frequency response of the system is better
under the control of the GPC algorithm proposed in this paper than PI during the simulation period,
except for the initial moment. The frequency deviation of the system is controlled generally within the
range of ±0.002 Hz with LIQHYSMES, which shows that the controller based on the GPC algorithm
Energies
2017,
185
Energies
2017,
10,10,
185
1111
ofof
2020
shownininFigure
Figure13,
13,it itcan
canbebeconcluded
concludedthat
thatthe
thefrequency
frequencyresponse
responseofofthe
thesystem
systemisisbetter
better
AsAsshown
under
the
control
of
the
GPC
algorithm
proposed
in
this
paper
than
PI
during
the
simulation
period,
under the control of the GPC algorithm proposed in this paper than PI during the simulation period,
Energies 2017, 10, 185
11 of 20
except
forthe
theinitial
initialmoment.
moment.The
Thefrequency
frequencydeviation
deviationofofthe
thesystem
systemisiscontrolled
controlledgenerally
generallywithin
within
except
for
therange
rangeofof±0.002
±0.002Hz
Hzwith
withLIQHYSMES,
LIQHYSMES,which
whichshows
showsthat
thatthe
thecontroller
controllerbased
basedononthe
theGPC
GPC
the
algorithm
offers
better
stability
and
robustness.
The
frequency
oscillation
is
smoother
and
the
range
algorithm
offers
better stability
and robustness.
The frequency
oscillation
is smoother
and the
offers better
stability
and robustness.
The frequency
oscillation
is smoother
and the range
is range
smaller
smaller
withthe
theLIQHYSMES
LIQHYSMES
unitcompared
compared
withthe
theconditions
conditions
it.It Itcan
canbe
beseen
seenthat
thatthe
the
isis
smaller
unit
with
with
the with
LIQHYSMES
unit compared
with
the conditions
without
it. without
Itwithout
can be it.
seen
that
the
LIQHYSMES
LIQHYSMES
unit
plays
a
positive
role
in
suppressing
the
oscillation
of
the
load
frequency
caused
LIQHYSMES
playsrole
a positive
role in suppressing
theof
oscillation
of the loadcaused
frequency
caused
byby
unit plays a unit
positive
in suppressing
the oscillation
the load frequency
by wind
power
wind
power
fluctuation
in
an
isolated
micro
energy
grid.
wind
power fluctuation
in an
isolated
micro
fluctuation
in an isolated
micro
energy
grid.energy grid.
-3
x -310
x 10
20 20
GPC
without
SMES
GPC
without
SMES
PI without
SMES
PI without
SMES
GPC
SMES
GPC
withwith
SMES
PI with
SMES
PI with
SMES
15 15
Δf/Hz
Δf/Hz
10 10
5 5
0 0
-5 -5
0 0
10 10
20 20
30 30
Time/s
Time/s
40 40
50 50
60 60
Figure 13.
13. The
Thefrequency
frequency deviation
deviation of
of the
the micro
micro energy
energy grid.
grid.
Figure
Figure
13. The
frequency deviation
of the
micro energy
grid.
the
initial
moment
the
simulation,
the
control
more
effective
than
the
GPC
algorithm,
At
the
initial
moment
of
the
simulation,
the
PI
control
more
effective
than
the
GPC
algorithm,
AtAt
the
initial
moment
ofof
the
simulation,
the
PIPI
control
isisis
more
effective
than
the
GPC
algorithm,
because
there
is
little
historical
data
provided
for
the
online
identification
of
the
system
predictive
because
there
is
little
historical
data
provided
for
the
online
identification
of
system
predictive
because there is little historical data provided for the online identification of the system predictive
model
parameters
causing
the
large
prediction
deviations
and
the
remarkable
frequency
fluctuations.
model
parameters
causing
the
large
prediction
deviations
and
the
remarkable
frequency
fluctuations.
model
parameters
causing
the
large
prediction
deviations
and
the
remarkable
frequency
fluctuations.
Actually
this
situation
can
avoided
providing
historical
inputs
and
outputs
data
the
Actuallythis
thissituation
situationcan
canbebe
beavoided
avoidedbyby
byproviding
providinghistorical
historicalinputs
inputsand
andoutputs
outputsdata
dataonon
onthe
the
Actually
parameters
of
the
predictive
model
to
be
pre-set
before
the
simulation.
parameters of the predictive model to be pre-set before the simulation.
Figures
and
showthe
theactive
activeoutput
output
power
thecomponents
components
the
micro
energy
grid
Figures
14and
and1515show
outputpower
powerofof
ofthe
components
of
themicro
microenergy
energygrid
grid
Figures
1414
ofof
the
controlled
by
the
two
methods.
SMES
has
the
ability
to
respond
quickly
to
the
frequency
oscillation,
controlled
by
the
two
methods.
SMES
has
the
ability
to
respond
quickly
to
the
frequency
oscillation,
controlled by the two methods. SMES has the ability to respond quickly to the frequency oscillation,
while
theresponse
responsespeed
speedofofthe
theLH
LH
22 storage
unit
slower
due
largerinertia.
inertia.Since
Since
the
output
while
storage
unit
is
slower
due
to
Since
the
output
while
the
2 storage
unit
isis
slower
due
toto
itsitslarger
the
output
power
each
smart
load
notadjustable,
adjustable,
the
total
active
output
power
the
smart
loads
can’t
power
ofeach
eachsmart
smartload
loadisisnot
adjustable,the
thetotal
totalactive
activeoutput
output
power
ofthe
thesmart
smartloads
loadscan’t
can’t
power
ofof
power
ofof
change
smoothly.
In
addition,
electric
vehicles
based
on
V2G
technology
can
also
be
used
as
energy
change
smoothly.
In
addition,
electric
vehicles
based
on
V2G
technology
can
also
be
used
as
energy
change smoothly. In addition, electric vehicles based on V2G technology can also be used as energy
storage
devices
participateininthe
thesystem
systemofofload
loadfrequency
frequencycontrol.
control.
storage
devices
storage
devices
totoparticipate
0.03
0.03
MT
Δ PΔ
MT
P
Δ PΔ
EV
PEV
Δ PΔ
SL
PSL
SMES
Δ PΔ
SMES
P
LH2
Δ PΔ
LH2
P
0.02
0.02
Δ P/pu.
Δ P/pu.
0.01
0.01
0 0
-0.01
-0.01
-0.02
-0.02
-0.03
-0.03
0 0
10 10
20 20
30 30
Time/s
Time/s
40 40
50 50
60 60
Figure 14. The output power increment of micro turbine (MT), EV, smart loads, non-optimized SMES
and LH2 storage unit controlled by PI.
Energies 2017, 10, 185
Energies 2017, 10, 185
12 of 20
12 of 20
Figure 14. The output power increment of micro turbine (MT), EV, smart loads, non-optimized SMES
and LH2 storage unit controlled by PI.
and LH2 storage unit controlled by PI.
Energies
2017, 14.
10, The
185 output power increment of micro turbine (MT), EV, smart loads, non-optimized SMES12 of 20
Figure
0.03
0.03
ΔP MT
ΔP MT
ΔPEV
ΔPEV
ΔP SL
ΔP SL
ΔPSMES
ΔPSMES
ΔPLH2
ΔPLH2
0.02
0.02
ΔP/pu.
ΔP/pu.
0.01
0.01
0
0
-0.01
-0.01
-0.02
-0.02
-0.03
-0.03 0
0
10
10
20
20
30
30
Time/s
Time/s
40
40
50
50
60
60
Figure 15.
15. The
output
power
increment
of
MT,
EV,
smart loads,
loads, non-optimized
non-optimizedSMES
SMES and
and LH
LH2 storage
Figure
Theoutput
outputpower
powerincrement
incrementof
ofMT,
MT,EV,
EV, smart
smart
storage
Figure
15. The
loads, non-optimized
SMES and
LH22storage
unit controlled by generalized
generalized predictive
predictive control
control (GPC).
(GPC).
unit controlled by generalized predictive control (GPC).
For an isolated micro energy grid with RESs, the abrupt change in load demand is also a
For an isolated
grid
with
RESs,
the abrupt
change
in loadindemand
is also a challenge
For
isolatedmicro
microenergy
energy
grid
with
RESs,
the abrupt
change
load demand
is also a
challenge for the system to maintain frequency stability. Assuming that there are step disturbances
for the system
maintain
frequencyfrequency
stability. stability.
Assuming
that there
arethere
stepare
disturbances
in load
challenge
for thetosystem
to maintain
Assuming
that
step disturbances
in load demand (ΔPL = −0.1 pu, ΔPL = 0.12 pu, and ΔPL = 0.06 pu at t = 5 s, t = 40 s and t = 80 s,
demand
(∆PL = −(ΔP
0.1Lpu,
∆PLpu,
= 0.12
and ∆P
= 0.06
t = 5pu
s, tat= t40= s5and
80 ss,and
respectively).
in
load demand
= −0.1
ΔPLpu,
= 0.12
pu,L and
ΔPpu
L =at
0.06
s, t t==40
t = 80 s,
respectively). The fluctuation of the wind power is added to obtain the combined power disturbances
The fluctuation
the wind power
is added
to is
obtain
power disturbances
shown in
respectively).
Theoffluctuation
of the wind
power
addedthe
to combined
obtain the combined
power disturbances
shown in Figure 16.
Figure in
16.Figure 16.
shown
0.08
0.08
0.06
0.06
0.04
0.04
ΔPw/pu.
ΔPw/pu.
0.02
0.02
0
0
-0.02
-0.02
-0.04
-0.04
-0.06
-0.06
-0.08
-0.08
-0.1
-0.1
-0.12
-0.12 0
0
10
10
20
20
30
30
Time/s
Time/s
40
40
50
50
60
60
Figure 16. The power disturbances applied in the case.
Figure
Figure 16.
16. The
Thepower
powerdisturbances
disturbancesapplied
applied in
in the
the case.
case.
Figure 17 shows the frequency deviation results when PI control and GPC algorithm are applied
Figure 17
frequency
deviation
results
when
PI
and
GPC
are
17 shows
showsthe
thewith
frequency
deviation
results
whenthe
PI control
control
andoscillation
GPCalgorithm
algorithm
are applied
applied
to theFigure
LFC. Comparing
PI control,
GPC can
suppress
frequency
more rapidly
and
to
the
LFC.
Comparing
with
PI
control,
GPC
can
suppress
the
frequency
oscillation
more
rapidly
and
to
the
LFC.
Comparing
with
PI
control,
GPC
can
suppress
the
frequency
oscillation
more
rapidly
and
the peak of it is also smaller with or without the LIQHYSMES unit. In the case with LIQHYSMES
the
peak of
of itit isisalso
alsosmaller
smallerwith
withoror
without
LIQHYSMES
unit.
In the
LIQHYSMES
the
without
thethe
LIQHYSMES
unit.
Inthe
the
casecase
withwith
LIQHYSMES
unit,
unit,peak
the advantage
of the proposed
GPC algorithm
in suppressing
frequency
oscillation
is more
unit,
the
advantage
of
the
proposed
GPC
algorithm
in
suppressing
the
frequency
oscillation
is
more
the
advantage
of the
GPC the
algorithm
suppressing
the frequency
oscillation
is more obvious.
obvious.
Figures
18 proposed
and 19 show
active in
power
contribution
of the various
components
of the
obvious.
Figures
18
andthe
19active
show power
the active
power contribution
ofcomponents
the various of
components
of
the
Figures
18
and
19
show
contribution
of
the
various
the
system.
the
system. In the event of abrupt change in load demand, SMES can provide or consume powerInfrom
system.
In
the
event
of
abrupt
change
in
load
demand,
SMES
can
provide
or
consume
power
from
event
of
abrupt
change
in
load
demand,
SMES
can
provide
or
consume
power
from
the
system
in
the system in response to a rapid change in load frequency.
the
system
in
response
to
a
rapid
change
in
load
frequency.
response
to aon
rapid
in load frequency.
Based
the change
PSO algorithm,
the superconducting coil parameters of LIQHYSMES are
Based
onthe
thePSO
PSO
algorithm,
the superconducting
coil parameters
of LIQHYSMES
are
Based
on
algorithm,
the
superconducting
parameters
of 20.
LIQHYSMES
areof
optimized,
optimized, and the iterative process of optimization iscoil
shown
in Figure
The number
particles
optimized,
and
the
iterative
process
of
optimization
is
shown
in
Figure
20.
The
number
of
particles
and
iterative
ofof
optimization
isisshown
Figure
number
particles
is set
50
is setthe
as 50
and theprocess
number
the iterations
set as in
200.
Here,20.
theThe
input
signal of
R(s)
is chosen
as aas
step
is
set
as
50
and
the
number
of
the
iterations
is
set
as
200.
Here,
the
input
signal
R(s)
is
chosen
as
a
step
and
thewith
number
of the iterations
signal
the amplitude
of 0.1.is set as 200. Here, the input signal R(s) is chosen as a step signal with
signal
with the of
amplitude
of 0.1.
the amplitude
0.1.
Energies 2017, 10, 185
Energies2017,
2017,10,
10,185
185
Energies
13 of 20
13ofof20
20
13
0.06
0.06
0.06
GPCwithout
withoutSMES
SMES
GPC
GPC
without
SMES
withoutSMES
SMES
PI
PIPIwithout
without
SMES
GPCwith
withSMES
SMES
GPC
GPC
with
SMES
withSMES
SMES
PI
PIPIwith
with
SMES
0.04
0.04
0.04
ΔΔf/Hz
f/Hz
0.02
0.02
0.02
000
-0.02
-0.02
-0.02
-0.04
-0.04
-0.04
-0.06
-0.06
-0.06
000
10
10
10
20
20
20
30
30
30
Time/s
Time/s
Time/s
40
40
40
50
50
50
60
60
60
Figure17.
17.The
Thefrequency
frequencydeviation
deviationofofthe
themicro
microenergy
energygrid.
grid.
Figure
Figure
The
frequency
deviation
0.15
0.15
0.15
P
PP
ΔΔΔ
MT
MT
MT
P
PP
ΔΔΔ
EV
EV
EV
P
PP
ΔΔΔ
SL
SL
SL
P
PP
ΔΔΔ
SMES
SMES
SMES
P
PP
ΔΔΔ
LH2
LH2
LH2
0.1
0.1
0.1
ΔΔP/pu.
P/pu.
0.05
0.05
0.05
000
-0.05
-0.05
-0.05
-0.1
-0.1
-0.1
-0.15
-0.15
-0.15
000
10
10
10
20
20
20
30
30
30
Time/s
Time/s
Time/s
40
40
40
50
50
50
60
60
60
Figure18.
18.The
Theoutput
outputpower
powerincrement
incrementof
ofMT,
MT,EV,
EV,smart
smartloads,
loads,non-optimized
non-optimizedSMES
SMESand
andLH
LH
2storage
storage
Figure
non-optimized
SMES
and
LH
smart
loads,
Figure
18.
The
output
power
increment
of
MT,
222 storage
unitcontrolled
controlledby
byPI.
PI.
unit
0.15
0.15
0.15
P
PP
ΔΔΔ
MT
MT
MT
P
PP
ΔΔΔ
EV
EV
EV
P
PP
ΔΔΔ
SL
SL
SL
P
PP
ΔΔΔ
SMES
SMES
SMES
P
PP
ΔΔΔ
LH2
LH2
LH2
0.1
0.1
0.1
ΔΔP/pu.
P/pu.
0.05
0.05
0.05
000
-0.05
-0.05
-0.05
-0.1
-0.1
-0.1
-0.15
-0.15
-0.15
000
10
10
10
20
20
20
30
30
30
Time/s
Time/s
Time/s
40
40
40
50
50
50
60
60
60
Figure19.
19.The
Theoutput
outputpower
powerincrement
incrementof
ofMT,
MT,EV,
EV,smart
smartloads,
loads,non-optimized
non-optimizedSMES
SMESand
andLH
LH2222storage
storage
Figure
SMES
and
LH
The
output
power
increment
of
MT,
EV,
non-optimized
storage
unitcontrolled
controlledby
byGPC.
GPC.
unit
Energies 2017, 10, 185
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14 of 20
3
2.8
2.6
Isc0/kA
2.4
2.2
2
1.8
1.6
1.4
0
20
40
60
80
100
120
Iterations Number
140
160
180
200
(a)
1.5
1.4
1.3
Lsc/H
1.2
1.1
1
0.9
0.8
0.7
0
20
40
60
80
100
120
Iterations Number
140
160
140
160
180
200
(b)
1.3
1.25
Esc0/MJ
1.2
1.15
1.1
1.05
1
0.95
0
20
40
60
80
100
120
Iterations Number
180
200
(c)
Figure 20. Iteration process, (a) initial current; (b) coil inductance; (c) initial stored energy.
Figure 20. Iteration process, (a) initial current; (b) coil inductance; (c) initial stored energy.
The optimized LIQHYSMES unit is applied to the load frequency control of the micro energy
The optimized LIQHYSMES unit is applied to the load frequency control of the micro energy grid,
grid, and the performance of the non-optimized LIQHYSMES unit is compared in Table 2.
and the performance of the non-optimized LIQHYSMES unit is compared in Table 2.
As shown in Figures 21 and 22, the
system
optimized
SC is able to maintain better frequency
Table
2. Thewith
Parameters
of SC.
stability when different load disturbances occur. As the wind power fluctuating, the output power
Parameter
Non-Optimized
SC
of SMES unit with optimized
SC increases
comparing SC
with Optimized
the non-optimized
SC controlled by the
I SC 023 and 24. The
1.5 kA
1.784
kAin the case with the combined
two methods as shown in Figures
tendency is the
same
LSC25 and 26. 5 H
disturbances as shown in Figures
1.241 H
ESC 0
5.625 MJ
1.975 MJ
Energies 2017, 10, 185
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Energies2017,
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15 of 20
The proposed GPC algorithm utilizes CARIMA that features an easy online identification.
Meanwhile, it can improve the robustness of the controller. However, as the high penetration rate of
As shown
shown inin Figures
Figures21
21 and
and 22,
22, the
thesystem
system with
with optimized
optimized SC
SC isisable
able toto maintain
maintain better
better
As
the DGs in the energy internet, CARIMA may result in prediction deviations for the LFC. Therefore, it
frequency
stability
when
different
load
disturbances
occur.
As
the
wind
power
fluctuating,
the
frequency stability when different load disturbances occur. As the wind power fluctuating, the
is necessary to have stochastic studies with given confidence interval in this condition.
output
power
of
SMES
unit
with
optimized
SC
increases
comparing
with
the
non-optimized
SC
output power of SMES unit with optimized SC increases comparing with the non-optimized SC
controlledby
bythe
thetwo
twomethods
methodsasasshown
shownininFigures
Figures23
23and
and24.
24. The tendency is the same in the case
controlled
Table 2. The Parameters
of SC.The tendency is the same in the case
with
the
combined
disturbances
as
shown
in
Figures
25
and
26.
with the combined disturbances as shown in Figures 25 and 26.
The
proposed
GPC
algorithm
utilizes
CARIMA
that
features
anSC
easyonline
onlineidentification.
identification.
The proposed GPC Parameter
algorithm utilizes
CARIMA SC
that features
an
easy
Non-Optimized
Optimized
Meanwhile,ititcan
canimprove
improvethe
the robustness of thecontroller.
controller.However,
However,asasthe
thehigh
highpenetration
penetrationrate
rateofof
Meanwhile,
ISC0robustness of the
1.5 kA
1.784 kA
the
DGs
in
the
energy
internet,
CARIMA
may
result
in
prediction
deviations
for
the
LFC.
Therefore,
the DGs in the energy internet,
deviations
for the LFC. Therefore,
LSCCARIMA may result
5 H in prediction1.241
H
necessarytotohave
havestochastic
stochastic
studieswith
with
given
confidenceinterval
interval
thiscondition.
condition.
ESC0 studies
5.625
MJconfidence
1.975 MJ
ititisisnecessary
given
ininthis
-3
-3
x x1010
1010
GPC
with
unoptimized
SMES
GPC
with
unoptimized
SMES
with
unoptimized
SMES
PIPI
with
unoptimized
SMES
GPC
with
optimized
SMES
GPC with optimized SMES
with
optimized
SMES
PIPI
with
optimized
SMES
88
66
Δ f/Hz
Δ f/Hz
44
22
00
-2-2
-4-4
-6-6 0
0
1010
2020
3030
Time/s
Time/s
4040
5050
6060
Figure
21.
The
frequency
deviation
only
with
wind
power
fluctuation.
Figure21.
21.The
Thefrequency
frequencydeviation
deviationonly
onlywith
withwind
windpower
powerfluctuation.
fluctuation.
Figure
0.03
0.03
GPC
with
unoptimized
SMES
GPC
with
unoptimized
SMES
with
unoptimized
SMES
PIPI
with
unoptimized
SMES
GPC
with
optimized
SMES
GPC
with
optimized
SMES
with
optimized
SMES
PIPI
with
optimized
SMES
0.02
0.02
Δ f/Hz
Δ f/Hz
0.01
0.01
00
-0.01
-0.01
-0.02
-0.02
-0.03
-0.03
00
1010
2020
3030
Time/s
Time/s
4040
5050
Figure
22.
The
frequency
deviation
with
combined
disturbances.
Figure22.
22.The
Thefrequency
frequencydeviation
deviationwith
withcombined
combineddisturbances.
disturbances.
Figure
6060
Energies
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2017,10,
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185
Energies
Energies 2017,
2017, 10,
10, 185
185
16
16of
of20
20
16
16 of
of 20
20
0.03
0.03
0.03
Δ PMT
ΔΔPPMT
MT
Δ PEV
ΔΔPPEV
EV
Δ PSL
ΔΔPPSL
SL
Δ PSMES
ΔΔPPSMES
SMES
ΔPLH2
ΔΔPPLH2
LH2
0.02
0.02
0.02
P/pu.
ΔP/pu.
Δ P/pu.
Δ
0.01
0.01
0.01
0
00
-0.01
-0.01
-0.01
-0.02
-0.02
-0.02
-0.03
-0.03
-0.030
00
10
10
10
20
20
20
30
30
30
Time/s
Time/s
Time/s
40
40
40
50
50
50
60
60
60
Figure 23. The output power increment of MT, EV, smart loads, optimized SMES and LH2 storage
Figure
23.
The
output
power
increment
MT,
smart
loads,
optimized
SMES
and
LH
Figure23.
23.The
Theoutput
outputpower
power
increment
of
MT,
EV,
smart
loads,
optimized
SMES
and
LH22 storage
storage
Figure
increment
ofof
MT,
EV,EV,
smart
loads,
optimized
SMES
and LH
unit
2 storage
unit controlled by PI only with wind power fluctuation.
unit
PI
only
power
fluctuation.
unit controlled
controlled
by
PI with
only with
with wind
wind
power
fluctuation.
controlled
by PIby
only
wind
power
fluctuation.
0.03
0.03
0.03
Δ PMT
ΔΔPPMT
MT
Δ PEV
ΔΔPPEV
EV
Δ PSL
ΔΔPPSL
SL
Δ PSMES
ΔΔPPSMES
SMES
Δ PLH2
ΔΔPPLH2
LH2
0.02
0.02
0.02
P/pu.
ΔP/pu.
Δ P/pu.
Δ
0.01
0.01
0.01
0
00
-0.01
-0.01
-0.01
-0.02
-0.02
-0.02
-0.03
-0.03
-0.030
00
10
10
10
20
20
20
30
30
30
Time/s
Time/s
Time/s
40
40
40
50
50
50
60
60
60
Figure 24. The output power increment of MT, EV, smart loads, optimized SMES and LH2 storage
Figure
24.
The
output
power
increment
MT,
smart
loads,
optimized
SMES
and
LH
Figure24.
24.The
Theoutput
outputpower
power
increment
of
MT,
EV,
smart
loads,
optimized
SMES
and
LH22 storage
storage
Figure
increment
ofof
MT,
EV,EV,
smart
loads,
optimized
SMES
and LH
unit
2 storage
unit controlled by GPC only with wind power fluctuation.
unit
by
only
power
fluctuation.
unit controlled
controlled
by GPC
GPC with
only with
with wind
wind
power
fluctuation.
controlled
by GPC
only
wind
power
fluctuation.
0.15
0.15
0.15
Δ PMT
ΔΔPPMT
MT
Δ PEV
ΔΔPPEV
EV
ΔPSL
ΔΔPPSL
SL
Δ PSMES
ΔΔPPSMES
SMES
Δ PLH2
ΔΔPPLH2
LH2
0.1
0.1
0.1
P/pu.
ΔP/pu.
ΔP/pu.
Δ
0.05
0.05
0.05
0
00
-0.05
-0.05
-0.05
-0.1
-0.1
-0.1
-0.15
-0.15
-0.15
0
00
10
10
10
20
20
20
30
30
30
Time/s
Time/s
Time/s
40
40
40
50
50
50
60
60
60
Figure25.
25.The
Theoutput
outputpower
powerincrement
increment
of
MT,
EV,
smart
loads,
optimized
SMES
and2 storage
LH2 storage
Figure
ofof
MT,
EV,EV,
smart
loads,
optimized
SMES
and LH
unit
Figure
MT,
smart
loads,
optimized
SMES
and
Figure 25.
25. The
The output
output power
power increment
increment
of
MT,
EV,
smart
loads,
optimized
SMES
and LH
LH22 storage
storage
unit controlled
by
PI combined
with combined
disturbances.
controlled
by
PI
with
disturbances.
unit
unit controlled
controlled by
by PI
PI with
with combined
combined disturbances.
disturbances.
Energies 2017, 10, 185
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17 of 20
17 of 20
0.15
Δ PMT
ΔPEV
ΔPSL
Δ PSMES
ΔPLH2
0.1
ΔP/pu.
0.05
0
-0.05
-0.1
-0.15
0
10
20
30
Time/s
40
50
60
Figure 26.
26. The
Theoutput
outputpower
powerincrement
increment
MT,
smart
loads,
optimized
SMES
and2 LH
2 storage
Figure
ofof
MT,
EV,EV,
smart
loads,
optimized
SMES
and LH
storage
unit
unit controlled
by GPC
combined
disturbances.
controlled
by GPC
with with
combined
disturbances.
7. Conclusions
Conclusions
7.
Energy storage
storage devices
devices are
are necessary
necessary in
in the
the energy
energy internet
internet due
due to
to an
an increasing
increasing number
number of
of RESs
RESs
Energy
are
adopted
in
the
future.
The
LIQHYSMES
unit
can
obtain
better
economic
benefits
with
promising
are adopted in the future. The LIQHYSMES unit can obtain better economic benefits with promising
applications. The
The LFC
LFC controller
controller based
based on
on GPC
GPC algorithm
algorithm is
is designed
designed and
and applied
applied to
to the
the micro
micro grid
grid
applications.
energy
including
the
LIQHYSMES
unit.
To
obtain
better
control
effect,
the
SC
parameters
are
energy including the LIQHYSMES unit. To obtain better control effect, the SC parameters are optimized
optimized
as well. Simulations
the load control
frequency
control
based
on the model
equivalent
model
the
as
well. Simulations
of the load of
frequency
based
on the
equivalent
of the
microofgrid
micro
grid
energy
are
carried
out
and
the
results
are
summarized
as
follows.
energy are carried out and the results are summarized as follows.
1.
1.
The LIQHYSMES unit can be used to achieve the energy storage and transformation in the
The LIQHYSMES unit can be used to achieve the energy storage and transformation in the energy
energy internet. It is also helpful for the efficient use of renewable energy through solving the
internet. It is also helpful for the efficient use of renewable energy through solving the frequency
frequency instability problems of the isolated micro energy grid.
instability problems of the isolated micro energy grid.
2. In the isolated micro energy grid including the LIQHYSMES unit, the proposed controller based
2. on
In the
isolated
micro
including
the LIQHYSMES
unit,
proposed
controller
based
GPC
algorithm
canenergy
obtaingrid
better
robust performance
on LFC
in the
complex
operation
situations,
on
GPC
algorithm
can
obtain
better
robust
performance
on
LFC
in
complex
operation
situations,
namely, random renewable energy generations and continuous load disturbances. It plays a
namely, random
energy generations
and continuous
load where
disturbances.
plays a
significant
role inrenewable
the load frequency
control, especially
in the cases
the loadItdemand
significant
role
in
the
load
frequency
control,
especially
in
the
cases
where
the
load
demand
changes violently in the system with RESs.
changes
violently SC
in the
system with
RESs.
3. The
optimization
parameters
of the
SMES can offer better technical advantages in alleviating
3. the
Theload
optimization
SC
parameters
of
the
SMES
can offer better technical advantages in alleviating
frequency fluctuations in different
cases.
the load frequency fluctuations in different cases.
For the future work, an experimental study which implements the proposed method in reality
will be
out. work, an experimental study which implements the proposed method in reality
Forcarried
the future
will be carried out.
Acknowledgments: The work is funded by the National Science Foundation of China (51277135, 50707021).
Acknowledgments: The work is funded by the National Science Foundation of China (51277135, 50707021).
Author Contributions: Jun Yang and Xin Wang conceived and designed the study; Xin Wang performed the
Author
Contributions:
Jun Yang the
andexperimental
Xin Wang conceived
and designed
the study;
Xin Wang
the
experiments;
Lei Chen analyzed
results; Jifeng
He contributed
analysis
tools; performed
Xin Wang and
experiments; Lei Chen analyzed the experimental results; Jifeng He contributed analysis tools; Xin Wang and Jun
Jun Yang wrote the paper.
Yang wrote the paper.
Conflictsof
ofInterest:
Interest:The
Theauthors
authorsdeclare
declareno
noconflict
conflictof
ofinterest.
interest.
Conflicts
Energies 2017, 10, 185
18 of 20
Nomenclature
∆D
Unpredictable wind power and loads variation
∆Ed
ESC0
Emax
Emin
∆f
Ht
∆Id
ISC0
J1 , J
KSMES
Kid
LSC
na
nb
nd
∆PSMES
∆PLH2
∆PMT
∆PE
∆PSL
∆PL
Deviation of the SC voltage
Initial stored energy of SC
Maximum controllable energy
Minimum controllable energy
Deviation of the frequency
Gird inertia
Deviation of the SC current
Initial current of SC
Objective functions
Gain of the SMES
Gain of the feedback in SMES
Coil inductance of SC
Prediction time domain
Control time domain
Interference time domain
Output power increment of SMES
Output power increment of LH2 storage unit
Output power increment of MT
Output power increment of EV
Output power increment of SL
Load disturbance
∆PW
Fluctuation of the wind power generation
R
R(s)
tsim
Speed regulation of MT
Input signal of LFC
Total simulation time
TDC
TEEC
Tc
TFC
Tf
Tt
Te
∆u MT
∆u1
∆u E
∆u2
∆uSL
∆u3
∆uSMES
∆u4
∆u LH2
∆u5
δSMES
δLH2
δMT
δe
δSL
µ MT
µe
W1 , W2
Converter time constant
Electrolyser time constant
Liquefier time constant
FC time constant
Governor time constant
Generator time constant
EV time constant
∆X MT
Valve position increment of the governor
ξ (t)
White noise sequence
LFC signal for MT
LFC signal for EV
LFC signal for SL
LFC signal for SMES
LFC signal for LH2 storage unit
Power ramp rate limit of SMES
Power ramp rate limit of LH2 storage unit
Power ramp rate limit of MT
Power ramp rate limit of EV
Power ramp rate limit of SL
Power increment limit of MT
Inverter capacity limit of EV
Weighting factors
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