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Wishart, Michael, Dewadasa, Manjula, Ziari, Iman, Ledwich, Gerard, &
Ghosh, Arindam (2011) Intelligent distribution planning and control incorporating microgrids. In IEEE Power Engineering General Meeting (IEEE
PES 2011), 25-29 July 2010, Detroit.
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1
Intelligent Distribution Planning and Control
incorporating Microgrids
Michael T. Wishart, Member, IEEE, Manjula Dewadasa, Member, IEEE, Iman Ziari, Student
Member, IEEE, Gerard Ledwich, Senior Member, IEEE, Arindam Ghosh, Fellow, IEEE1
Abstract-- This paper proposes a comprehensive approach to the
planning of distribution networks and the control of microgrids.
Firstly, a Modified Discrete Particle Swarm Optimization
(MDPSO) method is used to optimally plan a distribution system
upgrade over a 20 year planning period. The optimization is
conducted at different load levels according to the anticipated
load duration curve and integrated over the system lifetime in
order to minimize its total lifetime cost.
Since the optimal solution contains Distributed Generators
(DGs) to maximize reliability, the DG must be able to operate in
islanded mode and this leads to the concept of microgrids. Thus
the second part of the paper reviews some of the challenges of
microgrid control in the presence of both inertial (rotating direct
connected) and non-inertial (converter interfaced) DGs. More
specifically enhanced control strategies based on frequency
droop are proposed for DGs to improve the smooth
synchronization and real power sharing minimizing transient
oscillations in the microgrid. Simulation studies are presented to
show the effectiveness of the control.
Index Terms — Microgrid, intelligent control, power system
planning & control.
T
I. INTRODUCTION
HE planning of a reliable distribution network that
satisfies the annual load growth for the planning period is
a significant issue for distribution network companies striving
to survive in the competitive electricity market [1]. The
resulting distribution network upgrade may consist of
installation of new substations, the upgrade of existing power
system infrastructure (substations & lines) and/or the
installation of Distributed Generation (DG).
If DG forms part of the solution, then to maximize the
system reliability, the DG should be able to operate in an
islanded mode. In the islanded mode of operation the DG
supplies the local load and is disconnected from the main
supply grid. This leads to the concept of a microgrid [2] where
a number of DGs can supply local loads in an autonomous
manner.
This paper considers two main aspects:
• The optimal planning of distribution systems.
• The control of the resulting DG (microgrid) network.
1
M. Wishart, M. Dewadasa, I. Ziari, G. Ledwich & A. Ghosh are with
Queensland University of Technology, Brisbane, Australia (e-mails:
mike.wishart@gmail.com,
i.ziari@qut.edu.au,
j.dewadasa@qut.edu.au,
a.ghosh@qut.edu.au, g.ledwich@qut.edu.au)
Firstly, an integrated planning method is presented for
determining the optimal solution for rural distribution systems
given a certain load duration curve and growth parameters.
The method considers DGs, capacitors and transformer and
line upgrades in its solution to minimize the total lifetime cost
of the system. Since all the solution technologies are available
in discrete ratings, the planning problem is discrete in nature
which causes the objective function to have a number of local
minima. Heuristic methods are commonly used for solving
this type of optimization problem. This paper employs Particle
Swarm Optimization (PSO) as a heuristic method [3, 4]. Due
to the discrete nature of the objective function, a Discrete PSO
(DPSO) is used. To further mitigate the local minimum
problem, a Modified DPSO (MDPSO) is presented in this
paper. In this approach, the diversity of the optimizing
variables is increased using the Genetic Algorithm (GA)
mutation and crossover operators to avoid trapping in local
minima. The objective is to minimize the lifetime cost over the
planning period including line loss and the reliability costs
along with the investment needed in DGs, capacitors, lines,
and transformers. The bus voltage, feeder current, and the DG
output power are incorporated in the optimization procedure
as constraints.
Secondly, since the solution contains DG, issues for control
of a microgrid need to be addressed. Power management
strategies are vital for an autonomous microgrid in the
presence of multiple DG units, where no single dominant
energy source is present to supply the energy requirement [5].
Additionally, fast and flexible power control strategies are
necessary to damp out transient power oscillations [6]. Many
researchers have addressed the operational, control and
protection issues in microgrids [7-11]. The real and reactive
power output of a generator can be independently controlled
by changing the voltage angle (based on frequency) and the
magnitude respectively [12, 13]. Therefore, frequency and
voltage droop controls are the most common methods used to
share the real and reactive load power in a microgrid. In this
paper, following issues are addressed under microgrid
operation, control and protection.
1. Steady state control and power sharing.
2. Transients between converter interfaced DGs.
3. Dynamic power sharing amongst different DG types.
4. Incorporation of non-schedulable (renewable) DGs.
5. Protection of microgrid in grid connected & islanded
modes of operation.
2
Decentralized control amongst DG sources is implemented
as a simple and cost effective solution. Each DG has its own
local control for connection and disconnection from
microgrid, and controlling the real and reactive power. Both
inertial & non-inertial (converter-interfaced) DGs are
considered.
II. OPTIMAL DISTRIBUTION NETWORK PLANNING
A. Problem Formulation
The main objective of the Optimal Distribution Network
Reinforcement (ODNR) problem is to maintain (or improve)
voltage profile and avoid overloading subject to an increasing
load, all at a minimum lifetime cost. The lifetime cost includes
investment in DGs, capacitors, lines and transformers as well
as operation & maintenance (O&M), losses and reliability
cost. As constraints, the bus voltage should be kept within the
standard range and the feeder current should be maintained
lower than the rated current.
Given that all of the objective function elements are simply
converted into a composite equivalent cost, this problem can
be solved using a single-objective optimization method. This
objective function is defined as follows:
Y
OF =
1
∑ (1+ r )
y =0
y
y
y
( CCAP
+ COy & M + CLy + C Ry + CES
) + DP (1)
where OF is the objective function which is the net present
value of the total cost, CCAP is the total capital cost, CO&M is
the total operation and maintenance cost, CL is the loss cost,
CR is the reliability cost, CES is the energy saving resulted by
installing DGs, r is the discount rate, Y is the number of
planning intervals in the study timeframe, and DP is the
constraint penalty factor. The reliability cost is calculated in
equation (2) below. This cost is based on the total unsupplied
demand after an outage:
LL
C Ry = k NS × ∑ Tll × DNS lly
(2)
ll =0
where CRy is the reliability cost in the planning interval y, kNS
is the cost of energy not supplied ($/MWh), and DNSlly is the
demand not supplied in load level ll in planning interval y.
The energy saving (CES) is calculated in a similar manner to
the line loss cost.
B. Methodology
A novel sequential technique is proposed in this paper to
solve the ODNR problem for a planning time framework as
the load demand is growing. The proposed algorithm is shown
in Fig. 1.
As observed in this flowchart, the algorithm is composed
of two parts. In the first part, an initialization is performed.
The second part is the main body of the procedure. Further
details can be found in [15].
Fig. 1. Flowchart of the proposed technique.
C. Implementation of MDPSO
PSO was introduced originally by Kennedy and Eberhart in
1995 [14]. In this optimization method, the algorithm uses a
population of individuals in parallel to search possible areas of
a multi-dimensional space for the optimal solution. The
individuals are called particles and the population is called a
swarm [4].
As the discrete version of PSO, DPSO is applied to discrete
problems like ODNR where part or all of the particles have an
integer value. During this procedure, the optimal solution can
be achieved by rounding off the actual particle value to the
nearest integer value during the iterations. In [4], it is
mentioned that this rounding off process does not influence
the performance of DPSO. The particle which is composed of
the optimizing variables was chosen as shown in Fig. 2.
The discrete nature of the planning problems like ODNR
causes a number of local minima in the objective function. As
a result of this, a modification is applied to the employed
DPSO to avoid trapping in local minima. For this purpose,
the diversity of the optimizing variables is increased by
employing GA mutation and crossover operators [15] to 50%
of the population. This reference [15] also indicates that this
optimization method is more robust and accurate compared
with some other optimization methods, such as pure DPSO,
GA, Simulated Annealing, and Discrete Nonlinear
Programming.
3
period of planning. Fig. 4 summarizes the incremental costs
for each planning period. It can be seen that the DGs are
added in the first planning period, while the transformer is
upgraded in period 3, lines are upgraded in periods 3 & 4 and
capacitors are added in all periods. The reliability cost
indicates that the reliability and losses are maintained
reasonably constant throughout the 20 year planning horizon.
Fig. 2. The structure of a particle.
D. Results
The 18-bus IEEE distribution system [16, 17] is used to
validate the proposed optimization method. A five load level
characteristic is used to approximate the load duration curve
in this paper, helping to decrease the computation time. It is
assumed that the load is at the peak level for 0.05% of the
total time. The load level is 81.25% of the peak level for
4.95% of the total time, 62.5% for 35% of the total time, 50%
for 35% of the total time, and 37.5% for 25% of the total time.
The characteristics of the test system are listed in Table I.
The prices in this table are in the same range as [18-20] and
show a similar trend to the generating electricity cost from
different types of technology in the UK [21]. The transformer
upgrade cost is calculated based on a constant cost (e. g. labor
cost) which is assumed to be $100000 and an additional cost
based on the installation cost of the new and old HV/MV
transformers and related facilities. It is assumed that the old
transformer and associated facilities can be sold at half price.
It is assumed that the loads, located in the distribution
network, are growing every five years during the planning
period. Therefore, each planning interval is 5 years and four
periods, each lasting 5 years, are defined (i.e. giving a 20 year
lifetime). The results corresponding to this scenario are
summarized in Figs. 3 & 4. Fig. 3 shows the test system
configuration after planning in the last period. The red
elements in this figure illustrate the optimal location of
capacitors and DGs and the required line upgrades in the last
Fig. 3. The test system configuration after planning in the last time interval.
7
10
Period 4
Period 3
Period 2
Period 1
6
10
Cost ($)
TABLE I
CHARACTERISTICS OF THE TEST SYSTEM
Parameter
Value
$50000+$550/kVA
DG Installation Cost
¢11.4/kWh
DG O&M Cost
300 kVA
DG Base Unit
$3000+$35/kvar
Capacitor Installation cost
$1/kvar
Capacitor O&M Cost
300 kvar
Capacitor Base Unit
Line Upgrading Cost
(120000+30000×ΔLT)/km
$2000/km
Line O&M Cost
Load Growth
40% per planning interval
for Peak Load Level
Load Growth
13% per planning interval
for Other Load Levels
0.07
r
20 years (4 planning intervals)
Y
0.01 (fault/km.yr)
Failure Rate
30 minutes
DG Time
30 minutes
Switching Time
180 minutes
Repair Time
5
10
4
10
3
10
Line
Transformer
Capacitor
DG
Cost Elements
Loss
Reliability
Energy Saving
Fig. 4. A summary of the optimization results.
The optimal solution clearly includes DGs. To maximize
reliability, these DGs must be able to operate in autonomously
as a microgrid. Indeed, this has been implicit in the calculation
of the reliability cost. The next section explores the issues
related to autonomous microgrid operation, control and
protection. Control strategies are proposed using frequency
and voltage droop control to improve the dynamic power
sharing in a hybrid microgrid.
III. OPERATION, CONTROL AND PROTECTION OF MICROGRIDS
A. Steady State Control and Power Sharing
Real and reactive load power sharing amongst DGs in a
microgrid can be achieved using frequency and voltage droop
control respectively. In a conventional frequency droop
control, each DG uses the frequency at its point of connection
(PC) to determine the amount of real power injection required.
Thus, the system frequency will act as the communication
signal amongst the DGs to share the real power appropriately.
4
The conventional frequency droop characteristic can be
expressed as [12, 22-23]
f ∗ = f r + m × ( Pr − P ∗ )
(3)
where f* is the instantaneous frequency setting for a DG
considered, fr is the rated frequency of the system, Pr is the
rated real power output of the generator and P* is the
measured actual real power output of the DG. The droop
coefficient is denoted by m.
Moreover, the output voltage magnitude of a DG can be
controlled to change the reactive power supplied to the
system. However, in the presence of multiple DGs,
maintaining a voltage to a pre-defined value can cause
reactive power circulation amongst the sources. The best
solution to solve this problem is to implement voltage droop
control in DGs. This voltage droop results in reactive load
power sharing in the microgrid. The conventional voltage
droop control characteristic is given by [12, 22]
V ∗ = Vr + n × (Qr − Q∗ )
(4)
where V* is the instantaneous voltage magnitude setting, Vr is
the rated voltage of the microgrid system, Qr is the rated
reactive power output of the generator and Q* is the measured
actual reactive power output. The voltage droop coefficient is
denoted by n.
B. Transient between converter interfaced DGs
High gain in droop control improves the power sharing
however can lead to transient oscillations. To minimize the
transient oscillations, it is desirable to have low transient
gains. Angle based droop control is proposed in [24] for a
microgrid which consists of several converter interfaced DGs.
The author compared the performance of angle droop to
conventional frequency droop and it was shown that angle
droop control can minimize the real power fluctuations during
load changes in a microgrid. The proposed angle droop can be
applied to a converter interfaced DG microgrid to share the
real power amongst DGs. Power sharing accuracy can be
increased by selecting the output inductance of converters to
be inversely proportional to DG rating.
C. Dynamic Power Sharing Amongst Different DG Types
The limitations of using conventional frequency droop
have been investigated by previous researchers. Several
drawbacks can be identified using conventional frequency
droop such as slow transient response, frequency and
amplitude deviations, and high dependency on the converter
output impedance [25]. System stability is one of the major
concerns in microgrids with higher penetration levels of DGs,
especially if high feedback gains are used to achieve proper
power sharing [23]. Also, both inertial and non-inertial DGs
can be present in a microgrid. However, the dynamic response
of inertial and non-inertial DGs is different. The non-inertial
DGs, connected through converters, can have a significantly
faster response than inertial DGs. This can lead to dynamic
sharing problems and further oscillations. To minimize these,
DGs are required to respond in a similar rate during a transient
[23].
A modified droop control characteristic is proposed to
improve the dynamic power sharing of a microgrid containing
both inertial and non-inertial DGs. This ensures that the
change of load is proportionally picked up by all the DGs. The
proposed droop control is called as “integral to system droop
line” and only implemented on converter interfaced DGs in
the microgrid. In the proposed integral to system droop line
control, steady state gain and transient gain are able to be set
independently using an integral controller. Thus, system can
respond with a medium gain during a transient event but reach
a steady state point corresponding to a high gain. Once an
appropriate time constant is selected for the integrator,
converter interfaced DGs can respond in a similar manner to
inertial DGs. This results in a smooth transition to system
steady state. To implement the proposed droop, the frequency
droop in (3) is modified by introducing an integration process
to the DG to reach the steady state frequency droop point in
the system. The error between calculated droop frequency in
(3) and frequency at the PC is passed through an integrator to
force the operating frequency of DG to reach the steady state
droop point within a defined time period. The proposed
method has the ability not only to minimize transient
instability but also improve the proper power sharing amongst
DG sources. Moreover, the proposed droop allows using high
gain in steady state droop since it is providing sufficient
flexibility avoiding transient instability. The proposed droop
control is given by
∫
f d = f ∗ + ( f ∗ − f pc ) dt
(5)
where fd is the modified droop frequency for the DG, f* is the
droop given in (3) and fpc is the frequency at PC. The time
constant of the integrator is selected according to the inertial
DG dynamics (i.e., time constant of governor) to ensure a
similar response from the non-inertial DGs in the system.
However, it is to be noted that real power injection to the
system can be controlled by changing the output voltage angle
of a converter. Therefore, an angle ( φ ) corresponding to the
frequency deviation (i.e., the amount of real power required to
inject into the system) given by (5) is calculated and used in
reference generation to the converter. For example, if output
feedback voltage control is used to control three phase
converters, the reference voltages for three phases are
generated using voltage magnitude obtained from voltage
droop and calculated angle corresponding to droop frequency
in (5). In this case, the reference for phase A can be generated
as
Va = Vm Sin(2π f pct + φ )
(6)
5
where Vm is the voltage magnitude calculated from the voltage
droop in (4), fpc is the PC frequency obtained from a phase
locked loop (PLL) and φ is the angle corresponding to droop
frequency fd in (5). All the converter interfaced DGs are
controlled using the proposed modified droop control to
enhance better dynamic power sharing amongst inertial and
non-inertial sources in a microgrid during a transient event.
To demonstrate the concepts, consider the microgrid
system shown in Fig. 5, which includes both inertial and noninertial sources. The dynamic power sharing before and after
implementing integral to system droop line control in
converter interfaced DGs is investigated. It is assumed that
DG1 is a diesel generator (i.e., an inertial), while DG2 and
DG3 are converter interfaced DGs (i.e., non-inertial). Three
impedance type loads, load1, load2 and load3 are connected
at buses 2, 4 and 6 respectively. The system parameters are
given in Table II.
fluctuations during both DG synchronization and load
changes.
Fig. 6. Real and reactive power sharing with conventional droop.
Fig. 5. Diesel generator with two converter interfaced DGs
TABLE II
SYSTEM PARAMETERS
System data
Value
System frequency
50 Hz
System voltage
0.415 kV rms (L-L)
DG1 (diesel) power rating
(12 + j 8) kVA
DG2 power rating
(15 + j 10) kVA
DG3 power rating
(10 + j 6.7) kVA
Feeder impedance (Z12=Z23)
(0.025+ j 1.2566) Ω
Load impedance
load1=load2
(15+ j 11.781) Ω
load3
(5+ j 3.92) Ω
Frequency droop coefficient
DG1
m1=0.0417 (Hz/kW)
DG2
m2=0.0333 (Hz/kW)
DG3
m3=0.050 (Hz/kW)
Voltage droop coefficient
DG1
n1=1.5 (V/kVAR)
DG2
n2= 1.2 (V/kVAR)
DG3
n3=1.79 (V/kVAR)
As a reference, the system response is investigated by
implementing the conventional frequency droop in all DGs
during synchronization and load changes. It is assumed that
DG1 is connected to the system supplying load1 and load2
operating in conventional frequency and voltage droop in (3)
and (4). DG2 and DG3 are then synchronized to the system at
3.5 s and 6.5 s respectively. Subsequently, load3 is connected
to the system at 9 s. Finally, load1 and load3 are disconnected
from the system at 12 s.
The real and reactive load power sharing amongst DGs is
shown in Fig. 6. DG2 and DG3 start to inject real power after
their connection. The results show frequency and real power
Fig. 7. The variation of PC frequency of each DG.
These oscillations are due to the different dynamic response
of inertial and non-inertial DGs. Thus, the system takes
several cycles to reach steady state. The variation of the PC
frequency of each DG is shown in Fig. 7.
Next, the same system is simulated by applying the
proposed droop in (5) to converter interfaced DGs. The
response of the DGs is shown in Fig. 8. It is evident that there
are no power oscillations during the DG synchronization and
load changes in the system. Moreover, the real power outputs
of DGs are proportional to the generator ratings as expected
from the droop coefficient selection. Thus, the power sharing
amongst DGs is very accurate. The variation of system
frequency during this simulation is also shown in Fig. 9. This
shows that no large frequency oscillations are observed in the
system.
It can therefore be concluded that in the presence of both
inertial and non-inertial sources in a microgrid, conventional
frequency droop can initiate frequency and real power
oscillations during synchronization and load changes.
However these oscillations can be avoided by implementing
integral to system droop line control in converter interfaced
DGs. This results in improved dynamic power sharing in the
microgrid while at the same time maintaining accurate steady
state sharing.
6
Fig. 8. Real and reactive power sharing with integral to system droop line.
Fig. 9. The variation of system frequency.
D. Incorporation of Non-schedulable DGs
In this section, power management and control strategies
required to incorporate non-schedulable (renewable energy
based) DGs and battery storage into a microgrid are discussed.
The microgrid can include diesel generator(s), battery storage
(BS), wind and solar PVs. The control of microgrid should
enable the plug and play capability of DG sources, thus
maximizing the benefits of renewable based energy sources.
Decentralized control amongst DG sources is proposed as a
simple and cost effective solution. Each DG has its own local
control for connection and disconnection from the microgrid,
and for controlling the real and reactive power output.
The BS is connected to the microgrid through a converter
ensuring bidirectional power flow between microgrid and
battery. Therefore, the BS can act as either a load or a source
to absorb or inject real power into the microgrid. Also, the BS
can assist in controlling the microgrid frequency. Moreover,
the converter associated with BS has the ability to regulate the
voltage at PC by injecting reactive power into the microgrid.
The converter rating determines the maximum reactive power
injection capacity into the system. The BS is employed with
an intelligent control system (ICS) (or battery management
system (BMS)) to manage the power effectively. The ICS in
BS is continuously monitoring the state of charge (SOC) of
the battery. If the battery is not fully charged and there is
surplus power in the microgrid, the surplus power is used to
charge the batteries. The battery storage can be controlled as
“operating reserve” to supply or absorb any transient power
during changes in generation or loads within the energy limits.
For example, when the load changes in the microgrid, the BS
can react very quickly to match the load power change. The
ICS is responsible for managing the operating reserve in the
battery and controlling the battery charging and discharging.
The DGs connected through wind and PVs are controlled
using maximum power point tracking (MPPT) to enhance the
benefits of renewable energy sources. Therefore, any deficit in
load power is supplied by other dispatchable sources (i.e.,
diesel, BS) operating in frequency and voltage droop control.
Voltage control of each dispatchable DG and voltage droop
amongst DGs ensures the voltage regulation, stability and
proper reactive power sharing, avoiding reactive power
circulation in the microgrid. The proposed frequency droop
lines for BS and diesel generator are defined to ensure the
battery is charged when there is excess power available in the
microgrid.
The droop lines for the diesel generator and BS are shown
by DE and AFG respectively in Fig. 10. The line segment AF
represents the droop for battery charging while the droop for
battery discharging is represented by line FG. According to
the droop lines shown in Fig. 10, BS starts to supply the load
power once the diesel generator reaches its maximum power
output at rated frequency. However, it is to be noted that slope
of the droop line is controlled by the ICS and it can be
changed towards points H or K. The reason for proposing
adaptive droop slope is to give ICS an opportunity to enhance
the flexibility of control of the BS in the microgrid. For
example, consider the power sharing in the presence of a few
BS systems in a microgrid. In this circumstance, the slope of
the droop line can be changed according to SOC of the
batteries to enable the power sharing effectively since power
sharing according to each BS converter rating is not viable.
These control actions can be embedded in the ICS to respond
whenever required. However, when there is a surplus of
generated power (i.e. as determined using the system
operating frequency), the BS can be charged. During this
charging, the slope of the droop is selected appropriately by
the ICS. The droop line is determined using the microgrid
frequency, the required battery charging power (or current)
and the time of day.
Fig. 10. Frequency droop characteristics for BS and diesel generator.
7
The main aim of proposing an adaptive droop for charging
is to manage the battery charging effectively maintaining the
microgrid stability. The advantages of having adaptive droop
for charging are listed below.
(i) The output power of wind and PV fluctuates with time, is
difficult to predict and these intermittent sources are
controlled in MPPT. If wind and PV start to inject more
power into microgrid than the loads require, the system
frequency (fsys) starts to rise. In this circumstance, the
excess power can be used to charge the battery
appropriately by selecting the appropriate slope for the
droop. However, once the battery is fully charged, the ICS
changes the droop line such that power absorption is zero.
If the frequency increases further as a result of additional
power generation, generation shedding is implemented to
manage the frequency rise and stability of the microgrid.
(ii) The ICS of the BS continuously monitors the state of
charge and the time of day. If the peak load demand is
about to occur and the battery is not fully charged (to the
operating reserve level) the excess power available from
the diesel generator can be used to rapidly charge the
battery. To increase the rate of battery charging, the droop
line should be moved towards point B in Fig. 10.
However, the maximum charging current will be limited
by the converter rating. Alternatively, the rate of battery
charging can be decreased by moving the droop line
towards point C if required. It is to be noted that battery
charging should not violate the maximum charging current
given by battery manufacturer and charging should be
carried out according to specifications (i.e., constant
current and voltage). Also, the ICS changes the droop line
if the SOC becomes low allowing system frequency to
drop further triggering non-critical (prioritized) frequency
based load shedding.
E. Protection of Microgrid in Grid Connected & Islanded
Modes of Operation
Overcurrent (OC) protection is conventionally employed to
protect radial distribution networks due to its simplicity and
low cost [26, 27]. However, if DGs are added, several
protection issues can arise and are well documented [28-32].
The current practice of automatic DG disconnection for every
fault during loss of main grid supply drastically reduces the
DG benefits [33]. Also, the islanded operation with DGs is
usually not allowed since restoration by reclosing is difficult
and power quality within the islanded section cannot be
guaranteed [30]. However, to enhance the benefits of optimal
distribution network planning as explained in Section II,
protection issues should be solved, thus allowing both grid
connected and islanded mode operations.
Several protection schemes have been proposed and most
of them need a reliable communication medium. In [31],
protection algorithms are proposed using a neural network for
a DG connected network to locate a fault and isolate the faulty
zone. A protection scheme based on communication to a
multi-source distribution system has been proposed in [34]
while the sign of wavelet coefficients of the fault current
transient is used to locate and isolate a faulted segment in
[35]. A new inverse time admittance (ITA) relay characteristic
is proposed for DG connected networks in [36]. The ITA relay
is capable of operating without any communication between
relays in the presence of different fault currents, thus
providing the required protection for both grid connected and
islanded mode of operation.
To enhance the proposed planning in Section II and
proposed control strategies in Section III, a reliable protection
scheme can be implemented considering the aforementioned
solutions.
IV. CONCLUSION
This paper has presented an optimal approach for planning
distribution networks including Distributed Generation (DG).
A Modified Discrete Particle Swarm Optimization (MDPSO)
algorithm has been utilized to optimize the network over its
lifetime with consideration for the anticipated future load
duration curve. The resulting optimal solution is shown to
contain multiple DGs from the first 5 year planning period
onwards. To maximize reliability, some DGs must be operated
in islanded mode and this leads to the concept of a microgrid.
The paper continues by investigating operational issues of
microgrids. An “integral to system droop” control scheme is
proposed to achieve desirable steady state and dynamic
performance. Simulation results are presented to demonstrate
the effectiveness of the scheme in improving the dynamic
stability and damping system transients. A methodology for
incorporating renewable energy sources is presented using the
same form of droop control. Finally protection issues are
briefly discussed and a new inverse time admittance relay is
recommended.
Optimally designed distribution systems invariably contain
microgrids. This paper has shown pathways for successful
operation of microgrids including incorporation of renewable
sources.
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VI. BIOGRAPHIES
Michael T. Wishart (M’91) obtained a Ph.D. in Electrical Engineering from
University of Natal, South Africa in 1994. Prior to joining Queensland
University of Technology in 2009, he spent 15 years in industry in various
Research and Development roles in the Power Electronics and Power Systems
areas. This included periods working for Eskom in South Africa and for
various Schneider Electric subsidiaries in the Asia Pacific region. His research
interests are in Power Systems, Power Electronics and Intelligent Control.
Manjula Dewadasa (S’08, M’10) is working as a research associate at
Queensland University of Technology, Australia. He received the Ph.D in
Electrical Engineering from Queensland University of Technology, Australia
in 2010 and B.Sc. (Hons) in Electrical Engineering from the University of
Peradeniya, Sri Lanka in 2005. He worked as an Electrical Engineer for two
years in a leading manufacturing company before starting the PhD studies at
QUT in 2007. His interests are in fault analysis, power system protection and
distributed generation.
Iman Ziari (S’09) received his B.E. in Electrical Engineering from Sahand
University of Technology, Iran in 2000 and his M.Sc. (Eng.) degree from Iran
University of Science and Technology (IUST) in 2004. His interests are in
Distribution Network Planning, Optimization and Power Quality.
Gerard Ledwich (M’73, SM’92) received the Ph.D. in electrical engineering
from the University of Newcastle, Australia, in 1976. He has been Chair
Professor in Power Engineering at Queensland University of Technology,
Australia since 2000. His interests are in the areas of power systems, power
electronics, and controls. He is a Fellow of I.E.Aust.
Arindam Ghosh (S’80, M’83, SM’93, F’06) is Professor of Power
Engineering at Queensland University of Technology, Brisbane, Australia. He
obtained a Ph.D. in electrical engineering from University of Calgary, Canada
in 1983. Prior to joining the QUT in 2006, he was with the Dept. of Electrical
Engineering at IIT Kanpur, India, for 21 years. He is a fellow of Indian
National Academy of Engineering (INAE) and the IEEE. His interests are in
Control of Power Systems and Power Electronic devices.