Fan zhang etd

OPERATION OF NETWORKED MICROGRIDS IN
THE ELECTRCAL DISTRIBUTION SYSTEM
by
FAN ZHANG
Submitted in partial fulfillment of the requirements for the
degree of Master of Science
Thesis Advisor: Dr. Mingguo Hong
Department of Electrical Engineering and Computer Science
CASE WESTERN RESERVE UNIVERSITY
August, 2016

ii
CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
We hereby approve the thesis of
Fan Zhang
Candidate for the degree of Master of Science*
Committee Chair
Dr. Mingguo Hong
Committee Member
Dr. Kenneth Loparo
Committee member
Dr. Marija Prica
Date of Defense
May 31, 2016
*We also certify that written approval has been obtained for any
proprietary material contained therein

iii
Table of Contents
List of Tables .................................................................................................................v
List of Figures...............................................................................................................vi
Acknowledgement ........................................................................................................ix
Abstract..........................................................................................................................x
1. Introduction................................................................................................................1
1.1 Microgrids and Microgrid Community................................................................1
1.2 Electromagnetic Simulation Using the PSCAD Software ...................................4
1.3 The MGC Model and Study Outline....................................................................5
2. The Microgrid Community Model in PSCAD...........................................................7
2.1 Structure of Microgrid Community......................................................................7
2.2 MGC Components and Control Methods: .........................................................11
2.3 Hierarchical Control strategies...........................................................................24
3. Simulation Results in Grid-Connected Operation ...................................................27
3.1 AGC Deployment...............................................................................................28
3.2 Economic Dispatch ............................................................................................29
3.3 Power Quality Analysis......................................................................................32
3.4 Discussions.........................................................................................................34

iv
4. Simulation Results in Standalone Operation ...........................................................36
4.1 Intentional Islanding...........................................................................................37
4.2 Steady State Operation.......................................................................................37
4.3. Small Disturbance Stabilities ............................................................................38
4.4 Power Quality Analysis......................................................................................41
4.5 Discussions.........................................................................................................43
5. Energy Scheduling of Microgrid Community under Uncertainties.........................45
5.1 Stochastic approach on MGC energy management ...........................................45
5.2 System model.....................................................................................................48
5.4 Problem Formulation..........................................................................................51
5.5 Model Implementation.......................................................................................54
5.6 Illustration of the Results using a Two Period Example....................................55
6. Conclusion ...............................................................................................................59
Bibliography ................................................................................................................61

v
List of Tables
Table [1]. Basic parameters of synchronous generator model.................................................11
Table [2]. Basic parameters of PV array..................................................................................17
Table [3]. Basic parameters of battery model..........................................................................20
Table [4]. Active Power Setpoints and Load Adjustments......................................................30
Table [5]. Voltage per unit value at each node of MGC..........................................................38
Table [6]. Times and scales of small disturbance events.........................................................39
Table [7]. Load forecasting......................................................................................................56
Table [8]. Wind generation forecasting ...................................................................................56
Table [9]. Profit adjustment in three scenarios ........................................................................57

vi
List of Figures
Fig. 1. Example of DR island system of IEEE standard 1547.4.................................................3
Fig. 2. Structure of microgrid community .................................................................................7
Fig. 3. Structure of Microgrid A ................................................................................................8
Fig. 4. Structure of Microgrid B ................................................................................................9
Fig. 5. Structure of Microgrid C ..............................................................................................10
Fig. 6. Synchronous generator model in PSCAD ....................................................................12
Fig. 7. (a) P-f droop control. (b) Q-V droop control................................................................13
Fig. 8. Control diagram of synchronous generator model .......................................................13
Fig. 9. (a) Wind turbine with IG. (b) Wind turbine with DFIG ...............................................15
Fig. 10. (a) I-V curve of PV array. (b) P-V curve of PV array. ...............................................16
Fig. 11. Equivalent circuit of PV model ..................................................................................16
Fig. 12. PV model in PSCAD ..................................................................................................18
Fig. 13. Battery charging and discharging characteristic curve...............................................19
Fig. 14. Three phase full bridge inverter model.......................................................................21
Fig. 15. Control diagram of the inverter model .......................................................................22
Fig. 16. Simulation of AGC deployment.................................................................................29

vii
Fig. 17. Active power (MW), voltage (kV) and frequency (HZ) at microgrid community PCC.
(a) Active Power. (b) Voltage. (c) Frequency..........................................................................31
Fig. 18. Active power and voltage of microgrid A. (a) Active power interchange (negative
MW for exporting). (b) Voltage...............................................................................................31
Fig. 19. PCC Active power and voltage of microgrid B. (a) Active power interchange
(negative MW for exporting). (b) Voltage...............................................................................31
Fig. 20. PCC Active and of microgrid C. (a) Active power interchange (negative MW for
exporting). (b) Voltage (kV)....................................................................................................32
Fig. 21. BESS deployment (microgrid A) at t = 15 s and SG6 active power transient
(microgrid C) (ripples around t = 15 s). (a) BESS. (b) SG6. ...................................................32
Fig. 22. Voltage and current waveforms at microgrid community PCC. ................................33
Fig. 23. Voltage and current waveforms at microgrid PCCs. ..................................................33
Fig. 24. Current waveforms of DERs. .....................................................................................34
Fig. 25. Voltage and frequency at microgrid community PCC before and after microgrid
islanding (at t = 15 s). (a) Voltage. (b) Frequency...................................................................37
Fig. 26. System frequency and microgrid PCC voltages (in kV) during small disturbance
events; and microgrid A BESS real and reactive power deployment. (a) Frequency. (b)
Voltage at 832. (c) Voltage at 890. (d) Voltage at 844. (e) Voltage at 860. (f) BESS
deployment...............................................................................................................................40
Fig. 27. (a)(b): Voltage and frequency at 890..........................................................................41

viii
Fig. 28. (a)(b)PCC voltage and current waveforms of Microgrid A........................................42
Fig. 29. Renewable distributed generation output current in Microgrid A. (a) Wind (IG). (b)
Wind (DFIG). (c) Solar PV. (d) BESS ....................................................................................42
Fig. 30. FFT analysis of voltage and current waveforms at the microgrid PCCs. (a) and (b)
Voltage and current FFT of microgrid A. (c) and (d) Voltage and current FFT of microgrid B.
(e) and (f) Voltage and current FFT of microgrid C. (g) and (h) Voltage and current FFT at
node 852. (i) Current FFT of BESS in microgrid A. ...............................................................43
Fig. 31. Order of two stages.....................................................................................................49
Fig. 32. Scenario tree ...............................................................................................................54

ix
Acknowledgement
I would like to express a special thank you for my advisor, Prof. Mingguo Hong, for
his guidance, encouragement and patience through my research work. I benefit a lot
from our discussions on research direction, future career, writing and presentation skills,
and the most important, the way to cracking problems.
I would also like to thanks Prof. Kenneth Loparo and Prof. Marija Prica to be my thesis
committee. I appreciate your time reading my thesis and attending my defense, along
with the valuable suggestions to my research work.
At last I want to express my appreciation for my friends and family, for their support
and time helping me get through difficulties during my graduate study.

x
Operation of Networked Microgrids in the Electrical
Distribution System
Abstract
by
FAN ZHANG
The networked microgrids, or microgrid community (MGC) have been recognized as a
promising future electrical infrastructure, for its operational and economic benefits
along with unique control and challenges. Built as a practical example of IEEE Standard
1547.4, the MGC in this study contains three microgrids interconnected through the
medium voltage (MV) feeder system. To study the static and dynamic performance of
the system, a detailed electromagnetic model was constructed in PSCAD software.
Typical scenarios are simulated in both grid connected and islanded modes to
demonstrate the operational feasibility of MGC in responding to secondary control
signals, and the effectiveness of primary controls in facilitating energy transaction and
mitigating fluctuations in voltage and frequency during network disturbances. Also to
study the energy management of the MGC, a two-stage stochastic mixed integer
programming model was built in the AIMMS modeling environment. Optimal
operational decisions are solved with uncertainties in load, renewable generation and
bulk grid electricity price.

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1. Introduction
1.1 Microgrids and Microgrid Community
In recent year, the academic community, governments and the industry are awakened
to the importance of distributed generation. According to the definition by the U.S.
Department of Energy, a microgrid is “a group of interconnected loads and distributed
energy resources within clearly defined electrical boundaries that acts as a single
controllable entity with respect to the grid” [1]. The key features for microgrids include
the ability to operate in both grid connected and islanded modes, and integration of
renewable generation. Broader views of microgrids can be found in the literature where
the boundaries of microgrids are flexibly defined [2] [3]. By delivering electricity to
local demands, distributed generation incurs minimal losses as compared to the
traditional transmission and distribution (T&D) infrastructure. Microgrid, as a
regionally controllable entity of distributed energy resources (DER) and interconnected
loads, is a central pillar of today’s energy revolution.
North America is one of the world’s leading markets of microgrids. Reports show that
until 2015, 1,437 microgrid projects worldwide with more than 13 GW were identified.
With the rapid but stable growth, it is predicted that the revenues for microgrid would
grow from $4.3 billion in 2013 to $19.9 billion in 2020 [4] [5]. In this trend, North
America takes up around 66% of the global microgrid electricity, including 1,542 MW
online and more than 1,363 MW under development [6].

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The global efforts to make electric energy production more clean, safe, economic and
environmentally friendly also accelerate the growths of both research and commercially
oriented microgrid projects. The deployment of renewable resources including solar
photovoltaic (PV) arrays, wind and hydro, is able to reduce the dependency on fossil
fuel energy and thus significantly decrease the carbon dioxide emissions and other
subsides of traditional energy production. While transmission system is considered to
be inadequate and fragmented for renewable generation, microgrid, however, with less
requirements for site, could maximize the use of DERs. Additionally, integrating the
local DERs and consumption in a certain area in one microgrid system that can be
isolated from large electric grid, can shield the impacts of bulk system failures and
further increase the reliability and resiliency of distribution systems [2] [7]. In addition,
microgrids can decrease the energy costs by deploying renewable resources, and
enabling the customers’ active involvement in demand response.
According to the IEEE standard 1547.4, a hierarchy of microgrids of different capacity
can be constructed in the distribution system [3], as Fig. 1 shows, the distributed
resource islands range from feeder microgrids and substation microgrids in medium
voltage (MV) network to facility microgrids in low voltage (LV) system.
Interconnected with each other in the distribution system, the aggregation of the
microgrids can be defined as networked microgrids, or microgrid community (MGC).
MGC inherits the key features of microgrid, being able to operate in both grid connected
mode and islanded mode [8]. Also MGC is expected to fulfil similar operational and
economic capabilities as microgrids, such as to ancillary services, enhanced system

3
reliability, service to critical loads during grid outage and participation in electricity
market.
DG
Substation
feeds
Step-down
transformers
Secondary
island
Substation
bus island
Substation
island
Circuit
island
Lateral
island
N.O. Adjacent
circuit
Facility
island
Bus
N.C
.
N.C
.
N.C
.
DG
DG
DG
DG
DG DG
DG
DG
DG
DG
DG
Fig. 1. Example of DR island system of IEEE standard 1547.4
Concerns and issues mainly arise in the areas of feasibility of MGC operation and the
effectiveness of control strategies. So far, most research studies [9] [10] [11] focus on
energy management and power sharing strategies with the assumption that microgrids
are sharing a common bus, while in reality the microgrids are most likely
interconnected with the MV distribution system at different buses. Many research
studies have been mainly engaged in optimal energy transaction strategies to
accomplish economic objectives. However, the performances of the MV distribution
system in steady state operation and during dynamic events have not been adequately
studied. It is unclear whether the resultant energy schedules are operationally feasible
or not.
In this thesis, the steady state and dynamic performances of MGC will be thoroughly
studied at first through detailed electromagnetic simulation in PSCAD; and then

4
economic energy scheduling based on stochastic optimization will be performed on the
MGC. These studies intend to understand the response characteristics of MGC during
both normal periodic economic dispatch, and abrupt network events such as large load
changes and short circuit faults. Power electronic converter based generation resources,
such as PV, wind and battery energy storage systems (BESS) are fully represented with
switching models in the simulation, to reveal potential power quality issues in the MGC
such as harmonic distortions. Moreover, the stochastic optimization based energy
scheduling extends the study of MGC operation by modeling generation and load
uncertainties in MGC energy management. The future work will attempt to integrate
the various operational stages of MGC, to dynamically model short-term power
management requirements in the long-term energy scheduling studies. This research
vision is supported by the fact that in small-scale power systems, power management
and energy management are not as well decoupled as in bulk power systems.
1.2 Electromagnetic Simulation Using the PSCAD Software
In developing advanced control strategies to support reliable and economic operation
of the MGC involving the MV distribution system, it is important to perform simulation
studies as the field experiments are usually time-consuming and expensive.
The power system electromagnetic simulation tool, PSCAD/EMTDC software [12] is
the software platform we have chosen to use in our study. While steady state
simulations can be built based on average models and studied through power flows,
transient state simulation requires detailed modeling of each components to analyze the

5
whole system dynamics. The PSCAD tool enables us to evaluate the MGC steady state
and transient performances through simulations of both AC and DC sub-systems
including the power electronic converters, and hence obtain accurate descriptions of the
performance of large-scale integrated systems.
1.3 The MGC Model and Study Outline
This thesis work has created a nested microgrid community model in the MV
distribution network with three facility microgrids that each represent a remote
community, a commercial building and an industrial factory emergency backup
generation facility. Constructed based on IEEE 34 feeder grid [13] with light,
distributed and unbalanced loads, this MGC contains a rich mix of conventional power
plant representing combined heat and power (CHP) facilities, renewable energy sources
and energy storage resources. Fig. 2 shows the structure of MV distribution system and
the location of three microgrids. It is assumed that the hierarchical control and
communication infrastructures for power and energy management have already existed
at each level of the network, including the MGC and each individual microgrid. Also
the fast response primary controls are implemented at local devices to react to the
changes in operating condition and adjusting setpoints requirements.
The main goal of the first part of the thesis, is to validate 1) the operational feasibility
of MGC under static and dynamic conditions, and 2) the effectiveness of local primary
controls in facilitating energy transactions while maintaining network security in
voltage and frequency, such as responding to secondary control signals like economic

6
dispatch, and operational mode transitions. Then the second part will focus on the
optimal energy scheduling to meet the economic objectives.
This thesis is organized into 6 Chapters. Chapter 1 discusses the background of
microgrid and microgrid community, and presents an overall introduction to the
research work. Chapter 2 describes the PSCAD electromagnetic model of microgrid
community, and provides the detailed model of synchronous generators, wind and solar
generation units, and battery management system as well as their local primary control
strategies. Chapters 3 and 4 perform simulation case studies on the MGC in both grid
connected mode and islanded mode to validate the system operational feasibility and
effectiveness of primary and secondary controls in supporting economic dispatch and
power sharing upon abrupt load changes. Chapter 5 constructs a two-stage stochastic
mixed integer programming model to perform the optimal energy scheduling for the
MGC. Finally, Chapter 6 presents the conclusion and future work.

7
2. The Microgrid Community Model in PSCAD
2.1 Structure of Microgrid Community
In this thesis, the networked microgrids is constructed within 24.9 kV IEEE 34 bus test
feeder as described in [13], which represents the practical MV distribution network of
U.S. state of Arizona. The three microgrids in the community are located in remote
sections downstream from node 832, which is also defined as the point of common
coupling (PCC) of MGC, and the distance between each two are far enough so that the
study in voltage regulation may not be affected by potential issues. For each microgrid
and microgrid community, the locations of PCC are selected according to their
capacities, which means the generation within one entity can serve the most demand of
its majority local customers in normal operation, and even supply power to adjacent
loads beyond its PCC if necessary. The detailed structure of MGC and each microgrid
will be described in the following.
Fig. 2. Structure of microgrid community
800
806 808 812 814
810
802 850
818
824 826
816
820
822
828 830 854 856
852
832
888 890
838
862
840
836
860
834
842
844
846
848
864
858
Microgrid B
Microgrid C
Microgrid A

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Microgrid A
Bus 890
Solar
SG3
Battery
Transmission
line
Wind 1
(IG)
Wind 2
(DFIG)
4.16
kV
25
kV
25
kV
0.6
kV
Fig. 3. Structure of Microgrid A
As shows in Fig. 3, this microgrid is connected to bus 890 and represents a remote
community with a natural gas synchronous generator, a battery energy storage system
(BESS), a solar PV and two wind generating units. The following is the list of
parameters and power management strategies of each distributed resource.
1) Synchronous generator: rated at 200 kW, this generator is implemented with real
power frequency (P-f) turbine governor droop control and Q-V droop control in
excitation system.
2) BESS: rated at 500 kW, the battery interfaced with the Microgrid A through a 4-
quadrant controlled inverter.
3) Solar arrays: rated at 210 kW, the PV generation unit is controlled by maximum
power point tracking (MPPT), and connected to Microgrid A via a DC/AC inverter.

9
4) IG and DFIG wind generating units: wind turbines with an induction generator (IG)
rated at 200 kW, and with a doubly fed induction generator (DFIG) rated at 60 kW.
Both of them are on MPPT.
The loads modeled in this microgrid are all ZIP types, referring to constant power,
constant current, and constant impedance, and the amount of critical loads is 610 kW.
Microgrid B:
Bus 844
Solar
R
L
R
L
R
L
R
L
R
L
R
L
SG1 SG2
R
L
R
L 24.9 kV
11.2 kV
0.48 kV 0.48 kV
Fig. 4. Structure of Microgrid B
Shown in Fig. 4, this microgrid is connected to bus 844, representing a commercial
building with totally over 800 kW onsite power sources. The generations include two
combined heat and power (CHP) synchronous generators, and a solar energy unit on
rooftop.
1) CHP synchronous generators: rated at 200 kW each, similar to the ones in Microgrid
A.
2) PV array: rated at 460 kW, the solar plant is on MPPT.
The critical loads in Microgrid B is 440 kW, which are all ZIP loads.

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Microgrid C:
Bus 860
R
L
SG4 SG5
R
L
R
L
R
L
R
L
SG6
R
L
R
L
24.9 kV
11.2 kV 11.2 kV
0.6 kV 0.6 kV 0.6 kV
Fig. 5. Structure of Microgrid C
Shown in Fig. 5, Microgrid C is connected to bus 860, as a backup generation in a
factory, which are commonly off line. This microgrid is consist of three combined cycle
synchronous generators, rated 200 kW each, and respond to the unit commitment and
economic dispatch signals from microgrid community and serve as the emergency
backup unit during grid outage. The amount of critical ZIP loads in the factory is 155
kW.
For steady state operation of MGC, the distributed resources are not always working at
nominal power output: the generation of renewable energies may vary according to
climate conditions such as wind speed and solar radiation, while the synchronous
generators are deployed considering supply-demand balance and operating reserves.
Thereby within each microgrids, the majority of local demands is fulfilled by the local
generations. During events or in islanded mode of operation, the DERs in three
microgrids are also able to support the loads in nearby bus, so that the balance between
demand and supply in MGC can be achieved.

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2.2 MGC Components and Control Methods:
Synchronous generator
Fig. 6 is the synchronous generator model built in PSCAD, and the basic parameters
for the generator model are listed in Table [1]. To allow the dynamic simulation of the
generator rather than simple AC source, this model construct the detailed turbine
governor, generator and excitation circuit. The governor is controlled by real power-
frequency (P-f) droop and will convert the power from other sources to the mechanical
power, and output the torque to generator. Then energy will store in the magnetic field
and flow into the stator’s circuit as the rotor moves. Another droop control, reactive
power-voltage (Q-V) droop is implemented on the exciter of the generator.
Table [1]. Basic parameters of synchronous generator model
Rated RMS line to neutral voltage 0.346 kV
Rated RMS line current 0.24 kA
Base frequency 60 Hz
Neutral series resistance 1.0e4 p.u.
Neutral series reactance 0.0 p.u.
Iron loss resistance 300 p.u.
Armature (Ra) 0.002 p.u.
Potier Reactance (Xp) 0.13 p.u.
D: Unsaturated reactance (Xd) 1.79 p.u.
D: Unsaturated transient reactance 0.169 p.u.
D: Unsaturated transient time 4.3 s
D: Unsaturated sub-transient reactance 0.135 p.u.
D: Unsaturated sub-transient time 0.032 s
Q: Unsaturated reactance (Xq) 1.71 p.u.
Q: Unsaturated sub-transient reactance 0.2 p.u.
Q: Unsaturated sub-transient time 0.05 s

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Fig. 6. Synchronous generator model in PSCAD
Droop control strategy:
Droop control is a popular and well developed techniques in power system primary
control. By investigating in models of transmission lines originally, the relationship was
found that power angle depends mainly on real power while the differences in voltage
depends on reactive power. Also from the swing equations we know that system
frequency is closely related to phase angle. Then the simplified equations that
demonstrate those behaviors was developed as follows,
(2.1)
(2.2)
In droop controls, we are able to regulate each unit of frequency and voltage by
controlling the real and reactive power flows in the network, as described in Fig. 7.
Active power regulations are always achieved by turbine governor of synchronous
machines, as mentioned before. If the frequency is less than expected, the mechanical
0 0
( )
P
f f k P P
   
0 0
( )
V
V V k Q Q
   

13
torque will increase to generate more real power for frequency recovery, the process
can be described using the curve in Fig. 7. Also if the voltage in the system is higher
than desired, the droop control in the exciter will decrease the field voltage of the
generator so that the voltage will return to normal level.
Fig. 7. (a) P-f droop control. (b) Q-V droop control.
Based on this, the control diagram for the synchronous generator of this project is
designed and described in Fig. 8. As mentioned before, the P-f droop which regulating
the real power output of the generator is installed in governor, and Q-V droop will
control the terminal voltage of AC exciter.
Fig. 8. Control diagram of synchronous generator model

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Wind energy source
Renewable energy sources (DERs), including wind and solar, have their advantages in
modernized power network, such as low pollution and low operation cost. Wind turbine
is the common device to capture wind energy and convert it into electricity. For the
normal three-blade wind turbine, the description of electrical power in related with wind
speed and characteristic of machine is formulate in equation (2.3) and (2.4), according
to [14]:
(2.3)
(2.4)
Where
W
P : output power of wind turbine (W).
W
V : wind velocity (mi/h).
B
 : blade angular velocity (rad/s).
 : tip speed ratio.
 : blade pitch angle (degrees).
P
C : power coefficient.
 : air density ( 3
/
lbf ft )
A : blade impact area ( 2
ft )
2 0.17
1
( 0.022 5.6)
2
P
C e 
  
  
3 2
1 1
2 2
W P W B P W
P AC V A C V
   
 

15
There are two main types of wind turbines based on their speed variations, the fix speed
wind turbines, and variable speed wind turbines. The former one allows the generator
to directly connect to grid as the variations in speed are relatively low, in this simulation,
we choose the Induction Generator (IG) as the model in microgrid, as shown in Fig.
9(a). And in order to improve power factor, large capacitors are also installed for large
turbines. While the later one, we choose Doubly Fed Induction Generator (DFIG) to
represent generator torque controllable wind turbines. As shown in Fig. 9(b), the stator
of the turbine is directly connected to the grid whereas the rotor to back to back PWM
converter. The electronic devices IGBTs used in the converter enable the DFIG to
control real and reactive power by controlling the excitation current.
Squirrel
cage IG
DFIG Converter
Converter
Fig. 9. (a) Wind turbine with IG. (b) Wind turbine with DFIG
Solar PV
Solar energy also gained significant popularity as a renewable source of power system
in recent years, especially in Europe, Asia Pacific and Americas [15]. For example, the
U.S. has installed 7.3 GW in 2015, which will bring to a total capacity of 27.4 GW in
solar market.
(a) (b)

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Collected from sunlight, the PV cells can convert the energy it receives to electrical
power due to the photovoltaic effect. The power and current a typical PV module
generates can be described using the characteristic curves in related with terminal
voltage, and the curve shows in Fig. 10. Below the knee point [14], the module behaves
like a constant current source, while at the other side of the knee point, it is similar to a
voltage source and will accelerate to reach zero in output power. The model for PV has
been well developed already PSCAD technical support team based on those curves in
reference [12] and [15].
Fig. 10. (a) I-V curve of PV array. (b) P-V curve of PV array.
The model was built based on the equivalent circuit as shown in Fig. 11,
SC
I
d
I sh
I
sh
R
d
V


sr
R
V


I
and the corresponding mathematical model in equation (2.5).
(2.5)
(a) (b)
Fig. 11. Equivalent circuit of PV model
0[exp( ) 1] ( )
/
sr sr
SC
C sh
V IR V IR
I I I
nkT q R
 
   

17
SC
I : photo current, related to solar radiation and cell temperature.
0
I : dark current, related to cell temperature.
q : electron charge.
k : Boltzmann constant.
C
T : cell temperature.
The basic parameters selected in this thesis are listed in Table [2]:
Table [2]. Basic parameters of PV array
Number of modules connected in series/array 20
Number of modules connected in parallel/array 20
Number of cells connected in series/module 108
Number of cells connected in parallel/module 10
Reference irradiation 1000
Reference cell temperature 25
Effective area per cell 0.01
Series resistance/cell 0.02 ohm
Diode ideality factor 1.5
Band gap energy 1.103 eV
Saturation current at reference condition per cell 1e-9 kA
Short circuit current at reference condition per cell 2.5 A
Temperature coefficient of photo current 0.001

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The PSCAD of solar PV model in PSCAD is shown in Fig. 12. The solar cells are
connected to system through DC/DC converter in which the maximum power point
tracking (MPPT) control strategy is implemented, and a DC/AC inverter which controls
voltage and real power output.
Fig. 12. PV model in PSCAD
Control Method: Maximum power point tracking (MPPT) is commonly used in controls
of renewable energy sources such as solar and wind, to maximize the output power. As
demonstrated in the curves of Fig. 10, PV arrays behave like a constant current source,
so the terminal voltage can be used to control the output power. In this way, if the solar
cell operate at a voltage below the optimal power point, or the knee point of the curve,
as the current in this area is constant, the control strategy will increase the terminal
voltage until the maximum power point reached.
The module of MPPT in this thesis implements the popular Perturb and Observe
algorithm, that is, by observing the changes in power along with increasing or
decreasing terminal voltage, it would always find the direction tuning the voltage by
comparing the new calculated power to the previous one. To achieve this algorithm, the
DC/DC converter is placed and the transistor is used to regulate the terminal voltage.

19
Battery Energy Storage
Energy storage system is a key part in microgrid, as it acts as back up energy source in
case of grid failure or outage, demonstrates seamless transition of power during events,
help other devices to ride through low voltage, provides spinning reserve services to
the microgrid and supplies power for intended or unexpected grid islanding. Battery
energy storage system (BESS) constructed in this thesis, is consist of two main part:
battery and inverter with its control circuit.
The electromagnetic battery model was developed in PSCAD [16] based on the
charging/ discharging characteristic of rechargeable batteries, shown in Fig. 13,
Fig. 13. Battery charging and discharging characteristic curve
and the model can be described by Shepherd’s equation (2.6) and (2.7).
(2.6)
(2.7)
Where
0
1
[ (1 ) ]
bat
SOC
E E K Q A exp B SOC Q
SOC

       
bat bat bat bat
V E R I
  

20
bat
E : internal voltage (V).
0
E : battery voltage constant (V).
SOC: state of charge (%).
Q: battery capacity (Ah).
A: exponential zone amplitude.
B: exponential zone time constant inverse.
bat
V : terminal voltage (V).
bat
I : bettary current (A).
bat
R : equavalent internal resistance (Ohm).
K: polarization constant, or polarization resistance.
In this thesis, the battery model was constructed and the basic parameters are set in
Table [3].
Table [3]. Basic parameters of battery model
Nominal voltage 0.9 kV
Rated Capacity 0.0065 kAh
Fully charged voltage 0.95 kV
Nominal capacity 0.006175 kAh
Internal resistance 0.46 Ohm
Nominal discharging current 0.0013 kA
Voltage at exponential point 1 kA

21
DC/AC Inverter
Inverter is a technology that convert DC power to AC system or vice versa, based on
the control of power transistors. The topology selected for this design is a full bridge
three phase configuration, Fig. 14 shows its PSCAD model, which is well developed
and commonly used in microgrids. At each bridge, a power transistor in parallel with a
diode is implemented as the switch to conduct current or turn off the branch according
to control signals sent to its gate.
Fig. 14. Three phase full bridge inverter model
In this thesis, a current-controlled voltage source inverter is accomplished to control the
real and reactive power, and the IGBTs are selected to work as the switches as their
ability to response fast and operate in high voltages. The following four quadrant P/Q
control strategy is adopted to generate the reference signal for pulse width modulation
(PWM).
Four quadrant P/Q control: 4-quadrant control strategy for the battery energy storage
system is to control the instantaneous real and reactive power independently by

22
decoupling current in d-q frame. Fig. 15 describes the control diagram, the battery, as
the voltage source, is connected to the IGBTs to the right.
Qref
- +
PI
Q
Iq_ref
-
+ PI
Iq
-
+
Vq
+
Id
ωL
Pref
- +
PI
P Id_ref
-
+ PI
Id
-
-
Vd
+
Iq
ωL
X
Y
M
P
N/D
Edc/2
X
Y
M
P
D
Q
A
B
C
Ref_a
Ref_b
Ref_c
L
C
E
dc
P/Q Iac Vac
Firing Pulse
IGBT
Fig. 15. Control diagram of the inverter model
As reference [17] describes, by decoupling the three phase current sampled from the
grid from left to d
i and q
i , we first calculate the output P and Q of this inverter in
Equation (2.8).
(2.8)
While the phase lock loop is in steady state, 0
q
V  and Equation (2.8) can be written:
(2.9)
If the setpoints for this control system can be fast tracked, dref
i and qref
i will be closely
enough to d
i and q
i , in this way ref
P P
 and ref
Q Q
 . This is the theory how we can
control the real and reactive power independently by controlling their respective
references and dealing with the DC variables of d
V , dref
i and qref
i as follows:
3
( ) [ ( ) ( ) ( ) ( )]
2
3
( ) [ ( ) ( ) ( ) ( )]
2
d d q q
d q q d
P t V t i t V t i t
Q t V t i t V t i t

   



     


3
( ) ( ) ( )
2
3
( ) ( ) ( )
2
d d
d q
P t V t i t
Q t V t i t

 



   



23
(2.10)
Also, we deduce the equations for dynamics of AC side of the VSC and get (2.11) and
(2.12).
(2.11)
(2.12)
Those equations describes the VSC model in dq-frame, while td
V and tq
V are control
inputs and sd
V , sq
V are disturbance inputs. We define d
m and q
m as the reference
signals in dq-frame for generating firing pulses, so
(2.13)
While d
u and q
u are the results of PI controllers which processing d
i and q
i
respectively, as shown in the control diagram, and described by equation (14).
(2.14)
By tuning the parameters of PI controller and sketching the control diagram, this thesis
designed the 4 quadrant control of the inverter for BESS.
2
( ) ( )
3
2
( ) ( )
3
dref ref
d
qref ref
d
i t P t
V
i t Q t
V





  


0
0
( )
( )
d
q on d td sd
q
d on q tq sq
di
L L i R r i V V
dt
di
L L i R r i V V
dt



    



      


( ) ( )
2
( ) ( )
2
DC
td d
DC
tq q
V
V t m t
V
V t m t





 


0
0
2
( )
2
( )
d d q sd
DC
q q d sq
DC
m u L i V
V
m u L i V
V



  



   


( )
( )
d
d on d
q
q on q
di
u L R r i
dt
di
u L R r i
dt

  



   



24
2.3 Hierarchical Control strategies
The hierarchical controls are essential in supporting the operation of microgrid
community. In the hierarchical control structure, the primary controls are fundamental
commonly implemented locally to control the electrical devices. Many research studies
on nested power systems such as microgrid community show that, for the secondary
controls, a two-level framework is typically used, one at the microgrid level and another
at the microgrid community level, controlled by microgrid energy management system
(M-EMS) and MGC energy management system (MC-EMS) respectively. As for the
tertiary control in this thesis, it is concerned with scheduling resources to meet load
demand over a look-ahead time period, and will be discussed in Chapter 5.
Primary control
The primary controls refer to all the local controls described in 2.2, including frequency
and voltage droop controls (P-f/Q-V) for the synchronous generators, four-quadrant P-
Q controls for the BESS units, and MPPT controls for the solar and wind generations.
These represent the most common primary control methods that are currently applied
in distributed generation technology.
Secondary control
The secondary controls typically refer to the actions taken to balance energy and
maintain scheduled frequency in a longer time than primary controls, such as AGC
(Automatic Generation Control) and economic dispatch. Implemented at both the

25
microgrid level and microgrid community levels, the secondary control functions are
performed by the microgrid Energy Management System (M-EMS) and microgrid
community Energy Management System (MC-EMS). The higher level control center,
microgrid community EMS, will schedule the dispatch signals to microgrids. The EMS
of each microgrid can be placed on the active power and voltage regulation mode, i.e.,
the P-V mode, or on the active and reactive power regulation mode, i.e. the P-Q mode.
In the P-V mode, the microgrid secondary control follows scheduled active power
interchange Pref and voltage setpoints Vref at the microgrid PCC, and dispatches the
active and reactive powers of the generation resources in the microgrid. In the P-Q
mode, the microgrid secondary control dispatches the generation resources to follow
the scheduled active and reactive power interchange setpoints Pref and Qref measured
at their PCC.
Tertiary control
Tertiary control is used to relieve the secondary control in higher level, controlling the
power flow between the microgrid community and utility grid from economical and
optimal perspectives. With the tertiary control, the MGC energy management decisions
will be made while considering uncertainties in both load demand and wind power
generation in the future, and as the mismatches between plan and real-time operation,
system security will be achieved with optimal solution.
There are many proposed algorithms that perform energy transaction scheduling
(secondary and tertiary controls) to achieve optimal economic objectives. Centralized

26
tertiary controls is implemented at the distribution system level or in MC-EMS.
Centralized, decentralized, or distributed secondary control schemes can be
implemented at the microgrid community level in MC-EMS to compute and
communicate setpoints (Pref, Qref, Vref) to the individual microgrids. Procurement and
deployment of operating reserves on DER to respond to either frequency or voltage
events in the grid system will also become important requirements for power system
operational security. Each microgrid will carry operating reserves of active and reactive
power requirements (Pres, Qres). The active and reactive power operating reserves will
be procured and deployed through instructions from the MC-EMS.
The secondary and tertiary controls will not be elaborated in great detail here. In
Chapter 3 and 4, simulation cases are studied to identify the effectiveness of primary
controls and secondary controls in microgrid community operation, and Chapter 5
discusses the energy transaction schedules of tertiary controls.

27
3. Simulation Results in Grid-Connected Operation
In this case study, the 34 bus model is connected to distribution system through the
substation, and the microgrid community, downstream of bus 832, is in grid-connected
mode of operation. All microgrids in the microgrid community work in P-Q control
mode, and the MC-EMS will assign the setpoints of real and reactive power (Pref, Qref)
to the microgrids. The M-EMS follows the dispatch instructions and deploys their P-Q
control dispatchable generation resources. Through economic dispatch within the
microgrid (as a secondary control function), E-EMS is able to response to setpoints
(Pref, Qref) change and thus regulate the power interchanges at the microgrid PCC. In
addition, the MGC is also able to provide other ancillary services, including tracking
AGC signals to support bulk system adjustments [18], and deployment of operating
reserves to the loss of generation events.
A number of network operation scenarios are simulated in the following, illustrating
the performance of microgrid community on the system stability and power quality
when tracking the AGC or economic dispatch signals. As in MV distribution network
where R/X ratio is high, large rises or drops in supply of demand may potentially lead
to voltage instability, a key goal of this study is to evaluate network voltage and
frequency stabilities during the period when the generator drastically changes its active
power output levels to track the setpoints.

28
3.1 AGC Deployment
In this scenario, the factory generation in Microgrid C is offline, and the MGC attempts
to change the real power importing for the feeder system as an AGC (automatic
generation control) request sent from the transmission system operator, with Microgrid
A and B. The 34 feeder grid covers the district area and connected to transmission
system through the transformer substation. Prior to the change, the feeder system was
importing 480 kW. The new interchange target is 400 kW (import). The MC-EMS
received the signal to adjust its power interchange at PCC from 130 kW to 50 kW, and
microgrids A and B were dispatched to increase their active reactive power generation.
As the dispatch result, microgrid A provided 87.5% and microgrid B 12.5% of the total
increase amount at 27s. Later at 32s, a new AGC setpoint was sent to the feeder system
to import 200kW more, and the MC-EMS dispatched microgrids A and B so that the
total interchange at the distribution substation is 600 kW (import).
(c) (d)
(a) (b)

29
Fig. 16 shows the simulation results. Power exchanges at the substation for the 34
feeder system is in (a), and at PCC point of MGC and Microgrid A, B are shown in (b)
~ (d). It is noted that the AGC control signals initially caused significant voltage
changes in the MGC (as measured at bus 832), but the Q-V droop in both microgrids A
and B responded and mitigate the voltage drift as shown in (e).
3.2 Economic Dispatch
The economic dispatch objectives for both the microgrids and microgrid community
are to minimize operational costs while maintaining the branch flows in both lines and
transformers within security limits.
In this simulation, all microgrid generations in A, B and C are on line. With abrupt load
changes, a series of adjustments are made to the active power setpoints of synchronous
generators and BESS units according to the economic dispatch signals in M-EMS and
MC-EMS, without voltage support from reactive power dispatch. Meanwhile, power,
voltage and current measurements are taken at key nodal locations.
(e) (f)
Fig. 16. Simulation of AGC deployment
(a) Active Power exchange from upstream transmission system, measured at
distribution substation
(b) Active power measured at PCC of MGC or node 832
(c), (d): PCC Active power exchange of Microgrids A and B (- for exporting)
(e), (f): Voltage at PCC of MGC and system frequency

30
Initially the microgrid community operated in steady-state operation with all DERs
connected online supplying power. Table [4] recorded a series of generation setpoint
and load changes. The power interchange at microgrid community’s PCC varies with
time is depicted in Fig. 17(a), where the MGC moved from initially importing 200 kW
at t = 10 s to finally exporting 120 kW at t = 65 s. The PCC voltage and system
frequency measurements are shown in Fig. 17 (b) and (c).
Table [4]. Active Power Setpoints and Load Adjustments
T (s) Device P, Q T (s) Device P, Q
15 BESS +100 kW 39 L +30 kW
22 BESS +400 kVar 39 SG5 +15 kW
26 L +24 kVA 39 SG6 +15 kW
26 SG1 +12 kW 43 SG4 +15 kW
26 SG2 +12 kW 46 SG4 +20 kW
28 L +24 kVA 50 BESS +50 kW
29 SG1 +12 kW 51 SG5 +15 kW
29 SG2 +12 kW 51 SG6 +15 kW
31.5 L +32 kW 53 SG5 +20 kW
32 SG1 +16 kW 53 SG6 +20 kW
32 SG2 +16 kW 55 SG3 +15 kW
36 L +15 kW 57 SG3 +15 kW
36 SG4 +15 kW 59 SG3 +20 kW
+: Increased power output/withdraw for generator/load;
: Decreased power output/withdraw for generator/load;
L_890: Load of microgrid A.
As for Microgrids A, Fig. 18 shows the active power interchange (exporting) and
voltage RMS value at PCC. And in Fig. 19, the active power interchange and voltage

31
profile at the PCC of Microgrid B during the economic dispatch are shown. Also at the
PCC of Microgrid C, the active power interchange and voltage profile are shown in Fig.
20. It is also noted that the quick ramp of BESS active power at t = 15 s creates transients
across the microgrid community, as shown in Fig. 21.
Fig. 17. Active power (MW), voltage (kV) and frequency (HZ) at microgrid
community PCC. (a) Active Power. (b) Voltage. (c) Frequency.
Fig. 18. Active power and voltage of microgrid A. (a) Active power interchange
(negative MW for exporting). (b) Voltage.
Fig. 19. PCC Active power and voltage of microgrid B. (a) Active power interchange
(negative MW for exporting). (b) Voltage.
(a) (b)
(c)
(a) (b)
(a) (b)

32
Fig. 20. PCC Active and of microgrid C. (a) Active power interchange (negative MW
for exporting). (b) Voltage (kV).
Fig. 21. BESS deployment (microgrid A) at t = 15 s and SG6 active power transient
(microgrid C) (ripples around t = 15 s). (a) BESS. (b) SG6.
3.3 Power Quality Analysis
Samples of the three phase voltage and current waveforms at the PCC of microgrid
community in steady-state operation is shown in Fig. 22(a) and Fig. 22(b),
representing good power quality of the MV distribution system network. And Fig. 23
shows the samples of voltage and current waveforms at PCCs of Microgrid A, B and
C, in the same period with the ones in Fig. 22. Voltages in Fig. 23 (a), (c) and (e) are
always perfect sinusoid waves as the microgrids community operate in grid connected
mode. As for the currents, small harmonics exist in Microgrid A, as shown in Fig.
23(b), as it has high penetration of renewable generations, while Microgrid C has
smooth sinusoid current wave (Fig. 23(c)) as the generation units are synchronous
generator without electronics inverter interface. Also it is noted that the differences in
current magnitude across the three phases are results of unbalanced network loads.
(a) (b)
(a) (b)

33
Fig. 22. Voltage and current waveforms at microgrid community PCC.
(a) Voltage. (b) Current.
Fig. 23. Voltage and current waveforms at microgrid PCCs.
(a) Voltage of Microgrid A. (b) Current of Microgrid A.
(c) Voltage of Microgrid B. (d) Current of Microgrid B.
(e) Voltage of Microgrid C. (f) Current of Microgrid C.
To demonstrate that the harmonic injections are mainly from the renewable generations,
we investigate the study in the current waveforms of wind farms and PV arrays in
Microgrid A. In Fig. 24, (a) refers to the current waveforms of wind turbines with IG,
(b) is the output current wind with DFIG and (c) is the ones from solar PV.
(a) (b)
(a) (b)
(c) (d)
(e) (f)

34
Fig. 24. Current waveforms of DERs.
(a) Current of wind with IG. (b) Current of wind with DFIG. (c) Current of PV arrays.
3.4 Discussions
According to the simulation results, we can conclude that the microgrid community is
able to maintain voltage and frequency stabilities when the generator outputs vary
drastically. The key issues can also be observed from the simulation case study:
1) Distributed generation power is mostly consumed by microgrid loads or network
loads that connected close to the microgrid PCC, avoiding long distance power
transmission between buses within the MGC. Distributed generation resources actively
perform voltage regulation based on local voltage measurements with weak coupling
among each other, since the dynamic interactions among the voltage regulation controls
are damped by the nearby voltage-dependent loads. This network operation feature has
positively contribute to voltage stability in distribution systems.
2) In the grid connected mode of operation, the P-f droop control and Q-V droop
control of a generation resource are decoupled from each other. Since f represents the
(a) (b)
(c)

35
frequency of bulk power system, it is not affected by local voltage variations and load
changes.
3) The parameters of Q-V droop controls on synchronous generators are critical to the
network stability. The system would be prone to voltage instability due to high values
of droop response R. In addition, the PI control of excitation voltage should be on
slower time scale than the P-f droop control of turbine governor, by reducing gain and
time constant.
4) The ramping of generation resources with power electronics interfaced, such as a
BESS unit with smart inverter, is very fast. Therefore, the ramping rate, or active power
change (increase or decrease) per ramping action, should be limited to avoid
undesirable voltage and current transients in the network.

36
4. Simulation Results in Standalone Operation
The microgrid community can also operate in the islanded mode due to intentional
islanding request or unintentional islanding event. The primary challenge for the MGC
to be disconnected from bulk system is to mitigate voltage and current transients during
the transition process. In this case, unintentional islanding is particularly more
challenging as it requires quick power outage detection and deployment of fast response
generation resources such as batteries, and the energy stored in this battery system
should also be enough to support the load demand until diesel units deploys the reserves
and compensates the power outage. During the islanding event, the power interchange
at the PCC of microgrid community should be limited, otherwise large oscillations may
occur in the islanded system. Successful transitions depend on both fast-response
deployment of the hardware equipment involved and the effective detection of
microgrid operating conditions prior to islanding. There have been many discussions in
the research literature on the strategies for unintentional islanding and blackstart of
microgrids [19]. The islanding strategy for a community of multiple microgrids is a
separate study topic by itself and will not be discussed in details here. In the following
simulation study, intentional islanding will be modeled where the microgrid community
has dispatched adequate generation to meet its local load demand before islanding took
place.

37
4.1 Intentional Islanding
Prior to islanding, the microgrid community has already bring all of its generations in
Microgrid A, B and C online and serve almost all local demand by dispatching the
generations. Therefore, the active power interchange at the microgrid community PCC
is nearly zero, while the reactive power interchange is exporting 90 kVar to the bulk
system. Fig. 25 shows the voltage and frequency around the time (t = 15 s) when the
PCC switch is opened. No significant transients are seen to be present in either voltage
or frequency.
Fig. 25. Voltage and frequency at microgrid community PCC before and after
microgrid islanding (at t = 15 s). (a) Voltage. (b) Frequency.
4.2 Steady State Operation
For the operation of islanded operation of the microgrid community, the essential
challenges are the frequency and voltage regulations through effective primary and
secondary controls. With the primary droop controls described in Chapter 2, all
synchronous generation units are placed on the P-Q control mode with both P-f and Q-
V droops. The economic dispatch of the microgrid community is expected to send real
and reactive power setpoints to the microgrids to limit the voltage and frequency
excursions at nominal levels, similar to the manner of AGC (automatic generation
(a) (b)

38
control) function of bulk power system operation. Moreover, the proper contingency
reserves (Pres, Qres) procured by the microgrids should also be considered, as they are
essential for the microgrid community to ensure both frequency and voltage security of
the islanded distribution system network in case where unexpected events occur, such
as abrupt changes in load, generation and network topology. With the absence of such
secondary control functions, this study will mainly examine the voltage and frequency
stabilities after the microgrid community has become islanded. Table [5] records the
voltage level in per unit measured at the 15 buses in the microgrid community. From
the data we could conclude that during steady state operations, the islanded microgrid
community is able to support the local demand while managing the voltage security.
To further demonstrate the system security, small disturbance events are emulated in
the following simulation case while the voltages and currents are monitored at key
nodal locations.
Table [5]. Voltage per unit value at each node of MGC
Node Voltage Node Voltage Node Voltage Node Voltage Node Voltage
832 0.9946 834 0.9955 860 0.9961 838 0.921 862 0.9959
858 0.9949 842 0.9963 836 0.9959 846 0.9968 848 0.9968
864 0.9949 844 0.9966 840 0.9958 888 0.9845 890 0.9618
4.3. Small Disturbance Stabilities
A series of small disturbances is emulated for the islanded microgrid community as
shown in Table [6]. Fig. 26 describes the responses of voltage and frequency during the
disturbances in the period from 20 second to 37 second. It shows that although voltage

39
and frequency stabilities are maintained throughout the events, the excursions can travel
outside the normal ranges. Corresponding secondary controls must be deployed on time
to adjust generation setpoints to steer frequency and voltage to within the security
ranges (e.g., 59.5–60.5 HZ for frequency and 0.95–1.05 p.u. for voltage according to
[6]), to prevent equipment damage from prolonged voltage and frequency excursions.
In this study, the BESS in microgrid A was deployed to generate 330 kVar reactive
power at t = 22 s, recovering the sagging system voltage across the microgrid
community (Fig. 26).
Table [6]. Times and scales of small disturbance events
T (s) Device P, Q T (s) Device P, Q
20 L_890 -20 kW 37 Solar_844 -20 kW
23 SGs 5, 6 20 kW 37 Load_860 -20 kW
28 L_860 -24 kW 106.5 Load_844 -52 kW
30 SGs 1, 2 24 kW 106.5 Load_844 -33 kVar
L_890: Load inside microgrid A (with PCC at node 890).
L_844: Load inside microgrid B (with PCC at node 844).
Solar_844: Solar PV inside microgrid B (with PCC at node 844).
(a) (b)

40
Fig. 26. System frequency and microgrid PCC voltages (in kV) during small
disturbance events; and microgrid A BESS real and reactive power deployment. (a)
Frequency. (b) Voltage at 832. (c) Voltage at 890. (d) Voltage at 844. (e) Voltage at
860. (f) BESS deployment.
Also a case where the voltage and frequency regulations are relying on droop controls
without MC-EMS dispatching signals is tested at 106.5 second. At 106.5s, a load in the
amount of 52kW and 33kVar was unintentionally shed at bus 844, causing small
disruptions in both voltage and frequency. The droop controls implemented in the local
generation resources quickly mitigated against the changes. The change at the amount
of 5% of total load was handled well by the system network without significant impact
to the local power quality. The abruption voltage and frequency at two key nodal
locations (bus 832, bus 890 and bus 844) are monitored and shown in Fig. 27.
(c) (d)
(e) (f)

41
Fig. 27. (a)(b): Voltage and frequency at 890.
(c)(d): Voltage and frequency of 844.
4.4 Power Quality Analysis
The voltage and current waveforms of the microgrid community in islanded operation
are also record. As shown in Fig. 28, harmonics contents in voltages and currents of the
community network system are insignificant. Also the behaviors of the DERs in power
quality are also analyzed in Fig. 29, illustrating the current waveforms from wind, solar,
and the inverter-based battery storage. The worst harmonic distortions occurred in
BESS 4-quadrant inverter current [Fig. 29 (c)]. However, as the FFT analysis of various
of degree harmonic distortion in current in Fig. 30, though the 5th
and 7th
harmonics of
the inverter output current are visibly slight higher, the harmonics in the systems are all
within security range.
(a) (b)
(c) (d)
(a) (b)

42
Fig. 28. (a)(b)PCC voltage and current waveforms of Microgrid A.
(a)(b)PCC voltage and current waveforms of Microgrid B.
(a)(b)PCC voltage and current waveforms of Microgrid B.
Fig. 29. Renewable distributed generation output current in Microgrid A. (a) Wind
(IG). (b) Wind (DFIG). (c) Solar PV. (d) BESS
(c) (d)
(e) (f)
(a) (b)
(c) (d)

43
Fig. 30. FFT analysis of voltage and current waveforms at the microgrid PCCs. (a)
and (b) Voltage and current FFT of microgrid A. (c) and (d) Voltage and current FFT
of microgrid B. (e) and (f) Voltage and current FFT of microgrid C. (g) and (h)
Voltage and current FFT at node 852. (i) Current FFT of BESS in microgrid A.
4.5 Discussions
This simulation cases have shown the feasibility of operating the microgrid community
in the islanded mode. The followings are the key observations from this study.
1) The requirement for primary control mechanism for carrying out a planned
islanding process is reduced, by minimizing the power interchange at the PCC using
through a secondary control function (such as the economic dispatch). As long as the
PCC power interchange is held at low enough levels, operational mode transition can
take place without significant voltage transients. In this case, the dynamic impacts to
a b
c d
e f
g h
i

44
the MGC are similar to that under the small disturbance, such as an abrupt load change
or loss of a small generation unit.
2) Due to the high R/X ratio of distribution lines and voltage dependent loads, the P-f
and Q-V droop controls of the generation resources are coupled during the standalone
mode of operation. The simulation results show that with well-adjusted droop control
constants, the distribution system operation can quickly settle at new equilibrium points,
thus the coupling does not lead to system voltage and frequency instability.
3) Robust system stability in the MGC can be achieved through the primary droop
controls during small disturbance events, such as synchronous generator output
ramping and abrupt load changes. The Q-V linear droop constant R of synchronous
generators must be kept at low levels, or significant voltage oscillations will occur.
4) Due to the relative small scale of the islanded MGC system, the frequency in steady-
state can easily travel outside their normal ranges due to changes in operating conditions.
Therefore more frequent deployment of secondary control functions are needed to
adjust generation setpoints in order to restore voltage and frequency to safety levels.

45
5. Energy Scheduling of Microgrid Community under
Uncertainties
5.1 Stochastic approach on MGC energy management
As discussed in Chapter 1, MGC (i.e., microgrid community) represents an important
future distribution system infrastructure that brings about a number of benefits from
the perspectives of environment, operation and economics. In the previous chapters,
we have shown the operational feasibility of MGC in the distribution system, and the
effectiveness of fast local primary control strategies and secondary controls in
facilitating power sharing, and frequency and voltage regulations. The dynamic
performance of the MGC with high penetration levels of renewables has been
demonstrated in both normal operating conditions and following grid events.
In the long-term energy management of the MGC, on the other hand, there are both
security and economics related issues due to the intermittency of renewable DERs and
uncertainties in load demand. The traditional operation planning is performed using
deterministic models over rolling planning horizon against the most up-to-date
forecasts. Planning studies based on these deterministic models may not best deal
with uncertainties, and can result in large mismatches between real-time operation and
scheduled plans. Mitigation against large planning mismatches often requires
expensive measures, leading to inefficient system operation and unnecessary costs. In
the operation planning for the small-scale microgrids, even small planning
mismatches may cause large deviations in network voltage or frequency. Therefore,

46
developing a strategy that takes into consideration the stochastic factors to achieve
optimal MGC energy management is necessary for system reliability and cost. Many
studies in stochastic optimization have emerged and strategies in solving the
uncertainties in microgrid systems have been proposed. In [20], a scenario based
stochastic programming method is used to minimize the cost of operation of a
microgrid. And in [21] a scenario generation based on Markov Chain has been
proposed to solve the energy management problem. While in [22], a generalized
Benders decomposition algorithm is developed.
In this chapter, the optimal energy scheduling under the uncertainties as a part of the
tertiary control will be studied for the microgrid community based on a two-stage
stochastic programming model. As mentioned before, the uncertainties in MGC
operation mainly come from the random mismatch in the forecasting results of
renewable generation and loads. In this study, one of the most challenging issues is to
model the stochastic elements. Many research studies assume that the randomness
follows a certain distribution. For example, in paper [23], statistical methods was used
to evaluate the randomness in wind and load; in [24] an Auto Regressive Moving
Average series from history data is applied to simulate the wind generation. In this
thesis, the continuous stochastic variables are decomposed and bundled into different
scenarios, with discrete probabilities assigned to the scenarios according to real
situations. Meanwhile, the problem formulation will take into account the
uncertainties in wind generation and load demand during the hourly study intervals,
assuming constant solar radiation and grid electricity prices in each hour. With the

47
modeling of uncertainties in wind and load demand, decisions are being made on the
commitment and economic dispatch of DGs that are backup synchronous generation
resources. In general, making planning decisions incorporating network flow and
voltage security requirements is complex as it needs to model both uncertainties and
the nonlinear power system network. Therefore, a strong assumption made in this
study is that the network impedance is small due to the small system scale. Network
flow and voltage security can be effective dealt with during real-time operation
through primary and secondary controls. To reduce the complexity of the operation
planning problem, the microgrid community is modeled as resources and loads
interconnected to a single bus.
In the following discussion, a mixed integer stochastic linear programming problem is
formulated with the following highlights:
1) A two-stage stochastic programming time series model was developed
incorporating uncertainties in renewable wind generation and load demand.
2) It minimizes the operating cost of the microgrid community by solving the
stochastic programming problem
3) Discrete scenarios are generated based on a probability distribution obtained from
the Monte Carlo method.

48
5.2 System model
According to the MGC model described previously, the MGC has three microgrids
interconnected within a MV distributions system. The goal of the optimization is to
minimize the total operating cost considering the electricity purchase from utility grid
and DERs. Considering the entire cluster of microgrids as being connected to one bus,
this model implements 6 synchronous generators, 2 wind farms, 2 solar plants, and 1
battery storage system. In the community, the maximum load demand of 1.5 MW is
served by power purchased from utility grid, as well as from the PV, wind, battery
and synchronous generators. During the daily operation of the MGC, as the essential
decision of the control center of the MGC is to make decisions on how much
electricity should be purchased from the bulk system and the hourly look-ahead
commitment and dispatch of the synchronous generators. Among the six synchronous
generators, the three in microgrid A and B are assumed to low cost combined heat and
power (CHP) units that are always on line and hence modeled as a single resource.
The other three synchronous generators in the microgrid C are assumed to be
combined cycle units and are also modeled as one aggregated resource. They can be
called on or off by unit commitment decisions.
In order to deal with the randomness, the two-stage stochastic programming model is
developed and the objective is to minimize the production cost of the MGC. The first
stage make here-and-now decisions while the wait-and-see decisions in second stage

49
represent the real time operation of MGC. Fig. 31 indicates the relationships and order
of the decisions in stage 1 and 2 in time line.
To make two-stage decisions:
Stage 1 Stage 2
Commitment
Decisions
Scenario
Realization
Dispatch
Decisions
5.3 Decision Variables and Parameters
The indices that indicating the time periods and scenarios are:
 t : Index of time period (1 to N
T ).
  : Index of scenarios (1 to N
 ).
The here-and-now decisions decision variables and parameters are, for given time
interval t:
 G
t
P : Continuous variable for planed power purchase from utility grid.
 1
DG
t
P : Continuous variable for aggregated CHP synchronous generator unit output
in microgrids A and B.
 2
DG
t
P : Continuous variable for aggregate output of combined cycle units in
Microgrid C.
Fig. 31. Order of two stages.

50
 t: Binary variable for unit commitment decision on the combined cycle units in
microgrid C.
Also the parameters are;
 W
t
P : Parameter for wind power generation forecasting.
 L
t
P : Parameter for load demand forecasting.
 G
t
 : Hour-ahead market price for energy buying from grid.
 DG
t
 : Price for operating DGs, including fuel cost.
 Start
t
 : Price of starting up the generation unit in Microgrid C.
 L
t
 : Price of selling electricity to customer.
 max
DG
P : Maximum power generation of a DG unit.
 min
DG
P : Minimum power generation of a DG unit.
Among those parameters and variables, the decision G
t
P is independent of real time
operation so that it needs to be made before any scenario realization.
The wait-and-see decision variables include, for time interval t and scenario :
 G
t
p 

 : Continuous variable for the adjustment of prescheduled power from grid,
indicating the difference between real purchases and scheduled amount in the
scenarios where the scheduled amount is not enough.
 G
t
p 

 : Continuous variable the adjustment of prescheduled power from grid,
indicating the difference between real purchases and scheduled amount in the
scenarios where the scheduled amount is more.

51
 1
DG
t
p and 2
DG
t
p  : Continuous variables for the real time outputs of synchronous
generators.
 t
 : Binary variable for unit commitment decision on the combined cycle units in
microgrid C.
And the wait-and-see parameters are:
 W
t
p  : Wind power generation.
 L
t
p  : Load demand.
 RT
t
 
: Real time market price for energy purchasing from grid.
 RT
t
 
: Real time market price for energy selling to grid.
 DG
t
 : Price for operating DGs, including fuel cost.
 Start
t
 : Price of starting up the generation unit in Microgrid C.
 L
t
 : Price of selling electricity to customer.
Ancillary variables linking the 1st
and 2nd
stage include:
 t
 : Binary variable for the status of real time power exchange at PCC point. This
variable ensures that only one action (buying electricity from grid and selling
electricity to grid) could take place in each scenario per time period.
5.4 Problem Formulation
The objective function is to minimize the total production costs in terms of 1st
and 2nd
stage decisions.

52
The objective function is to minimize the total cost in terms of 1st
and 2nd
stage
decisions.
Min 1 2
1
[ ( )]
N
T
st nd
t
C E C


 (6.1)
where , and
2 1 2
1
( ) ( )
N
nd RT G RT G DG DG DG DG Start L L
t t t t t t t t t t t t
E C p p p p p
            

       

   

           

The objective function includes the electricity power purchased from grid in 1st
stage,
and also the real time adjustments of grid power, operation and startup cost of DGs,
and the profit selling electricity to customers in 2nd
stage with the probabilities
assigned to each scenarios.
The objective function is subject to the following here-and-now constraints:
(6.2)
(6.3)
(6.4)
(6.5)
Constraint (6.2) ensures the power balance in each time period, and constraints (6.3)
and (6.4) describe the output limitations of the two DG units. Constraint (6.5)
indicates that the MGC is always importing electricity power from the utility grid.
Deterministic approaches solve the problem with one scenario representing the best
load and wind forecasts, and make optimal decisions under the single scenario. The
1st G G
t t
C P

 
1 1
0
G DG DG W L
t t t t t
P P P P P
    
1
min max
DG DG DG
t
P P P
 
2
min max
DG DG DG
t t t
P P P
 
 
0
G
t
P 

53
stochastic programming models, on the other hand, represent uncertainties with
scenarios of certain probabilistic distribution, and make here- and-now decisions by
minimizing the expected costs of stochastic events. Consequently, it can further
reduce the mismatch between the real- time operation and plan schedule and thus
minimize the expected production cost of MGC while enhancing the system service
reliability.
The followings are the wait-and-see constraints:
(6.6)
(6.7)
(6.8)
(6.9)
(6.10)
(6.11)
Constraint (6.6) and (6.7) shows the power balance in each scenarios of different time
periods, taking into consideration the total power supply from each sources and the
demand of customers. Constraints (6.8) to (6.9) act as the junction constraints
connecting 1st
and 2nd
stages and secure the limits on adjustments of real time power
exchange at the PCC point of MGC. As amount of real time power purchased from
grid may varies, constraint (6.8) and (6.9) links the adjustments in stage two to the
planned value in stage one. The ancillary variable t
 guarantee the absence of
1 1
0
G DG DG W L
t t t t t
p p p p p
    
    
G G G G
t t t t
p P p p
  
 
    
0 G G
t t t
p P
 


  
(1 ) 0
G G
t t t
P p
 
 
    
1
min max
DG DG DG
t
P p P

 
2
min max
DG DG DG
t t t
P p P
  
 
 

54
confliction in real time purchase with utility. Constraints (6.10) and (6.11) ensures the
DG units work in normal range and response to the unit commitment signal.
5.5 Model Implementation
To solve this two-stage stochastic programming problem, we first investigate efforts
in categorizing the uncertainties. In this work, the randomness are in the wind
generation and load demand, as mentioned before. Techniques will be applied to
generate the forecasting curves of wind generation and load demand based on climate
and history data, which represents the stochastic process. Due to the statistical
characteristic, the predicted scenarios can be combined and reduced based on the
probability distances, and grouped into three scenarios with equal distribution. Then
the scenario tree can be built based on this, shown as the figure. 32.
Fig. 32. Scenario tree.
We assume the stochastic scenario tree is branching in three intervals from each node
or scenario during one time slot, L for low, M for medium and H for high, based on
L
M
H
LL (p=0.09)
LM (p=0.12) ∙∙∙∙∙∙
LH (p=0.09)
ML (p=0.12)
MM (p=0.16) ∙∙∙∙∙∙
MH (p=0.12)
HL (p=0.09)
HM (p=0.12) ∙∙∙∙∙∙
HH (p=0.09)
0.3
0.4
0.3
0.4
0.3
0.3
0.4
0.3
0.3
0.4
0.3
0.3

55
the values of upper and lower bound. The starting node is also the result of
deterministic optimization, and within each interval, the probability of the value is
uniformly distributed.
The above formulation (6.1) through (6.11) has been implemented in the AIMMS
programming environment as a stochastic linear programming problem. The
stochastic programming model will be solved to optimize the production cost of the
MGC operation. Ultimately, the amount of grid power purchase will be recommended
to the MGC control center.
5.6 Illustration of the Results using a Two Period Example
In modeling the problem with two time intervals, the forecast of load demand value is
500 kW in period 1 (also stage 1) and 820 kW in period 2, while the wind generation
forecast is 70 kW in period 1 and 60 kW in period 2. These values are used to solve a
deterministic model (with a single scenario of probability 1.)
Moreover, scenario trees are generated and the expected value of these two
parameters can be found in table [7] and table [8]. Each scenario is assumed to be of
equal probability.

56
Table [7]. Load forecasting
Load demand
(kW)
1st
stage 2nd
stage
L 500 631.088
M 500 742.768
H 500 1215.328
Table [8]. Wind generation forecasting
Wind Generation
(kW)
1st
stage 2nd
stage
L 70 46.177
M 70 54.349
H 70 88.926
The AIMMS optimization model was executed where the proposed stochastic
programming model was solved using IBM commercial solver CPLEX 12.6.
In deterministic model, it is assumed that the forecast error are not considered. The
result shows that if using the deterministic approach to schedule the electricity supply,
the microgrid community will earn the profit of $332 by making the schedule as
purchasing 30 kWh in 1st
stage and 360 kWh in 2nd
stage, at the rate of $2.2/kWh in
the forward grid market. However, if uncertainties exist as shown in the three
stochastic scenarios, then the expected profit determined by deterministic model will
change due to mismatches in supply and demand, thus more electricity should be
purchase in real time market. The price in real time market is $2.5 for extra power

57
demand, and the rate of selling to customers or back to grid (if less demand than
scheduled) is $2/kWh. So the profit adjustments of deterministic decision in each
scenario, besides the purchase in forward grid market, are calculated and listed in
table [9].
Table [9]. Profit adjustment in three scenarios
Scenario Real time power purchase
G
p

 or G
p

 (kWh)
Probability
( 
 )
Calculation Profit ($)
 =L 184.911 0.3 RT G
p
 
  
 350.178
 =M 288.419 0.4 RT G
p
 
  
 143.162
 =H 726.402 0.3 RT G
p
 
  
 -916.005
Profit adjustment -$112.4833
So by applying deterministic approach, the expected total profit under three scenarios
is $219.5167.
By taking into consideration for the uncertainties in the future, the stochastic
programming approach, on the other hands, solve the scheduling problem at the
optimal profit of $281.421. To achieve this optimal solution, the result shows that in
stage one the MGC is supposed to purchase 30 kWh for stage two 363.201 kWh.
Compared with the solutions from deterministic method, this energy scheduling
method can obtain a lower operating cost, which saves $61.9043, so we can conclude
that the stochastic programming model has better solution dealing with uncertainties.
This is because that the stochastic method first considers the uncertainties in wind
generation and load demand as well as the real time market price for buying and

58
selling electricity energy, then calculates the expected wait-and-see cost in real time
operation, and accordingly adjusts the here-and-now decisions in the direction of
minimizing the total objective cost. Therefore by reducing the mismatches in the plan
and actual operation, the better decisions can be made to lower the risk of spending
extra money for uncertainties.

59
6. Conclusion
In this thesis, a PSCAD electromagnetic model of microgrid community was developed
as a practical example of IEEE standard 1547.4. Nested in IEEE 34 feeder grid, this
MGC is consist of three representative facility microgrids with high penetration of
renewable energy. We start with describing the frame of MGC, the structures of each
microgrids, the hierarchical control strategies, and the model and basic parameters of
physical devices including synchronous generator, solar arrays, wind turbine and
battery energy storage.
Based on the PSCAD model of MGC, a group of scenarios under both grid connected
mode and islanded mode were proposed, demonstrating the operation feasibility of the
system and the effectiveness of primary and secondary controls. As the result, the MGC
model is able to:
1. incorporate renewable generation,
2. respond to AGC and economic dispatch control signals,
3. provide ancillary services such as regulation reserves,
4. ensure system security in voltage and frequency under small disturbance,
5. operate standalone mode with seamless transition capability.
Then a two-stage mixed integer stochastic model was built in AIMMS software for
study the optimal energy scheduling problem of the MGC with uncertainties in load
demand and wind generation, as part of the tertiary control. In solving this optimization

60
problem, scenarios were created with equal probability based on statistic data, and
finally we get the solution to minimize the MGC operating cost.
We will continue our optimization research in the future work. Battery involvement in
reducing the operating cost, and the constraints on linearized power flows are the two
main issue we will study. As the microgrids will no longer be treated as connecting to
one bus, analysis on the power flow and voltage is necessary. Therefore, advanced
algorithms of linearizing the system constraints and enhanced decomposition method
need to be studied for solving a complex stochastic problem.

61
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