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System stability effect of large scale of EV and renewable energy deployment

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System stability effect of large scale of EV and renewable energy deployment

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Content I

SYSTEM STABILITY EFFECT OF LARGE SCALE OF EV
AND RENEWABLE ENERGY DEPLOYMENT

By
CONG HOU

A thesis submitted in partial fulfillment of
The requirement for the degree of

MASTER OF SCIENCE IN ELECTRICAL ENGINEERING
UNIVERSITY OF SOUTHERN CALIFORNIA
Viterbi School of Engineering
MAY 2015
II

ACKNOWLEDGMENT
I would like to express my sincere gratitude to the supervision of Dr.
Mohammed Beshir. He offered me this great opportunity to participate in USC
Smart Grid Regional Demonstration Project. His invaluable assistance and
guidance helped me in completing the project in time. Meanwhile I appreciate
the company and spirit support from XinYu. Finally, I am deeply indebted to
my beloved parents for their understanding, encouragement, and endless
support.

III

ABSTRACT
SYSTEM STABILITY IMPACT OF LARGE SCALE OF EV AND
RENEWABLE ENERGY DEPLOYMENT
By Cong Hou. M.S
University of Southern California
May 2015
This paper mainly focuses on the impact of large EV and renewable energy
(solar energy and wind energy) deployment on power system. This research
is based on several tests on the extended WSCC 9 bus system. In the first step
of this research, California power flow prediction by 2020, including load
flow data, EV data, PV generation data and wind generation data are made by
literature reviewing. In the next step, the WSCC 9 bus system and constructed
using software PSS/E. Then the PV , EV and wind turbine models are
developed and connected to the WSCC 9 bus system. Then the power flow
calculation, contingency analysis are conducted to validate the system steady
state stability. To test the system dynamic stability, several test cases are
developed according to the California power flow prediction data and the
dynamic behavior of the system is studied by simulating a 5-cycle bus fault.
The responses obtained from the simulation indicate that the system can keep
working in steady state when coupled with the EVs and renewable energy
according to the WECC planning standard and control theory.
IV

Table of Content

ACKNOWLEDGMENT ........................................................................... II
ABSTRACT .............................................................................................. III
Table of Content ....................................................................................... IV
CHAPTER 1 Introduction .......................................................................... 1
1.1 Background ....................................................................................................... 1
1.2 Study Purpose ................................................................................................... 5
1.3 Power System Stability ..................................................................................... 6
1.3.1 Power System Stability Introduction ...................................................... 6
1.3.2 Power System Stability Standard ............................................................ 8
1.4 Organization of This Thesis .............................................................................. 9
Chapter 2 Load Profile Definition ............................................................ 10
2.1 Daily Load ...................................................................................................... 10
2.2 EVs Load ........................................................................................................ 11
2.2.1 Total Number of Cars Prediction .......................................................... 11
2.2.2 EV Charging Level ............................................................................... 11
2.3 Photovoltaic Power Generation ...................................................................... 11
2.4 Wind Power Generation .................................................................................. 12
Chapter 3 Model Construction .................................................................. 14
3.1 WSCC 9 Bus System ...................................................................................... 14
3.1.1 Saved Cases File (*.sav) ....................................................................... 14
3.1.2 Single Line Diagram File (*.sld) .......................................................... 15
3.2 Generator Model ............................................................................................. 15
3.2.1 Generator Rotor Model ......................................................................... 16
3.2.2 Excitation System ................................................................................. 17
3.2.3 Speed Governor System ........................................................................ 18
3.3 EVs/Battery Model ......................................................................................... 18
3.3 EVs/Battery Model ......................................................................................... 19
3.5 Photovoltaic (PV) System Model ................................................................... 23
3.6 Extended Test System Construction ............................................................... 24

V

Chapter 4 System Stability Analysis ......................................................... 26
4.1 Steady State Analysis ...................................................................................... 26
4.1.1 Power Flow Calculation ........................................................................ 26
4.1.2 Contingency Analysis ........................................................................... 30
4.2 Dynamic Analysis ........................................................................................... 34
4.2.1 Dynamic Data ....................................................................................... 35
4.2.2 Dynamic Model .................................................................................... 36
4.2.3 Dynamic Simulation Setup ................................................................... 38
4.2.4 Test Case Development ......................................................................... 41
4.2.5 Dynamic Simulation Automation ......................................................... 44
Chapter 5 Conclusion and Future Work ................................................... 46
5.1 Test Case Results ............................................................................................ 46
5.2 Conclusions and Future works ........................................................................ 47
5.2.1 Conclusions ........................................................................................... 47
5.2.2 Future Work .......................................................................................... 48
Reference .................................................................................................. 49
Bibliography ............................................................................................. 51
Appendix 1 California Load Data .......................................................... - 1 -
Appendix 2 Iteration Method Characteristic ......................................... - 1 -
Appendix 3 Contingency analysis files code ......................................... - 2 -
Appendix 4 Models Data Structure ....................................................... - 4 -
Appendix 5 Extend test system dynamic data ....................................... - 9 -
Appendix 6 Test Case Bus Plot ............................................................ - 11 -

1

CHAPTER 1 Introduction
1.1 Background
Electric vehicles have become increasingly popular in California due to the high costs of
the operation of gas / diesel powered vehicles and the potential to reduce CO2 emission.
In 2013, there were about 70,000 battery electric vehicles (EVs) and 104,000 plug-in
hybrid electric vehicles (PHEVs)—small numbers compared to around 226 million
registered vehicles in the United States. Total U.S. sales of plug-in electric vehicles (PEVs)
have increased in recent years, but still represent only about 0.7% of new vehicle sales in
2014 so far, up from 0.6% in 2013 and 0.4% in 2012. California is home to almost half of
all of the nation's PEVs, but even in California, only about 5 out of every 1,000 registered
vehicles are PEVs.
[1]
The plug in electric vehicle sales in United States from 2010 to 2015
is shown below:

Fig 1.1 Plug In electric vehicles sales, United States: 2010-2015

Essentially, an electric vehicle (or EV) is any vehicle that uses an electric motor for
propulsion. Typically, these vehicles will fall into one of these categories
[2]
:
a. Hybrid Electric Vehicles
A hybrid electric vehicle uses a battery-powered electric motor to supplement its traditional
combustion engine. The addition of the electric motor helps to reduce idling and enables
the vehicle to operate with zero emissions at low speeds (typically below 40 miles per hour).
At higher speeds, the combustion engine drives the vehicle.
b. Plug-in Hybrid Electric Vehicles (PHEVs)
A plug-in hybrid vehicle is similar to a standard hybrid in that it combines an electric motor
2

with a traditional combustion engine. However, a plug-in hybrid utilizes a larger battery
that can be recharged by plugging the vehicle into an electrical outlet. The larger battery of
a PHEV and the ability to recharge by connecting to an electrical outlet increases the extent
to which electricity can be used for propulsion. This, in turn, increases the vehicle’s fuel
efficiency.
c. Battery Electric Vehicles (BEVs)
Battery electric vehicles are 100 percent electric. They include no combustion engine and
rely solely on their electric motors for propulsion. BEVs do not run on gasoline and
therefore produce zero tailpipe emissions. However, drivers of battery electric vehicles are
cautioned to remain aware of their vehicle's range. BEVs must be plugged-in to charge,
which means refueling is not as simple as pulling into the nearest gas station.

Renewable energy is generally defined as energy that comes from resources which are
naturally replenished on a human timescale such as sunlight, wind, rain, tides,
waves and geothermal heat.
[3]
Renewable energy replaces conventional fuels in four
distinct areas: electricity generation, hot water/space heating, motor fuels, and rural (off-
grid) energy services.
[4]
Climate change and global warming concerns, coupled with high
oil prices, peak oil, and increasing government support, are driving increasing renewable
energy legislation, incentives and commercialization.
[5]
The global renewable power
capacities from 2006 to 2012 is shown below:

Fig 1.2 Global renewable power capacities

The main categories renewable energy are:
a. Hydropower
Water is the leading renewable energy source used by electric utilities to produce power.
According to the National Hydropower Association, hydroelectric generating facilities
3

produced approximately 65.9% of the energy generated in the U.S. from renewable sources,
and 7% of total electricity generation. Hydropower is produced in 150 countries, with the
Asia-Pacific region generating 32 percent of global hydropower in 2010. China is the
largest hydroelectricity producer, with 721 terawatt-hours of production in 2010,
representing around 17 percent of domestic electricity use. There are now three
hydroelectricity stations larger than 10 GW: the Three Gorges Dam in China, Itaipu
Dam across the Brazil/Paraguay border, and Guri Dam in Venezuela.
[6]

Fig 1.3 is the Three Gorges Dam in China.

Fig 1.3 Three Gorges Dam on the Yangtze River in China

b. Wind Power
Airflows can be used to run wind generators. Modern utility-scale wind turbines range
from around 600 kW to 5 MW of rated power, although turbines with rated output of 1.5–
3 MW have become the most common for commercial use; the power available from the
wind is a function of the cube of the wind speed, so as wind speed increases, power output
increases up to the maximum output for the particular turbine.
[7]
Areas where winds are
stronger and more constant, such as offshore and high altitude sites, are preferred locations
for wind farms. Typical capacity factors are 20-40%, with values at the upper end of the
range in particularly favorable sites.
[8][9]
Fig 1.4 shows the Wind Farm in Oregon.

Fig 1.4 845MW Shepherds Flat wind farm in Arlington, Oregon
4

c. Solar Energy
Solar energy, radiant light and heat from the sun, is harnessed using a range of ever-
evolving technologies such as solar heating, photovoltaics, concentrated solar power, solar
architecture and artificial photosynthesis.
[10][11]

A photovoltaic system converts light into electrical direct current (DC) by taking
advantage of the photoelectric effect.
[12]
Solar PV has turned into a multi-billion, fast-
growing industry, continues to improve its cost-effectiveness, and has the most potential of
any renewable technologies together with CSP.
[13][14]
Concentrated solar power (CSP)
systems use lenses or mirrors and tracking systems to focus a large area of sunlight into a
small beam. Commercial concentrated solar power plants were first developed in the 1980s.
Fig 1.5 shows the solar power plant in San Bernardino.

Fig 1.5 354MW SEGS solar complex in San Bernardino, California

As the development of EVs and renewable energy, more and more EVs and renewable
energy facilities will be connected to the current power grid. In order to keep the power
grid stable, simulation must be done to research the system stability impact of the EVs and
renewable energy before EVs and renewable energy operating in scale.

5

1.2 Study Purpose
The main of objectives of the study are:
a. The WSCC 9 bus system and constructed using software PSS/E. Then the PV, EV and
wind turbine models are developed and connected to the WSCC 9 bus system.
b. Steady state analysis including power flow calculation and contingency analysis are
conducted to validate the system steady state stability.
c. Several test cases are developed according to the California power flow prediction data
and the dynamic behavior of the system is studied by simulating a 5-cycle bus fault to test
the system dynamic stability. A flow chat is shown below to indicate the study process.

Fig 1.6 Study process flow chat

Generator
EVs
Model
Construction
PV
WT
Steady State
Analysis
Dynamic
Analysis

Power Flow
Contingency
6

1.3 Power System Stability
1.3.1 Power System Stability Introduction
Power system stability issues have existed for about 100 years since the first power system
was invented. The definition of power system stability can be as follow
[15]
:
Power system stability is the ability of an electric power system, for a given initial
operating condition, to regain a state of operating equilibrium after being subjected to a
physical disturbance, with most system variables bounded so that practically the entire
system remains intact.
Power system stability is quite a complicated problem and influenced by many factors. The
main categories of power system stability are as follow
[16]
:

Fig 1.7 Power System Stability Categories

a. Rotor Angle Stability
Rotor Angle Stability is that after the occurrence of a disturbance, the capability to keep
the system synchronous machines operating synchronously. The system reaches
equilibrium when the electromagnetic torque is equal to the mechanical torque.
[17]

Instability will happen when the electromagnetic torque is not equal to the mechanical
torque and some machines speed increase until these machines are tripped.
[15][16]

The rotor angle stability comprises three types:
Transient Stability. Large-disturbance angle stability (Transient stability) is that the
capability of the power system to maintain angle stability after being subjected to a large
disturbance (such as a short circuit on a bus or the disconnection of a generator). Instability
problems are aperiodic and are mainly due to insufficient synchronizing torque.
[15][16]

Power System Stability
Rotor Angle Frequency V oltage
Small
Disturbance
Transient
Stability
Large
Disturbance
Small
Disturbance
Short Term Short Term Short Term
Short Term Short Term
7

Small-Signal Stability. Small-disturbance angle stability (small-signal stability) is that
the capability of the power system to maintain angle stability after being subjected to a
small disturbance. A small disturbance is that the linearized system still represents the
dynamics of the original system under this disturbance.
[15][16]

Frequency Stability. Frequency stability is that a power system capability to keep a
stable frequency after a disturbance due to an imbalance between generation and load. If
the system is frequency stable, it can maintain or restore balance between the generation
and the load with minimum load tripping.
[15]

b. Voltage Stability
Voltage stability is that the capability of keeping bus voltages within an acceptable level in
the power system (from 0.95p.u. to 1.05p.u. according to the WECC standard) after a
disturbance. When the equilibrium between the load demand and the supply from the
power system is reached, the power system can operate stably
[15]
. Voltage instability
occurs when the power consumption beyond the system generation. This could result in
tripping loads, transmission lines and/or a loss of synchronism of some generators
[18]
.
Voltage collapse arises during the final stage of voltage instability when very low bus
voltages lead to a partial or total blackout in the system
[15][16][19]
.
The voltage stability comprises the following types:
Large-disturbance voltage stability. Large-disturbance voltage stability is the capability
of maintaining bus voltages at a certain acceptable level after the system is subjected to a
large disturbance (system faults, loss of generation or presence of contingencies)
[15][16]
.
Small-disturbance voltage stability. Small-disturbance voltage stability is that the
capability of maintaining steady voltages after as small disturbance such as load variations
[15][16]
.

8

1.3.2 Power System Stability Standard
The system stability performance is verified according to the WECC/NERC Reliability
Criteria. Electric system reliability begins with planning. The NERC/WECC Planning
Standards state the fundamental requirements for planning reliable interconnected bulk
electric systems. The measurements define the required actions or system performance
necessary to comply with the standards. The guides describe good planning practices and
considerations
[20]
.
The WECC/NERC Planning Standard states that if the power system is dynamically stable,
the duration of voltage dip exceeding 20% is not exceeding 20 cycles after recovering from
the fault
[20]
. What is more, when the power system operates at steady state after recovering
from the fault, the post fault bus voltage should reach or approach the initial value. Fig 1.8
is the WECC/NERC voltage performance parameters
[20]
.

Fig 1.8 WECC/NERC voltage performance

The voltage dip and recovery voltage of test cases will be obtained to draw the conclusion.

9

1.4 Organization of This Thesis
The organization of this thesis is that: Chapter 2 describes the load profile prediction in
California by 2020 including the daily load, EVs load, PV power generation and wind
power generation. Chapter 3 mainly states the model construction such as the WSCC 9 bus
system, the generator models, the PV models, EVs models and wind turbine models.
Chapter 4 describes the system stability analysis based on the software PSS/E, including
the process and required file of steady state analysis and dynamic analysis. Moreover, the
test cases are developed according to the load profile prediction in Chapter 2. Chapter 5
tests the all cases and collects the required data then draws the conclusions.

10

Chapter 2 Load Profile Definition
In this chapter, the California load profile by 2020 will be defined including the daily load,
EVs load, PV power generation and wind power generation
2.1 Daily Load
In order to get the summer peak load and winter light load in 2020. Several materials are
taken for reference.
California Independent System Operator (ISO) is and independent, non-profit independent
system operator who oversees the California’s bulk electric power system, transmission
lines, and electricity market generated and transmitted by its member utilities. California
ISO gives everyday outlook of the load demand and supply in California.
According to the prediction load data by 2020 from California ISO, we can get a typical
summer peak load and winter light load in 2020, shown in Table 1. The complete load data
in 2020 is shown in Appendix 1.
Table 1 one summer day load and one winter day load in 2020
1 2 3 4 5 6 7 8 9 10 11 12
07.23
38660 36464 35024 34190 34291 35446 37949 42161 47410 52091 56303 59720
13 14 15 16 17 18 19 20 21 22 23 24
61971 63967 65273 65504 64968 63652 61505 59569 59087 56920 51677 45614

11.27 1 2 3 4 5 6 7 8 9 10 11 12
24931 23645 22876 22569 22897 23997 25059 26507 28407 30073 31039 31259
13 14 15 16 17 18 19 20 21 22 23 24
31262 30887 30445 30327 32172 36449 36362 35269 33839 32057 30412 27664

Then we plot the load curve using MATLAB shown in Fig 2.1.

Fig 2.1 Load Curve

11

2.2 EVs Load
2.2.1 Total Number of Cars Prediction
According to the California State Department of Motor Vehicle Statistics in 2013, we get
that the total registered is 32903847. Now (2014) the population in California is about
38million. It means that each person has a car on average. According to the forecast of
California Department of Finance, the population in California will be approximately 41.7
million. Therefore we assume that there will be 42 million cars by 2020.
2.2.2 EV Charging Level
Typically speaking, EV charging is performed at three voltage and current levels in U.S.
It is shown in Table 2.
Table 2 Charging Power Level
V oltage
(V AC)
Currents
(A)
Power
(KV A)
Frequency
(Hz)
Phase
Level 1 120 12 1.44 60 Single
Level 2 208/240 32 6.7/7.7 60 Single
Level 3 480 400 192 60 Three

The level 2 charging is most widely used and is chosen to calculate the EV load. The Table
3 shows the total EV load at different penetration levels.
Table 3 EV Load at Different Penetration Levels
Total Number of Cars by 2020 is 42 million
Penetration Level Number of EV(Million) Power (MW)
2% 1.0 7700
5% 2.5 19250
10% 5 38500
15% 7.5 57750

2.3 Photovoltaic Power Generation
Solar power in California has been growing rapidly because of high insolation, community
support, declining solar costs, and a Renewable Portfolio Standard which requires that 25%
of California's electricity come from renewable resources by 2016, and 33% by
2020.
[21]
Much of this is expected to come from solar power. In 2011, California's goal to
install 3,000 MW by 2016 was expanded to 12,000 MW by 2020
[22]
.
Fig 2.2 shows the California Photovoltaic Power Generation from 1999 to 2020.
12

Fig2.2 California Photovoltaic Power Generation

Therefore the Photovoltaic power generation by 2020 in California is 12000MW.
2.4 Wind Power Generation
Wind power in California refers to the 5829MW if wind powered electricity generating
capacity operating within California as of June 2013
[23]
. The Fig 2.3 shows the wind power
generation in California from 2001 to 2013
[24]
. According to the North American Electric
Reliability Corp (NERC) report, the wind power generation is expected to rise to
18000MW in 2020
[25]
.

Fig 2.3 California wind power generation
7 9 15 30 58 96 140 198
279
449
768
1022
1564
2559
5183
12000
0
2000
4000
6000
8000
10000
12000
14000
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2020
MW
Year
1534 1544 1571
2064 2064 2064 2064 2064 2184
3019 2992
4966
6204
18000
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2020
MW
Year
13

So the wind power generation in California by 2020 is 18000MW.
Overall, the load profile in California by 2020 is summarized in Table 4. And the data will
be used to construct the model and develop the test cases in the following Chapters.
Table 4 California load profile in 2020
MW
Daily load 65504
EV load 57750
PV power generation 12000
Wind power generation 18000
Note: EV load is at 15% penetration level.

14

Chapter 3 Model Construction
In this chapter, the WSCC 9 bus system is constructed using PSS/E. What is more, the
generators models, EVs model, PV model and wind turbine model are developed and
connected to the base WSCC 9 bus system. Fig 3.1 shows the model construction flow chat.

Fig 3.1 Models construction flow chat

3.1 WSCC 9 Bus System
This WSCC 9-bus test case represents a simple approximation of the Western System
Coordinating Council (WSCC) to an equivalent system with nine buses and three
generators.
The system base voltage levels are 13.8 kV, 16.5 kV, 18 kV, and 230 kV. The line complex
powers are around hundreds of MVA each. As a test case, the WSCC 9-bus case is easy to
control, as it has few voltage control devices.
[26]

Building the system in PSS/E, the saved cases file and single line diagram file are created
to represent the system.
3.1.1 Saved Cases File (*.sav)
PSS/E always operates on the work cases, and the required data is necessary to load into
the work cases and then conduct the simulation. Saved cases files are binary files contain
the system data such as bus information, connection situation, transformer data, machines
data and so on. Each saved cases is a complete power flow description
[27]
. Fig 3.2 shows
one saved case file open in PSS/E.
Base WSCC 9 Bus System
Model
Construction
PV
Wind
Turbine
Generator
EV/Battery
15

Fig 3.2 Saved case file in PSS/E

3.1.2 Single Line Diagram File (*.sld)
This file (*.sld) allows for performing network analysis studies on the grid. Sliders are
visual displays of the grid. It includes buses, branches, lines, loads, generators,
transformers etc... All components should be color coded based on voltage flow. The slider
file can also show the operational ratings (power flowing across the component relative to
the capacity) of the listed components.
According to the data file we can draw the single line diagram showing in Fig 3.3.

Fig 3.3 WSCC 9 bus system single line diagram

According to the Chapter 2, the California daily load in 2020 is about 65000MW so we
can assume that the bus represents the California. And 1MW in the diagram is equal
1000MW in reality.
3.2 Generator Model
The PSS/E generator models range from the simplest to complicated representations of the
16

synchronous machine. All models share certain common features, however. All generator
models ultimately present the electric transmission network with a positive sequence
source voltage where instantaneous amplitude and phase are known and where current is
to be determined. Although, physically, a generator is generally represents as a voltage
source behind the step-up transformer and an impedance, it is represented in PSS/E by a
Norton equivalent in which the voltage source is converted by an equivalent current source,
ISORCE
[28]
. Fig 3.4 shows the Norton source current.

Fig 3.4 Norton source current

Rotor flux linkage transients and magnetic saturation are the principal factors affecting the
dynamic behavior of synchronous machines in the perturbation frequency bandwidth, 0 to
about 10 Hz, covered by PSS/E dynamic simulation. The magnitude and phase of the
source current are determined at any instant as a function of the instantaneous values of
generator state variables (i.e., rotor circuit flux linkages, shaft speed, and rotor angle). The
value of the generator’s effective dynamic impedance, ZSORCE, may be either its transient
or sub-transient impedance, depending on which dynamic model is used to represent the
behavior of rotor circuit flux linkages
[28]
.
3.2.1 Generator Rotor Model
The PSS/E models library contains a group of generator models which represent different
types of generators and dynamic performance. The rotor model chosen is GENROU.
GENROU represents solid rotor generators at the sub-transient level. Fig 3.5 shows the
GENROU electromagnetic control model
[28]
.
17

Fig 3.5 GENROU electromagnetic control model.

3.2.2 Excitation System
The basic approaches to the excitation of large generators are as follows:
a. Rotating dc exciter
b. Rotating ac
c. Excitation power from generator terminals
The excitation system in all above cases consists of a high power source of direct current,
an intermediate power level controlling circuit, and an instrument power level voltage
18

regulator which determines the operation of the excitation system.
The chosen excitation system is the IEEE recommended dc exciter, IEEET1. Models
IEEET1 is widely used to represent systems with shunt dc exciters as well as systems with
alternator exciters and uncontrolled shaft-mounted rectifier bridges
[28]
.
3.2.3 Speed Governor System
The turbine-governor models are used to simulate the effect of the power plant on power
system stability and they represent the principal effects in conventional steam turbine,
nuclear, and hydro plants
[28]
.
The chosen IEEEG1 is the IEEE recommended general model for steam turbine speed
governing systems. By the appropriate choice of parameters, this model can be used to
represent a variety of steam turbine systems including non-reheat, tandem compound, and
cross-compound types. IEEEG1 can also approximate the behavior of hydro turbine-
governors
[28]
.
Overall, the chosen generator models comprises three modules:
a. The generator rotor model: GENROU
b. The excitation system: IEEET1
c. The speed governor system: IEEEG1
3.3 EVs/Battery Model
From the electrical perspective, the EV models can be represented as battery model. The
PSS/E library model CBEST simulates the dynamic characteristics of a battery. The
CBEST is modeled in the power flow as a generator with a large ZSORCE impedance
[28]
.
The battery system is comprised of a voltage source converter. Power into and out of the
battery is controlled by adjusting battery terminal voltage
[28]
.
The active power path in the CBEST model simulates power limitations into and out of the
battery as well as ac current limitations at the converter. The model assumes that the battery
power rating is large enough to cover all energy demands during the simulation
[28]
.
The reactive path is comprised of a voltage regulator model. Reactive current, instead of
internal voltage, is under direct control of the regulator
[28]
. Fig 3.6, 3.7, 3.8shows the
CBEST model control block
[29]
.

Fig 3.6 Active power path

19

Fig 3.7 Voltage source converter terminal voltage control

Fig 3.8 Reactive power path

3.3 EVs/Battery Model
The PSS/E model library contains WT3 wind turbine model can simulate the performance
of a doubly fed induction wind generator (DFIG), shown in Fig 3.9, with the active by a
power converter connected to the rotor terminal
[28]
.

Fig 3.9 DFIG

20

When a wind turbine generator is connected to the system and conduct the power flow
calculation, the wind control mode should be set as 2, i.e., a wind machine that controls a
remote bus voltage within the given range of reactive power [Qmin, Qmax]
[28]
.
With reference to the GE 1.5MW Wind Turbine
[34]
, the wind turbine models are comprised
of models as follows
[28]
:
a. WT3G1: generator/converter model
b. WT3E1: electrical control model
c. WT3T1: mechanical control model
d. WT3P1: pitch control model
Fig 3.10 shows the interaction between the models.

Fig 3.10 Interaction between wind turbine models

Fig 3.11 shows the WT3G1 model control block.
21

Fig 3.11 WT3G1 model

Fig 3.12 shows the WT3E1 model.

Fig 2.12 WT3E1 model

Fig 3.13 shows the WT3T1 model.
22

Fig 3.13 WT3T1 model

Fig 3.14 shows the WT3P1 model.

Fig 3.14 WT3P1 model
23

3.5 Photovoltaic (PV) System Model
The PSS/E model library contains the PV system model to simulate performance of a PV
plant connected to the power system via a power converter. The PV system model is based
on the PSS/E Wind Turbine model WT4, adding the capability of simulating the output
changes due to solar irradiance
[28]
.
When the PV system is connected to the power grid and conducting power flow calculation,
the wind control mode is set as 2, i.e. a wind machine which control a remote bus voltage
within the given range [Qmin, Qmax] of reactive power. For the most electronic devices,
the source reactance of the machine should be set: XSORCE=99999 (infinite)
[28]
.
The PV system models are comprised of models as follows
[28]
:
a. PVGU: power converter/generator
The power converter/generator module calculates the current injection to the grid based on
filtered active and reactive power commands from the electrical control module.
Fig 3.15 shows the PVGU1 model.

Fig 3.15 PVGU1 model

b. PVEU: electrical control module
[28]

The converter control module includes reactive and active power controls. The reactive
control calculates the reactive current command for the various control options.
The active power control compares the active power injected to the grid against the power
reference, and changes the active component of the injected current respectively. The
power reference is controlled by the amount of DC power coming from the PANEL module.
Fig 3.16 shows the PVEU1 model.
24

Fig 3.16 PVEU1 model

c. PANEL: linearized model of a panel’s output curve
[28]

The panel module calculates the DC power from the PV plant at a given irradiance level.
The user enters the maximum DC power a panel can produce at standard irradiance levels,
which is readily available from a PV manufacturer's I-P curves.
d. IRRAD: linearized solar irradiance profile
[28]

The IRRAD module allows the user to enter an irradiance profile in the form of up to ten
data points (time, irradiance level) as constants. At each simulation time step, the module
will calculate a linearized irradiance level. The irradiance level is initialized based on the
steady state power output.
3.6 Extended Test System Construction
After the construction of generator models, EV model, PV model and wind turbine model,
all the models will be connected to the base WSCC 9 bus system. Some modification is
done to make the system more practical.
a. Move the load from 230KV bus to 34.5 KV bus via a step down transformer (230/34.5).
As we know that the load should be connected to be distribution voltage level.
b. With reference to the GE 1.5MW wind turbine, the wind turbine is connected to 0.6KV
bus then connected to a 34.5KV POI bus via a step up transformer then connected to
another 34.5kv connect bus and connected to the 230KV bus via another step up
transformer
[28][30][32]
.
c. EVs and PV are connected to the 34.5KV bus first and connected to the 230kv bus via a
step up transformer
[28][30][33]
.
Fig 3.17 shows the extended test system.
25

Fig 3.17 Extended test system

26

Chapter 4 System Stability Analysis
In this chapter, the system stability analysis will be conducted including the steady state
analysis, dynamic analysis and test cases development. The steady state analysis includes
the power flow calculation and contingency analysis. The dynamic analysis includes the
dynamic file introduction and the model dynamic data statement. The test cases
development develops 29 cases according to the load profile definition in Chapter 2.
4.1 Steady State Analysis
Generally speaking, the steady-state analysis can include
[27]
:
a. Base case with all elements in service.
b. Single contingencies (N-1). Loss of any transmission line or transformer or generator.
c. These are often termed probable or credible contingencies.
d. Double contingencies (N-2). Simultaneous loss of two single-circuit transmission lines,
a double-circuit line or dc bipole. Variations on these contingencies exist worldwide
specifically with respect to the definition of double circuit and the option of non-
simultaneity of loss (N-1-1). These too are credible or probable contingencies.
e. Less probable contingences and/or extreme contingencies can include loss of entire
substations or multiple generators.
4.1.1 Power Flow Calculation
The most important power system simulation is power flow calculation. The power flow
calculation solve the question as follows
[27]
:
Given the load power consumption at all buses of the electric power system and the
generator power production at each power plant, what is the power flow in each line and
transformer of the interconnecting network?
The power flow problem pertains to balanced steady-state operation of the power system.
Because it considers balanced operation in which all negative- and zero-sequence voltages
are zero, the power flow calculation deals with the positive-sequence model of all system
components
[27]
.
Successful power system operation under normal balanced three-phase steady-state
conditions requires the following
[31]
:
a. Generation supplies the demand (load) plus losses.
b. Bus voltage magnitudes remain close to rated values.
c. Generators operate within specified real and reactive power limits.
d. Transmission lines and transformers are not overloaded.
The power-flow computer program (sometimes called load flow) is the basic tool for
investigating these requirements. This program computes the voltage magnitude and angle
at each bus in a power system under balanced three-phase steady-state conditions. It also
computes real and reactive power flows for all equipment interconnecting the buses, as
well as equipment losses.
27

PSS/E have the capability of power flow calculation and allows the users to choose
different iteration method
[27]
:
Table 4.1 Iteration Method
Iteration Method Activity Name
Gauss-Seidel SOLV
Modified Gauss-Seidel handles series
capacitors
MSLV
Full Newton-Raphson FNSL
Decoupled Newton-Raphson NSOL
Fixed-Slope Decoupled Newton-Raphson FDNS

Because power flow convergence properties are dependent upon network and load
attributes, each of the five iteration methods has its own strengths and weaknesses, showing
in the Appendix 2.
The process of calculating the extend test system power flow is shown in Fig 4.1 to Fig 4.4.

Fig 4.1 Choose the “Solve”

28

Fig 4.2 Choose the iteration method

29

Fig 4.3 Output information

Fig 4.4 Power flow animation diagram

As shown in Fig 4.5, the number above the line represents the active power and the number
below the line represents the reactive power. The positive icon represents injection to the
power system and negative icon represents absorption from the grid.
30

Fig 4.5 Power flow diagram instruction

4.1.2 Contingency Analysis
PSS/E has the capability of conducting the contingency analysis. Contingency analysis
covers a variety of analytical investigations performed by both system planners and
operators. The system planner’s objective is to identify the network elements that will be
required to maintain system operation within planning criteria. Single contingencies (N-1)
means loss of any transmission line or transformer or generator
[27]
.
The steps to conduct the contingency analysis are shown below:
a. Produce the Distribution Factor Data file from the subsystem file (*.sub), monitor file
(*.mon) and contingency file (*.con) (Activity DFAX).
b. Produce the Contingency Solution output file using the Distribution Factor Data files.
c. Produce the selected report using the Contingency Solution Output file

Fig 4.6 Contingency process and files

Contingency
Analysis
Contingency
(*.con)
Subsystem
(*.sub)
Monitor
(*.mon)
ACCC report
31

The required files are shown as follows:
a. Contingency file (*.con)
Programmed to remove equipment, one piece at a time from service; this is referred to as
a contingency. When the system is fully operational, it has no outages, and is referred to as
system intact or (N-0). When a single line is taken out of service, the case is then referred
to as an (N-1).
b. Monitor file (*.mon)
Tells the power flow simulator which branches to be supervised during the (N-1)
contingencies.
c. Subsystem file (*.sub)
Informs the power flow analysis to only look at a prescribed section, or zone, of the overall
network.
The code of the files are shown in Appendix 3.
The Fig 4.7~4.10 shows the contingency analysis set up using PSS/E.

Fig 4.7 Choose activity ACCC

32

Fig 4.8 Build Distribution Factor Data file using *.mon, *.con, *.sub

Fig 4.9 Choose the output file

33

Fig 4.10 Produce the ACCC report

Fig 4.11 shows the produced extended test system ACCC report.
34

Fig 4.11 Extended test system ACCC report

The monitored branch section shows all the branches that were overload due to a
contingency. The contingency section shows which branch was automatically taken out of
service by the ACCC, which resulted in an overload. The base case contingencies show the
system intact overloads before any branches were removed from service. The report shows
that the extended test system is no violations
4.2 Dynamic Analysis
The objective of conducting the dynamic analysis is to research the system dynamic
performance. It is to determine the system response after a prescribed stimuli. The stimuli
can be a bus fault, a generator outage and so on for a power system. From the mathematical
perspective, the system behavior is described by a set of differential equations. The
constants and variable parameters describe the condition of the system at that time as initial
conditions. The time derivative of each state variable in the system is calculated and from
the present value of each state variable and its rate of change (i.e. its time derivative), the
state variable values at next time step can be determined
[27]
. The set of differential
equations are determined by the system models. For the extended test system in this thesis,
the differential equations are determined by the dynamic models of generators, PV, EV and
wind turbines. Fig 4.12 shows the mathematical process of dynamic analysis.

35

Fig 4.12 Mathematical process of dynamic analysis

4.2.1 Dynamic Data
The data required to conduct the dynamic simulation are classified into four types
[27]
:
a. Constants are parameters that do not vary during the simulation.
b. State variables are quantities for which the instantaneous values are determined by
differential equations.
c. Algebraic variables are quantities for which the values at any instant can be determined
if all state variables and constants are known.
d. Input variables are quantities for which the values at any instant are specified by logic
outside of the dynamic simulation.
There are four storage arrays to store the system dynamic data
[27]
:
a. CON contains constants.
b. STATE contains state variables.
c. VAR contains algebraic variables.
d. ICON contains integer quantities which may be either constants or algebraic variables.
Corresponding to each ICON is an entry in the CHRICN array which may contain
character quantities.
These four general purpose arrays are treated as large storage bins. Each reference to a
model from the PSS/E Model Library and to a user-written model may be assigned a small
contiguous block of locations in one or more of these arrays. The block of consecutive
storage locations in these arrays is allocated for each model type (i.e., plant-related model,
load related model etc.) on a first come, first served basis
[27]
.
The filling of the model connection tables used by PSS/E supplied as well as user defined
models, then, requires enumeration of the CONs, STATEs, VARs and/or ICONs used for
each model reference
[27]
.
The number of locations that would be allocated in each of these arrays for all PSS/E
supplied models is shown in data sheets; refer to PSS/E Model Library
[27]
.
For example, the GENROU model contains the CONs, STATEs, VARs and ICONs at a
Dynamic
Simulation
Differential
equations
construction
Initial
condition
determination
Differential
equation
integration
using initial
condition
36

fixed sequence. Table 4.2 shows the each array the model contains and the parameters it
represents.

Table 4.2 GENROU model sample data
CONs # STATEs #
J 6 T’do (>0) (sec) K E’q
J+1 0.05 T’’do (>0) (sec) K+1 E’d
J+2 1 T’qo (>0) (sec) K+2 Ψkd
J+3 0.05 T’’qo (>0) (sec) K+3 Ψkq
J+4 3 Inertia, H K+4 Δspeed (pu)
J+5 0 Speed damping, D K+5 Angle (radians)
J+6 1.4 Xd
J+7 1.35 Xq
J+8 0.3 X’d
J+9 0.6 X’q
J+10 0.2 X’’d = X’’q
J+11 0.1 Xl
J+12 0.03 S(1.0)
J+13 0.4 S(1.2)

When using the GENROU model, the CONs and STATEs can be filled according to the
specific model and parameters you choose. The other model data is shown in Appendix 4.
4.2.2 Dynamic Model
In chapter 3, the generator model, EV model, PV model and wind turbine model are chosen
from the PSS/E library generally. However the parameters of each model are still need to
be determined. In section 4.2.1, we know that each model has its own parameters list and
fixed sequence. When we do the dynamic analysis, the general models are appeared in the
saved case file and the specific parameters need to be upload using the dynamic file (*.dyr).
The model dynamic file (*.dyr) file is the data sheet recoding the model parameters in order.
The logic record should follow the general format
[27]
:
a. Device models and protection models
BUSID ’model name’ data list /
b. Miscellaneous model of type “other”
BUSID ’model name’ data list /
Where:
BUSID Is the bus identifier (number or extended bus name) of the bus at which this
equipment model is to be placed (from bus for a line relay model), a dc line or FACTS
device name (in single quotes), area, zone, or owner number, or zero.
'model name' Is the name by which the model is referenced. The model name is limited
to 16 characters, and must be enclosed in single quotes.
37

data list Specifies the constant parameters associated with the model. Generally, these
parameters are specified in the order in which constants are listed on the data sheets in the
blocks labeled ICONs and CONs.
The data list of the models are shown below
[29]
:
a. GENROU
IBUS, ’GENROU’, ID, CON(J) to CON(J+13) /
b. IEEET1
IBUS, ’IEEET1’, ID, CON(J) to CON(J+13) /
c. IEEEG1
IBUS, ’IEEEG1’, ID, JBUS, M, CON(J) to CON(J+19) /
d. CBEST
IBUS, ’CBEST’, ID, CON(J) to CON(J+11) /
e. PVGU1
IBUS ‘USRMDL’ ID ‘PVGU1’ 101 1 0 9 3 3 CON(J) to CON(J+8) /
f. PVEU1
IBUS ‘USRMDL’ ID ‘PVEU1’ 102 0 4 24 10 4 ICON(M) to CON(M+3) CON(J) to
CON(J+23) /
g. PANELU1
IBUS ‘USRMDL’ ID ‘PANELU1’ 103 0 0 5 0 1 CON(J) to CON(J+4) /
h. IRRADU1
IBUS 'USRMDL' ID 'IRRADU1' 104 0 1 20 0 1 ICON(M), CON(J) to CON(J+19) /
i. WT3G1
IBUS, ’WT3G1’, ID, ICON(M), CON(J) to CON(J+4) /
j. WT3E1
IBUS, ’WT3E1’, ID, ICON(M) to ICON(M+5), CON(J) to CON(J+30) /
k.WT3T1
IBUS, ’WT3T1’, ID, CON(J) to CON (J+7) /
l.WT3P1
IBUS, ’WT3P1’, ID, CON(J) to CON (J+8) /
After collecting data from some materials according to the practical experience, the
dynamic data is shown in Appendix 5.

38

4.2.3 Dynamic Simulation Setup
The procedures of doing the dynamic simulation shown in Fig 4.13.
Fig
4.13 Dynamic simulation procedures

The operation in PSS/E is shown in Fig 4.16~Fig 4.21.
a. Power flow calculation.
Load the saved case and solve the power flow as the initial condition.

Fig 4.16 Solve power flow

b. Convert the generators
The generator and induction machine conversion activity CONG initializes the in-service
machines in the working case in preparation for dynamic simulation calculations.
39

Fig 4.14 Convert the generators

Fig 4.17 Convert load and generators

c. Convert Load
The load conversion activity CONL converts the constant MVA load for a specified
grouping of network loads to a specified percentage for constant current or constant
admittance load characteristics of the existing constant MVA load.

Fig 4.15 Convert Load

d. ORDR
Perform the optimal ordering function, setting up an internal ordering of system buses to
40

optimize the sparsity of the Jacobian and triangularized admittance matrices used in the
network solution activities.

Fig 4.18 Order network

e. FACT&TYSL
These steps give a refinement of the power flow solution to obtain the smallest possible
mismatch at all buses.
The triangular factorization activity FACT decomposes the network admittance matrix (Y
matrix) into its upper and lower triangular factors for use in the triangularized Y matrix
network solution (activity TYSL) or in the network balance of dynamic simulations.

Fig 4.19 FACT and TYSL

f. Read raw format dynamic data.
g. Choose the monitored channels
Choose the parameters you want to monitor and get the waveforms. For example, the all
buses voltage and angle can be chosen monitoring.
41

Fig 4.20 Choose channels

h. Start Run.

Fig 4.21 Run the simulation

4.2.4 Test Case Development
a. Test cases
According to the Chapter 2, the California load profile by 2020 is shown in Table 4.3.
Table 4.3 2020 California load profile
MW
Daily load 65504
EV load 57750
PV power generation 12000
Wind power generation 18000

42

We has made the assumption in Chapter 3 that 1MW in the extended test system is equal
to 1000MW in practice and several approximation is made so that the Table 4.4 shows the
load profile in the extended system.
Table 4.4 Extended system load profile
MW
Daily load 71
EV load 50
PV power generation 12
Wind power generation 18

In order to research the system stability impact of the EVs and renewable energy coupled
with the power grid, the test cases are shown in Table 4.5. The development of test cases
also satisfies the control variable method.

43

Table 4.5 Test cases
Case# EV PVG WTG
Case0 0 0 0
Case1a 10 0 0
Case1b -10 0 0
Case2a 20 0 0
Case2b -20 0 0
Case3a 30 0 0
Case3b -30 0 0
Case4a 40 0 0
Case4b -40 0 0
Case5a 50 0 0
Case5b -50 0 0
Case6 0 0 9
Case7 0 0 18
Case8 0 6 0
Case9 0 12 0
Case10a 50 0 18
Case10b -50 0 18
Case11a 50 12 0
Case11b -50 12 0
Case12a 10 12 18
Case12b -10 12 18
Case13a 20 12 18
Case13b -20 12 18
Case14a 30 12 18
Case14b -30 12 18
Case15a 40 12 18
Case15b -40 12 18
Case16a 50 12 18
Case16b -50 12 18

Note that the EVs positive number means operating in the mode V2G, injecting power to
the grid and the negative number means operating in the mode G2V, absorbing power from
the grid.
Each case is the combination of different number of EVs load, PV generation and wind
turbine generation.
b. Event.
When doing the dynamic analysis, the system can be simulated according to an event. The
event we choose is:
44

System operates to 5 second and works in steady state. Then fault happens at bus 5 and
lasts for 5 cycles. System operates to 25 second after fault is cleared. The timeline is shown
below:

Fault happens at bus 5
Operates to 25s

0s 5s Fault cleared at 5.083s
Fig 4.22 Timeline

4.2.5 Dynamic Simulation Automation
Program automation in PSS/E provides the mechanism to control PSS/E execution other
than by direct user interaction. This is the ability to define a set of operations for PSS®E
to perform in a file of some kind (explained in the following sections) and to tell PSS/E to
use the instructions in that file as a single command
[27]
.
There are several automation processors to control the PSS/E dynamic analysis
[27]
:
 An embedded Python interpreter
 The Batch (or BAT_) Command interpreter
 The Line Mode Interpreter (LMI)
 The PSS® E Engineering Basic (PSEB) macro processor
 The PSS® E Simulation Run Assembler (PSAS) macro processor
 The IPLAN simulator
In this thesis we use embedded Python to fulfill the dynamic analysis automation so that
we do not need to click different buttons many times.
The PSS/E has the capability of recording the API commands in the form of Python
statement. We just need to use the I/O Control>Start>Recording … option and choose the
Python file (*.py) before we take each step to do the dynamic analysis shown Fig 4.23.

45

Fig 4.23 Recording

After conduction the dynamic analysis, the Python file (*.py) has recorded all the operation
and the file can be used when you want to do the same simulation again. Taking the base
case for example, the Python code is shown below:

Fig 4.24 Python code for case0

46

Chapter 5 Conclusion and Future Work
5.1 Test Case Results
After conducting the dynamic simulation according to the different test cases, the output
file (*.out) can be obtained and get the all buses voltage plots. The bus 5 voltage will be
observed.
According to the WECC/NERC criteria in Chapter 1, the maximum voltage dip and the
ratio of final voltage and initial voltage of bus 5 voltage are recorded. Fig 5.1 shows the
case0 bus 5 voltage plot.

Fig 5.1 Case0 bus 5 voltage

The bus 5 voltage plot indicates that the system operates at steady state until 5s and bus
fault results that the voltage decreases to 0. After the fault is cleared, the system recovery
to steady state experiencing oscillation. The bus 5 voltage plot is shown in Appendix 6.
The test case results is shown in Table 5.1.

47

Table 5.1 Test cases results
Case# EV PVG WTG Vdipmax(%) Vrec(%)
Case0 0 0 0 0.51 100
Case1a 10 0 0 0.41 99.9
Case1b -10 0 0 0.30 99.9
Case2a 20 0 0 0.41 100
Case2b -20 0 0 0.30 100
Case3a 30 0 0 0.30 100
Case3b -30 0 0 0.41 100
Case4a 40 0 0 0.41 100
Case4b -40 0 0 0.41 100
Case5a 50 0 0 0.30 99.9
Case5b -50 0 0 0.30 99.9
Case6 0 0 9 0.20 99.9
Case7 0 0 18 0.20 99.9
Case8 0 6 0 0.30 100
Case9 0 12 0 0.30 100
Case10a 50 0 18 0.20 100
Case10b -50 0 18 0.20 100
Case11a 50 12 0 0.30 100
Case11b -50 12 0 0.30 100
Case12a 10 12 18 0.20 100
Case12b -10 12 18 0.20 100
Case13a 20 12 18 0.10 100
Case13b -20 12 18 0.10 100
Case14a 30 12 18 0.10 100
Case14b -30 12 18 0.10 100
Case15a 40 12 18 0.10 100
Case15b -40 12 18 0.10 100
Case16a 50 12 18 0.10 100
Case16b -50 12 18 0.10 100

5.2 Conclusions and Future works
5.2.1 Conclusions
 The test case result indicates that the current WSCC 9 bus system can operate stably
when connecting PV, wind turbines and EVs to the grid.
 The renewable energy can reduce the traditional fossil (coal, petroleum) fuel
utilization and be good to the environment.
48

 The widely used EVs can also minimize the vehicle exhaust emission and help
maintain the power grid stable.

5.2.2 Future Work
 Learn how to construct the model that is not provided by PSS/E Model Library
according to the elements in reality.
 Learn how to use Python language to control the PSS/E to complete the simulation
work faster and more easily.
 Learn how to do the power system reliability and the do the economy analysis.

49

Reference
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[2] ConEdison. http://www.coned.com/electricvehicles/types_of_EVs.asp
[3] Omar Ellabban, Haitham Abu-Rub, Frede Blaabjerg, Renewable energy resources: Current
status, future prospects and their enabling technology. Renewable and Sustainable Energy
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[6] Worldwatch Institute (January 2012). "Use and Capacity of Global Hydropower Increases".
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[8] "How Does A Wind Turbine's Energy Production Differ from Its Power Production?".
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[9] "Wind Power: Capacity Factor, Intermittency, and what happens when the wind doesn't blow?".
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[11] Solar Fuels and Artificial Photosynthesis. Royal Society of Chemistry
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March 2013)
[12] "Energy Sources: Solar". Department of Energy. Retrieved 19 April2011.
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[14] National Renewable Energy Laboratory: Solar Has the Most Potential Of Any Renewable Energy
Source, 30 July 2013
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power system stability IEEE/CIGRE joint task force on stability terms and definitions,” IEEE
Trans. Power Syst., vol. 19, 2004, pp. 1387-1401.
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[17] Mohamed El Chehaly, POWER SYSTEM STABILITY ANALYSIS WITH A HIGH
PENETRATION OF DISTRIBUTED GENERATION, thesis, Concordia University
[18] T.V. Cutsem and C. Vournas, Voltage stability of electric power systems, Springer, 1998.
[19] C.W. Taylor, N.J. Balu, and D. Maratukulam, Power System Voltage Stability, List of
References 92 McGraw-Hill, 1994.
[20] WECC/NERC Planning Standard
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[21] "California: Renewables Portfolio Standard". Database of State Incentives for Renewables &
Efficiency. 2014-06-25. Retrieved 2014-07-21.
[22] 12,000 MW of Renewable Distributed Generation by 2020, IREC
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Department of Energy. 2014-08-05. Retrieved 2014-10-24.
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system/
[27] Siemens PTI, PSS/E-33.4 Program Operation Manual
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Control, Springer, 1998

- 1 -

Appendix 1 California Load Data
2020 California Load from California Independent System Operator.
CA Load
Profile-2020.xlsx

Appendix 2 Iteration Method Characteristic

- 2 -

Appendix 3 Contingency analysis files code
Contingency file (*.con)
The code is shown below:
COM
COM Contingency Description File For WECC 9 bus_Modified System
COM
TRACE
CONTINGENCY BUS2-BUS7
TRIP LINE FROM BUS 2 TO BUS 7
END
CONTINGENCY BUS7-BUS8
TRIP LINE FROM BUS 7 TO BUS 8
END
CONTINGENCY BUS8-BUS9
TRIP LINE FROM BUS 8 TO BUS 9
END
CONTINGENCY BUS9-BUS3
TRIP LINE FROM BUS 9 TO BUS 3
END
CONTINGENCY BUS9-BUS6
TRIP LINE FROM BUS 9 TO BUS 6
END
- 3 -

CONTINGENCY BUS7-BUS5
TRIP LINE FROM BUS 7 TO BUS 5
END
CONTINGENCY BUS5-BUS4
TRIP LINE FROM BUS 5 TO BUS 4
END
CONTINGENCY BUS5-BUS6
TRIP LINE FROM BUS 4 TO BUS 6
END
CONTINGENCY BUS4-BUS1
TRIP LINE FROM BUS 4 TO BUS 1
END
END

Subsystem file (*.sub)
COM
COM System Discription File For WECC 9 bus_Modified System
COM
SUBSYSTEM OVERALL
BUS 1
BUS 2
BUS 3
BUS 4
BUS 5
BUS 6
BUS 7
BUS 8
BUS 9
END
END

Monitor file (*.mon)
COM
COM Monitored Element Description File for WECC 9_bus Modified System
COM
MONITOR BRANCHES IN SUBSYSTEM OVERALL
4 5
4 6
5 7
6 9
7 8
- 4 -

8 9
END
MONITOR VOLTAGE RANGE SUBSYSTEM OVERALL 0.95 1.05
MONITOR VOLTAGE DEVIATION SUBSYSTEM OVERALL 0.05 0.05
END

Appendix 4 Models Data Structure
Generator Model Data
GENROU

IEEET1

- 5 -

IEEEG1

EV/Battery Model

- 6 -

PV Model
PVGU1

PVEU1

- 7 -

PANLU1 and IRRADU1

Wind Turbine Model
WT3G1

- 8 -

WT3E1

WT3T1 and WT3P1

- 9 -

Appendix 5 Extend test system dynamic data

- 10 -

- 11 -

Appendix 6 Test Case Bus Plot
All test cases bus 5 voltage plot
Case1a

Case 1b
- 12 -

Case 2a

- 13 -

Case 2b

Case 3a

- 14 -

Case 3b

Case 4a

- 15 -

Case 4b

Case 5a

- 16 -

Case 5b

Case 6

- 17 -

Case 7

Case 8

- 18 -

Case 9

Case 10

- 19 -

Case 11

Case 12a

- 20 -

Case 12b

Case 13a

- 21 -

Case 13b

Case 14a

- 22 -

Case 14b

Case 15a

- 23 -

Case 15b

Case 16a

- 24 -

Case 16b

Abstract (if available)

Abstract This paper mainly focuses on the impact of large EV and renewable energy (solar energy and wind energy) deployment on power system. This research is based on several tests on the extended WSCC 9 bus system. In the first step of this research, California power flow prediction by 2020, including load flow data, EV data, PV generation data and wind generation data are made by literature reviewing. In the next step, the WSCC 9 bus system and constructed using software PSS/E. Then the PV, EV and wind turbine models are developed and connected to the WSCC 9 bus system. Then the power flow calculation, contingency analysis are conducted to validate the system steady state stability. To test the system dynamic stability, several test cases are developed according to the California power flow prediction data and the dynamic behavior of the system is studied by simulating a 5-cycle bus fault. The responses obtained from the simulation indicate that the system can keep working in steady state when coupled with the EVs and renewable energy according to the WECC planning standard and control theory.

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Asset Metadata

Creator Hou, Cong (author)

Core Title System stability effect of large scale of EV and renewable energy deployment

School Viterbi School of Engineering

Degree Master of Science

Degree Program Electrical Engineering

Publication Date 04/24/2015

Defense Date 03/23/2015

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag dynamic simulation,electric vehicle,OAI-PMH Harvest,PSS/E,renewable energy,Solar energy,system stability,wind power

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Beshir, Mohammed J. (committee chair), Jonckheere, Edmond A. (committee member), Maby, Edward W. (committee member)

Creator Email conghou@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-559125

Unique identifier UC11301633

Identifier etd-HouCong-3390.pdf (filename),usctheses-c3-559125 (legacy record id)

Legacy Identifier etd-HouCong-3390.pdf

Dmrecord 559125

Document Type Thesis

Format application/pdf (imt)

Rights Hou, Cong

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA