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Distribution system reliability analysis for smart grid applications

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Distribution system reliability analysis for smart grid applications

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Content I

Distribution System Reliability Analysis
for Smart Grid Applications
By
Tawfiq Masad Aljohani

Thesis Submitted to the Faculty of the
University of Southern California
In Partial Fulfillment of the Requirements for the Degree
Master of Science in Electrical Engineering
(Electric Power)

Approved March 2014 by the Graduate Supervisory Committee:
Mohammed Beshir, PhD (Chair)
Edward Maby, PhD (Member)
Edmond Jonckheere, PhD (Member)

II

[[And certainly did WE create man from an extract of clay ۞ Then WE
placed him as a sperm-drop in a firm lodging ۞ Then WE made the
sperm-drop into a clinging clot, and WE made the clot into a lump [of
flesh], and WE made [from] the lump, bones and WE covered the bones
with flesh; then WE developed him into another creation. So blessed be
ALLAH, the best of creators]]
- Holy Quran, Chapter 23, versus 12 through 14

III

Distribution System Reliability Analysis for Smart Grid Applications

Abstract
Reliability of power systems is a key aspect in modern power system planning, design,
and operation. The ascendance of the smart grid concept has provided high hopes of
developing an intelligent network that is capable of being a self-healing grid, offering the
ability to overcome the interruption problems that face the utility and cost it tens of
millions in repair and loss. To address its reliability concerns, the power utilities and
interested parties have spent extensive amount of time and effort to analyze and study the
reliability of the generation and transmission sectors of the power grid. Only recently has
attention shifted to be focused on improving the reliability of the distribution network, the
connection joint between the power providers and the consumers where most of the
electricity problems occur. In this work, we will examine the effect of the smart grid
applications in improving the reliability of the power distribution networks. The test
system used in conducting this thesis is the IEEE 34 node test feeder, released in 2003 by
the Distribution System Analysis Subcommittee of the IEEE Power Engineering Society.
The objective is to analyze the feeder for the optimal placement of the automatic
switching devices and quantify their proper installation based on the performance of the
distribution system. The measures will be the changes in the reliability system indices
including SAIDI, SAIFI, and EUE. The goal is to design and simulate the effect of the
installation of the Distributed Generators (DGs) on the utility’s distribution system and
measure the potential improvement of its reliability. The software used in this work is
DISREL, which is intelligent power distribution software that is developed by General
Reliability Co.

IV

A dedication to my inspiration, my beloved father, Masad Salem Aljohani:
I wanted first to thank you for everything you have done for me and my siblings, but I
figured out that all the listed words in our world are insufficient to express my great
admiration and gratitude to you, father. You have been my greatest joy and inspiration,
while your departure from this world has been my greatest pain. I miss you all the time,
and I ask ALLAH ALMIGHTY to rest you in the greatest places of Eden’s heavens.

V

Acknowledgments
First and foremost, I am grateful to ALMIGHTY ALLAH for his guidance and protection
throughout my life and for providing me with the ability, courage, and patience to carry
out this work.
I would like to express my gratitude to my adviser, Dr. Mohammed J. Beshir, for his
valuable support, discussion, and concern throughout my research. He was always very
helpful, friendly, patient, and a great adviser. I also would like to thank the University of
Southern California for the great academic environment they provide for their students,
specifically I thank Mrs. Diane Demetras and the Ming Hsieh EE department staff for
their privileged and smooth academic support they provided to me. It was one of my
greatest privileges to be a student of this highly respected institution. The thanks are also
extended to Taibah University for providing me with a scholarship to continue my
graduate education in power systems.
Also, I would to thank Dr. Sudir Agarwal of General Reliability for all of his
contributions to this thesis. Dr. Agarwal provided me with valuable feedback as well as
with the software that used in my work to determine the scope of the research, DISREL.
The appreciation as well is extended to Dr. Edward Maby and Dr. Edmond Jonkhere for
serving on my committee and taking the time to review the thesis.
Last, but certainly not least, I would like to express my gratitude to my mother, Maysar,
for being the most beloved mother in the world. Her strength, endless love, support,
wisdom, and mercy have made me the man I am today. I will eternally be grateful that
among the millions of mothers in this world, you are my mother. Special thanks go to my
wife, Sarah, for being a very supportive and loving partner. I thank you for being so
patient and kind for the long hours I was away due to my work. My unlimited gratitude
is greatly extended to my siblings Mohammed, Khalid, Fahad, Basim, Nahla and Norah,
for being the best family anyone could ever have. Words are insufficient to express my
deep regard for their unconditional love, support, and care.

VI

List of Appreciations

IEEE: Institute of Electrical and Electronics Engineering.
EPRI: Electric Power Research Institute.
SCADA: Supervisory Control and Data Acquisition.
SAIDI: System Average Interruption Duration Index.
SAIFI: System Average Interruption Frequency Index.
CAIDI: Customer Average Interruption Duration Index.
EUE: Expected Un-served Energy.
ASAI: Average Service Availability Index.
RBTS: Reliability Test System.
AMI: Advanced Metering infrastructure.
DG: Distributed Generation.
AR: Automatic Recloser.
AS: Automatic switch.
OD: Outage Duration.
OF: Outage Frequency.
U: Unavailability
A: Availability.
P: Probability of the component to be available.
Q: Probability of the component to be unavailable.
 : Failure rate of an electrical component.
MTTR: Mean Time To Repair.
MTTS: Mean Time To Switch

VII

Table of Contents

Chapter One: Introduction to the Work .......................................................................................... 1
1.1 Problem Definition and the Scope of Work .................................................................... 1
1.2 Introduction to the IEEE 34-Bus System ........................................................................ 4
1.3 The Outline of the work .................................................................................................. 6
Chapter Two: Reliability of the Power Distribution System........................................................... 7
2.1 Introduction .................................................................................................................... 7
2.2 Reliability of the Distribution Networks ......................................................................... 8
2.3 General Concept on Reliability Database ....................................................................... 9
2.3.1 Equipment Level Index ......................................................................................... 10
2.3.2 Delivery Point Indices .......................................................................................... 11
2.4 The Traditional Distribution System ............................................................................. 13
2.5 Microgrids and the Concept of Smart Grids ................................................................. 17
2.5.1 Reliability Modeling of DGs................................................................................. 18
2.5.2 The Operation of the Distributed Generation ........................................................ 20
2.5.3 Induction and Synchronous Distributed Generators .............................................. 22
Chapter Three: Literature Review ................................................................................................ 24
3.1 On the Reliability Indices and Current Techniques and Approaches ............................ 24
3.2 On the Smart Grid Concept .......................................................................................... 26
3.3 On the Optimal Placement of Switches ........................................................................ 31
Chapter Four: System Modeling ................................................................................................... 33
4.1 Introduction .................................................................................................................. 33
4.2 Network Modeling ........................................................................................................ 33
4.3 The Analytical Technique ............................................................................................. 35
4.4 Reliability Modeling of the Distributed Generators (DGs) ........................................... 39
4.5 System and Load Point Reliability Indices ................................................................... 40
4.5.1 Load Indices ......................................................................................................... 41
4.5.2 System Indices (Customer-Oriented Indices) ........................................................ 42
4.6 System Reconfiguration Algorithm Based on the Analytical Technique ...................... 45
Chapter Five: Results and Discussion .......................................................................................... 48
Case Study 1: The Reliability Impact of the Optimal Placement of Automatic Reclosers ........ 48
A. Considering the Installation of Only One AR ............................................................... 48
VIII

B. Considering the Installation of two ARs ....................................................................... 52
Case Study 2: Reliability Impact of Adding a New Source of Supply to the Radial Feeder ..... 57
Case Study 3: The Reliability Impact of the Distributed Generators on the Radial Feeder ....... 60
A. Considering the Installation of One DG Unit ................................................................ 60
B. Considering the Installation of Three DG Units............................................................ 63
Chapter Six: Conclusion ............................................................................................................... 69
6.1 Conclusion .................................................................................................................... 69
6.2 Future Work ................................................................................................................. 70
References .................................................................................................................................... 72
Appendix A: The Average Faliure Rates and MTTR Utilized in this Work ................................. 76
Appendix B: Load Curtaliment and Power Flow Using DISREL for the Original test system ..... 77

List of Tables
Table 1.1: The load information for 34 node system ..................................................................... 5
Table 2.1: Typical Substation Characteristic ............................................................................... 16
Table 2.2: Typical feeder characteristic ....................................................................................... 16
Table 4.1: Part of the input data of the tests system ..................................................................... 44
Table 5.1: The software recommendation for the optimal placements of the switches ................ 49
Table 5.2: The obtained savings for the utility for each option in case one ................................. 49
Table 5.3: The results of the optimal placements for the modified test feeder ............................. 53
Table 5.4: The obtained savings for the modified test system ..................................................... 53
Table 5.5: results of different reconfiguration schemes for case study 2 ..................................... 58
Table 5.6: the projected savings for each option in case study 2 .................................................. 59
Table 5.7: The results of the installation of one 1MW DG unit at node 890 ............................... 61
Table 5.8: The savings in case of connection 1 DG unit to the grid ............................................. 61
Table 5.9: Modelling the effect of adding distributed generator at nodes 890, 844, 820 ............. 64
Table 5.10: the savings in case of connecting three DG units....................................................... 64
Table 5.11: The results of modelling one AR with 3-1MW DG units on the test system ............ 67
Table 5.12: The savings for the above case study ....................................................................... 67

IX

List of Figures
Figure 1.1: The key factors of the smart grid concept .................................................................... 2
Figure 1.2: IEEE 34 node test feeder ............................................................................................. 4
Figure 2.1: Electrical sub-transmission and distribution interconnection joints ........................... 14
Figure 2.2: Example of suburban distribution substation ............................................................ 14
Figure 2.3: Basic components of distribution substations ............................................................ 16
Figure 2.4: The structure of a vertically owned power utility ...................................................... 19
Figure 2.5: Induction generator connected to the grid ................................................................. 22
Figure 4.1: Series and parallel power distribution components ................................................... 34
Figure 4.2: The generic tree structure .......................................................................................... 36
Figure 4.3: The concept of the upstream and downstream navigation ......................................... 38
Figure 4.4: A scheme for the modeling of the DG units into the distribution feeder ................... 39
Figure 4.5: Highlighted sections of the feeder to illustrate calculation of SAIDI and SAIFI ....... 44
Figure 5.1: Improvements in SAIDI for case study 1.A .............................................................. 51
Figure 5.2: Projected savings per option for case study 1.A ........................................................ 51
Figure 5.3: The optimal locations of ARs based on the results .................................................... 53
Figure 5.4: Projected savings for case study 1.B for each option ................................................ 55
Figure 5.5: The savings to the outage costs for case study 1 ....................................................... 56
Figure 5.6: Adding new source of supply to the IEEE test system .............................................. 58
Figure 5.7: The test feeder with only one DG unit connected ..................................................... 60
Figure 5.8: Customer interruption improvements per year for the feeder .................................... 62
Figure 5.9: The modified tests system with three DG units with the optimal location of AR ...... 63
Figure 5.10: The savings vs outage costs for case three .............................................................. 65
Figure 5.11: resulted customer interruptions for each option in case study 3 .............................. 65
Figure 5.12: SAIFI reductions for case study 3 ........................................................................... 66
1

Chapter One: Introduction to the Work

1.1 Problem Definition and the Scope of Work
The electric power industry has undergone extensive changes in recent decades. The
traditional role of the electricity service provider has changed from the concept of
vertically integrated companies that provide generation, transmission, and distribution
service all at once to the deregulated free market that introduces new players guided by
rules and requirements. The introduction of the smart grid as one of the emerging
solutions to the many problems the power industry currently faces has established high
hopes and raised questions about the capability and effectiveness of the smart grid to
mitigate power grid problems.
Among all of the challenges, improving the reliability of the electric infrastructure is one
of the most desired goals yet one of the most difficult things to achieve. Throughout the
years, research has been extensive in the area of power system reliability to insure that
the electric systems would be able to maintain power to its consumers at both acceptable
levels, and rates. Studies have mainly focused on the generation and transmission sectors
to solve the issue of the grid reliability. Not until recently has the power distribution area
received part of the attention. According to Brown (2009) improving the reliability of the
distribution network offers great potentials to save billions of dollars that the power
utilities in the U.S, in specific, and in the rest of the world in general, lost due to outages
and interruptions. To be more specific, almost 90% of the problems that occur in the
electric system happen between the distribution substations and consumer’s meters. Thus,
it is very clear that there is a tremendous need to solve these issues related to the
distribution system, which if solved, could save the utilities time, money, and efforts
better spent on improving the generation and transmission areas.
One of the solutions suggested to enhance the electric service is to incorporate the
concept of the smart grid to improve the electric distribution system. According to the
U.S. department of energy, there is no specific definition for the term smart grid, but that
the concept of the smart grid can cover many aspects such as [2]:
2

 Self-healing from power disturbance events
 Accommodating all generation and storage options
 Enabling active participation by consumers in demand response
 Operating resiliently against physical and cyber attack
 Providing power quality for 21st century needs
 Enabling new products, services, and markets
 Optimizing assets and operating efficiently

Figure 1.1 The key factors of the smart grid concept
My interest in this thesis is to examine and evaluate the effect of the first two points on
the reliability of the distribution system. The concept of a power grid free from disruption
is a goal that many studies have been carried out to verify. In my work, I intend to
measure the effect of the optimal placement of the automatic and smart switching devices
on the reliability of the grid, and how this would change the reliability indices of the
electric utilities such as SAIDI, SAIFI, CAIDI ...etc. In addition, the domination of the
distributed generation (DG) technology has raised many questions about the
3

reinforcement that these dispersed power sources can play when incorporated in a
distribution system. Specifically, when a major outage occurs in the system, there are
studies suggesting that isolating the main power grid into small microgrids will be one of
the solutions to overcome the problem of major power blackouts. These small microgrids
will depend upon sources, mainly the dispersed DG units, to continue customer’s service
regardless of the outage occurrence. In this thesis, I examined whether the proper
installation of the DG units in the distribution system will have a positive impact on the
reliability, and whether the installation of the DGs will be more viable and desirable than
other options that will be discussed later on in the work.
The work in this thesis was carried out using smart intelligent software (DISREL) that is
developed by General Reliability Inc. The work discusses theories and techniques
involved in evaluating the reliability of the distribution gird, and also test those
techniques using the software on the IEEE 34 node test system introduced in the
following section.

4

1.2 Introduction to the IEEE 34-Bus System
The work of this thesis is applied on the IEEE 34 node test system, which was released in
2003 as a study test by the Distribution System Analysis Subcommittee of the IEEE
Power Engineering Society. Figure 1.2 shows the one-line diagram for the original IEEE
node feeder before applying needed modifications to test the scope of the work. The test
system operates at 60 Hz, 24.9 kV and 4.16 kV, and 12 MVA, with both active and
reactive power loads at 3-phase and single phase systems. Throughout the work on this
thesis, I assume that the average customer consumes 2 kW. Based on this assumption,
and using the load information presented by the IEEE 34 node feeder report, the number
of households (customers) is assumed to be 812, as illustrated in table 1.1 which also
includes both the distributed and spot load information that has been used in this work.

Figure 1.2 IEEE 34 node test feeder

5

Table 1.1 The load information for 34 node system
Node kW
Number of
Customers
802 55 27
808 16 8
816 5 2
818 34 17
826 40 20
824 4 2
828 7 3
830 45 22
832 15 7
834 146 73
840 67 33
842 9 4
844 405 202
846 45 22
848 83 41
854 4 2
858 32 16
864 2 1
860 60 30
836 82 41
862 28 14
890 450 225

Since this test system is an actual feeder that operates in Arizona, it was challenging to
assess its reliability and weight the impacts of our suggested modifications that will be
presented in this work. Therefore, the study was started on this feeder as a base system
with no modification made at all.

6

1.3 The Outline of the work
The work on this thesis is organized as following:
Chapter one of this thesis’ work is dedicated to the introduction of the problem and the
scope of the work. It also includes a brief introduction to the test system that is going to
be utilized.
Chapter two provides a detailed introduction to the topics of interest in the work. This
chapter introduces many important terms such as the definition of the traditional
distribution network and its associated components, the reliability of the power
distribution system, the concept of the smart grid, and the different types of the
distributed generators, and the way they operate.
Chapter three provides the literature review for some of the most important work that
has been done on the related topics of the thesis. A concise discussion in this chapter
emphasizes the relevance of each study that has been discussed and the outcomes of each
one of them.
Chapter four presents and discusses the system’s modeling. The analytical technique
was utilized to analyze the test model and come up with suggestions and data that were
used in the simulation of the system using DISREL. This chapter also discusses the
proper operation of the DG units in the system for achieving the goal of the work, which
is to use them during major outages to maintain power as part of the distribution system,
which results in enhancement of system’s reliability.
Chapter five illustrates and discusses the results that were obtained by DISREL after
analyzing the system using the mentioned method. It also provides a discussion on the
outcome and provides related suggestions and comments.
Chapter six concludes the work on this thesis and provides suggestions for future work
that can be done to enhance the work in this area.

7

Chapter Two: Reliability of the Power Distribution System

2.1 Introduction
Power system reliability is a key aspect in power distribution system planning, design,
and operation. Electric power utilities are required to provide uninterrupted electrical
services to their customers at the lowest possible cost while maintaining an acceptable
level of service quality. The importance of reliability arises as it can express the cost of
service outages. A distribution system’s quality of service can be judged by its reliability
indices, which can be increased by automation of its feeder and associated parts, which
eventually will lead to a desired reduction in the power interruptions. Reliable power
distribution networks are those managing a high level of reliability.
The traditional power distribution grid is radial in nature, the power flows in one
direction from the distribution substation to the load point. The radial system has low
reliability, and those customers who are located at the end of the circuit, tend to be more
prone to power outages than any other customers [3]. In general, the radial configuration
is mostly found in suburban areas, where reliability tends to be alleviated due to shorter
circuit designs and less customer numbers, with no complicated reliability requirement as
those found in urban areas. Since there are no backup or alternative sources to back up
the traditional distribution systems, there is a high chance that a major fault on the feeder
would affect a substantial number of customers in the radial configuration. This occurs
where the circuit breaker or the recloser of the feeder clears the fault, thus interrupting the
customers of the downstream of the protective devices.
Many efforts have been made to quantify the losses of the utilities due to the faults and
outages in the distribution grid. There have been suggestions that many inconsistencies
are found in the reported collected data that measure the utilities interruption events and
its reliability indices’ performance [4]. Reference [4] presents a minimum set of data and
a suggested organization of the data used to make a comparison of distribution system
reliability performances.
8

The concept of reliability can be simply expressed as two states or conditions: up and
down. The first, up, would mean that the system is available (functioning) while the
latter, down, means the system is unavailable (failing). Electrically, when a device is
interrupted by a fault, the state or condition of this specific equipment would be adjusted
from the up state to the down state. The down state lasts until the equipment is fully
repaired. Once the equipment goes alive with the grid, the state condition returns to the
up state again. Yet, the electrical infrastructure, known to be one of the most
sophisticated systems ever created by mankind, contains hundreds of components that
interact with each other to make the system fully operational. This interaction adds more
complexity to the process of evaluating the reliability of the grid. The reliability problem
could become even worse if the failure of one component influences the failure of others,
increasing the possibility of cascading outages [5].

2.2 Reliability of the Distribution Networks
Power system reliability can be defined as the degree to which the performance of the
elements in a bulk grid results in electricity being delivered to customers within accepted
standards and with the desired amount of power. The degree of reliability may be
measured by the frequency, duration, and magnitude of adverse effects on the electric
supply [6, 7].
Reliability evaluation is divided into two basic categories [6]:
 System Adequacy: the ability of the power system to provide aggregate
electricity services to their customers at all times, therefore associated with static
conditions.
 System Security: the ability of the power system to withstand the sudden
disturbances and respond to dynamic changes in the network, therefore associated
with dynamic conditions such as transient or stability problems.

9

There are several key aspects that are directly related to the concept of power system
reliability [1]:
 Power Outages: short/or long-term loss of the electric services due to
unscheduled events such as faults and voltage drops (sags). There could be also
scheduled outages due to operational needs (i.e. for periodic maintenance).
 Open Circuit: a broken path in the electrical system due to an open switch or
damaged feeder.
 Momentary Interruptions: momentary interruptions are brief service disruptions
that usually take no longer than a few seconds. Sometimes, momentary
interruptions occur during the reclosing of automated switches.
 Fault: a fault is an abnormal current in the system that is caused by short circuit.
There are several forms of faults in the electrical system (i.e. single line to ground
fault, three phase fault). Most types of faults required human interventions in
order to end.
 Sustained Interruption: occurs when a customer is de-energized for minutes or
longer. This type of interruptions is usually caused by short circuit incidents.

2.3 General Concept on Reliability Database
Reliability indices are divided into two categories: indices for predicting future system
performance, and indices for reflecting historical performance. The latter is considered
effective in establishing the utility’s reliability criteria in transmission and distribution
systems planning. Furthermore, the historical performance can be indicated at either
equipment level or delivery point level.

 Equipment Level Index
Assess the average performance of individual or groups of equipment. It is
also used as the input data in the reliability evaluation of the planned
future systems.

10

 The Delivery Point Level Index
Assess the average performance of individual delivery points, delivery
point groups, and the overall system, which results in an estimation of the
effects of the grid configurations and the consequences of the load
interruptions on the system operation.

2.3.1 Equipment Level Index
The outages of different equipment in the network could be divided into two
types: one outage leads to a loss in the load, which is the basis for the delivery
point index calculation. The other type of outage is an outage that does not
necessarily lead to loss in the load, and is the basis for equipment index
calculation. Another classification of the outages is that used by the Canadian
Electricity Association (CEA), which classifies the outages as:

o Equipment-Related Outages: Caused by failure of the equipment itself.
o Terminal-Related Outages: Caused by the failure of the terminal devices
of the equipment.

The different classifications aim to guide us in reaching a better estimation of the
system’s reliability. Such classifications could also lead to an increase in the
quality of data, which could be exploited in enhancing system reliability and
defining problems accurately. It should be noted that very few outages database
available at present have the classification of equipment-related and terminal-
related outages, which contributed to some errors in the reliability data
assessment. There are three basic indices that are used for historical equipment
performance:

o Outage Duration (OD): also known as average outage duration and
mean time to repair; consider the outage time (in hours) for each outage
event to the overall outages in a given timespan.
11

o Outage Frequency (OF): the average number of outages in one year.
This index has always been mixed with another index called the outage
rate.
o Unavailability (U): also called the forced outage rate (FOR); the
probability that a unit will not be available for service when required. FOR
is defined as the number of hours the unit is on forced outage over the
total number of hours in a year (which is the sum of hours the power
station is available for service and hours the power station is in forced
outage) [6].

2.3.2 Delivery Point Indices
The delivery point can be defined as a junction in the transmission system in a
grid in which power is transferred from high voltage levels of the transmission
system to the low voltage level of the distribution system. An example of a
delivery point would be the low voltage side of a step-down transformer. The
delivery point indices can be categorized into either forced (planned) or
momentary (sustained) interruptions. The reliability data based on the delivery
point model analyzes the relationship between the delivery point interruptions and
equipment outages, enables us to have all the factors related to an individual
equipment outage, such as duration, cause, amounts of interruptions, reported and
analyzed within the data.

There are a number of indices that are commonly used to analyze the delivery
point indices. The most famous ones are SAIFI and SAIDI. A detailed discussion
on the reliability system indices, including SAIFI and SAIDI are presented in
chapter four of this work.

12

Ensuring system reliability is one of the main tasks that electric utilities can face.
Monitoring the system’s distribution infrastructure leads to increase in the reliability
index of the utility and overall enhancements of its service quality. It is well worth noting
that a system infrastructure contains various components within it that have a lifespan.
Once these components reach their functional lives it is better to replace them; otherwise,
they are susceptible to failing, which leads to detrimental effects in the system’s
operation [6]. Therefore, replacement of the aged components should be considered to
avoid system problems. The screening shall be focused on:

- Power & auxiliary transformers.
- Underground cables.
- Circuit breakers and switches.
- Overhead lines.
- Power poles.
- Generating units.

13

2.4 The Traditional Distribution System
The main mission of the distribution system is to deliver the electricity to the consumers.
This would be the final stage of power delivery which starts by transmitting the energy
from power plant generators, through high voltage transmission systems (AC or DC) and
ending up with stepping down the voltage to the distribution levels. There are many
components in the distribution system network, such as the overhead lines, underground
cables, distribution transformers, distribution substations, low voltage system, and
customers’ meters.
According to Brown (2009) 90% of all the reliability problems in the electrical
infrastructure occur in the area between the distribution substation and the consumer
meter. This calls for making a major investment in the distribution system to improve the
overall reliability of the power grid. Figure 2.3 shows the basic layout of the distribution
substation’s components [1]

2.4.1 Distribution Subsystems
There are many elements that compose the traditional power distribution systems.
The most important ones are:
Distribution Substation: transfers the electric power from the transmission
system to the distribution system of the load area. The input to the distribution
substations are transmission and/or sub-transmission lines at high voltage levels
(i.e. 115 kV) and the output are feeders that constitute the primary distribution
system. Figure 2.1 shows a typical interconnection point between the sub-
transmission and the distribution systems. Distribution substations usually come
in different shapes and sizes based on its area’s needs. Most of the traditional
distribution substations are built with limited bus configuration and redundancy.
Figure 2.2 shows a typical layout of a suburban distribution substation [1, 3].

14

Figure 2.1 Electrical sub-transmission and distribution interconnection joints

Figure 2.2 Example of suburban distribution substation [3]

Primary Distribution System: delivers the electricity from the distribution
substations to the distribution transformers, with voltage levels between 4.16 kV
to 34.5 kV. Major components in the primary distribution system are:
o Feeder: the output line (circuit) from the distribution substation, usually
designed at 400 amps with emergency rating that reach 600 amps [3].
Table 2.2 shows typical feeder characteristics. The feeder in the
distribution systems could be an overhead line or underground cable. It is
well worth to mention again that most of the U.S. distribution feeders are
radially operated.
o Sectionalizing Switches: switches that reconfigure the primary
distribution circuits by being either opened or closed depending upon the
system’s needs.
o Fuse Cutouts: protection devices; fuses cannot interrupt high fault
currents but can contribute effectively to the reduction of faults’ strength.
15

o Recloser: a self-contained circuit breaker with the ability to interrupt high
fault current. Usually, the reclosers are equipped with overhead
distribution lines to detect the momentary faults. The control of reclosers
ranges from the traditional electromechanical systems to the digital
electronic ones in SCADA. There are strong suggestions that the reclosers
can play a major role in improving the reliability of the distribution
systems [1].
o Automatic recloser [AR]: a developed type of circuit breaker that has the
capability to interrupts the faults automatically and without the human
interference. According to [8], ARs have great potentials to cure over 80%
of the overhead lines transients. Many argue that the auto-reclosers are the
gates to the so-called smart grid and/or smart distribution system, which
will be discovered later on in this work.
o Sectionalizers: do not have fault interrupting capabilities; sectionalizers
are protective devices that work in conjunction with downstream
protective devices to mainly isolate faulted sections of the feeders. This
provides a great advantage by allowing all the customers upstream of the
sectionalizer to be in service regardless of the fault on the feeder.
o Distribution Transformer: a transformer that converts the primary
distribution voltage levels to the utilization (end-use) voltage levels.
Distribution transformers are usually classified based on the different
factors such as types of insulation used, number of phases (single phase or
three phase distribution transformer) and voltage class. The typical size
ranges is between 5 kVA to 2500 kVA.
o Secondary Distribution System: delivers the electricity from the
distribution transformer to the customers’ meters. The typical voltages are
120/240 V single phase system, 120/208 V and /or 277/480 V three phase
system.

16

Table 2.1 Typical Substation Characteristic [3]

Substation Characteristic

Most Common Value Other Common Value
Voltage 12.47 kV
4.16, 4.8, 13.2, 13.8, 24, 34.5
kV
Number of station transformers 2 1~6
Transformer size 21 MVA 5~60 MVA
Number of feeders per bus 4 1~8

Table 2.2 Typical feeder characteristic [3]

Feeder Characteristic

Most Common Value Other Common Value
Peak current 400 A 100~600 A
Peak load 7 MVA 1~15 MVA
Power factor 0.98 lagging 0.8 lagging-0.95 leading
Distribution TR (1 - phase) 25 kVA 10-150 kVA

Figure 2.3 Basic components of distribution substations [1]
17

2.5 Microgrids and the Concept of Smart Grids
The development of smart grid has raised questions recently about the opportunities this
new technology offers to enhance the reliability of electric service. The term “smart grid”
refers to the modernization of the electric power grid via the application of information
and communication systems to incorporate alternative sources of energy into the power
grid [9]. Some of the main characteristics of a smart grid can be described as follows:
 Distributed Resources: power generation via the incorporation of dispersed
resources, energy storage applications, demand side management, and reduced
greenhouse gas emissions,
 Communication and control: advanced metering infrastructure (AMI), state
estimation, and high-speed communication process to the AMI.
 Improved power system reliability: improved fault detection, allow of self-
healing network, installation of automatic switches and optimal switch planning.
 Improved efficiency: end-use efficiency, greater utilization of generation,
improved delivery efficiency and energy measures.

As mentioned earlier, in the problem definition, in chapter one of this thesis, the US
department of energy argues that there is no clear definition of the concept of smart grid.
However, several interested parties have come up with their own definitions on this topic.
For example, the North American Electric Reliability Corporation (NERC) defines a
smart grid as:
The integration and application of real-time monitoring, advanced sensing,
communications, analytics, and control, enabling the dynamic flow of both
energy and information to accommodate existing and new sources of supply,
delivery, and use in a secure, reliable, and efficient electric power system, from
generation sources to end-user.
The implementation of real-time information systems and improved infrastructure, for the
control of installed distributed generation units is what could transform the conventional
distribution system into the concept of “smart distribution”, simply turning the
18

distribution system into a part of the whole smart grid [10]. The outcome could be an
intelligent exploitation of resources, to provide highly secure and reliable electric services
to the customers at a lower cost, while maintaining a clean environment.

2.5.1 Reliability Modeling of DGs
Known also as decentralized generation, dispersed generation, on-site generation;
distributed generation reduces the need of the transmission system by providing an on-
site generation from local (small) resources. In recent years, the power industry has been
undergoing a major change in its business and in its operational structure. Furthermore,
the late 1990’s witnessed the official starting points toward the trend to deregulate the
electric industry. This would forever change the traditional organization of the electric
utilities that dominated power generation, transmission, and distribution or what is known
as the vertically integrated utility structure. Instead, there were new players in the
electricity service market which managed a single function solely. The goal of
establishing a deregulated power system is to provide a competitive market with fair
choices to consumers. Figure 2.4 shows the traditional structure of the electric utilities
before deregulation [11].
19

Figure 2.4 The structure of a vertically owned power utility

One of the fundamental, well-known goals of electric utilities is to provide reliable power
services at reasonable costs, due to consumers being more cost sensitive. Because of the
market’s condition, and due to the high costs required to maintain a reliable power grid,
along with the need to modernize the aged grid, new players, such as the independent
power producers (IPPs) have been introduced. IPPs could play a major role in electric
generation by installing, monitoring and maintaining the DG units [1, 12]. Due to the
energy crisis in the late 1970’s, the United States Congress passed the Public Utility
Regularity Policies Act (PURPA) in 1978, to promote the use of dispersed energy
sources. The act forced the local electric utilities to buy energy from more efficient
sources in order to achieve conservation of energy supplied and optimize use of the
facilities and resources by the utilities, thereby ensuring equitable electric rates for the
consumers [13].

20

The DG units offer great benefits to the local power utilities, such as deferring hundreds
of millions of generation projects, helping the utilities in providing energy to their
consumers at acceptable levels of reliability (especially during interruptions), and
reducing the overall electricity costs while maintaining a healthier environment. The
distributed resources typically consist of small generators that range from 15 to 10,000
kW, which can be connected at the utility’s own distribution network or the customer’s
own site [12]. There are two types of DG units:
 Conventional Based Distributed Generators: internal combustion (IC) engines,
microturbines, small gas turbines, diesel generators, and AC storage applications.
 Renewable Based Distributed Generators: solar panels, wind, small hydro
…etc.
Connecting DG units to an existing distribution network involves a wide variety of
effects that must be highly considered, such as the possibility of voltage fluctuations, the
impact of interruption events, and the ability to meet the demand appropriately within a
specific time framework, the need for proper coordination of the protection relays, and
the availability of the fuel supply [9, 14]. Reference [15] provides discussion on the
importance and relevance of transformers interconnection to the protection system of the
DG units. The advantages and disadvantages of five different transformer-interconnection
methods are elaborated in details in order to weight the effects of different operational
schemes that are resulted from the connection and/or disconnection of the DG units
within the distribution network.

2.5.2 The Operation of the Distributed Generation
The integration of DG units into the distribution network needs voltage regulation to
ensure proper reliability and stability of operation. Otherwise, there would be extreme
hazard that may emerge due to voltage and/or reactive power oscillations. The voltage
control monitors voltage vs. reactive regulation levels, so that whenever the level of
reactive power increased in the grid (more capacitive), the voltage set point reduces.
21

Conversely, if the level of reactive power reduced (inductive), the voltage set point
increases [16, 17].
Most of the time when the DG units are installed, the distribution network receives power
from both the local electric utility’s grid and the connected DG units. However, if the
grid experienced a major outage, the microgrid concept will take place and operate in
islanded operation mode. This creates the need to establish a power vs. frequency control.
The power flows in the system only when there is sufficient generation to meet the
demand, and this applies to the concept of microgrid. When the microgrid is islolated
during the outage, the new frequency level of the islanded part will be lower than the
nominal level due to the fact that it was receiving power from the local grid. This
contributes to the huge need to receive an adequate power supply from the DG units
within a specific time framework. On the other hand due to the network situation needs
during the peak demand, the microgrid will provide the power to the local grid, resulting
in the frequency level being higher than the nominal levels. This requires action to
regulate the frequency, so that power flows to the islanded part of the grid to meet the
demand of consumers [17]. The proper operation of the DG units during the IEEE 1547
events provide important benefits to the electric utilities by improving the reliability
indices, especially those related to the customer services such as SAIDI and SAIFI.
Another important fact in DG operations is that if the DG unit is conventional based, then
it will be used as a backup supply to the grid during emergencies and outages only. While
if the DG unit is a renewable based, it will be operating in parallel with the local grid,
connecting and disconnecting based on the need of the grid [14]. As I will discuss later
on in the system modeling section in chapter four, the conventional-based DG units can
only be connected to the distribution grid as a voltage source since its parallel operation
with the utility generation sources are not advised due to potential stability concerns.

22

2.5.3 Induction and Synchronous Distributed Generators
There is also another classification of the DG units based on types: induction and
synchronous distributed generators. Reference [18] presents a comparative analysis
between the induction and synchronous machines for distributed generation applications.
1. Induction Distributed Generators
Mostly associated with wind powers, induction machines are typically rated less
than 500 KVA. Induction generators can provide the distribution grid with real
power (W) but need a reactive power source (vars) especially if the capacity of
the induction machines exceeds 10 kW. Exceeding 10 kW may require the
installation of a voltage support system, such as capacitors, to maintain power
factors of at least 0.95 for smaller units and near unity power factor for larger
ones. It is very important to give attention to this issue, since the induction
generator can become self-excited and thus affect the voltage system of the grid
when the reactive power exceeds the regular consumption. Thus, it is
recommended to provide the induction machines with both over and under-
voltage protection. Figure 2.5 shows a simple interconnection of wind generator
to the distribution grid.

Figure 2.5 Induction generator connected to the grid

23

2. Synchronous Distributed Generators
Currently, most distributed generators are synchronous generators, such as the
conventional generators. In most cases, the synchronous distributed generators are
connected with the distribution systems as active constant power. These types of
generators are usually equipped with dc field winding in order to provide a solid
excitation system, that it is used to control two main variables. The first is to
control the voltage at the specified distribution voltage levels, while the second is
set to control the power factor [15, 18].

24

Chapter Three: Literature Review

3.1 On the Reliability Indices and Current Techniques and
Approaches
For many decades, electric power utilities have focused much of their attention on
enhancing the reliability of the generation and transmission sectors in electrical
infrastructures. It was not until recently that utilities have started to realize the great
economic benefits that can be achieved by improving the reliability of the distribution
network. Since then, efforts have been made to quantify the cost of customer
interruptions and thus evaluate the worth of electric service reliability. Goel and Billinon
(1994) discuss the evaluation of the reliability worth index for making critical decisions
in power system distribution planning and design. The index, Interrupted Energy
Assessment Rate (IEAR), correlates the cost of the customers’ interruptions to the
reliability indices. Furthermore, reference [19] compares three different methods on two
test systems to evaluate the IEAR index for specific load points, and the distribution grid
in general. The techniques are discussed in [19] are: Contingency Enumeration Method
(CEM), the Basic Indices Method (BIM), and System Indices Method (SIM). Based on
their results, reference [19] indicates that both BIM and SIM methods (both configuration
sensitive compared to CEM) are effectively simpler and more computationally desired to
evaluate the worth of the electric service reliability for short duration interruptions, but
cause, however, large errors in long duration interruptions. Therefore, the use of CEM
between them would be more logical.

Arritt and Dungan (2011) discuss the recent essential features of the present distribution
systems’ analysis tools used by utilities and major research groups such as Electric Power
Research Institute (EPRI) and the Distribution System Analysis Subcommittee (DSAS)
of the IEEE Power and Energy Society (PES). The paper analyzes the key design
functions needed to evolve the distribution systems into the smart grid concept.
Moreover, it emphasizes the importance of collecting reliable metering data in the smart
25

grid infrastructure for a better understanding of the customer’s behavior. An improved
model will result in providing more accurate end-user data and diversity factors which
eventually will effectively aide in planning and operating the distribution network within
the context of the smart grid.

It is also suggested that reconfiguration of the distribution feeder can contribute in
reducing the overall operating cost in real-time operations. Zhou, Shirmohammadi,
Edwin Liu (1997) present an algorithm that aims to minimize the operational costs over a
period of time rather reducing operational losses at a specific point in the operation. The
algorithm is based on the optimal opening/closing of switches in a desirable and timely
manner. This could eventually improve the reliability indices by reducing the average
outage time, and by providing the ability to shift loads from heavily loaded feeders to
others during outages.

Reference [21] argues that incorporating probabilistic reliability techniques could be
more effective in evaluating the reconfiguration of the distribution systems than using the
Monte Carlo simulation techniques, which consumes larger computational times
according to their suggestion. Moreover, [21] applies their algorithm on a modified 33
bus system and provided minimal cut sets of components between the feeders and load
points. To ensure optimal placements of the switches in the system, [21] used the Binary
Particle Swarm Optimization (BPSO) technique, which highlights the use of only one set
of topological input data to evaluate the minimal cut sets for each load point. The
performance measure for the distribution grid would be both the reliability indices and
the system losses. The distribution power loss for each configuration in this reference is
calculated using the algorithm described in [22].

26

3.2 On the Smart Grid Concept
There have been considerable volumes of research to quantify the benefits that can arise
from the integration of the smart grid applications into the enhancement of the reliability
of the power distribution grids. The U.S. Department of Energy states:
Think of the smart grid as the internet brought to our electric system. Devices
such as wind turbines, plug-in hybrid electric vehicles, and solar arrays are not
part of the smart grid. Rather, the smart grid encompasses the technology that
enables us to integrate, interface, with intelligently control these innovations
and others.

Canizares, Bhattacharya, and Paudyal (2011) proposed a comprehensive distribution
optimal power flow model that can be used to integrate the local distribution feeders into
a smart grid. In their model, they suggest a three-phase model of distribution systems,
which is a more accurate and precise operation and could prepare the path for an
integrated optimal approach. The integration of the distribution systems into a smart grid
environment requires the installation of smart control devices such as real-time
information systems, and Advanced Metering Infrastructure (AMI), which will
eventually help to make centralized control of the distribution grid more flexible in the
presence of multiplayer power markets. The customer will have access to the real-time
information systems which report updated information such as energy prices, emissions,
consumption, weather condition, and data that are related to the customer’s own energy
management. Data is shared, using AMI and communication equipment, with the local
Load Dispatch Centers (LDCs), allowing them to be constantly briefed. The improved
control infrastructure of the smart grid would provide the LDCs with capability to have
real-time based control and operation of the distribution grid.

27

Al-muhaini, Heydt (2013) proposed a Markov model approach that measures the
reliability of the power grid by incorporating both conventional and/or renewable
distributed generation (DG) units. Since renewable DGs are not dispatchable, the
proposed model assumes that if the DG unit is a renewable base load unit, it will be
operated in parallel with the utility supply and to be connected or disconnected based on
grid’s need. It also assumes that the conventional DG units considered as backup, will be
operated and connected to the grid during emergencies only. To evaluate the reliability of
the distribution network including the DG, the output power of the incorporated DGs
must supply the demand adequately during the interruptions. During the DG islanded
mode, it is said that the DG may not be able to provide sufficient power to meet the
demand quickly, which will result in a reduction of the reliability indices that are related
to the islanded system. During the isolation, if the DG available capacity is greater than
the demand, the DG will be able support the load. However, if the DG is unable to
provide enough loads, then the DG will be disconnected, since power generated does not
match load demand with losses, and might only be used to supply critical loads. P5
concludes that the DG units operating at an islanded mode can enhance SAIDI, but at the
same time could lead to an eventual increase in the interruption frequency (SAIFI).
Song, Yun, Kwon and Kwak (2013) presented a strategic design for the implementation
of the smart distribution management system in South Korea, known as KSDMS. They
argued that the system topology in a smart distribution system must be looped and
meshed, rather than radial, to achieve high reliability goals.

The Consortium for Electric Reliability Technology Solutions (CERTS) was formed in
1999 with the main goal of improving the reliability of the U.S. electric power system
and preparing a more competitive power market [17]. Lasseter, Eto, Schenkman, Stevens,
Vollkommer, klapp, Linton, Hurtado, Roy (2011) discussed the CERTS Microgrid
system and applied different classes of tests that focus on evaluating the power flow
control and the operation limits of the microgrid. CERTS’s critical feature is its
presentation to the surrounding distribution grid as a single self-controlled entity. CERTS
provided a simple view of distributed generation and its associated load as subsystems of
28

the overall grid that can be islanded and operated independently. The DGs source can
operate in parallel or in island, which ensures the power source to the microgrid in case
of islanding due to a major outage. Using the CERTS system, [20] demonstrated the
process of the integration of the DG units into a microgrid, including a peer-to-peer and
plug-and-play functionality. The tests considered critical conditions and recorded the
timely response of the system in isolation mode and at autonomous reconnection to the
grid.

Reference [24] believes that the concept of microgrids can contribute effectively in the
implementation of many smart grid functions, such as enhancing the reliability of the
distribution grid and applying the concept of a self-supporting network. Lasseter
suggested that the focus of improvements in the upcoming years would be in the
distribution sector of the power systems, with no major changes applied to the power
system’s transmission system. For example, incentives related to renewable energy are
going to strengthen the desire of utilities and academia to conduct research on the
distribution grids. [24] argues that by bringing power sources closer to load centers, the
electric infrastructure will experience tremendous enhancements in voltage profile,
minimizing both transmission and distribution losses, and enhancing the use of heat while
exploiting wasted heat from conventional DGs. Thereby hundreds of millions of dollars
will be saved by postponing the major generation and transmission projects. [24] believes
that attention must be given on how to effectively dispatch as many microgrids as
possible in local distribution grids in order to achieve the desired smart grid goals as soon
as possible.

Mozina (2013) discusses the potential features of the smart grid and the impact of the
interconnections of renewable energy sources (of 10 MW or less) on the power
distribution grid from a reliability point of view. For a better discussion on DG
installations, reference [15] reviews the recent efforts that have been made to standardize
the interconnection requirements for DG units. Taking into considerations the need of
29

establishing a specialized committee in response to the growing interest in DGs, IEEE
has formed many specialized committees, such as IEEE 1547, that aim to provide
technical standards requirements for such interconnection. IEEE 1547 includes standards
such as the following:
 Standard for Conformance Test Procedure for Equipment Interconnecting
Distributed Resources with Electric Power Systems (IEEE 1547.1): published
in 2005, the scope of this standard is to specify the type, production, and
commissioning tests that shall be performed so that the interconnection equipment
of a distributed resources (DR) to an electric power system (EPS) meet the
requirements of IEEE 1547.1
 Application Guide for IEEE 1547 Standard for Interconnecting Distributed
Resources with Electric Power Systems (IEEE 1547.2): approved in 2008, this
guide provides technical background to support the understanding of IEEE 1547
standard. It facilitates the use of IEEE 1547 by presenting technical descriptions,
schematics, application guidance, and interconnection examples.
 Guide for Monitoring, Information, Exchange, and Control of Distributed
Resources Interconnected with Electric Power Systems (IEEE 1547.3):
established in 2007, this document describes the methodologies and parameters
used for monitoring, information exchange, and control of DRs associated with
power systems.
In addition, reference [15] provides a number of state guidelines for DG interconnections
that must be followed by local electric utilities:
 California Rule 21: Electric Rule 21 is a tariff that describes the interconnection,
operating and metering requirements for DGs associated with the local utility’s
distribution grid. Due to the high price of power in California, most DGs are used
to either shave the peak load or as load following, providing that the DG unit is
supplying a portion of the grid with power independently. A key provision of this
rule is the application of directional power relaying (32), to detect loss of utility
parallel operation, which is only applicable for DGs that are connected in order to
shave the peak load of the utility, not to provide it with sole generated power.
30

 New York State Requirements: requirements of installing DGs in the state of
New York do not require the installer to get approval for protective relays from
utilities if the interconnection’s protection is in accordance with the state
requirements.
 Texas State Requirements: provides interconnection requirements for DGs that
are connected to local utilities grid via underground connections.
Then, [15] provides discussion on the interconnection protection’s requirements for a safe
and reliable operation of DG units in parallel with the utility’s grid. The majority of
distribution systems in the U.S. are radial by nature. Thus, connecting dispersed
generation sources will lead to automatic reconfiguration of the feeder circuit layout and
would expose the grid to the hazard of overvoltage strikes. IEEE 1547 addresses the
concerns of overvoltage that may occur due to the parallel DGs interconnection as
follows:
The grounding scheme of the DG interconnection shall not cause overvoltage
that exceed the rating of the equipment connected to the area electric power
system and shall not disrupt the coordination of the ground fault protection on
the area power system.
Reference [15] discusses five different schemes of transformer connections that are
currently used to interconnect the dispersed DG sources to the utility grid, and illustrates
the advantages and disadvantages of each one on the connection process. [15] mentions
some of the methods and practices that are currently used in DGs interconnection
protection such those in the detection of loss of parallelism with the utility grid and DGs
tripping and restoring practices.

31

3.3 On the Optimal Placement of Switches
One scope of this thesis is to examine the fact that the automatic switches can play an
important role, when installed properly and optimally, in enhancing the reliability of the
distribution grid. The main difference between the automatic switch and the normal
switch is that the latter can isolate the faults without a major interference from the human
factor. It is very important to draw attention to some of the work that has been conducted
to reach an algorithm for the optimal placement of the sectionalizing devices, since the
proper installation of these devices, whether manual or automatic, may lead to a
significant improvement to the system’s reliability.

Reference [25] studied the impact of automatic switches on the reliability of distribution
grids based on the implementation of Monte Carlo’s simulations. After a fault is cleared,
the system’s sectionalizing devices (i.e. automatic switches, circuit breakers) reconfigure
so that power is restored to as many customer as possible. Most of these sectionalizing
devices are still handled manually; having a negative effect on the reliability indices of
utilities. [25] studied whether installing automatic switches would result in improvement
in reliability indices as suggested. To prove their point of view, [25] argue that the
selection of optimal locations for the automatic switches would guarantee that their
neighboring feeders would have the capability to pick up the load of feeders affected by
an outage. The study was carried out on a small distribution system that included three
substations that supplied three feeders in an urban area. [25] installed six automatic
switches in different locations in the test system, where reliability parameters had been
obtained for each component based from historical records of random events. After
evaluating reliability indices’ results using Monte Carlo simulations for two different
positions on their test system, [25] concluded that automatic switches improved both
SAIDI and SAIFI more than manual switches, and that the optimal placement of the
automatic switches was also be a major factor in enhancing these indices.

32

Billinton and Jonnavithula (1996) proposed a new formulation methodology for the
placement of the sectionalizing devices taking into account the cost of investment,
maintenance and reliability. The proposed method was based on the optimization
technique of the simulated annealing in order to balance the number and the location of
the switching devices to achieve the desired optimal outcome. The method was tested on
two distribution test systems developed from the RBTS system. The proposed
methodology and results can be found in [26].

Moreover, many papers have proposed different algorithms and methodologies and have
testified to the effectiveness of the optimal installation of the switches. For example,
reference [27] proposed an algorithm for determining the optimal composition and
placement of the automatic switches in a distribution automation system. The proposed
algorithm has been tested on an eight-feeder distribution system located in Korea. The
test verified that proper installation will yield in an improvement in the operational
efficiency of the distribution grid.

33

Chapter Four: System Modeling

4.1 Introduction
One of the most important goals of any power utility is to provide reliable electricity to
their customers at very reasonable costs. In attempts to meet this ultimate goal, research
has been extensive to enhance the reliability of the electric infrastructure. There are many
examples of research that was enabled by the reliability models in the electric system
such as:
 Assessing the effect of constructing a new source of supply on the electrical grid
performance.
 Provide options for reliability improvements and test the feasibility and
effectiveness of each option.
 Identify the locations on the existing system that require reliability improvements.
 Design systems that can offer different levels of reliability based on the need.
This chapter addresses the technique used in the work of this thesis, which is the
analytical technique. It also introduces very important terms that are related to the
reliability of the distribution system.

4.2 Network Modeling
An electric feeder consists of many components that work together as a system that is a
subsidy of a larger system. The transfer of the physical infrastructure into a reliability
model can be achieved by the transformation of the actual component’s connections into
parallel and series component connections, which is also known as the network modeling.
Network modeling is a component-based method where each component is best
described by its probability of being available, P, and a probability of not being available,
Q, where Q = 1- P. The availability of an electric component (P) can be calculated as
follow:

34

Where the  and MTTR are the failure rate and Mean Time To Repair for a component.
If two components are in series, then it is pretty clear that both of them need to be
available in order for them to be operational, whereas when two components are in
parallel, then only one of them is need to be available for the other component to be also
operational [1]. Figure 4.1 shows a schematic representation for both parallel and series
components in the electrical grid.

Figure 4.1 Series and parallel power distribution components

The left-hand side of the figure above shows two overhead lines that are connected in
series, where the loss of one of them would mean the loss of the other, while on the right-
hand, two distribution transformers that are connected in parallel. If one of the
transformers goes out of service, the downstream load can still be served using the
second transformer that is available. Thus, the probability of a series path being available
is the product of the individual component availability, while the probability of a parallel
path is equal to the product of the individual component unavailability.

8760
8760 MTTR
P
 


35

P series = P component.
Q parallel = Q component.
It is clear that reliability in general enhances when we have meshed (parallel) systems.
However, speaking about the power distribution grids, most of the U.S. distribution
networks are radial in nature. Thus, it would make no sense to request adding parallel
components (overhead lines for instance) to improve the reliability of its operation.
Rather, the grid must maintain its radial shape for both economic and operational
concerns. Therefore, the goal is to improve the reliability reasonably without altering the
system’s radial configuration, which is a part of the scope of work of this thesis.

4.3 The Analytical Technique
The analytical technique is one of the fundamental methods in assessing the reliability of
power distribution networks. It models the overall effect of the outages and contingency
events on the system, and specifically on each component. According to Brown et al.,
applying the analytical technique on the distribution grid will yield the expected number
of annual and sustained interruptions, the interruption duration (in hours), and the
components operational switching and failures. In addition, the technique provides the
ability to model the impact of the modifications on the existing systems, which is
considered a major advantage of using this technique even on the very sensitive
distribution systems. The analytical technique can be summarized as follows:
 Apply a contingency event (fault) on a component that has a probability of
occurrence h.
 Simulate the distribution grid response to the applied fault and assess the impact
on the component overall.
 Weight the impact of the fault by h.
 Have all the contingencies been simulated? If not, go back and repeat the process
by applying a new contingency.
36

 End.
As mentioned earlier in this report, most of the distribution feeders in the U.S. are
operating radially, meaning that there is only one way for power flow which is from the
source, which is the substations that step-down the voltage to the distribution level, to the
rest of the feeder in one direction only. One of the central tools in using the analytical
technique for the radial distribution systems is using the navigation of the radial feeder,
so it is very important to understand some of the related concepts [1].

4.3.1 Radial Structure and Navigation
Since the radial distribution feeder consists of components that are connected in
series, we can use the so-called generic tree method to identify the paths for each
component to the source of power. Each component is designed to have only one
path to the source; otherwise the system will violate the definition of radial
operation [1]. Figure 4.2 shows the generic radial tree structure.

Figure 4.2 The generic tree structure.
37

Key definitions of the terms listed on the generic tree structure are the following:
o Radially Operated: The components that constitute a radial electrical
feeder are connected in series. The failing of one component could result
in the failing of the whole feeder.
o Upstream: The direction from the component toward the source of the
power.
o Downstream: The direction from the component to the rest of the feeders,
away from the source of power.
o Parent: The first upstream component.
o Children: The first downstream component.
o Siblings: components with the same parent.

The importance of the above structure is shown when analyzing the system using
the navigation method, where there are two ways to do so: upstream and
downstream searches. As shown in figure 4.3 below, the upstream searches starts
out from the component toward the source of the power going through its
subsequent parents, while the downstream searches start from the component also
but in the opposite direction, away from the source and through its subsequent
children to the end of the feeder [1].
38

Figure 4.3 The concept of upstream and downstream navigation
The use of the upstream and downstream navigation is extremely important in
assessing the reliability of the distribution system. These two methods enable the
examiner to search properly for the source of power, protection devices, switching
devices and potential isolation zones, and the number of the affected customers.
When a fault occurs at one point of the radial feeder, the circuit breaker opens to
clear out the fault. The process of searching for the location of the fault could take
up to several hours (depending on both length and location of the feeder) before
reaching the faulted section of the feeder. Thus, one of the contributions that
smart grids can offer, in regards to providing a reliable distribution system, is to
sense the contingency events, isolate the faulted areas, and return the power to as
many customers as possible. It also plays an important role in reducing repair time
by locating the troubled area within the feeder. The optimal installation of the
switching devices throughout the feeder offers the capability of a self-healing
39

distribution feeder, which this thesis will try to examine using the intelligent
software DISREL.

4.4 Reliability Modeling of the Distributed Generators (DGs)
Chapter 2 of this work illustrated the definition, potentials, and types of available
distributed generators in the market currently. As mentioned earlier, the traditional power
system distribution has only one source of power that is moved in one direction only,
from the substation to the customers distributed along the feeder. However, the
introduction of distributed generator technology has made it possible for a certain
location on the feeder to be connected to a source of power that flows upstream from the
DG unit to the substation. This provides great benefits to the reliability of the distribution
system when, in case of contingency events, the reconfiguration of the distribution feeder
using the automatic switches fails to secure a flow of power to a substantial number of
customers.
The reliability model of the DG units in this work assumes that all the DG units
connected to the radial feeder are dispatchables, which mean that they provide electricity
upon connection to the load points. Furthermore, the objective to reach in this work is to
quantify the reliability impact caused by the connection of the DG units during outages
and interruptions on the system, using reliability indices like SAIDI, SAIFI, CAIDI and
EUE.

Figure 4.4. A scheme for the modeling of the DG units into the distribution feeder [1].
40

When a major fault occurs, the distributed generators offer the distribution system a great
potential to operate independently, or in other words to be islanded from the main grid. In
this scenario, the utility will insure a continuous power supply to parts of their grid,
which would automatically result in improving its reliability indices.
The distributed generator units are set to remain offline during the normal operation of
the grid, where an open switch connects the DG unit to the grid when needed. However,
it must be noted that the DG unit cannot operate in parallel with the grid, since such
action will likely result in undesirable consequences, specifically causing stability
problems. Thus, it is more convenient to consider the DG unit as a voltage source that
provides both active and reactive power to the distribution feeder without being
connected to its voltage system [1, 3]. Figure 4.3 shows a DG unit as a voltage source. In
the left-hand of the figure, the DG unit can be connected to the system via an open
switch, while the right-hand side shows how a certain load in the feeder, using switches
and automatic devices, can be isolated from the main power source in the substation and
be connected to the DG unit source [1]. When connected to the grid, the DG unit starts to
serve the downstream load. There is a high chance also to serve the upstream load that is
not located in the isolation area of the feeder. However, it must be noted that proper
installation is required in this case. We do not want power flow from the DG unit to the
substation in large amounts, which would initiate problems for the transformers
connected to the substations. Another important advantage of modeling the DG units is to
calculate the impact that can made during peak demand time, which would reduce the
overall loading of the feeder, resulting in improving the systems reliability in general.
Chapter five represents a reliability model for the installation of the distributed generators
on the IEEE 34-node system.

4.5 System and Load Point Reliability Indices
It is very important to understand the many terms and concepts that are essential in
assessing the reliability of the distribution system. These concepts are related to
component modeling. Chapter two of this work introduced the concepts in general. In this
41

section of this chapter, we give easy descriptions for the terms that are related to
component modeling, for a better understanding of the overall system modeling.
 Mean Time to Repair (MTTR): to assess the reliability of the distribution grid,
each component must be listed with its availability to serve in the utility. Thus,
when this component (i.e. transformer, breaker, overhead line) fails for any
reason, it will be considered unavailable until it is repaired or substituted with a
similar back up unit. The expected time for this failed component to be restored to
the service from the moment it fails is what is known as MTTR [1, 12].
 Mean Time to Switch (MTTS): the time it needs for a switch to operate,
when a fault occur on the distribution system is MTTS. It is very important to
note that the automatic switches have less MTTS than the manual switches, since
the latter is an automatic operation from the control center and the former is
manual, requiring physical operation by the operator.

4.5.1 Load Indices
Each component in the electrical infrastructure has a failure rate associated with its
operation. The importance of these rates is central in distribution system reliability.
Reference 7 defined the failure rate as the number of expected failures for a single
component in a given time interval. It is worth noting that the failure rates are average
values and could be significantly different from one component to another. For a
series system like the IEEE 34 node feeder, the load indices can be calculated as
follow:

unit for time operating Total
failures of Number
 
42

4.5.2 System Indices (Customer-Oriented Indices)
System indices are very important figures in evaluating the reliability of the
distribution grid, by expressing the interruption statistics in regard to the system’s
customers. Also, these indices measure the validity of suggested project options in
terms of electric services, where a viable option is one that improves certain indices
while maintaining a reasonable implementation cost [12, 28]. The indices that are
related to the scope of this thesis are:
 The System Average Interruption Frequency Index (SAIFI): the average
number of sustained service interruptions per customer during the year. It is the
ratio of the annual number of interruptions to the number of customers. In other
words, it measures how many times the average customer has been out of service.

Where N are the number of customers at node i.

 The System Average Interruption Duration Index (SAIDI): the average
duration of interruptions per customer during the year. It is the ratio of the annual
duration of interruptions (sustained) to the number of customers. In other words, it
measures how long the average customer was without power.

Where r
i
is the annual outage time.

 The Customer Average Interruption Duration Index (CAIDI): the average
interruption duration for the affected customers during a year. In other words,
CAIDI is determined by the division of SAIDI to SAIFI.
T
i
N
N


Served Customers of Number Total
ons Interrupti Customer of Number Total
SAIFI 
T
i i
N
N r


Served Customers of Number Total
ons Interrupti Customer of Duration Total
SAIDI 
43

 The Average Service Availability Index (ASAI): the customer-weighted
availability of the year and defined as the ratio of the total number of customer
hours available for service to the total customer hours demanded, which is
considered as 8760 hours (the total number of hours in a year).

It is well worth noting here that the average values of ASAI for the electric
utilities in the US is over 0.99, due to the fact that service interruptions in hours is
compared to the total number of hours in a full year.

 Expected Unserved Energy (EUE): this index is extremely valuable in
calculating the outage costs for a utility, based on the unserved energy in a year
due to interruptions.

Where L
a(i)
represents the average load connected to load point i.

SAIFI
SAIDI
N
N r
i
i i
 


ons Interrupti Customers of Number Total
ons Interrupti Customer of Duration Total
CAIDI 
8760
8760

 


T
i i T
N
N r N
Demand Service Hours Customer
ty Availabili Service Hours Customer
ASAI 
ri i a L EUE  

) (
44

Table 4.1 Part of the input data of the tests system
Node
Failure rate (
 )
MTTR
(hours)
No. of
Customers
kVA
842 0.2 4 4 9.375
844 0.25 4 202 421.875
846 0.15 4 22 43.75
848 0.11 4 41 86.458

Below is a quick calculation of SAIFI and SAIDI for the highlighted part shown
in figure 4.4 This part of the IEEE 34 system includes 4 nodes that are going to be
considered in the indices calculation. The data is provided in table 4.1 above, and

Figure 4.5 Highlighted sections of the feeder to illustrate the calculation of SAIDI and SAIFI

Similarly, we can illustrate the calculation for SAIDI as following:

....... 41 22 202 4
....... ] 41 11 . 0 [ ] 22 15 . 0 [ ] 202 25 . 0 [ ] 4 2 . 0 [
   
       
 SAIFI
....... 41 22 202 4
....... ] 4 41 11 . 0 [ ] 4 22 15 . 0 [ ] 4 202 25 . 0 [ ] 4 1 2 . 0 [
   
           
 SAIDI
45

However, this is only done to illustrate how to calculate the system indices. It is
very important to note that this system is radial, where its components are
connected in series. The nodes in this lateral are dependent on other nodes in the
system which have different failure rates and repair hours.

In this work, the intelligent software used, which is DISREL, has the ability to
calculate the system indices upon modeling it in the software. In addition, it can
provide multiple suggestions to enhance the indices, which will be covered later
on in this work. Analyzing the response of the system based on the changes of
these indices is going to be addressed as well.

4.6 System Reconfiguration Algorithm Based on the Analytical
Technique

The distribution system could be reconfigured to restore service, if possible, using the
upstream and downstream search methodologies of the analytical technique. This could
be accomplished depending on the locations of the automatic reclosers in the system,
which will be simulated in the following steps, in order to assess the reliability of the
distribution feeder after its reconfiguration based, on the placement of the reclosers.
1. Contingency event occurs at component C with frequency h and repair time
MTTR. Main CB isolates the fault.
2. Search for the first upstream automatic sectionalizing device, R
1
.Will opening R1
restore power to upstream customers? If no, then the fault is located between R1
and the substation. Go to step 6. Record the system indices and the MTTR of the
system in this case.
3. Search for sectionalizing device, R
2
, downstream of R
1
. Will opening R2 restore
power to upstream customers? If no, then the fault is located upstream of R2 and
downstream of R1. Record the system indices and the MTTR of the system in this
case.
46

4. If yes, then this step should restore power to all customers upstream of R
2
, and the
fault should be located in the area downstream of R
2
. Record the system indices
for the reconfigured feeder. Are there more sectionalizing devices on the feeder?
If no, then proceed to step 6.
5. Return to step 3 and perform similar search.
6. If fault is cleared, then return to step 1 until fault occur.
7. End
We will be able to assess the improvement of reliability based on the cases mentioned
above. The system indices should weight the investment made in the installation of the
automatic switches on the feeder, based on the above scheme. Improvement in SAIDI
and SAIFI is targeted, while the EUE would help in evaluating project viability. If the
installation of these switches improves utility revenues, then the installation shall be
considered optimal when providing the highest possible profit. If not, then the utility
must apply more studies in order to assess whether the system could be reconfigured
using automatic switches to achieve the goal of making the installation economically
more desirable.
It is very important to note that the switching time of the automatic sectionalizing devices
should be the incremental time needed to apply the downstream search [1]. The upstream
and downstream restoration could help in achieving the utility’s main goal of improving
the reliability of the distribution feeder. For example, if there is no way to restore power
to customers due to an outage located upstream of switch S, step 3, then the utility would
already know the location of the fault, which would greatly reduce the duration of the
interruption and repair hours. This may lead to significant improvement in SAIDI, even if
it did not do the same for SAIFI, since the customers in this case would still experience
the same number of interruptions but with less duration. Similarly, this could be done
with restoring as many customers as possible when the simulation is applied as shown in
the steps. For instance, if the fault occurs in the area downstream of R
2,
then all of the
customers upstream of R
2
and downstream of R
1
would be restored to service which
would be shown in the calculated indices in this case, while the utility would be able to
reduce the MTTR by starting to repair the fault that would be 100% located downstream
47

of R
1
. Figure 5.1 in the next chapter shows the IEEE 34 node system modified with the
installation of the automatic reclosers that should increase system reliability based on the
software modeling results. DISREL provides the calculation of the indices, based on the
system after installing the automatic reclosers in the locations shown in the figure.

48

Chapter Five: Results and Discussion

Case Study 1: The Reliability Impact of the Optimal Placement of
Automatic Reclosers

A. Considering the Installation of Only One AR
In this thesis, I analyzed the potential effect of optimal installation of automatic reclosers
on the distribution feeders, using the analytical technique. The application on this
analysis is done using DISREL, an intelligent software that uses the concept of brute
force in analyzing test systems, quantifies the effects of modifications, and provides
recommendations for optimal installation of switches. Appendix A provides the failure
rates and MTTR values that have been used in modelling the original IEEE 34-node
feeder in this work, which are based on the average values obtained from reference [1].
The automatic recloser (AR) is a protection device that has the ability to detect a fault and
open for a pre-programmed time before closing again automatically and without the
interference of the human factor. If optimally installed, ARs can help in achieving the
concept of a self-healing power grid, which is one of the smart grid’s main concepts
according to the U.S. department of energy. These powerful tools would be undoubtedly
one of the main components of a modern, smart, and efficient power distribution grid.
At the beginning we model the original IEEE 34-node feeder using DISREL. Table 5.1
shows the results of the software recommendations for the optimal installations of
automatic reclosers.

49

Table 5.1. The software recommendation for the optimal placements of the switches

Case Description

SAIFI SAIDI CAIDI ASAI
EUE
(kW/yr)
Outage Cost
($)
Base Case 5.35187 927.25385 173.25789 0.998235822 25,245 251,769.00
Add AR [832-858] 4.91459 840.828 171.088 0.998400271 22,888 228,293.00
Add AR [858-834] 4.87825 848.89636 174.01645 0.998384893 23,109 230,501.00
Add AR [834-860] 4.84965 865.86755 178.54211 0.998352587 23,572 235,100.00
Add AR [860-836] 4.90973 873.88715 177.99095 0.998337328 23,791 237,276.00
Add AR [834-842] 5.08451 886.42841 174.33902 0.998313487 24,134 240,694.00
Add AS [832-858] 5.35187 880.33508 164.49146 0.99832511 23,965 238,592.00
Add AS [858-834] 5.35187 884.95972 165.35558 0.998316288 24,092 239,856.00

The base case in this study would be the original IEEE 34-node test system, which
indicates that any contingency event would result in the given indices for the system,
which means the loss of all the customers in service following any outage event. Chapter
four discusses a developed algorithm that is based on the analytical technique, which
illustrates the main concepts of the isolation and restoration process in the distribution
grid when installing automatic reclosers or switches. This is done by coordinating the
MTTS of the automatic reclosers to be the time reclosers would take to locate the nearest
sectionalizing devices.

Table 5.2 The obtained savings for the utility per each option in case one

Case Description

Savings ($)
Base Case No savings (base study case)
Add AR [832-858] 23,476.00
Add AR [858-834] 21,268.00
Add AR [834-860] 16,669.00
Add AR [860-836] 14,493.00
Add AR [834-842] 11,075.00
Add AS [832-858] 23,476.00
Add AS [858-834] 23,476.00

The provided results by the software suggest that the optimal location to install the
automatic recloser is between nodes 832-858. Furthermore, it is shown that SAIDI has
50

been reduced from 927.25 to 840.83 minutes per year, indicating an average of 9.32%
reduction, or in other words improvement, to the duration of interruptions that the
average customer will experience over the course of a year. This will allow the utility to
improve its service to the customer, in case of a fault occurring downstream of the
automatic recloser installed on this specific location. The recloser would automatically
isolate the affected area of the feeder; thus, restoring the service to the upstream
customers, which improves SAIDI in this case. However, if the fault is located upstream
of the automatic recloser, then there is no way to restore the power to the downstream
customer unless there is another source of power that can feed these customers. Yet, this
will give the utility the advantage of reducing the repair hours by detecting the location of
the fault quicker than the case of the original feeder, where there is no sectionalizing
switch at all.
Figure 5.1 shows a comparison for the options provided in this case study. However, this
does not necessarily mean that this option will yield the best outcomes in regard to
SAIFI. SAIFI measures the sustained interruptions an average customer will experience.
For the best option provided by the software, which is installing the automatic recloser in
between 832-858, SAIFI also witnessed a reduction from 5.35 to 4.92 interruptions per
customer over the course of a year, which is equal to 8.16% improvements, while in some
other options results in greater reduction (9.34% when install the auto-recloser in
between 858-834 instead).

51

Figure 5.1 Around 10% improvements in SAIDI for case study 1.A

Figure 5.2 Projected Savings per option in Case study 1.A

52

Figure 5.2 shows a pie chart for the projected savings per the installation of each
provided option. Installing the automatic recloser between nodes [832-858] reduces the
outage costs from $251,769.00 to $228,293.00, representing a total savings of $23,476,
for this option only. However, it is expected that installing another automatic
sectionalizing device would yield better improvements and savings to the system, which
will be tested in the next section of this case study.

B. Considering the Installation of two ARs
One of DISREL’s virtues is that it can quantify the outage costs for each option based on,
EUE, which represents the expected unserved kW per year due to interruptions.
According to [29], the U.S. utilities’ losses due to energy not being served to the
customer is more than $13 billion a year, which does not include the damages that might
happen to the equipment. The outage cost in this study is assumed to be $10 for each
kWh lost, which is reasonable when compared with the real life outage costs that were
estimated in reference [30].
Table 5.2 presents in order the savings that can be recovered by installing one automatic
recloser in the distribution feeder. Yet, there are over 800 customers in this feeder (based
on our assumption that a single household consumes an average of 2 kW). Thus, and as
the analytical analysis suggests, the installation of the another automatic recloser would
probably result in a greater improvement to the reliability of the distribution network and
more savings earned in outcome. In this section also, we assume that the first recloser is
installed as DISREL suggests in between 832-858 and we will investigate the effect that
other automatic sectionalizing devices may leave on the grid. Therefore, we modify the
IEEE 34-node test feeder to include the recloser in the above mentioned place. Table 5.3
shows the results of the modified IEEE test system shown in figure 5.3.

53

Figure 5.3 The optimal locations of ARs based on the results.
Table 5.3. The results of the optimal placements for the modified test feeder

Case Description

SAIFI SAIDI CAIDI ASAI EUE (kW)
Outage Cost
($)
Base Case 5.35187 927.25385 173.25789 0.998235822 25,245 251,769.00
Base Case + one AR 4.94058 833.42004 168.68883 0.998414338 22,686 226,282.00
Add AR [834-860] 4.66379 801.66541 171.89131 0.998474777 21,822 217,657.00
Add AR [860-836] 4.69352 804.74646 171.45895 0.998468876 21,906 218,496.00
Add AR [834-842] 4.78457 817.78149 170.92068 0.99844408 22,262 222,051.00
Add AR [832-888] 4.79635 816.11261 170.15291 0.998447299 22,213 221,571.00
Add AR [842-844] 4.85976 825.29413 169.82184 0.998429835 22,465 224,082.00
Add AS [834-860] 5.32914 812.24915 152.41666 0.99845463 22,110 220,465.00
Add AS [860-836] 5.39499 816.2149 151.29141 0.998447061 22,218 221,545.00

Table 5.4. The obtained savings for the modified test system

Case Description

Savings ($)
Base Case + one AR 25,487.00
Add AR [834-860] 34,112.00
Add AR [860-836] 33,273.00
Add AR [832-888] 30,198.00
Add AS [834-860] 31,304.00
Add AS [860-836] 30,224.00
Add AR [834-842] 29,718.00
Add AR [842-844] 27,687.00
54

Based on the results above, the installation of two automatic reclosers projects more
improvement to the system indices, with higher revenues. The software suggests that
installing the recloser at 834-860 will raise a total savings of $34,112. This can be
justified by the fact that the utility would be able to isolate faults that are probably located
near the densest area in the feeder, where around 40% of the total load is located in the
distance between 858 to the end of the lateral at 848. In addition, this would reduce the
repair hours for the utilities to fix the issues as soon as possible and restore the power
more quickly in the areas where the customers are still experiencing service interruption.
For example, and similar to the algorithm written based on the analytical application in
chapter four, an outage would make the automatic recloser opens to isolate and clear the
fault. If the customers who are upstream of the automatic recloser (located between 832-
858) are still out of service after isolating the fault, then mostly the fault will be in the
area upstream this recloser to the distribution substation. This concept can be applied to
all the automatic reclosers (or switches) until the location of the fault is detected, which
would reduce the repair hours, thus improving SAIDI index. The installation of these two
automatic devices is considered the optimal solution in this case, since it both reduces
SAIFI and SAIFI by 12.9% and 13.54% respectively. Figure 5.5 shows a multi-scale
graph comparing the savings to the outage costs for this case. It can be shown that when
we install a second AR at 834-860, the system achieves the highest savings by closing the
gap with the outage costs.

55

Figure 5.4 Projected savings for case study 1.B for each option

It is also noted that there are no significant changes applied to CAIDI. This is due to the
fact that CAIDI mathematically equals to SAIDI divided by SAIFI, which means that
when we have improvements on both SAIDI and SAIFI, these improvements are not
going to be reflected in CAIDI unless there are outstanding improvement in SAIDI only.
Regard ASAI, which indicates the average customer-access to the service during a year,
it is noted that ASAI maintains higher reliability values (over 0.99 in all the cases) since
the measure here is how much energy an average customer receives to the amount this
customer demanded from the utilities. In general, ASAI index values are over 0.99 for
most U.S. power utilities. Thus, we can conclude that both CAIDI and ASAI are not good
reliability measures as SAIFI and SAIDI.
The installation of an effective, high-quality automatic recloser costs $20,000 - $30,000
in total [12, 31]. In our case study, the installation of two automatic reclosers will save a
utility an average of $34,112, calculated based on the outage costs. Thus, it is more likely
that this kind of investment will ensure a payback to the electric utility in less than 1.8
years from the installation taking place. However, it is worth mentioning that the return
might be sooner than suggested, since the savings in our study are marked for only one
distribution feeder, which is only considered as generalized saving values, whereas the
56

local utilities, in most cases, have hundreds of distribution feeders in their electrical
infrastructure. Thus, this study should be considered effective in evaluating the impact of
this smart-grid concept on the overall distribution system that consists of hundreds of
similar radial distribution feeders.

Figure 5.5 The savings to the outage costs for case study 1.

57

Case Study 2: The Reliability Impact of Adding a New Source of Supply
to the Radial Feeder

We illustrated in chapter four the system modeling of the DG units and its potential
impact on the reliability of the distribution feeders. Furthermore, the installation of the
DG units would be a more attractive choice for the utilities to achieve higher reliability
goals than building another source of supply for the existing grid, which accounts for
more costs and potential loading issues in the future. In addition, the DG units would
offer reconfiguration schemes for the feeder during outages, maintaining service to a
substantial number of customers and improving the utilities indices.
One of the great advantages that make the DG more desirable is the cost of its
installation, although a high one could be taken care of by Independent Power Producers
(IPPs), where these investors would receive incentives in the form of deferral credits for
replacing distribution facility requirements [12]. I illustrate in this section a reliability
model of the DG units on the test system, and try to make a comparison to the traditional
path through modeling and constructing a new source of supply to the distribution
system. DISREL can provide the system responses to the reconfiguration of the system
using the DGs, and quantify each option it suggests by making comparisons on the
outage costs for each case. EUE is calculated by the software based on the frequency, the
duration, and the time the load was interrupted. Figure 5.6 shows the modified IEEE 34-
node feeder when the utility decides to construct a new source of supply to offer more
reconfiguration options and to improve the reliability indices. Table 5.6 shows the system
indices after modeling the modified test system.

58

Figure 5.6 Adding new source of supply to the IEEE test system

Table 5.5 results of different reconfiguration schemes for case 2

Case Description

SAIFI SAIDI CAIDI ASAI EUE (kW)
Outage Cost
($)
Base Case + New Source 5.35187 687.95905 128.54552 0.998691082 18,735 186,839.00
Add AR [832-888] 4.20221 504.3049 120.00947 0.999040544 13,761 137,123.00
Add AR [888-890] 4.30497 516.63666 120.00924 0.99901706 14,098 140,488.00
Add AR [834-860] 4.84965 626.57263 129.19943 0.998807907 17,063 170,170.00
Add AR [858-834] 4.87825 609.60144 124.96304 0.998840153 16,599 181,154.00
Add AR [832-858] 4.91459 601.53308 122.39732 0.998855531 16,379 163,363.00
Add AS [832-858] 5.35186 641.04083 119.77911 0.99878037 17,456 173,662.00
Add AS [888-890] 5.35186 590.32611 110.30298 0.99887687 16,091 159,942.00

Modeling of the modified system, shown in figure 5.6, has yielded the expected outcomes
that would result from building a new source of supply, which is a significant
improvement in the system indices, and projected savings for the utility. However, it is
must be noted that adding a new source of supply without the installation of circuit
breakers or reclosers are useless, since there would be no ability to isolate the faults and
transfer the unaffected customers from one source to the others available.

59

In this case, the reliability of the distribution feeder has improved significantly with
adding automatic reclosers, where the optimal place to install recloser is suggested by the
software to be between nodes 832-888. Both SAIDI and SAIFI have shown substantial
reductions as shown in the table 5.5. Also, based on the outage costs, the software
indicated significant savings, reaching $114,646 at highest when the automatic recloser is
installed in the best location as suggested above. The savings were calculated based on
the difference of the outage costs for the original IEEE 34-node system (without making
any case modification) which were originally provided by the software as $251,769.

Table 5.6 the projected savings for each option in case study 2

Case Description

Savings ($)
Base Case + New Source 64,930.00
Add AR [832-888] 114,646.00
Add AR [888-890] 111,281.00
Add AR [834-860] 81,599.00
Add AR [858-834] 86,198.00
Add AR [832-858] 88,406.00
Add AS [832-858] 78,107.00
Add AS [888-890] 91,827.00

The costs of the installation of the recloser would repay itself within less than one year;
however, it is not a practical solution to recommend installing a new source of supply to
improve the reliability in the distribution grids. The real capital cost would be higher
since it includes the construction of new feeders, a substation with its associated auxiliary
equipment, in addition to the costs of manpower, and the required land and time that it
would take to complete such a project. In this work, we will show in the next section that
the application of the smart grid concepts, represented by the installation of the DG units,
offer much great benefits, with more reasonable logics than the traditional approach of
building a new source may achieve.

60

Case Study 3: The Reliability Impact of the Distributed Generators on
the Radial Feeder

A. Considering the Installation of One DG Unit
I have emphasized in this thesis that the DG units would considered great tools to
enhance the reliability of the distribution grid. The DG units are one of the concepts of
the smart grid applications as discussed in previous chapters. In this case study, I quantify
its effect by modeling different sizes of distributed generators on the test system using
DISREL. At the beginning, I would like to investigate modeling a 1MW distributed
generator, connected to node 890, where around 30% of the customers were found.
Figure 5.7 shows the modified test model to include the 1 MW distributed generator.

Figure 5.7 The test feeder with only one DG unit connected

Table 5.7 shows the results after the modeling of the test system using DISREL. The base
case would be here the modified system shown in figure 5.7 above. It is noted that adding
automatic switches will not be effective option, since the switches do not have the
capability of interrupting the faults occurring on the feeder, as this mission can be done
only using the circuit breaker and/or automatic recloser.
61

Table 5.7 The results of the installation of one 1MW DG unit at node 890

Case Description

SAIFI SAIDI CAIDI ASAI EUE (kW)
Outage Costs
($)
Original Case 5.35187 927.25385 173.25789 0.998235822 25,245 251,769.00
Base Case + one DG 5.35187 764.83942 142.91068 0.998544812 20,840 207,732.00
Add AR [852-832] 4.24777 516.23724 121.53123 0.999017835 14,082 140,250.00
Add AR [854-852] 4.30945 520.5567 120.79435 0.999009609 14,198 141,414.00
Add AR [830-854] 4.49053 546.98199 121.80791 0.998959303 14,913 148,562.00
Add AR [828-830] 4.6694 567.53998 121.54451 0.998920202 15,474 154,167.00
Add AR [888-890] 4.94313 593.51715 120.06919 0.99887079 16,203 161,381.00
Add AS [830-854] 5.35186 644.85278 120.49136 0.998773098 17,576 174,718.00
Add AS [850-816] 5.35186 707.7287 132.23979 0.998653472 19,288 191,773.00

Table 5.8 shows the savings that can be obtained by the installation of one 1MW
distributed generator at node 890.

Table 5.8 The savings in case of connection 1 DG unit to the grid

Case Description

Savings ($)
Base Case with DG 44,037.00
Add AR [852-832] 111,519.00
Add AR [854-852] 110,355.00
Add AR [830-854] 103,207.00
Add AR [828-830] 97,602.00
Add AR [888-890] 90,388.00
Add AS [830-854] 77,051.00
Add AS [850-816] 59,996.00

The results of the base case illustrate the need for the automatic reclosers/CBs when we
install a DG to the distribution system; otherwise, there would be no benefit since the
fault will certainly block the connection of the DG units during outages.
The distributed generator could be sized based on the need, whereas in this feeder, a 1
MW DG unit provides approximately the same benefits that could be added by the
installation of a 6 MW, since the amount of load connected to the feeder is around 1.7
62

MW, which would make no sense to connect a DG unit that provides power more than
the customers demand.
Figure 5.8 shows the customer minutes per year. For the original IEEE 34 node feeder,
the estimated interruption minutes per year for the whole feeder are provided by the
software to be 753,000 minutes year. When I considered connecting a DG unit to the
system, as shown in figure 5.7, the total interruption minutes per year were greatly
reduced when considering several scenarios for adding automatic reclosers/or switches.
For instance, the installation of one automatic recloser between nodes 852-832 to the
modified system seen in figure 5.7 will reduce the interruption minutes to almost 419,000
minutes per year, which is approximately 44.32% reduction than the original test system
(with no DG or AR connected at all). In the case of any contingency event, and as
discussed earlier in chapter four, the DG unit will provide the system the ability to
operate as a small microgrid, providing service to the unaffected parts of the feeder and
improving the system indices, specifically SAIDI, which will provide huge savings to the
involved parties (both the utility and the IPPs) as shown in table 5.8.

Figure 5.8 Customer interruption improvements per year for the feeder
63

B. Considering the Installation of Three DG Units
To improve reliability, it is more worthwhile to consider installing more dispersed
generation sources on the feeder. I modified the test system by adding three distributed
generators, each of them is 1 MW in capacity. Based on the number of customers, it is
suggested that the three DG units are better connected to nodes 890, 844 and 822. Table
5.10 shows the results of modeling the modified system. It is noted that when we
combine the distributed generators with an automatic recloser between nodes 834-842, as
shown in figure 5.9, the system experienced significant improvements in reliability
indices, resulting at the same time in great revenue for the electric provider.

Figure 5.9 The modified tests system with three DG units with the optimal location of AR

Based on the results, when we install an automatic recloser along with the three DG units
in the specified location, the system’s SAIDI would decrease from 927.25 minutes
(obtained in the IEEE original feeder) to 425.52 minutes, which correspond to over 54%
in SAIDI improvements. For SAIFI index, the modified system has reduced the
frequency of interruptions from 5.352 to 3.532 per customer, marking a 34% reduction in
SAIFI. However, I noticed again that there is no significant change applied to CAIDI
even in the case of the DG, which shows that we cannot consider CAIDI a real measuring
for system reliability improvements.

64

Table 5.9 Modelling the effect of adding distributed generator at nodes 890, 844, 820

Case Description

SAIFI SAIDI CAIDI ASAI EUE (kW)
Outage Costs
($)
Base Case + 3 DGs 5.35187 650.94885 121.63016 0.998544812 17728 176,779.00
Add AR [834-842] 3.53278 425.52283 120.44984 0.99919039 11583 115,427.00
Add AR [842-844] 3.65604 440.31363 120.43468 0.999162257 11994 119,501.00
Add AR [834-860] 4.84965 589.5625 121.56792 0.9988783 16056 160,110.00
Add AR [860-836] 4.90973 597.58228 121.71393 0.998863041 16274 162,286.00
Add AS [834-860] 5.35186 610.02368 113.98345 0.998839378 16613 165,211.00
Add AS [860-836] 5.35186 618.92767 115.64722 0.998822451 16856 167,628.00

Table 5.10 shows the savings that can be obtained by the installation of three-1MW
distributed generators at node 890, 844, 820.

Table 5.10 the savings in case of connecting three DG units

Case Description

Savings ($)
Base Case + 3 DGs 74,990.00
Add AR [834-842] 136,342.00
Add AR [842-844] 132,268.00
Add AR [834-860] 91,659.00
Add AR [860-836] 89,483.00
Add AS [834-860] 86,558.00
Add AS [860-836] 84,141.00

Figure 5.10 shows the savings obtained by installing the three DG units on the
distribution feeder. In the case when adding the automatic recloser between 834-842 or
842-844, it is projected that the utility will experience the greatest savings among other
options, which can be proven by the fact that the system will be able to reconfigure in
order to maintain service to a substantial number of customers during different outage
scenarios.

65

Figure 5.10 The savings vs outage costs for case three

Figure 5.11 the resulted customer interruptions for each option in case study 3

66

Figure 5.12 SAIFI reductions for case study 3.

Figure 5.11 shows the number of interruptions for the feeder’s customers per year when
considering the installation of the AR between 842-844 along with the DG units. In this
case, the interruptions events have been reduced from 4346 to 2869, saving the utility
from experiencing a total number of 1477 interruptions, which accounts to almost 34% in
improvements. Figure 5.12 illustrates the reductions to SAIFI index when considering the
DG units with each of the suggested optimal locations for the automatic recloser.
We also run DISREL to obtain the optimal locations for adding another automatic
recloser to check if there would be further enhancement on the system. Table 5.11 shows
the results after modeling the system including three distributed generators connected to
the feeder along with the automatic switch between nodes 834-842 as a base case. The
savings can be shown in table 5.12.

2
2.5
3
3.5
4
4.5
5
5.5
6
Base Case Add AR
[834-842]
Add AR
[842-844]
Add AR
[834-860]
Add AR
[860-836]
Add AS
[860-836]
Add AS
[834-860]
Improvements in SAIFI
67

Table 5.11 The results of modelling an AR with 3-1MW DG units on the test system

Case Description

SAIFI SAIDI CAIDI ASAI EUE (kW)
Outage Costs
($)
Base Case + 3 DGs +
one AR
3.39936 409.51285 120.46751 0.999220848 11147 111,082.00
Add AR [834-860] 3.07393 371.92035 120.99179 0.999292374 10124 100,893.00
Add AR [860-836] 3.11177 375.9744 120.82339 0.999284685 10235 101,990.00
Add AR [842-844] 3.33297 401.5462 120.47683 0.999236047 10930 108,911.00
Add AR [818-820] 3.356 404.3096 120.47354 0.999230743 11006 109,674.00
Add AS [860-836] 6.10885 389.38931 63.74179 0.999259174 10600 105,268.00
Add AS [818-820] 6.30034 400.90695 63.63257 0.999237239 10913 108,374.00

Table 5.12 The savings for the above case study

Case Description

Savings ($)
Base Case + 3 DGs +
one AR
140,687.00
Add AR [834-860] 150,876.00
Add AR [860-836] 149,779.00
Add AR [842-844] 142,858.00
Add AR [818-820] 142,095.00
Add AS [860-836] 146,501.00
Add AS [818-820] 143,395.00

Based on the results shown above, we can see that installing another automatic recloser
can add further improvement to the reliability of the distribution grid; however, the
improvement in the system indices are not significant from the one obtained in the
previous case where we install only one automatic recloser in the system. Thus, it is
recommended, in case of this feeder only, that one automatic recloser would be enough to
achieve the targeted reliability goal, and to isolate a proper portion of the network as a
small islanded microgrid during outages, to maintain service to a substantial number of
customers. The reduction in the SAIDI and SAIFI indices when adding multiple reclosers
could be attributable to the fact that the DG units have another virtue in improving
reliability by taking the form of peak shaving, where the DG units can generate more on-
site power than the demand on the feeder, allowing more power to support the grid during
68

normal operation. The savings in the table above compares the revenue the utility could
achieve by considering each option separately. Again, the savings here are calculated
based on the outage costs that the software provides.

69

Chapter Six: Conclusion

6.1 Conclusion
This thesis has tried to analyze and examine the effects of smart grid applications on the
reliability of power distribution systems. Unlike the generation and transmission sectors,
the distribution of power systems did not receive much attention until recent years, where
many have emphasized the great potential that can be achieved in this field of the
electrical systems. The concept of the smart grid is very broad and difficult to summarize.
However, the effects of two main applications of the smart grid have been examined in
this work, which are the optimal use of the automatic sectionalizing devices, such as the
automatic recloser and the automatic switches, and the accommodation of distributed
generation. Throughout this work, the main concepts that are related to the power system
distribution reliability were defined and discussed in detail, along with proper illustration
of the recent efforts that have been done in this area. Then, an analysis of the reliability
modeling of the distribution system using the analytical approach was presented. Also, I
have developed an algorithm that can both describe and quantify the role of the automatic
reclosers/switches in improving the system indices. In addition, I investigated the system
modeling of the distributed generator units in the distribution system and how they can
improve the overall system reliability.
To verify the hypothesis of this thesis, several case studies were applied using smart
software named DISREL. Using this software, results were obtained for each case study.
The results show that the optimal installation of the automatic reclosers will enable the
concept of a self-healing power distribution grid that can recover quickly and
automatically from major disturbance events, and restore power to as many customers as
possible, resulting in significant improvement in the system indices, for instance in
SAIDI and SAIFI. This has resulted also in great savings to the power providers, since
the utility will be able, using the automatic reclosers and/or switches, to reduce the
interruption duration which reduces the expected unserved kWh to the consumers. In
addition, the application of the distributed generators has been applied using the software.
Results have shown that the DG units can apply the concept of the microgrid, isolating an
70

important portion of the distribution feeder to maintain service to significant numbers of
customers. The distributed generators would be a great option factored into the
distribution system near the customers, rather than having them in the utility’s own
substations. Furthermore, system indices’ improvements are projected to be higher in
cases of installing DG units rather than considering the traditional choice, which is
building a new source of supply. This choice would also be very costly since it will
include a very high capital costs (i.e., the cost of the construction of new underground is
between 1.5 to 10 million dollar per mile). In all, I have reached the conclusion that both
the installation of automatic sectionalizing devices and distributed generation units will
achieve the concept of a smart grid, providing a more intelligent and reliable power
system distribution network.

6.2 Future Work
There are several studies that can be conducted in the near future for further investigation
into related topics to this thesis. For example, an economic analysis can be investigated to
determine the overall costs of the installation of different kinds of distributed generators.
Most of the time, the DG units are taken care of by the independent power producers
(IPPs) who enter into a contract with the local utilities to manage and monitor both the
installation and operation of the DG units in partnership with the utility. Usually, the
utility provides deferral credits to the IPPs for doing so. However, economic studies can
assess in detail the benefits each party can acquire from such a partnership.
In addition, the introduction of the distributed generators raises questions about potential
stability problems. To be specific, during normal operation where there is no contingency
event, there is suggestion that the DG unit can provide power to toward the power
distribution substation, which may be in a large amount, and can result in detrimental
impacts on the equipment beyond the substation. This may require a coordination of the
nearby protection devices so that it can be able to operate efficiently when it receives
power from two directions. Thus, these areas can be investigated to determine the
impacts of the DG units on such topics.
71

Another important topic to be examined is the effect of the current storage options in
improving the reliability of the power distribution system. For instance, an analysis of the
electric vehicles’ potentials, in addition to a deep investigation into the participants’ (the
consumers in this case) behavior and commitment, is very important in order to quantify
and determine the impact of this newly emerged technology on the power grid in general,
and on its reliability in specific.

72

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76

Appendix A
The average failure rates and MTTR utilized in this work

Component
ID
Component
Type
Failure rate
(Occ/yr)
MTTR (hrs)
Isolation time
(minutes)
L800-802 Line 0.1 4 120
L802-806 Line 0.15 4 120
L806-808 Line 0.2 4 120
L808-810 Line 0.25 4 120
L808-812 Line 0.15 4 120
L812-814 Line 0.1 4 120
L814-850 Regulator 0.1 4 120
L816-818 Line 0.25 4 120
L816-824 Line 0.15 4 120
L818-820 Line 0.15 4 120
L820-822 Line 0.065 4 120
L824-826 Line 0.2 4 120
L824-828 Line 0.15 4 120
L828-830 Line 0.2 4 120
L830-854 Line 0.25 4 120
L832-888 Transformer 0.15 4 120
L832-858 Line 0.25 4 120
L858-864 Line 0.15 4 120
L834-842 Line 0.2 4 120
L834-860 Line 0.2 4 120
L836-840 Line 0.2 4 120
L836-862 Line 0.15 4 120
L842-844 Line 0.25 4 120
L844-846 Line 0.15 4 120
L846-848 Line 0.11 4 120
L850-816 Line 0.2 4 120
L852-832 Regulator 0.2 4 120
L854-852 Line 0.1 4 120
L854-856 Line 0.15 4 120
L858-834 Line 0.12 4 120
L860-836 Line 0.1 4 120
L862-838 Line 0.15 4 120
L888-890 Line 0.2 4 120
L834-842 Line 0.2 4 120

77

Appendix B
Load Curtailment and Power Flow Details of the Original IEEE 34-node Test
System Using DISREL

COMPONE FLOW % of
Comp Rating Sys Load

GenSour 1634 2 100
L800-802 1634 18 100
L802-806 1579 18 97
L806-808 1579 18 97
L808-810 0 0 0
L808-812 1563 18 96
L812-814 1563 18 96
L814-850 1563 18 96
L816-818 34 0 2
L816-824 1524 17 93
L818-820 0 0 0
L820-822 0 0 0
L824-826 40 0 2
L824-828 1480 17 91
L828-830 1473 17 90
L830-854 1428 16 87
L832-858 959 11 59
L832-888 450 5 28
L834-842 542 6 33
L834-860 237 3 15
L836-840 67 1 4
L836-862 28 0 2
L842-844 533 6 33
L844-846 128 1 8
L846-848 83 1 5
L850-816 1563 18 96
L852-832 1424 16 87
L854-852 1424 16 87
L854-856 0 0 0
L858-834 925 10 57
L858-864 2 0 0
L860-836 177 2 11
L862-838 0 0 0
L888-890 450 5 28
BkrSour 1634 2 100

78

Event# 1 >>L800-802 FAULTED

Event Simulation Time From 0.00 hours To 4.00 hours
Freq= 0.1000000015 Occ/Yr, Dur= 4 hrs
Prob= 0.4000000060 Hrs/Yr

>>> 100% Load Curtailed

Event# 2 >>L802-806 FAULTED

Event Simulation Time From 0.00 hours To 5.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 5 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 3 >>L806-808 FAULTED

Event Simulation Time From 0.00 hours To 6.00 hours
Freq= 0.2000000030 Occ/Yr, Dur= 6 hrs
Prob= 1.2000000477 Hrs/Yr

>>> 100% Load Curtailed

Event# 4 >>L808-810 FAULTED

Event Simulation Time From 0.00 hours To 3.00 hours
Freq= 0.2500000000 Occ/Yr, Dur= 3 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 5 >>L808-812 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 2 hrs
Prob= 0.3000000119 Hrs/Yr

>>> 100% Load Curtailed

Event# 6 >>L812-814 FAULTED

Event Simulation Time From 0.00 minutes To 30.00 minutes
Freq= 0.1000000015 Occ/Yr, Dur= 30 minutes
Prob= 0.0500000007 Hrs/Yr

>>> 100% Load Curtailed

Event# 7 >>L814-850 FAULTED

Event Simulation Time From 0.00 hours To 4.00 hours
Freq= 0.1000000015 Occ/Yr, Dur= 4 hrs
Prob= 0.4000000060 Hrs/Yr

>>> 100% Load Curtailed

Event# 8 >>L816-818 FAULTED

Event Simulation Time From 0.00 hours To 3.00 hours
Freq= 0.2500000000 Occ/Yr, Dur= 3 hrs
79

Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 9 >>L816-824 FAULTED

Event Simulation Time From 0.00 hours To 5.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 5 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 10 >>L818-820 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 2 hrs
Prob= 0.3000000119 Hrs/Yr

>>> 100% Load Curtailed

Event# 11 >>L820-822 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
Freq= 0.0649999976 Occ/Yr, Dur= 2 hrs
Prob= 0.1299999952 Hrs/Yr

>>> 100% Load Curtailed

Event# 12 >>L824-826 FAULTED

Event Simulation Time From 0.00 hours To 6.00 hours
Freq= 0.2000000030 Occ/Yr, Dur= 6 hrs
Prob= 1.2000000477 Hrs/Yr

>>> 100% Load Curtailed

Event# 13 >>L824-828 FAULTED

Event Simulation Time From 0.00 hours To 5.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 5 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 14 >>L828-830 FAULTED

Event Simulation Time From 0.00 hours To 6.00 hours
Freq= 0.2000000030 Occ/Yr, Dur= 6 hrs
Prob= 1.2000000477 Hrs/Yr

>>> 100% Load Curtailed

Event# 15 >>L830-854 FAULTED

Event Simulation Time From 0.00 hours To 3.00 hours
Freq= 0.2500000000 Occ/Yr, Dur= 3 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

80

Event# 16 >>L832-858 FAULTED

Event Simulation Time From 0.00 hours To 5.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 5 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 17 >>L832-888 FAULTED

Event Simulation Time From 0.00 hours To 5.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 5 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 18 >>L834-842 FAULTED

Event Simulation Time From 0.00 hours To 6.00 hours
Freq= 0.2000000030 Occ/Yr, Dur= 6 hrs
Prob= 1.2000000477 Hrs/Yr

>>> 100% Load Curtailed

Event# 19 >>L834-860 FAULTED

Event Simulation Time From 0.00 hours To 6.00 hours
Freq= 0.2000000030 Occ/Yr, Dur= 6 hrs
Prob= 1.2000000477 Hrs/Yr

>>> 100% Load Curtailed

Event# 20 >>L836-840 FAULTED

Event Simulation Time From 0.00 hours To 5.00 hours
Freq= 0.2000000030 Occ/Yr, Dur= 5 hrs
Prob= 1.0000000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 21 >>L836-862 FAULTED

Event Simulation Time From 0.00 hours To 5.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 5 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 22 >>L842-844 FAULTED

Event Simulation Time From 0.00 hours To 3.00 hours
Freq= 0.2500000000 Occ/Yr, Dur= 3 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 23 >>L844-846 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
81

Freq= 0.1500000060 Occ/Yr, Dur= 2 hrs
Prob= 0.3000000119 Hrs/Yr

>>> 100% Load Curtailed

Event# 24 >>L846-848 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
Freq= 0.1099999994 Occ/Yr, Dur= 2 hrs
Prob= 0.2199999988 Hrs/Yr

>>> 100% Load Curtailed

Event# 25 >>L850-816 FAULTED

Event Simulation Time From 0.00 hours To 6.00 hours
Freq= 0.2000000030 Occ/Yr, Dur= 6 hrs
Prob= 1.2000000477 Hrs/Yr

>>> 100% Load Curtailed

Event# 26 >>L852-832 FAULTED

Event Simulation Time From 0.00 hours To 4.00 hours
Freq= 0.2000000030 Occ/Yr, Dur= 4 hrs
Prob= 0.8000000119 Hrs/Yr

>>> 100% Load Curtailed

Event# 27 >>L854-852 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
Freq= 0.1000000015 Occ/Yr, Dur= 2 hrs
Prob= 0.2000000030 Hrs/Yr

>>> 100% Load Curtailed

Event# 28 >>L854-856 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 2 hrs
Prob= 0.3000000119 Hrs/Yr

>>> 100% Load Curtailed

Event# 29 >>L858-834 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
Freq= 0.1199999973 Occ/Yr, Dur= 2 hrs
Prob= 0.2399999946 Hrs/Yr

>>> 100% Load Curtailed

Event# 30 >>L858-864 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
Freq= 0.1199999973 Occ/Yr, Dur= 2 hrs
Prob= 0.2399999946 Hrs/Yr

>>> 100% Load Curtailed
82

Event# 31 >>L860-836 FAULTED

Event Simulation Time From 0.00 hours To 2.00 hours
Freq= 0.1000000015 Occ/Yr, Dur= 2 hrs
Prob= 0.2000000030 Hrs/Yr

>>> 100% Load Curtailed

Event# 32 >>L862-838 FAULTED

Event Simulation Time From 0.00 hours To 5.00 hours
Freq= 0.1500000060 Occ/Yr, Dur= 5 hrs
Prob= 0.7500000000 Hrs/Yr

>>> 100% Load Curtailed

Event# 33 >>L888-890 FAULTED

Event Simulation Time From 0.00 hours To 6.00 hours
Freq= 0.2000000030 Occ/Yr, Dur= 6 hrs
Prob= 1.2000000477 Hrs/Yr

>>> 100% Load Curtailed

Abstract (if available)

Abstract Reliability of power systems is a key aspect in modern power system planning, design, and operation. The ascendance of the smart grid concept has provided high hopes of developing an intelligent network that is capable of being a self‐healing grid, offering the ability to overcome the interruption problems that face the utility and cost it tens of millions in repair and loss. To address its reliability concerns, the power utilities and interested parties have spent extensive amount of time and effort to analyze and study the reliability of the generation and transmission sectors of the power grid. Only recently has attention shifted to be focused on improving the reliability of the distribution network, the connection joint between the power providers and the consumers where most of the electricity problems occur. In this work, we will examine the effect of the smart grid applications in improving the reliability of the power distribution networks. The test system used in conducting this thesis is the IEEE 34 node test feeder, released in 2003 by the Distribution System Analysis Subcommittee of the IEEE Power Engineering Society. The objective is to analyze the feeder for the optimal placement of the automatic switching devices and quantify their proper installation based on the performance of the distribution system. The measures will be the changes in the reliability system indices including SAIDI, SAIFI, and EUE. The goal is to design and simulate the effect of the installation of the Distributed Generators (DGs) on the utility’s distribution system and measure the potential improvement of its reliability. The software used in this work is DISREL, which is intelligent power distribution software that is developed by General Reliability Co.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Aljohani, Tawfiq Masad (author)

Core Title Distribution system reliability analysis for smart grid applications

School Viterbi School of Engineering

Degree Master of Science

Degree Program Electrical Engineering

Publication Date 05/05/2014

Defense Date 03/12/2014

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

Tag automatic reclosers,automatic switches,distributed generation,distribution system reliability,OAI-PMH Harvest,power system reliability,smart grid

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 tmjohani@hotmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-410756

Unique identifier UC11296227

Identifier etd-AljohaniTa-2493.pdf (filename),usctheses-c3-410756 (legacy record id)

Legacy Identifier etd-AljohaniTa-2493.pdf

Dmrecord 410756

Document Type Thesis

Format application/pdf (imt)

Rights Aljohani, Tawfiq Masad

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