VOLTAGE STABILITY ASSESSMENT OF DUBAI POWER GRID by

VOLTAGE STABILITY ASSESSMENT OF DUBAI POWER GRID

USING A DETAILED LOAD MODEL

by

Salha Ali Al Disi

A Thesis Presented to the Faculty of the American University of Sharjah

College of Engineering in Partial Fulfillment of the Requirements

for the Degree of

Master of Science in Electrical Engineering

Sharjah, United Arab Emirates

June 2013

© 2013 Salha Al Disi. All rights reserved.

Approval Signatures

We, the undersigned, approve the Master’s Thesis of Salha Ali Al Disi. Thesis Title: Voltage Stability Assessment of Dubai Power Grid Using A Detailed Load Model Signature Date of Signature___________________________ _______________ Dr. Ahmed Osman-Ahmed Associate Professor Department of Electrical Engineering Thesis Advisor ___________________________ _______________ Dr. Awad Ibrahim Al-Baraasi Libya's Deputy Prime Minister Thesis Co-Advisor ___________________________ _______________ Dr. Ayman El-Hag Associate Professor Department of Electrical Engineering Thesis Committee Member ___________________________ _______________ Dr. Amr El-Nady Associate Professor Department of Electrical and Computer Engineering University of Sharjah Thesis Committee Member ___________________________ _______________ Dr. Mohamed El-Tarhuni HeadDepartment of Electrical Engineering ___________________________ _______________ Dr. Hany El Kadi Associate DeanCollege of Engineering ___________________________ _______________ Dr. Leland Blank Interim Dean College of Engineering ___________________________ _______________ Dr. Khaled Assaleh Director of Graduate Studies

Acknowledgments

All the praises and thanks are to Allah the Exalted, the Merciful for bestowing on me

his blessings and providing me with the strength to attain my Master Degree, which

has been a real challenge, yet an interesting experience. I am indebted to many

people, who in one way or another, contributed and extended their valuable support in

the preparation and completion of this thesis.

First and foremost, I would like to express my sincerest gratitude and appreciation to

my thesis advisors, Dr. Ahmed Osman and Dr. Awad Ibrahim, for their persistent

guidance, encouragement, understanding and patience throughout the course of my

thesis, whilst allowing me the room to work in my own way. Without their support

and motivation this thesis would not have been possible.

I also would like to express my warmest thanks and appreciation to my thesis

committee members, Dr. Ayman El-Hag and Dr. Amr Elnady, for their constructive

comments and valuable suggestions. My special thanks and appreciation also go to

Dr. Mohamed El-Tarhuni, Head of Department of Electrical Engineering, for his

unlimited support and assistance during my study at AUS.

My appreciation must be extended to Dubai Electricity and Water Authority (DEWA)

Management for sponsoring me to attain my Master Degree at the American

University of Sharjah (AUS).

I am also grateful to my family, and most especially my husband, for their

unconditional support, care and patience during the toughest times. I would like also

to express my gratitude to all my friends and colleagues at DEWA and AUS for

helping me get through the difficult times, and for all the emotional support they

provided.

I cannot end without emphasizing my sincere and profound gratitude to Dr. Awad

Ibrahim again for believing in me, asserting me to continue my graduate studies and

sparing me lots of his valuable time despite his tremendous responsibilities and

commitments.

“To the memory of my Parents

May Allah rest their souls in Heaven”

6

Abstract

Voltage stability problem has become one of the major concerns for power utilities in

recent years. This is due to the exponentially growing demands and the associated

stress on the power transmission resources. Moreover, voltage instability has been

responsible for severe network collapses world-wide and subsequently, the possible

threat of voltage instability is becoming more pronounced in power utilities. Dubai

Power Grid is undergoing similar circumstances. The increased stress on the power

resources in addition to the high proportion of motor driven loads, embedded in Air

Conditioning (AC) appliances, have raised the necessity to assess the voltage stability

of Dubai Power Grid. During large system disturbances, the transmission system

voltage can fall below a critical threshold, resulting in induction motors stalling or

tripping depending on several factors such as motor type, size and control. The

severity increases during peak load conditions, when the system load is dominated by

AC appliances. Recently, Dubai Power Grid had experienced several system

disturbances that were accompanied by small/large voltage variations. These

variations were followed by inadvertent disconnection of load. The existing Dubai

Power Grid load model is not capable of reflecting the actual system behavior

following the experienced disturbances. Having an accurate load model capable of

capturing load behavior during system disturbances is crucial in voltage stability

assessment. This thesis presents a detailed load model for Dubai Power Grid and

validates it against recorded disturbances. The updated load model will be used to

assess voltage stability margin against the increasing use of power transmission

resources, growing demand and associated stress on available and planned active and

reactive power resources.

Search Terms: Voltage Stability Assessment, Load Modeling, Measurement-Based

Approach, Component-Based Approach, Parameter Estimation.

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Table of Contents

Abstract ........................................................................................................ 6

List of Figures ..................................................................................................... 9

List of Tables .................................................................................................... 15

List of Abbreviations ........................................................................................ 17

List of Symbols.................................................................................................. 18

Chapter 1: Introduction ................................................................................. 20

1.1 Overview ............................................................................................................20

1.2 Motivation ..........................................................................................................21

1.3 Thesis Objectives...............................................................................................22

1.4 Contributions .....................................................................................................23

1.5 Thesis Outline ....................................................................................................23

Chapter 2: Theoretical Background .............................................................. 24

2.1 Power System Stability Definition...................................................................24

2.2 Power System Stability Classification .............................................................24

2.2.1 Rotor Angle Stability ..............................................................................26

2.2.2 Frequency Stability .................................................................................27

2.2.3 Voltage Stability .....................................................................................27

2.3 Voltage Stability Assessment ...........................................................................29

2.3.1 Static Voltage Stability Analysis ...........................................................30

2.3.2 Dynamic Voltage Stability Analysis .....................................................34

2.4 Power System Components Modeling for Voltage Stability Assessment.....35

2.4.1 Loads........................................................................................................35

2.4.2 Generators................................................................................................35

2.4.3 Reactive Power Compensation ..............................................................36

2.4.4 Protection and Controls ..........................................................................36

2.5 Load Modeling For Voltage Stability Assessment .........................................36

2.6 Basic Load Modeling Concepts........................................................................38

2.6.1 Static Load Model ...................................................................................39

2.6.2 Dynamic Load Model .............................................................................41

2.6.3 Acquisition of Load Model Parameters.................................................42

2.7 Literature Review on Load Modeling ..............................................................43

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Chapter 3: Updating Dubai Power Grid Load Model .................................. 57

3.1 Overview of Dubai Power Grid........................................................................57

3.2 Problem Statement.............................................................................................59

3.3 Methodology ......................................................................................................64

3.3.1 Component Based Approach Processes ................................................66

3.3.2 Measurement Based Approach Processes .............................................68

3.3.3 Validation of the Developed Load Model.............................................88

Chapter 4: Voltage Stability Assessment of Dubai Power Grid ................... 94

4.1 Overview of Dubai Power Grid Planning Standards ......................................94

4.2 Voltage Stability Assessment Methodology....................................................96

4.2.1 Software Tool ..........................................................................................96

4.2.2 Load Model Representation ...................................................................96

4.2.3 Study Considerations and Scenarios......................................................97

4.2.4 Steady State Voltage Analysis Results................................................100

4.2.5 Dynamic Voltage Analysis Results .....................................................114

Chapter 5: Conclusions, Recommendations and Future Work.................. 120

5.1 Conclusions......................................................................................................120

5.2 Recommendations ...........................................................................................122

5.3 Future Work .....................................................................................................123

References .................................................................................................... 125

Appendix A .................................................................................................... 129

Appendix B .................................................................................................... 130

Appendix C .................................................................................................... 138

Vita .................................................................................................... 150

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List of Figures

Figure 2.1: Classification of Power System Stability .................................................. 26 Figure 2.2: Voltage Stability Phenomena and Time Responses .................................. 28 Figure 2.3: A Simple Two Bus System ....................................................................... 31 Figure 2.4: PV Curves for Some Power Factor Values ............................................... 32 Figure 2.5: Different VQ Curves and Critical Operating Points ................................. 33 Figure 2.6: Induction Motor Equivalent Circuit .......................................................... 41 Figure 2.7: Component-Based Modeling Approach .................................................... 43 Figure 3.1: Dubai Power Grid Geographical Map ....................................................... 57 Figure 3.2: Electricity Installed Capacity & Peak Demand (2002-2012) .................... 58 Figure 3.3: Percentages of Energy Consumption for different Consumer Categories

by Year 2012 ............................................................................................ 59 Figure 3.4: Dubai Power Grid Load Composition (Existing Load Model) ................. 60 Figure 3.5: Recorded Voltage at MUSH 132 kV bus during MUSH Disturbance ...... 61 Figure 3.6: Frequency, System Requirement and Interchange Flow Trend during

MUSH Disturbance .................................................................................... 62 Figure 3.7: Zoomed in part of the Frequency Trend Curve during MUSH Disturbance ................................................................................................ 62 Figure 3.8: Recorded vs. Simulated Voltage Trend at MUSH 132 kV Bus – MUSH

Disturbance ............................................................................................... 63 Figure 3.9: Simulated Total System Load during MUSH Disturbance

(Existing Load Model) ............................................................................. 63 Figure 3.10 : Illustration of the Developed Load Modeling Methodology.................. 65 Figure 3.11: Result of Implementing Component Based Approach on Dubai Power

Grid ........................................................................................................... 68 Figure 3.12: Equivalent Circuit of the Selected Aggregate Load Model Structure ..... 69 Figure 3.13: Parameter Estimation Procedures ............................................................ 74 Figure 3.14: Measured Instantaneous Voltage and Current Signals –

Incident No.1 ............................................................................................ 75

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Figure 3.15: Calculated Line Positive Sequence Voltage and Current – Incident No.1 ............................................................................................ 75

Figure 3.16: Measured Instantaneous Voltage and Current Signals –

Incident No.2 ............................................................................................ 76 Figure 3.17: Calculated Line Positive Sequence Voltage and Current –

Incident No.2 ............................................................................................ 76 Figure 3.18: Measured Instantaneous Voltage and Current Signals –


Incident No.3 ............................................................................................ 77 Figure 3.20: Measured Instantaneous Voltage and Current Signals –


Incident No.3 ............................................................................................ 78 Figure 3.22: Measured and Estimated Active and Reactive Power – Incident No.1 ... 79 Figure 3.23: Measured and Estimated Active and Reactive Power – Incident No.2 ... 80 Figure 3.24: Measured and Estimated Active and Reactive Power – Incident No.3 ... 80 Figure 3.25: Measured and Estimated Active and Reactive Power – Incident No.4 ... 80 Figure 3.26: Aggregated Motor Currents ..................................................................... 81 Figure 3.27: Aggregated Motor Torque ....................................................................... 82 Figure 3.28: Aggregated Motor Speed ......................................................................... 82 Figure 3.29: Measured Instantaneous Voltage and Current Signals ............................ 83 Figure 3.30: Calculated Line Positive Sequence Voltage and Current ........................ 83 Figure 3.31: Measured and Simulated Active and Reactive Power............................. 84 Figure 3.32: Fast Tripping Pattern-Ilustration-1 .......................................................... 85 Figure 3.33: Fast Tripping Pattern-Ilustration-2 .......................................................... 85 Figure 3.34: Different Tripping Schemes (Fast Tripping Pattern) .............................. 86 Figure 3.35: Slow Tripping Pattern-Ilustration-1 ........................................................ 87 Figure 3.36: Slow Tripping Pattern-Ilustration-2 ........................................................ 87

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Figure 3.37: Extracted Slow Tripping Pattern related to the Triggering Voltage

Dip ............................................................................................................ 88 Figure 3.38: Recorded vs. Simulated Voltage Trend at MUSH 132 kV bus –

MUSH Disturbance .................................................................................. 90 Figure 3.39: Recorded vs. Simulated Voltage Trend at MUSH 132 kV bus –

MUSH Disturbance (New and Old Load Model) .................................... 90 Figure 3.40: Simulated Total System Load during MUSH Disturbance

(New Load Model) ................................................................................... 91 Figure 3.41: Recorded vs. Simulated Voltage Trend at MUSH 132 kV bus –

WRSN Disturbance .................................................................................. 92 Figure 3.42: Recorded vs. Simulated Voltage Trend at BKRA 400 kV bus –

WRSN Disturbance .................................................................................. 92 Figure 3.43: Simulated Total System Load during WRSN Disturbance ..................... 93 Figure 4.1: Typical Substation Layout a 400/132 kV Substation ................................ 98 Figure 4.2: Example of N-1 Contingencies ................................................................. 99 Figure 4.3: Example of N-2 Contingencies ................................................................. 99 Figure 4.4: Example of N-3 Contingencies ................................................................. 99 Figure 4.5: Calculating Active Power Transfer Margin from PV Curves ................. 100 Figure 4.6: Calculating Reactive Power Reserve Margin from VQ Curves .............. 101 Figure 4.7: PV Curves for NHDA 400/132 kV Substation – Base Case and All

Contingencies ......................................................................................... 102 Figure 4.8: PV Curves for NHDA 400/132 kV Substation –

All N-1 Contingencies ............................................................................ 102 Figure 4.9: PV Curves for NHDA 400/132 kV Substation –



Worst Contingencies .............................................................................. 104 Figure 4.12: PV Curves for MUSH 400/132 kV Substation –

Worst Contingencies .............................................................................. 106

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Figure 4.13: PV Curves for CARX 400/132 kV Substation – Worst Contingencies .............................................................................. 106

Figure 4.14: PV Curves for MBCH 400/132 kV Substation –

Worst Contingencies .............................................................................. 107 Figure 4.15: QV Curves for NHDA 400/132 kV Substation –

Base Case and All Contingencies ........................................................... 108 Figure 4.16: QV Curves for NHDA 400/132 kV Substation –

All N-1 Contingencies ............................................................................ 109 Figure 4.17: QV Curves for NHDA 400/132 kV Substation –



Worst Contingencies .............................................................................. 110 Figure 4.20: VQ Curves for MUSH 400/132 kV Substation –

Worst Contingencies .............................................................................. 112 Figure 4.21: VQ Curves for CARX 400/132 kV Substation –

Worst Contingencies .............................................................................. 112 Figure 4.22: VQ Curves for MBCH 400/132 kV Substation –

Worst Contingencies .............................................................................. 113 Figure 4.23: Illustration of Different Fault Locations ............................................... 115 Figure 4.24: Voltage Trends at NHDA 400 kV Bus for the Worst Contingencies

(Normal Fault Clearance Time) ............................................................. 116 Figure 4.25: Voltage Trends at NHDA 132 kV Bus for the Worst Contingencies

(Normal Fault Clearance Time) ............................................................. 117 Figure 4.26: Voltage Trends at NHDA 400 kV Bus for the Worst Contingencies

(Breaker Failure- Backup Protection Time) ........................................... 117 Figure 4.27: Voltage Trends at NHDA 132 kV Bus for the Worst Contingencies

(Breaker Failure- Backup Protection Time) ........................................... 118 Figure A.1: Simulink Model For Load Model Parameter Estimation ....................... 129 Figure B.1: Dubai Power Grid Planned 400 kV Network Topology – Year 2014 .... 130 Figure B.2: Dubai Power Grid Geogrphical Map ...................................................... 131

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Figure C.1: PV Curves for MUSH 400/132 kV Substation – Base Case and All Contingencies ................................................................ 138

Figure C.2: PV Curves for MUSH 400/132 kV Substation –

All N-1 Contingencies ................................................................................... 138 Figure C.3: PV Curves for MUSH 400/132 kV Substation –

All N-2 Contingencies ................................................................................... 138 Figure C.4: PV Curves for MUSH 400/132 kV Substation –

All N-3 Contingencies ................................................................................... 139 Figure C.5: QV Curves for MUSH 400/132 kV Substation –

Base Case and All Contingencies ................................................................ 140 Figure C.6: QV Curves for MUSH 400/132 kV Substation –



All N-3 Contingencies ................................................................................... 141 Figure C.9: PV Curves for CARX 400/132 kV Substation –

Base Case and All Contingencies ................................................................ 142 Figure C.10: PV Curves for CARX 400/132 kV Substation –



All N-3 Contingencies ................................................................................... 143 Figure C.13: QV Curves for CARX 400/132 kV Substation –

Base Case and All Contingencies ................................................................ 144 Figure C.14: QV Curves for CARX 400/132 kV Substation –



All N-3 Contingencies ................................................................................... 145 Figure C.17: PV Curves for MBCH 400/132 kV Substation –

Base Case and All Contingencies ................................................................ 146

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Figure C.18: PV Curves for MBCH 400/132 kV Substation –



All N-3 Contingencies ................................................................................... 147 Figure C.21: QV Curves for MBCH 400/132 kV Substation –

Base Case and All Contingencies ................................................................ 148 Figure C.22: QV Curves for MBCH 400/132 kV Substation –



All N-3 Contingencies ................................................................................... 149

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List of Tables

Table 3.1: Substations and Lines Statistics for Dubai Power Grid (2002-2012) ......... 58 Table 3.2: Load Tripping Rules for Motor Load (Existing Load Model) ................... 61 Table 3.3: Sample of Load Classes/Mixes in Dubai Power Grid – System Level ...... 66 Table 3.4: Typical Load Composition for Different Load Classes .............................. 67 Table 3.5: Modified Load Composition for Dubai Load Composition ....................... 67 Table 3.6: Parameters to be identified for the Aggregate Load Model ....................... 73 Table 3.7: Estimated Load Composition and ZIP Load Model Parameters for each

Incident ...................................................................................................... 81 Table 3.8: Estimated Unified Aggregate Motor Load Parameters .............................. 81 Table 3.9: Extracted Load Tripping Scheme (Fast Tripping Pattern) ......................... 86 Table 4.1: Steady State Voltage Levels for 400 kV and 132 kV levels....................... 95 Table 4.2: Maximum Active Power Transfer Margin for NHDA 400/132 kV

Substation – Base Case and All Contingencies ....................................... 105 Table 4.3: Maximum Active Power Transfer Margin for NHDA, MUSH, CARX and

MBCH 400/132 kV Substation – Base Case and Worst Contingencies .. 107 Table 4.4: Available Reactive Power Reserve Margin for NHDA 400/132 kV

Substation – Base Case and All Contingencies ....................................... 111 Table 4.5: Available Reactive Power Reserve Margin for NHDA, MUSH, CARX and

MBCH 400/132 kV Substation – Base Case and All Contingencies....... 113 Table 4.6: Details of Simulated Disturbances ............................................................ 114 Table B.1: Comprehensive Contingency List for NHDA 400/132 kV Substation .... 132 Table B.2: Comprehensive Contingency List for MUSH 400/132 kV Substation .... 134 Table B.3: Comprehensive Contingency List for CARX 400/132 kV Substation .... 135 Table B.4: Comprehensive Contingency List for MBCH 400/132 kV Substation.... 136 Table B.5: WECC Voltage Stability Criteria ............................................................. 137 Table C.1: Maximum Power Transfer Margin for MUSH 400/132 kV Substation –

Base Case and All Contingencies ............................................................ 139 Table C.2: Available Reactive Power Margin for MUSH 400/132 kV Substation –

Base Case and All Contingencies ............................................................ 141

16

Table C.3: Maximum Power Transfer Margin for CARX 400/132 kV Substation – Base Case and All Contingencies ............................................................ 143

Table C.4: Available Reactive Power Margin for CARX 400/132 kV Substation –

Base Case and All Contingencies ............................................................ 145 Table C.5: Maximum Power Transfer Margin for MBCH 400/132 kV Substation –

Base Case and All Contingencies ............................................................ 147 Table C.6: Available Reactive Power Margin for MBCH 400/132 kV Substation –

Base Case and All Contingencies ............................................................ 149

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List of Abbreviations

AC Air Conditioner AGC Automatic Generation Control AVR Automatic Voltage Regulator BKRA Bukidra CARX Car Complex CDSM Composite Dynamic Static Model DCP District Cooling/Chiller Plant DEWA Dubai Electricity and Water Authority DFR Digital Fault Recorder EPRI Electric Power Research Institute EUVLS Embedded Under Voltage Load Shedding GA Genetic Algorithm GABPE Genetic Algorithm Based Parameter Estimation HILP High Impact Low Probability HVDC High Voltage Direct Current LTC Load Tap Changer LV Low Voltage LV AC Low Voltage Alternating Current MBCH Mamzer Beach MUSH Mushrif NERC North American Electric Reliability NHDA Nahda PSO Particle Swarm Optimization PSS/E Power System Simulator for Engineers PTI Power Technologies Incorporation PV Active Power versus Voltage RMS Root Mean Square SCADA supervisory control and data acquisition SVC Static Var Compensator SVS Static Var System ULTC Under Load Tap Changer VQ Voltage versus Reactive Power WECC Western Electricity Coordination Council WRSN Warsan ZIP Constant Impedance- Constant Current- Constant Power

18

List of Symbols

A Torque Coefficient Proportional to the Square of the Speed B Torque Coefficient Proportional to the Speed C Constant Torque Coefficient α1 Percentages of Active Power Consumed by Static Load α2 Percentages of Reactive Power Consumed by Static Load β1 Percentage of Active Power Consumed by Dynamic Load β2 Percentage of Reactive Power Consumed by Dynamic Load E Electromotive Force I Current I+ Positive Sequence Current ids Direct Access Stator Current iqs Quadratic Access Stator Current P Active Power p.u. Per Unit PIM Active Power Consumed by Induction Motor PL Active Power Consumed by Aggregate Load PZIP Active Power Consumed by Static Load Q Reactive Power θ Power Angle QIM Reactive Power Consumed by Induction Motor QL Reactive Power Consumed by Aggregate Load QZIP Reactive Power Consumed by Static Load R Resistance Rr Rotor Resistance Rs Stator Resistance Te Electrical Output Torque TL Load (Mechanical) Torque To Initial Load (Mechanical) Torque V Voltage V+ Positive Sequence Voltage vdr Direct Access Rotor Voltage vds Direct Access Stator Voltage vqr Quadratic Access Rotor Voltage vqs Quadratic Access Stator Voltage ωb Motor Angular Electrical Base Frequency ωr Rotor Angular Electrical Speed ωe Stator Angular Electrical Frequency

19

X Reactance Xlr Rotor Leakage Reactance Xls Stator Leakage Reactance Xm Magnetizing Leakage Reactance ψdr Direct Access Rotor Flux Linkage ψds Direct Access Stator Flux Linkage ψmd Direct Access Magnetizing Flux Linkage ψqr Quadratic Access Rotor Flux Linkage ψqs Quadratic Access Stator Flux Linkage ψmq Quadratic Access Magnetizing Flux Linkage

20

Chapter 1: Introduction

1.1 Overview

Currently, most of the power systems around the world are being operated

under much more stressed conditions than were usual in the past. Environmental

pressure on transmission expansions, exponentially growing demands and penetration

of new types of loads (such as inverter-based appliances) at demand side are some of

the responsible factors for these stressed conditions. Under such stressed operational

conditions, a power system may exhibit instability behaviors that are characterized by

either slow or sudden voltage drops, i.e. voltage instability. Under certain conditions,

voltage instability may escalate to a form of voltage collapse which intimidates

system security. This was evidenced in several network collapses and blackouts

world-wide. Subsequently, voltage stability has become a major concern for power

system utilities [1].

Dubai Power Grid, being planned and operated by Dubai Electricity and Water

Authority (DEWA), is undergoing similar circumstances that made voltage stability a

critical issue. The growing demands and associated stress on the available and

planned system resources as well as the relatively limited geographical area of the city

had resulted in allocating most generation stations at one side of the city, hence,

feeding the load centers through long transmission circuits. Additionally, the

electrical load of Dubai has a particular nature; a significant amount of the supplied

load, especially in summer, is dominated by induction motor driven loads, specifically

Air Conditioning (AC) appliances. During system disturbances, such as faults,

transmission voltages may drop below certain thresholds resulting in either motor

stalling or tripping. Excessive motor tripping or stalling may result in either voltage

collapse or cascaded generator tripping, particularly if the reactive power

compensation facilities of the power system are not adequately sized. Therefore,

proper modeling of power system loads, with a focus on induction motor driven loads,

is essential for voltage stability assessment of a power system.

21

This thesis shall comprehensively assess Dubai Power Grid voltage stability margin

against the increasing use of power transmission resources, growing demand and

associated stress on available and planned active and reactive power resources; with a

special attention to the special load nature of the city.

1.2 Motivation

Dubai Power Grid had experienced several system disturbances that were

accompanied by small/large voltage variations. These voltage variations were

followed by unplanned disconnection of load. The largest amount of load loss was

37% of the total system load encountered following a single phase to ground fault.

This indicates the large proportion of the motor driven load represented mainly by AC

appliances. In general, motor driven loads such as AC appliances may respond to a

voltage dip in different ways depending on several factors such as motor type, size

and control. For example, a window AC unit typically has simple control; hence,

following a voltage dip the electrical torque may decrease below the mechanical load

torque causing the motor to stall, and the motor will eventually be disconnected by

thermal overload protection. On the other hand, a split system AC unit has contactors

that may respond to a voltage dip by disconnecting the motor before it stalls. The

consequences of excessive loss of load or excessive stalling may intimidate voltage

stability and hence system security. Such short-term voltage instability scenarios need

be studied in order to examine the dynamic interactions between the system loads and

the power grid.

Though, all the previous voltage stability studies that were conducted for

Dubai Power Grid show that voltage stability is maintained for all reasonable

contingencies and even for most contingencies beyond the normal design criteria. The

results of these studies are not in line with the actual system behavior following the

experienced disturbances. The reason behind these discrepancies is that all the

previous voltage stability studies were based on a tentative dynamic load model which

has three basic (load versus voltage) components: constant power, constant current

and constant impedance. These components of load model, while useful in the

absence of better information, do not always give an adequate characterization of a

system’s load versus voltage characteristic. Indeed, understanding the load nature is

22

very important for voltage stability assessment. The potential benefits of improved

load representation fall into the following categories [2]:

• If the existing load representation produces overly-pessimistic results:

- At planning level, the benefits of improved modeling will be in avoiding

premature investments by deferring or avoiding the expense of unnecessary

system modifications or equipment additions.

- At operational level, the benefits will be in increasing power transfer limits,

and more flexibility in operating the system with resulting economic

savings.

• If the existing load representation produces overly-optimistic results:

- At planning level, the benefit of improved modeling will be in avoiding

system inadequacies that may result in costly operating limitations.

- At operational level, the benefit may be in preventing system emergencies

resulting from overly-optimistic operational limits

Based on the above, it is becoming necessary to update Dubai Power Grid

Load Model to make it capable of representing, with reasonable accuracy, the load

behavior when subjected to actual voltage variations and hence assess the voltage

stability of Dubai Power Grid based on this updated load model.

1.3 Thesis Objectives

The objectives of this thesis are:

• To provide a methodology for developing aggregate load models for utility

power systems using real system data and measurements and test it on a real

power system.

• To update Dubai Power Grid load model, using the developed methodology,

to make it capable of capturing load behavior during system disturbances and

enhance the accuracy of voltage stability studies.

23

• To evaluate the voltage performance of Dubai Power Grid against the

increasing use of transmission system resources, growing demand and

associated stress on available active and reactive power resources.

1.4 Contributions

The main contributions of this thesis are:

• Developing a new hybrid methodology for developing aggregate load models

consisting of a combination of component based approach and measurement

based approach using real system data and measurements.

• Developing the load model using small voltage variation disturbances and

validating it against large voltage variation disturbances.

• Analysis of load characteristic during small and large voltage variations based

on field measurements, identification of load model parameters and embedded

load self-disconnection of induction motor loads (represented mainly by Air

Conditioning AC Appliances).

1.5 Thesis Outline

Chapter 1 of this thesis gives a general overview of the research topic, the

motivation and the objectives for carrying this research. Chapter 2 presents the

theoretical background of voltage stability problem, analysis techniques, load

modeling and its importance in voltage stability assessment, followed by a literature

review on the previous work conducted in the area. Chapter 3 starts with overview of

Dubai Power Grid and the problem statement, followed by the developed load

modeling methodology and its implementation for updating Dubai Power Grid Load

Model. Chapter 4 presents the results of voltage stability assessment of Dubai Power

Grid based on the developed load model. The conclusion of this research and

recommendations for future work are outlined in Chapter 5.

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Chapter 2: Theoretical Background

2.1 Power System Stability Definition

A typical modern power system is a high-order non-linear multivariable

dynamic system. The dynamic behavior of a modern power system is influenced by

its various components and their characteristics and response rates. Power system

stability has fundamental mathematical substructures that are comparable to the

stability of any other dynamic system. Accurate definitions of stability are available in

literatures that deal with the mathematical foundations of dynamic system stability.

According to IEEE/CIGRE Joint Task Force on Stability Terms and Definitions,

Power System Stability is defined as [3]:

Power system stability is the ability of an electric power system, for a given initial operating condition, to regain a state of operating equilibrium after being subjected to a physical disturbance, with most system variables bounded so that practically the entire system remains intact.

Power system components operate in a constantly changing environment

(dynamic behavior). As inferred from the above definition, the stability of the power

system, when exposed to a disturbance, depends on the system initial operating

condition as well as the nature of the disturbance [3].

2.2 Power System Stability Classification

Although power system stability is a single problem in principle, the various

forms of instabilities that a power system may experience cannot be appropriately

understood and effectively dealt with by treating it as such. Overall, stability is a

condition of balance between opposing forces. Different sets of opposing forces may

experience sustained imbalance depending on the system operating condition,

network topology, and the form of disturbance. Accordingly, power systems may

experience different types of instability. Therefore, it is essential to classify power

system stability phenomena to help power system engineers to analyze these

instabilities [1].

Previously, transient stability (large disturbance rotor angle stability) has been

the main stability concern for most power systems, and hence has been the focus of

25

industry’s attention. Different forms of system instability have emerged as a

consequence of power systems evolvement aroused from continuing growth in

interconnections, use of new technologies and controls, and the increased operation in

highly stressed conditions. For example, voltage stability, frequency stability and

inter-area oscillations have become greater concerns than in the past. A clear

understanding of different types of instability and how they are interrelated is

necessary for the satisfactory design and operation of power systems [3].

The classification of power system stability as proposed by per IEEE/CIGRE

Joint Task Force on Stability Terms and Definitions is based on the following

considerations [4]:

• The physical nature of the resulting form of instability as indicated by the

main system variable in which instability can be observed.

• The size of the disturbance which affects the method of calculation and

prediction of stability.

• The power system components and the time duration that must be taken into

consideration in order to assess stability.

In other literatures, power system stability is classified according to the

driving force of instability into generator-driven (rotor angle and frequency stability)

and load-driven (voltage stability). However, the terms (Generator-Driven) and

(Load- Driven) does not exclude the contribution of other system components to the

instability mechanism [1]. Power system stability can be classified according to

physical nature into three categories: rotor angle stability, frequency stability and

voltage stability. Figure 2.1 shows a general classification of the power system

stability problem [3].

26

Figure 2.1: Classification of Power System Stability [3]

2.2.1 Rotor Angle Stability

Rotor angle stability can be defined as the ability of synchronous machines of

an interconnected power system to stay in synchronism after being exposed to a

disturbance. It depends on the capability of the synchronous machines in the system to

maintain and/or restore balance between electromagnetic torque and mechanical

torque. The resulting instability occurs in the form of growing angular swings of some

generator units and subsequently to their loss of synchronism with other generator

units [3]. Rotor angle instability may be encountered in one of two forms [4]:

1) Undamped mechanical oscillations (lack of damping torque) initiated by

small system disturbances, and thus called steady-state or small signal

stability.

2) Monotonic rotor acceleration leading to loss of synchronism (lack of

synchronizing torque) initiated by large system disturbances and thus called

transient stability.

The time frame of both forms of rotor angle instability is in the range of few

seconds, where automatic voltage regulators (AVRs), excitation systems, turbine and

governor dynamics act, hence, the categorization of both rotor angle and the transient

stability as short term phenomena [1].

27

2.2.2 Frequency Stability

Frequency stability is the ability of a power system to maintain stable

frequency following a severe system incident. It depends on the ability to maintain

and/or restore balance between system generation and load, with minimum

inadvertent loss of load. The resulting instability occurs in the form of sustained

frequency swings and subsequently to cascaded tripping of generating units and/or

loads. The time frame for frequency instability ranges from few seconds (short term

phenomena corresponding to under-frequency load shedding schemes) to several

minutes (long term phenomena corresponding to prime mover dynamics) [3].

2.2.3 Voltage Stability

Voltage stability is the ability of a power system to maintain steady voltages at

all its buses after being exposed to a disturbance from a given initial operating

condition. It depends on the ability to maintain and/or restore balance between load

supply from the power system and load demand. Instability that may result occurs in

the form of a progressive drop or rise of voltages of some or all system buses [3].

Voltage stability covers a wide range of phenomena. Therefore, voltage stability

means different phenomenon to different engineers. It is a fast phenomenon for

engineers involved with induction motors, air conditioning loads or HVDC links. It is

however a slow phenomenon for engineers involved with mechanical tap changers

and other manual operator-initiated actions. Figure 2.2 shows that many system

components and controls play a role in voltage stability [5]. In various literatures,

voltage stability has been classified into short and long-term. Short-term voltage

stability corresponds to a time frame of about a few seconds, and is characterized by

the dynamics of components such as induction motors, static var compensators

(SVCs) and excitation of synchronous generators. On the other hand, long-term

voltage stability corresponds to slower time frames, around several minutes, and is

characterized by dynamic recovery of the load due to the action of on-load tap

changers, current limiter control in generators, corrective control actions such as

reactive compensation and load shedding, operator control actions, etc. [5].

28

Figure 2.2: Voltage Stability Phenomena and Time Responses [5]

Conditions causing the voltage instability may include one or more of the following

situations [4]:

• The power flow in the transmission lines is too high.

• The voltage/reactive power control resources are too far from the load centers.

• The source voltages (at generation side) are too low.

• The reactive power compensations facilities are insufficient.

Voltage instability may result in the loss of load in an area, or tripping of

transmission lines and other elements by their protective systems leading to cascading

outages. Loss of synchronism of some generators may also occur as a consequence of

these outages or due to other specific operating conditions that violate field current

limit [1].

The driving force for voltage instability is usually the loads. Following a

disturbance, power consumed by the loads tends to be restored by the action of motor

slip adjustment, distribution voltage regulators, tap-changing transformers, and

thermostats. Restored loads may increase the stress on the transmission network by

29

increasing the consumption of reactive power and causing additional voltage

reduction. Voltage instability may occur when load dynamics attempt to restore power

consumption beyond the capability of the transmission network and the connected

generation [1] and [6].

While the most common form of voltage instability is the progressive drop of

bus voltages, the jeopardy of overvoltage instability has been experienced by some

systems. It is caused by a capacitive behavior of the network (EHV transmission lines

operating below surge impedance loading) as well as by underexcitation limiters

preventing generators and/or synchronous compensators from absorbing the excess

reactive power. In this case, the instability is associated with the inability of the

generation and transmission system to operate below some load level. Transformer

tap changers may cause long-term voltage instability while attempting to restore this

load power [3].

2.3 Voltage Stability Assessment

Voltage stability assessment for a given power system involves the

examination of two aspects [4]:

• Proximity to voltage instability: how close is the system to voltage instability?

Distance to voltage instability can be measured in terms of physical quantities

such as system load level, active power flow through a critical interface and/or

reactive power reserve. However, the most appropriate measure for any given

situation is system specific, as it depends on many aspects including planning

versus operating decisions. Consideration must be given to potential

contingencies such as line outages, loss of a generation unit or a reactive

power source, etc.

• Mechanism of voltage instability: how and why does voltage instability

occur? What are the key factors contributing to voltage instability? What are

the voltage-weak areas? What are the most effective measures for improving

voltage stability?

30

Voltage stability assessment tools fall into two approaches: static and

dynamic. Static voltage stability analysis, which is based on power flow solutions, is

performed to identify the weak regions in terms of reactive power deficiency of the

system and determine the critical contingencies and voltage stability margins for

various power transfers within the power system. On the other hand, dynamic voltage

stability analysis, which is based on conducting time-domain simulations, is

performed to assess the ability of the power system, with a significant share of

rotating load, to operate satisfactorily following disturbances [7]. A comprehensive

voltage stability assessment would include both steady state and dynamic analyses

techniques as suggested in [8].

2.3.1 Static Voltage Stability Analysis

Static Voltage Stability Analysis (also called steady state voltage stability

analysis) is often used to analyze slower form of voltage instability making use of

power flow simulation as a primary study method. It is commonly tackled by plotting

PV and VQ curves. These two methods determine steady state loadability limits,

which are associated to voltages stability [5]. PV and VQ curves are useful for the

following purposes [9]:

• Defining voltage collapse point(s) in the power system network.

• Determining the maximum power transfer between different network buses

before voltage collapse point.

• Sizing the reactive power compensation devices required at relevant buses to

prevent voltage collapse.

• Assessing the impact of generator, loads and reactive power compensation

devices on the network.

In order to clearly understand PV and VQ curves, consider the two bus system as

shown Figure 2.3.

31

Figure 2.3: A Simple Two Bus System

The active and reactive power consumed by load at the receiving end bus is given by

(2.1) and (2.2), respectively [1]:

𝑃 =𝐸𝑉𝑋

𝑠𝑖𝑛𝜃 (2.1)

𝑄 =𝐸𝑉𝑋

𝑐𝑜𝑠 𝜃 −𝑉2

𝑋 (2.2)

By the help of the trigonometric identity (sin2 𝜃 + cos2 𝜃 = 1) we get:

𝑄 +𝑉2

𝑋2

+ 𝑃2 − 𝐸𝑉𝑋2

= 0 (2.3)

The voltage at the receiving end bus can be written as:

𝑉 = 𝐸2

2− 𝑋𝑄 ± 𝐸

2

4− 𝑋2𝑃2 − 𝑋𝐸2𝑄 (2.4)

Sample PV curves corresponding to different PF values are shown in Figure 2.4.

32

Figure 2.4: PV Curves for Some Power Factor Values

The upper part of the curves represents the stable region with 𝑑𝑃/𝑑𝑉 < 0,

while the lower part of the curves represents the unstable region with 𝑑𝑃/𝑑𝑉 > 0.

Voltage at the point of maximum loading margin, often referred to as ‘nose point’, is

known as “Critical Voltage”. This point is popularly referred to as collapse point [1].

Similarly, equation (2.2) can be rearranged as:

𝑉2 − 𝐸𝑉 cos 𝜃 + 𝑄𝑋 = 0 (2.5)

Therefore,

𝑑𝑄𝑑𝑉

=𝐸𝑐𝑜𝑠𝜃 − 2𝑉

𝑋 (2.6)

The voltage stability limit is reached at 𝑑𝑄/𝑑𝑉 = 0, this is the critical

operating point of the system [1]. The different VQ curves and critical operating

points are shown in Figure 2.5.

33

Figure 2.5: Different VQ Curves and Critical Operating Points

The right hand side of the curves represents the stable region with

dQ/dV > 0, while the left hand site of the curves represents the unstable region

with dQ/dV < 0. The bottom of the curves, often referred to as ‘knee point’,

represents the voltage stability limit [1].

2.3.1.1 PV Curves

PV curves are useful for conceptual voltage stability analysis and for studying

small or radial systems. This method is also used for large and meshed systems where

P is the total load in an area and V is the voltage at a critical or representative bus [5].

In principle, PV curve is a representation of voltage change as a result of increased

active power transfer between two systems (or subsystems). Tracing PV curves

requires a parametric study involving a series of power flows while monitoring the

changes in one set of power flow variables with respect to another. As power transfer

is increased in steps, voltage decreases at some buses on or near the transfer path. The

transfer capacity where voltage reaches a low value criterion (typically 90 to 95% of

the rated voltage) is the low voltage transfer limit. Transfer can continue to increase

until the solution identifies the proximity to the voltage instability, which is a “nose

point” on the PV curve where the voltage drops steeply in response of an increase in

34

the transfer power flow. Load flow solution will not converge beyond this limit,

indicating a voltage collapse transfer limit [10].

2.3.1.2 VQ Curves

VQ curves are used to determine the reactive power injection required at a bus

in order to control the bus voltage or set it to a required value. Like the PV curves, the

VQ curves are obtained through a series of load flow calculations. Starting with the

existing reactive loading at a bus, the voltage at the bus can be computed for a series

of power flows as the reactive load is increased in steps, until the power flow

experiences convergence difficulties indicating the proximity of a voltage collapse.

The bottom of the VQ curve, where the change of reactive power dQ, with respect to

voltage dV (dQ/dV) is equal to zero, represents the voltage stability limit. Since all

reactive power compensation devices are designed to operate satisfactorily when an

increase in Q results in an increase in V, the operation on the right side of the VQ

curve is stable, whereas the operation on the left side is unstable. The bottom of the

VQ curves defines also the minimum reactive power requirement for the stable

operation. Hence, the VQ curve can be used to examine the type and size of

compensation needed to provide voltage stability. Practically, this can be performed

by superimposing the VQ characteristic curves of the compensator devices on that of

the system [10].

2.3.2 Dynamic Voltage Stability Analysis

Dynamic analysis (also referred to as time-domain analysis) can capture the

evolvement of the instability process by simulating the transient response and timing

of control actions. This is done by solving a set of differential and algebraic equations

representing the system under study. Power systems networks typically include a

large number of dynamic and static components, where each individual component

may need several differential and algebraic equations to be represented. Accordingly,

the total number of differential and algebraic equations of a real power system can be

relatively large [11].

Voltage stability is a dynamic problem by nature; hence, dynamic voltage

stability analysis provides the most accurate response of the actual dynamics of

35

voltage instability when the appropriate modeling is included. Accurate dynamic

simulation reveals system trajectory after a disturbance; hence it is an essential tool

for post-disturbance analysis and the coordination of protection and control devices.

The dynamic voltage stability analysis is normally conducted by simulating the events

and chronology leading to voltage instability [1].

In contrast to static analysis in which equilibrium points of a PV/VQ curves

are time-independent, dynamic voltage stability analysis defines the time-dependent

voltage performance of the system. This method reveals the transient and/or the

longer-term voltage stability of a power system under study [9].

2.4 Power System Components Modeling for Voltage Stability Assessment

The accuracy of voltage stability assessment of a power system is associated

with the accuracy of modeling its components. Power system components need to be

carefully represented in the power system model to simulate their actual behavior

following system disturbances. Therefore, transformer Under Load Tap Changer

(ULTC) action, reactive power compensation at load side and voltage regulators in

the sub-transmission system must be carefully considered. The following are

descriptions of major power system elements that have a significant impact on voltage

stability, hence, need to be carefully represented in power system model [4]:

2.4.1 Loads

Voltage instability is a load driven disturbance; therefore, load characteristics

could be critical to voltage stability analysis. It is important to account for voltage

and frequency dependence of loads in the power system model, thus it is important to

carefully represent induction motors specifically. The appropriate representation of

load characteristics at low voltages is also essential [4].

2.4.2 Generators

Power system disturbances leading to voltage instability often involve

generation-load imbalances. This causes redistribution of power flow and reactive

losses. It is essential to represent how generation units respond to such disturbances

in the power system model [5]. For voltage stability, it is necessary to include the

36

droop characteristics of the AVR rather than to assume zero droop. If load

compensation exists, its effect should be represented. Field current and armature

current limits should also be represented specifically rather than using fixed values for

maximum and minimum reactive power limits. Additionally, for longer term

simulations in the range of few minutes, Automatic Generation Control (AGC)

functions need to be represented appropriately [4].

2.4.3 Reactive Power Compensation

Reactive power compensation is often the most effective way to improve both

power transfer capability and voltage stability. Reactive power compensation can be

divided into series and shunt compensation. It can also be divided into active and

passive compensation. Common forms of reactive compensation are series capacitor

banks, shunt reactors and capacitor banks and static var compensators (SVCs) [5].

For example, when SVCs are operating within the normal voltage control

range, they maintain bus voltage with slight droop characteristics. However, when

they operate at the reactive power limits, they become more or less similar to simple

capacitors or reactors. This could have a significant impact on voltage stability.

Therefore, the characteristics of reactive power compensation should be represented

appropriately in voltage stability studies [4].

2.4.4 Protection and Controls

Generating units and transmission networks protection and control must be

appropriately included in voltage stability analysis. Examples of these protection and

controls are generator excitation protection, armature over current protection,

transmission line overcurrent protection, capacitor bank controls, undervoltage load

shedding, etc. [4].

2.5 Load Modeling For Voltage Stability Assessment

In performing power system analysis, models must be developed for all

relevant power system components. These would include generating stations,

transmission, distribution equipment, and load devices. Models for generation,

transmission and distribution equipment had received great attention. Moreover, their

37

characteristics and parameters are generally very well-known they must comply with

the governing grid codes and regulations. On the other hand the representation of the

loads has received less attention and continues to be an area of greater uncertainty.

Several studies have shown that load representation can have a significant impact on

analysis results, especially in voltage stability assessment. Therefore, efforts directed

at improving load modeling are of major importance. The modeling of loads is

complicated due to a number of factors, including [2]:

• Abundance and diversity of load components.

• Location and ownership of load devices by customer sites, which make them

not directly accessible to the electric utility.

• Continuous change in load composition with time of day and week, seasons

and weather.

• Lack of precise information about load composition.

• Uncertainties about the characteristics of many load components, particularly

with respect to large voltage or frequency variations.

Consequently, load modeling in power system studies is based on a

considerable amount of simplification. Excluding detailed voltage stability analysis,

the aggregated load is represented in power system models as seen from bulk power

delivery points. The aggregated load represented at a transmission bus usually

includes, in addition to the connected load devices, the effect of sub-transmission and

distribution system lines, cables, reactive power compensation, LTC transformers,

distribution voltage regulators, and even relatively small synchronous or induction

motor [12].

Several literatures indicated that both the static and dynamic properties of

power system loads have a major impact on system stability. Load model uncertainty

was proved, in many publications, to be the major source for simulation inaccuracy.

Therefore, in any system stability study, especially voltage stability studies, it is

necessary to model loads accurately. Having accurate load models capable of

capturing load behavior during system disturbances enhances power system planners’

ability to anticipate potential risks and design power systems more precisely.

Therefore, AC appliances must be properly considered while modeling the electrical

38

load of the power system, in order to accurately predict system behavior following

small and large disturbances [13].

2.6 Basic Load Modeling Concepts

The term “LOAD” can have several meanings in power system engineering

depending on the context. The different meanings of the term "LOAD" include [2]:

• Load Device: A device connected to a power system that consumes power.

• System Load: The total power (active and/or reactive) consumed by all

devices connected to a power system.

• Generator or Plant Load: The power output of a generator or generating

plant.

• Bus Load: A portion of the system that is not explicitly represented in a

system model, but rather is treated as if it were a single power-consuming

device connected to a bus in the system model. Bus Load is the one that is of

main concern of this thesis.

When describing the composition of the load, the following terms are recommended

[2]:

• Load Component: A load component is the aggregate equivalent of all

devices of a specific or similar type, e.g., air conditioner, fluorescent lighting,

etc.

• Load Class: A load class is a category of load, such as, residential,

commercial, or industrial. For load modeling purposes, it is useful to group

loads into several classes, where each class has similar load composition and

load characteristics.

• Load Composition: The fractional composition of the load by load

components. This term may be applied to the bus load or to a specific load

class.

• Load Class Mix: The fractional composition of the bus load by load classes.

• Load Characteristic: A set of parameters, such as power factor, variation of

active power with voltage, etc., that characterize the behavior of a specified

39

load. This term may be applied to a specific load device, a load component, a

load class, or the total bus load.

The following terminology is commonly used in describing different types of load

models [2]:

A load model is a mathematical representation of the relationship between a bus voltage (magnitude and frequency) and the power (active and reactive) or current flowing into the bus load. The term “load model” may refer to the equations themselves.

Load models are conventionally categorized into static load models and dynamic load

models. The following sections explain both categories.

2.6.1 Static Load Model

Static load model expresses the active and reactive powers at any instant of

time as functions of the bus voltage magnitude and frequency at the same instant.

Static load models are used both for essentially static load components, e.g., resistive

and lighting load, and as an approximation for dynamic load components, e.g., motor-

driven loads [2]. The frequency dependence of loads is not addressed in this thesis,

since in voltage stability incidents the frequency exclusions are not of primary

concern. Various static load-voltage static models are explained below:

• Exponential Load Model : A static load model that represents the power

relationship to voltage as an exponential equation, usually in the following form

[2]:

𝑃 = 𝑃𝜊 𝑉𝑉𝜊𝛼

(2.7)

𝑄 = 𝑄𝜊 𝑉𝑉𝜊𝛽

(2.8)

Vo, Po and Qo are the reference values for voltage, active power and reactive power

respectively. The exponents α and β depend on the type of load. Common values for

the exponents α and β can be found in many literatures including [1] and [5].

However, three particular cases of load exponents are noteworthy:

40

• Constant Impedance Load Model (often noted Z): A static load model where

the power varies directly with the square of the voltage magnitude. It may also

be called a constant admittance load model and it is the first form of the

exponential load model when α = β =2.

• Constant Current Load Model (often noted I): A static load model where the

power varies directly with the voltage magnitude. It is the second form of

exponential load when α = β =1.

• Constant Power Load Model (often noted P): A static load model where the

power does not vary with changes in voltage magnitude. It may also be called

constant MVA load model and it is the third form of exponential load model

when α = β =0.

• Polynomial Load Model (often noted ZIP): A static load model that

represents the power relationship to voltage magnitude as a polynomial

equation, usually in the following form [2]:

𝑃𝑍𝐼𝑃 = 𝑃𝜊 𝑝1 𝑉𝑉𝜊2

+ 𝑝2 𝑉𝑉𝜊 + 𝑝3 (2.9)

𝑄𝑍𝐼𝑃 = 𝑄𝜊 𝑞1 𝑉𝑉𝜊2

+ 𝑞2 𝑉𝑉𝜊+ 𝑞3 (2.10)

With 𝑝1 + 𝑝2 + 𝑝3 = 1 and 𝑞1 + 𝑞2 + 𝑞3 = 1.

Any combination of constant impedance, constant current and constant load

models is called polynomial load model (ZIP).

The main advantage of exponential load model is its simplicity of parameters

identification procedure, since it has only two parameters (α and β) instead of six in

the polynomial (ZIP) load model (p1, p2, p3, q4, q5 and q6). However, the polynomial

(ZIP) load model could appear more of interest due to its physical meaning of

combining more than one load type. If for instance an active load is composed of 50%

of motors and 50% of impedance, it may be modeled using a ZIP load model with

a1 = a3 = 0.5 and a2 = 0 considering that the motor behaves roughly as a constant

power load.

41

2.6.2 Dynamic Load Model

Dynamic load model expresses the active and reactive powers at any instant of

time as functions of the voltage magnitude and frequency at past instants of time and,

usually, including the present instant. Difference or differential equations can be used

to represent such models. A Dynamic load model presents a time dependency that

generally describes a recovery of the load: following a voltage variation, the load

reacts instantaneously before recovering towards a power closer to the previous load

consumption. This class of model can describe phenomena as different as fast

recovery of a motor or as slow recovery of a thermostatic controlled load [2]. Power

systems with large percentage of induction motors require load dynamic

representation. Other dynamic aspects of load components are: the extinction of

discharge lighting with voltage drop, thermostatic control of loads such as space

heaters/coolers, operation of protective relays of some electric devices and response

Under Load Tap Changers (ULTC) of distribution transformers and voltage regulators

[4].

• Induction Motor Model: Most dynamic simulation programs include a

dynamic model based on the equivalent circuit shown in Figure 2.6. Other

features available in some programs are additional rotor circuits, saturation,

low voltage tripping, and variable rotor resistance [2].

Figure 2.6: Induction Motor Equivalent Circuit

It is important to note that the “slip” used in this model is the frequency of the

bus voltage minus the motor speed. Some programs incorrectly use either average

system frequency or 1.0 in place of the bus frequency. As with the frequency-

42

dependent load, such approximations will incorrectly represent damping effects.

Several levels of detail, based on this equivalent circuit, may be available in dynamic

simulation programs, including:

• A dynamic model including the mechanical dynamics but not the flux

dynamics.

• Addition of the rotor flux dynamics.

• Addition of the stator flux dynamics.

Stator flux dynamics are normally neglected in stability analysis and the rotor flux

dynamics may sometimes be neglected, particularly for long-term dynamic analysis.

Low voltage tripping is an important feature for voltage stability analysis and other

studies involving sustained low voltage [2].

2.6.3 Acquisition of Load Model Parameters

There are two basic approaches to the identification of load model parameters, these

are [4]:

• Component-Based Approach: This approach was developed by Electric

Power Research Institute (EPRI) under several research projects since 1976. It

involves building up the load model from information of constituent parts as

illustrated in Figure 2.7.

• Measurement-Based Approach: In which the load characteristics are

measured at representative substation and feeders at selected time of the day

and season. Alternatively load characteristics are monitored continuously from

naturally occurring system variations. The measurements are then used to

extrapolate the parameters of the load throughout the system. Measurement-

based load modeling provides a closer insight at the real-time power system

loads and their dynamic behavior. Nowadays, static and dynamic power

system behavior can be extracted from a number of data sources including

Power Quality (PQ) monitors, Digital Fault Recorders (DFR), etc.

43

Figure 2.7: Component-Based Modeling Approach [2]

Most dynamic simulation programs allow multiple generators, multiple motor

loads, and a single static load model to be connected to a bus. Generalization of this

capability was recommended in [12] to allow multiple loads of various types on a bus.

Each individual load type (static, induction motor, etc.) may have multiple

representations. For example, a bus load may consist of one or more static models,

one or more induction motors, and a synchronous motor. Each load type may have

load shedding or disconnection logic [12].

2.7 Literature Review on Load Modeling

In the past two decades, load modeling has received a great deal of attention.

Recent findings about the impact of load-to-voltage dependency on power system

voltage stability spurred further interest on the load modeling work. Research results

have shown that the voltage-dependent characteristics of bulk loads not only affect the

voltage secure operating region of a system but also influence the speed of voltage

collapse. A literature review of the key efforts done in Load Modeling during the past

two decades, arranged in chronological order, is given below.

44

In [14], M. K. Pal concluded that motor loads represent a special problem for

system voltage stability. Instability of motors may lead to system collapse and

accordingly, a detailed dynamic analysis is required to identify such instabilities.

In [6], D. J. Hill addressed the modeling of high voltage bus load models for

use in voltage stability studies. The paper concluded that models must account for the

lower level and regulating devices. Thus the models must be dynamic; for instance,

the effect of load recovery following a disturbance is important. The natural

differential equations for induction motors, heating, and tap-changing near a static

load are highly nonlinear and so difficult to parameterize for model estimation. A

somewhat simpler, but still nonlinear first order model was proposed by the author

based on assuming exponential recovery.

In [15], C.J Lin et.al implemented a measurement-based approach for dynamic

load model development. The approach has the advantage of direct measurement of

actual load behavior and can yield load models directly in the form needed for

existing computer program input. The paper described the procedure used for

applying a set of measured data from on-line transient recording system to develop

dynamic load models for the Taipower system. The authors concluded that the

developed second-order and third-order model structures are better in capturing load

behaviors during transients than the first-order load model structure used in earlier

publications. The authors stated that this improvement of capturing load behaviors, at

the expense of complexity in load model, shown up in both real and reactive powers.

They, however, acknowledged that it remains to be seen how many faults need to be

observed in order to derive an accurate dynamic load model.

Reference [2] presents the efforts done by IEEE Task Force on Load

Representation for Dynamic Performance to review the state of art of load

representation for dynamic performance analysis. The task force concluded that

considerable progress has been made in understanding load characteristics and,

particularly, in the methods for determining improved load model data. It was

recommended that serious attention should be given by power system analysts to the

load model and data used in their studies by following the below steps:

45

1. Familiarization with recent literature.

2. Selection of most realistic models for near-term use, based on available data.

3. Investigation of possible existing sources of data on system load characteristics,

such as billing data, load research studies and system disturbance monitors.

4. Development of a plan for acquisition of improved load data, either through

measurements or load mix/composition analysis.

In [16], D. Karisson et al presented a methodology for experimental

determination of aggregate dynamic loads in power systems. The methodology is

oriented to large disturbance studies and motivated towards voltage stability analysis.

The developed models can be expressed as first-order or higher-order differential

equations with a special structure related to steady state and transient load response.

The paper also presented convenient block diagram representation in terms of

nonlinear functions and linear transfer functions. The parameters of the model can be

identified from field measurement data using a combination of closed form formulae

(for step and ramp responses) and least square curve fitting. Identification of a load

model with recovery in the time scale of minutes has been carried out based on field

measurements. Load recovery was originated from thermostat controlled heating

devices; however, the effect of faster recovery in electrical motors has not been dealt

with.

Reference [17] presents the efforts done by W. Xu et al to investigate the

nature of voltage stability from the perspective of load dynamics. The study was

conducted using a generic load model that represents the dynamic behavior of

aggregate loads. The mechanism of voltage collapse was revealed by examining the

interaction of load dynamics with the supply network. The study concluded that:

• The dynamic behavior of aggregate loads, which can be characterized by time

constants, transient and steady-state load characteristic parameters is the main

factor for determining the dynamics of voltage collapse.

• The rate and the form of voltage instability under a large disturbance are functions

of load time constants, transient load characteristic parameters and the degree of

imbalance between load demand and network supply

46

• Network Jacobian matrix and steady-state load characteristics jointly determine

the small-signal voltage stability limit of a power system. The degree of small-

signal voltage stability, however, is also affected by the transient characteristics of

loads.

• The generic dynamic load model can easily be expanded to model more complex

forms of load responses.

• Modeling loads in an aggregate form simplifies voltage stability analysis.

Reference [12] addresses the efforts done by the IEEE Task Force on Load

Representation for Dynamic Performance as a follow up to their previous work

addressed in [2]. The objective of this publication is to promote better and advanced

load modeling, and to facilitate data exchange among users of various simulation

programs. For transient stability, longer-term dynamics, and small-disturbance

stability programs, the paper recommended the structure of multiple load types

connected to a load bus. The presented static models are suitable for power flow

simulations, and for dynamic simulations at locations where results are not sensitive

to load modeling. Additionally, the paper recommended induction motor models for

use at locations where results are sensitive to load modeling and for longer-term

dynamic simulations. The paper presented also the suitable data to be used with each

model.

Reference [18] presented a nonlinear composite dynamic-static load model

(CDSM) that was developed by P. Ju et al. The main features of CDSM are:

1. It includes the effect of voltage angle on transient active power, which may be

dominative in some cases.

2. It has a parallel structure, which means the total power consists of quasi-

steady-state component, voltage magnitude dependent transient component,

voltage angle dependent transient component, and frequency dependent

transient component.

3. It is applicable, with different time scales, to both angle stability and voltage

stability studies.

The authors also developed a genetic algorithm based parameter estimation (GABPE)

approach. The most attractive performance of the developed approach is its robustness

47

in a complex parameter search space. This approach has been successfully applied to

induction motor models, input-output models and neural network models. The

application results prove that GABPE is simple yet powerful.

In [19], A. Borghetti et al compared between three different simplified models

for describing the behavior of a load consisting of a static load and an aggregate of

induction motors. The different load models have been introduced at the far end of a

longitudinal-structured power system, to account for one of the critical conditions for

voltage stability. The authors concluded that:

• When a severe network disturbance occurs, voltage drops at the load side. The

induction motors absorb thus a greater amount of reactive power which leads

to a further decrease of the voltage at the load side and may result in the stall

of the same motors.

• In critical load conditions, this phenomenon can be predicted only if an

accurate dynamic model for the motor is employed.

• The knowledge of the percentage of asynchronous motors with respect to the

total load, as well as the mechanical load vs. speed dependence is of a primary

importance for selecting the appropriate load model for voltage stability

assessment.

In [20], W. Xu et al performed extensive load tests on the B.C. Hdyro system.

Important factors affecting test design and load parameter estimation were identified

and typical load parameters were determined. Finally, a simple and effective load test

procedure was proposed for future tests. The main conclusions of this paper are:

• Load responses due to disturbances caused by transformer tap changing are

sufficient to capture the dynamic and static load characteristics that are critical

to power system voltage security and stability studies. Transformer tapping

disturbances can be easily implemented without significant adverse effects on

the customers. Thus a special instrument may be built for regular load

characteristics monitoring. The instrument relies on the load responses to

voltage disturbances caused by such means as transformer tap changing to

estimate the load real and reactive power demand sensitivities to supply

voltages.

48

• Load parameters corresponding to both the summer and the winter seasons

indicate that the loads have significant real and reactive power demand

sensitivities to the supply voltage. This is largely due to the differences in the

seasonal-dependent load composition. It indicates the need for performing

load tests in different seasons.

In [21], L. M. Hajagos et al performed laboratory measurements and derived

models of modern loads subjected to large voltage changes and investigated their

effect on voltage stability studies. Low-voltage, long-time models of different type of

loads such as modern Air Conditioners (ACs), discharge lighting, and devices

containing electronic regulated power supplies were developed. Results of the

measurements showed that modern loads typically have a higher power factor, wider

voltage range, and more constant power characteristics than their predecessors. It was

also found that simulations performed with conventional load models produced both

conservative and optimistic results compared with studies using the accurate load

models derived in this project. Adherence to familiar-looking load model parameters

(e.g. 50% constant current, 50% constant impedance) does not necessarily accurately

represent load behavior. The paper strongly recommended that if detailed studies

involving load models are being performed, the accurate parameters derived in this

project can be used for the most important loads.

In [22], K. Tomiyama et al performed a series of measurements on single-

phase air conditioners in order to understand their voltage and frequency

characteristics and their interaction with the network dynamics. The voltage and

frequency characteristics of two conventional single-phase air conditioners and an

inverter air conditioner have been measured. The measurements indicated that:

1. The P of the conventional air conditioners studied is practically invariant

within a steady-state voltage range of 0.9 p.u. to 1.1 p.u. The Q of the

conventional air conditioners increases significantly when the voltage is high

and the frequency is low.

2. The P and Q of the inverter air conditioner are practically invariant within the

same voltage range and a frequency range of 55 Hz to 65 Hz.

49

3. Both conventional and inverter air conditioners stall around 0.6 p.u. voltage

when the voltage is decreased gradually. The inverter air conditioner drops out

at around 0.5 p.u. voltage, but the conventional air conditioner stays on line

and can aggravate low voltage situations.

4. Starting a conventional air conditioner at the rated voltage requires the

minimum energy and the minimum time integral of Q. The inverter air

conditioner starting characteristics are invariant within the studied voltage range

of 0.9 p.u to 1.1 p.u.

5. With severe voltage dips, the conventional air conditioner can stall and draw

significant amounts of P and Q for a few seconds before it drops out. The

inverter air conditioner drops out at the end of severe voltage dips. The

inverter air conditioner behaves well during voltage collapse situations while

the conventional air conditioner can aggravate the situations.

Simple models have been developed to represent the measured characteristics. The

load modeling approach has been used to correlate the observed bus load

characteristics to the estimated cooling load magnitude. The authors hoped that the

characteristics of the base load component and those of the cooling load component

can be determined separately.

In [23], S.Z Zhu et al analyzed the relation between the mechanism of voltage

stability and load models. Two methods, Instrumental Variable (IV) and Minimum

Sum of the Absolute Residues (MSAR) were used to identify the parameters of

nonlinear and linear load model. Eigenvalue-Structure Analysis (ESA) and two other

sensitivity methods based on ESA were used to analyze the voltage stability

sensitivity on state variables or control variables and sensitivity of total generated

reactive power. The study concluded that the load parameters and models have an

important effect on the study of voltage stability. The calculation results of voltage

stability with constant resistance are more conservative than the results obtained with

load parameters from real field test.

Reference [24] presented an improved composite load model structure that can

be used for measurement-based approach. This model was developed by J.H. Shi et

al. The authors stated that the improved composite load modeling structure was

50

compared with a typical dynamic load model and found that it can describe the

reactive power of the loads more accurately.

In [25], K. Morison et al provided a practical approach to develop models for

use in voltage stability analysis. Based on billing data or load inventory surveys, the

load is classified into broad types such as residential, industrial, and commercial load

and each is subdivided into more specific components including induction motors and

other elements. Time-domain simulations were used to establish the load

characteristics and general purpose models were synthesized from the results for use

in static analysis.

Reference [26] presented a neural network methodology for dealing with static

and dynamic load modeling developed by D. Chen et al. The load patterns were

classified by feed forward neural networks. Based on the static load model and

dynamic load model, either static voltage stability analysis or dynamic voltage

stability analysis can be made. The sensitivities involved in neural-network models

for loads were derived, and were then used in the Jacobian matrix, and further for the

modal analysis. Both static and dynamic voltage stability analyses were performed

which included use of the neural-network-based load models. The neural-network

methodology is tested on both the IEEE-14 bus system and real field data.

In [27], P. Pourbeik et al addressed the application of detailed load modeling

of large power system. The paper illustrated the importance of load modeling

assumptions to the results of a voltage stability study. In particular the following

factors were found to play a major role in the outcome of the study results:

• Load Amount: As the amount of load increases it becomes harder and harder

to sustain stability. That is, with more load there are more motors being slowed

down by a fault and accordingly more reactive support is needed.

• Fault Duration: The longer the fault lasts, the longer motor loads will

decelerate and thus the more likely motors will stall thus dragging system

voltages down.

• Fault Severity: The more severe the fault is, the greater the decelerating torque

on the motors. Also, for multi-phase unbalanced faults, the negative sequence

braking torque will further decelerate motors. Most transmission faults tend to

51

be single-line to ground faults, for which the consequences can be

significantly less severe than a 3-phase bolted fault.

• Fault Location: The closer the fault is to the substation, the lower the bus

voltage at the substation and the more severe the problem will be.

• Transmission Outages: The outage of a major transmission line may severely

compromise the ability of the transmission system to support voltages. This

will hamper the ability of the system to reaccelerate the affected motor loads.

• Motor Loads Amount and Characteristics: The lower the motor inertias are the

quicker the motors will decelerate. Also, the motor torque-speed curve will

dictate the break-down torque of the motors and thus the point at which they

stall.

Reference [28] addresses the efforts done by V. Stewart who deployed a

simple methodology for modeling the effects of stalled motor loads, particularly

residential ACs and refrigeration motor loads, for short-term voltage stability analysis

using a sample utility system. The methodology included using a constant-

impedance/admittance model for stalled motors, based on an assumed percentage of

motor loads and remaining loads using a complex load model with large and small

motor loads, discharge lighting constant power, and other loads. The active and

reactive power demands of stalled motor loads were determined based on basic

equations and assumed values of power factor and locked-rotor current for motors,

and then applied during dynamic simulations. Dynamic simulations using a sample

utility system were used to evaluate the performance of the stalled motor and complex

load models. The dynamic voltage profiles obtained illustrated effectively the impact

of stalled motor loads on system voltage stability.

In [29], B. K. Choi et al identified four dynamic linear and nonlinear models

based on eleven sets of measurement data gathered at different loading conditions in

Taiwan Power System. The process of model validation was performed using the

cross-validation technique. Analysis of parameter variation of each dynamic load

model with respect to different loading conditions was also conducted. From this

analysis, it was observed that the parameter values of each dynamic model can be

quite different for different loading conditions, which suggests that parameters of load

models should be properly updated in order to represent the load characteristic at each

52

loading condition. A simple and efficient method was presented to estimate a

representative parameter set for different loading conditions. The authors emphasized

that accurate dynamic load models can capture dynamic behaviors of reactive power

as well as real power during disturbances. However, the use of dynamic load model

increases the state–space dimension of the system. Considerable care must be taken in

attempting to validate the derived load models for practical use on the basis of time-

domain dynamic simulation. This is due to the fact that several components in power

system, including excitation system model, power system stabilizer model, and load

model, can significantly affect the time-domain simulation results. The authors

concluded finally that the linearized model proposed in this paper and the small

induction motor model appears to be good aggregate dynamic load models for both

real and reactive load behaviors.

In [30], K. Morison et al highlighted that the complexity of loads make it

impossible to represent loads exactly, and therefore, uncertainty is present in any

simplified or aggregate load models developed to represent load dispersed throughout

distribution systems. Sensitivity analyses should be conducted in order to assess the

impact of variations in the load parameters on both the system dynamic response and

also stability limits. Some of the recommended sensitivities which should be

considered include:

• The percentage of induction motors in the aggregate load model

• The induction motor parameters.

• The characteristic of the induction motor driven load.

• The effect of motor protections.

B. WU et al investigated the effects of air conditioner load on voltage stability

of an urban power system based on reactive power-voltage characteristics analysis of

Air Conditioners (ACs). The study which was addressed in [31] concluded that high

percentage of ACs at load bus will reduce the maximum reactive load supplied from

the bus and increase the critical voltage, and therefore push the bus voltage security

into danger. A further analysis of loading margin employing continuation power flow

shows that AC load may reduce the maximum loadability of the system, and plays an

adverse role in the voltage stability of power systems. The paper recommended the

53

installation of sound reactive power compensation measure to enhance voltage

stability of urban power system.

In [32], L. Y. Taylor et al made use of the actual voltage signature captured by

data fault recording equipment of a practical multi-fault multi-breaker failure to depict

an accurate load model capable of representing the incident. An aggregate load model

made up of additional distribution system impedance, 50% small induction motor, and

50% static load resulted in successful replication of the delayed voltage recovery

event through dynamic simulations. The same model has been successfully utilized in

the replication of voltage signatures resulting from milder fault events. However, it

was recognized that the aggregate load model could possibly be improved by

including the embedded load tripping due to induction motor protective relay action.

T. Parveen et al developed an approach for decomposition/separation of the

composite induction motors load from measurement at a system bus [33]. In power

system transmission buses, load is represented by static and dynamic loads where the

induction motor is considered as the main dynamic loads and in the practice for major

transmission buses there will be many and various induction motors contributing to

the load composition. Particularly, at an industrial bus most of the load is dynamic.

Rather than trying to extract models of many machines, the paper seeks to identify

three groups of induction motors to represent the dynamic loads. Three groups of

induction motors were used to characterize the load. These were: the small motors

group (4 kW to 11 kW), the medium motors group (15 kW to 180 kW) and the large

motors group (above 630 kW). At first, these groups with different percentage

contribution of each group were composite. After that from the composite models,

each motor percentage contribution was decomposed by using the least square

algorithm. To apply this theory to other types of buses such as residential and

commercial it is good practice to represent the total load as a combination of

composite motor loads, constant impedance loads and constant power loads. To

validate the theory, the 24hrs of Sydney West data was decomposed according to the

three groups of motor models.

In [34], K. Yamashita et al used measured data to identify static and dynamic

load model parameters. Transfer function-based simple dynamic load model was

applied for identifying static and dynamic load model parameters. The derivation

54

method of static load model parameters, with and without load drop, was developed.

The load model responses calculated by the measurement data (load bus voltage and

frequency) and the identified static load model parameters were consistent with the

measurement data. The derivation method of dynamic load model parameters without

load drop through high frequency measurement data was also developed. The

dispersion of the identified dynamic load model parameters is sufficiently small,

which means the dynamic load model is effective for representing load response with

voltage drop within around 30%. However, the response of the transfer function-

based dynamic load model were not consistent with around 20 % of the measurement

data mainly because the load time constant immediately after the fault was different

from the time constant immediately after the fault clearance. This tendency can be

seen in the response of induction motors.

I. F. Visconti et al proposed a methodology of measurement-based load

modeling for transient stability analysis [35]. The paper presented practical results

compared to power quality recordings. The technique proposed to estimate load

model parameters based on Genetic Algorithms (GA). The following steps summarize

the system identification procedure for load modeling:

• Select input-output system data.

• Transform, resample and cluster data.

• Choose an appropriate model structure.

• Estimate load model parameters.

• Validate load model.

A second order autoregressive model proved to cover a wide range of situations.

Moreover, the third and fifth order autoregressive models were also tested but the

results were not much different from second order models. The authors

recommended that measurement-based load modeling should be adopted by utilities

interested in taking full advantage of their resources and data storage by power quality

monitors. They concluded that measurement-based load modeling is the state-of-the-

art on load representation and shall improve reliability on bulk power system stability

studies.

55

In [36], S. A. Al Dessi et al developed a detailed dynamic load model for

Dubai Power Grid based on a monitoring campaign of the transmission network and

staged testing of sample Low Voltage Air Conditioning (LV AC) appliances. The

study highlighted the growing dimension of the fast embedded under voltage load

shedding (EUVLS) phenomenon which is driven by the advent of the inverter based

appliance electrical interfaces, and the protection settings of the main components of

the large chillers and cooling plants. The adopted load model has shown that the

EUVLS phenomenon can significantly affect the nature and the design of the

mitigation measures to secure the voltage stability of the power system, especially the

required operating range of the fast acting MVAr reserves such as SVS. Due to load

diversity, the study suggested monitoring the amplitude of the EUVLS phenomenon

with dedicated DFR devices in order to reduce the severity of the assumptions

performed and to remain on the secure side of the voltage stability assessment.

In [37] P. Regulski et al presented a compact solution based on instantaneous

measurements to extract parameters of a load model, which includes estimation of

power components based on an Improved Recursive Newton Type Algorithm and

estimation of parameters of a dynamic Load Model using Genetic Algorithms. The

paper demonstrated the influence of the preprocessing of the instantaneous values on

the final estimation of load parameters. The method has been tested in two stages

using 9-buses P.M.Anderson test system built in DIgSILENT. Firstly, the quality of

extracted RMS values has been presented, followed by the estimation of parameters

of a dynamic load model performed by GA. The procedure returned considerably

good results proving that the combination of both methods is suitable for this task.

M. Tomoda et al proposed a dynamic load model considering a change of flux

inside the induction motor for transient stability analysis and presented a new method

for estimating unknown parameters of the dynamic load model [38]. The model is a

parallel composite of a constant impedance load and an induction motor load behind a

series constant reactance. This model can represent the behavior of actual load by

using appropriate parameters. However, the problem of this model is that it has a large

amount of parameters and it is not easy to estimate a lot of unknown parameters.

Therefore, the authors proposed an estimating method based on Particle Swarm

Optimization (PSO), which is a non-linear optimization method, by using measured

56

instantaneous voltage sag data. The model was tested and it was confirmed that the

proposed method was successful and PSO is effective in parameter estimation.

In [39], B. Ho-Kim et al addressed the composite load model in which the ZIP

model and the induction motor model are used for the static and dynamic load models

respectively. The paper described the parameter estimations needed for the selected

load model. The authors obtained the measurements using the TSAT simulation

program. The parameter estimation using the least square method is then verified

using these measurements. The author concluded that, despite some oscillations, the

results are satisfactory in general and the calculation time is fast. Accordingly, they

recommended using the parameter estimation method for real time applications.

In summary power system loads have a significant impact on the stability of a

system. A proper understanding of load behavior is crucial in voltage stability

analysis, as it can provide information necessary for the accurate assessment of

different scenarios. However, accurate load modeling continues to be a difficult task

due to the complex and changing nature of the load and the difficulty in obtaining

accurate data on its characteristics, hence, uncertainty is encountered in any simplified

or aggregate load model. Therefore, sensitivity studies are recommended to determine

the impact of the load characteristics on the study results of interest. This will help in

guiding the selection of a conservative load model or focusing attention on where load

modeling improvements should be sought.

57

Chapter 3: Updating Dubai Power Grid Load Model

3.1 Overview of Dubai Power Grid

The Dubai Electricity and Water Authority (DEWA) was formed by a decree

issued by His Highness Sheikh Maktoum bin Rashid Al Maktoum on January 1,

1992, to take over and merge the Dubai Electric Company and the Dubai Water

Department that had been operating independently for several years until then. Both

these organizations were established in 1959 through the foresight and initiative of

His Highness Sheikh Rashid bin Saeed Al Maktoum, the former Ruler of Dubai, as

government supported bodies with the objective of making available to the people of

Dubai an adequate and reliable supply of electricity and water [40]. Figure 3.1 is a

geographical map of Dubai showing the existing power system [41].

Figure 3.1: Dubai Power Grid Geographical Map [41]

Dubai Power Grid has been developing rapidly since the foundation of the

utility in the emirate of Dubai in 1959. Initially, the generation and transmission

voltage was at 6.6 kV and gradually a higher voltage of 33 kV was introduced in

1969, which remained as the primary transmission voltage till 1977. During 1978, the

132 kV system was introduced in Dubai and in 1993, 400 kV lines and substations

58

were introduced. There has been a considerable growth in the power system

infrastructure since the introduction of the first 132 kV substations in 1978. The rapid

growth of the system can be seen from the statistics related to the installed capacity

and peak demand as shown in Figure 3.2, as well as the substations and lines statistics

shown in Table 3.1 [41].

Figure 3.2: Electricity Installed Capacity & Peak Demand (2002-2012) [41]

Table 3.1: Substations and Lines Statistics for Dubai Power Grid (2002-2012) [41]

Description Voltage Unit 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Number of substations

400 kV Nos 7 7 7 8 10 11 12 14 14 17 18

132 kV Nos 31 33 37 52 69 88 116 139 153 165 184

Overhead Lines

400 kV km 291 291 291 344 583 637 766 870 870 875 876

132 kV km 543 543 543 535 529 517 466 466 459 437 437

Underground Cables

132 kV km 228 258 266 369 495 642 867 1002 1137 1250 1486

The total number of consumers by end of year 2012 is 624445 classified into:

Residential, Industrial, Commercial and Others (Non-Commercial such as Mosques,

Police Stations, Government Hospitals, Government Schools, DEWA Offices & Staff

premises, etc.). The total electricity consumption by year 2012 reached 35124

59

(GWhs). Figure 3.3 shows the percentage of different consumer categories and the

corresponding electricity consumption for 2012 [40].

Figure 3.3: Percentages of Energy Consumption for different Consumer Categories - Year 2012 [40]

3.2 Problem Statement

A significant part of the load in Dubai and the Gulf countries in general is

dominated by AC appliances, especially during summer period. Recently, the

demand side at Dubai Power Grid had incurred enormous penetration of large and

small motor equipment ranging from small home Air Conditioning (AC) appliances to

large District Cooling/Chiller Plants (DCP’s). During large system disturbances, the

transmission system voltage can fall below a critical threshold, resulting in induction

motors stalling or tripping depending on several factors such as motor type, size and

control. Voltage collapse can occur when stalled motors remain connected to the

system, or motors previously disconnected are automatically reconnected causing a

large, mostly reactive, starting current. On the other hand, in case of excessive motor

tripping, the system voltages may increase above the specified planning and

operational limits. Consequently, and in case the reactive power compensation

facilities were not adequately sized in the power system, the excess reactive power

will be absorbed by generators driving them operate in an under-excited mode which

may eventually lead to their cascading tripping.

The Dubai Power Grid blackout of the 9th of June 2005 showed that load

variation, which took place after the under frequency load shedding was not in line

with the expected behavior. Moreover, on 18th of June 2006, 37% of the supplied

load was inadvertently shed at customer side following a single phase to ground fault

60

at 'L' Power Station 400 kV bus. Both incidents raised the importance of having

accurate representation of the supplied loads considering frequency and voltage

dependency. Accordingly, in 2008, DEWA decided to develop a more detailed load

model for Dubai Power Grid to be used for dynamic stability studies (especially the

ones leading to severe transients of the power system). A dedicated monitoring

campaign was conducted on five selected 132/11 kV substations at 11 kV side. The

selected monitored substations represent different load classes and mixes including:

residential, commercial, industrial, and district cooling.

The load modeling effort also included laboratory staged tests performed on

representative set of LV AC appliances that are commonly used in Dubai. The overall

monitoring campaign and the laboratory tests gave clear evidence that a portion of the

load is subject to a sudden disconnection following voltage disturbances. The efforts

done were addressed in [36]. In summary, the developed load model adopts the

assumption of a homogenous load composition at all load substations, which is 35%

static load and 65% motor load (of which 35% are large motors and 30% are small

motors) as illustrated in Figure 3.4 .

Figure 3.4: Dubai Power Grid Load Composition (Existing Load Model) Moreover, five different stages were proposed to represent motor tripping following

the occurrence of voltage dips. The percentage of tripped motors is directly related to

the magnitude of the dip and all the tripping was assumed to occur simultaneously

(after 250 ms delay time) as described in Table 3.2 below.

61

Table 3.2: Load Tripping Rules for Motor Load (Existing Load Model)

Stage Voltage Threshold

Load Tripping Fraction Sensing Time Tripping Time

1 90% 19% 50 ms 200 ms 2 72.5% 37% 50 ms 200 ms 3 55% 55% 50 ms 200 ms 4 37.5% 77% 50 ms 200 ms 5 20% 77% 50 ms 200 ms

On 20th of October 2009, a single phase to ground fault at the 400 kV bus of

Mushrif (MUSH) 400/132 kV load substation resulted in tripping 8% of the total

supplied load. Figure 3.5 shows the recorded voltage at MUSH 132 kV bus. The short

circuit resulted in a sudden voltage drop. After fault clearance, the voltage recovered

to about 100 kV. After reacceleration of the motors the voltage recovered to about

117 kV within 0.5 seconds demonstrating a fast voltage increase. Thereafter, the

voltage recovered to the pre-fault voltage during a 10 seconds period, demonstrating a

slow and smooth voltage increase. The same conclusions can be deduced from

frequency trend of the same disturbance. Figure 3.6 shows the frequency, system

requirement and interchange flow trend on 20th of October 2009. A zoomed in part of

the frequency trend curve during this incident is shown in Figure 3.7.

Figure 3.5: Recorded Voltage at MUSH 132 kV bus during MUSH Disturbance [42]

62

Figure 3.6: Frequency, System Requirement and Interchange Flow Trend during MUSH Disturbance [42]

Figure 3.7: Zoomed in part of the Frequency Trend Curve during MUSH Disturbance [42]

It is obvious from Figure 3.6 and Figure 3.7 that there is a temporary

frequency rise due to the short circuit at Mushrif. After fault clearing, the frequency

drops back again and then rise again within 10 seconds indicating reduction in the

system load and hence a slow voltage recovery in the voltage during the same period.

63

The existing load model was used to replicate the disturbance by performing

dynamic simulation. However, there were great discrepancies between the measured

and simulated voltages as shown in Figure 3.8. This is due to the fact that the

developed load model was built on the assumption of simultaneous tripping of motor

loads following voltage dips whereas the actual disturbance indicated that a part of the

load tripped during 0.5 second period after fault clearance, and another part tripped

during 10 second period. Moreover, the simulation resulted in tripping a higher

amount of load than the actual recorded amount as shown in Figure 3.9. Therefore, it

is obvious that the developed load model needs further modification in order to

represent, with accepted accuracy, the actual load behavior of Dubai Power Grid

following system disturbances.

Figure 3.8: Recorded vs. Simulated Voltage Trend at MUSH 132 kV Bus – MUSH Disturbance

(Existing Load Model)

Figure 3.9: Simulated Total System Load during MUSH Disturbance (Existing Load Model)

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6 7 8 9 10

V(p.u.)

Time (s) V_MUSH_132_Recorded V MUSH 132kV - Existing Load Model

3000

3500

4000

4500

5000

5500

6000

0 1 2 3 4 5 6 7 8 9 10

Total System Load (MW)

Time (s)

Tripped Load = 12%

64

3.3 Methodology

Whatever the methodology used by the utility to build the load model, the

developed load model should answer three questions [42]:

1. What is the appropriate load model structure that is capable of representing the

aggregated load?

2. For the selected load model structure, how should the load model parameters be

identified?

3. Does the developed load model have an adequate generalization capability?

Based on the analysis done in [30], the following basic principles of load modeling

were developed:

1. The load model is built mainly for performing stability studies; therefore, it should

be easily integrated into the power system analysis tool.

2. The developed load model should be general and valid under most scenarios. For

special studies, a special load model should be built according to the study

requirement.

3. The load model should have clear physical interpretations in order to be accepted

and easily implemented by the system analysts and operators

The above principles were taken into consideration while formulating the

methodology to update the existing Dubai Power Grid Load Model. The developed

methodology is a combination of the two basic approaches to the identification of

load model parameters addressed in 2.6.3, namely, Component Based Approach and

Measurement-Based Approach. This hybrid methodology is suitable for developing

load models for any power system based on real data and actual system

measurements. The new hybrid methodology is described in details in the following

sections and its different process is illustrated in Figure 3.10

65

Figure 3.10 : Illustration of the Developed Load Modeling Methodology

66

3.3.1 Component Based Approach Processes

It has been found from literature that surveying “load composition” instead of

“load component” to develop models for dynamic simulations is more practical to

derive the basic load models for short-term voltage stability studies. This means,

individual devices are not surveyed, but rather load classes [30].

3.3.1.1. Classification of Load Classes and Mixes

Classification of load into classes and mixes can be done at either transmission

or distribution system level, depending on the nature of the study that shall be

performed using the developed load model. For global system studies, classification

of load at transmission system level is adequate. However, for special load zone

studies, load classification needs to be done at the distribution system level. In this

thesis, the purpose of the developed load model is to assess the overall system voltage

stability; therefore, the load classification is done at the transmission system level,

which means at the end of the process, the identified load composition will be unified

for the whole system. Another reason behind this choice of classification level is that

Dubai Power Grid is compact and almost every load zone has similar load

composition, i.e. each load zone is feeding all different types of load classes with very

close percentages. Moreover, the system is growing quickly requiring shifting load

substations from a zone to a nearby one resulting in higher uncertainty of load

composition for each zone. Based on this, and with the help of the concerned

departments in DEWA, the existing 132/11 kV load substations were classified into

classes and mixes including: Residential, Commercial, Industrial,

Residential/Commercial etc. Table 3.3 is an excerpt of the obtained information.

Table 3.3: Sample of Load Classes/Mixes in Dubai Power Grid – System Level

No. Load

Substation Name

Voltage Level (kV)

Load Class/Mix Percentage (%)

Residential Commercial Industrial District Cooling

Plant Total

1 BDXB 132/11 50 50 100 2 DMAL 132/11 50 50 100 3 OTWN 132/11 15 15 5 65 100 4 FRDH 132/11 50 50 100 5 HLAL 132/11 25 30 45 100 6 KHLS 132/11 15 10 75 100

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3.3.1.2. Identification of Load Composition for Each Load Classes and

Mixes

Resistive, Small Motor, Large Motor, Discharge Lighting, and other

components are identified based on the load classification (percentage of Residential,

Commercial and Industrial) and composition (percentage of each component in each

load class) obtained from published typical models. Unless better information is

available, this typical data can be used in deriving the overall load model. Table 3.4

is an example of the available typical load compositions that utilities serve [30].

Table 3.4: Typical Load Composition for Different Load Classes [30]

Load Class Load Composition (%)

Residential Commercial Industrial

Resistive 25 14 5

Small Motor 75 51 20

Large Motor 0 0 56

Discharge Lighting 0 35 19

Table 3.4 was modified based on the conducted demand side survey to reflect

the load compositions for each load class in Dubai Power Grid. The fact that some

residential and commercial areas are being cooled by District Cooling/Chiller Plants

was behind adding a percentage of large motors to these two classes. On the other

hand, the discharge lighting was aggregated with the resistive load and given a more

general name, Static. The modified load compositions are shown in Table 3.5.

Table 3.5: Modified Load Composition for Dubai Load Composition

Load Class

Load Composition (%)

Residential Commercial Industrial District Cooling

Plant

Static 25 49 24 10

Small Motor 75 51 20 20

Large Motor 0 0 56 70

Moreover, based on the conducted demand side survey small motors were

divided into two categories: Conventional (non-inverter based) and Non-Conventional

(inverter based). The result of the implemented component based approach processes

68

is illustrated in Figure 3.11 which shows Average Load Compositions at system level.

As explained earlier, the identified load composition can be used for global system

voltage stability studies, which is in line with the scope of this thesis. For local

voltage stability studies concerning a certain load zone, detailed load compositions for

the zone under study may be required.

Figure 3.11: Result of Implementing Component Based Approach on Dubai Power Grid

3.3.2 Measurement Based Approach Processes

System identification is considered as the theoretical foundation of

measurement-based approach. It has developed into a mature engineering discipline,

which is widely applied in many branches of modern engineering. The process of

system identification involves finding a mathematical model (suitable model

structure) with a set of parameters that is capable of accurately replicating the

dynamic response of the system [13]. Once the model structure is selected, the model

parameters can be estimated using parameter estimation techniques. The objective

function for parameter estimation is usually defined as the minimization of the

difference between model outputs and actual measurements. Generally, measurement

based approach for load modeling consists of three steps; selection of a suitable

aggregate load model structure, parameter estimation and load model validation [43].

69

3.3.2.1. Selection of Aggregate Load Model Structure

The selection of the load model structure depends on various aspects including

the following [30]:

1. The load model structure should not be very simple. A very simple load model

will result in a big bias error, and hence great discrepancy between the

measured and estimated quantities.

2. The load model structure should not be very complex. A very complex load

model will result in a high model variance error. Therefore, while a complex

load model may fit the training data well, nevertheless, it may have poor

generalization capability.

Besides that, the applicability of the selected load model structure in power system

simulation software is essential. Most power system simulation software package,

such as Siemens PTI Power System Simulator for Engineers (PSS/E®), include

various aggregate load model structures that are user friendly and covers wide load

compositions. For more information, refer to PSS/E® user manual [10]. In this thesis,

the aggregate load model structure selected for parameter estimation consists of a

static part (Constant Impedance, Constant Current and Constant Power (ZIP)) in

parallel with dynamic part (induction motor) as shown in Figure 3.12. This load

model structure is suitable for reflecting the load composition identified for Dubai

Power Grid.

Figure 3.12: Equivalent Circuit of the Selected Aggregate Load Model Structure [29]

70

The static part of this aggregate load model structure is the polynomial (ZIP) load

model described earlier in 2.6.2 and represented by equations 2.9 and 2.10. On the

other hand, the dynamic part of the selected aggregate load model structure is the

induction motor model. In this thesis, the fifth order model of induction motor is used

for parameter estimation and is represented by the following five differential

equations [44]:

𝑑𝜓𝑞𝑠𝑑𝑡

= 𝜔𝑏 𝑣𝑞𝑠 −𝜔𝑒𝜔𝑏

𝜓𝑑𝑠 +𝑅𝑠𝑋𝑙𝑠

(𝜓𝑚𝑞 − 𝜓𝑞𝑠) (3.1)

𝑑𝜓𝑑𝑠𝑑𝑡

= 𝜔𝑏 𝑣𝑑𝑠 +𝜔𝑒𝜔𝑏

𝜓𝑞𝑠 +𝑅𝑠𝑋𝑙𝑠

(𝜓𝑚𝑑 − 𝜓𝑑𝑠) (3.2)

𝑑𝜓𝑞𝑟𝑑𝑡

= 𝜔𝑏 𝑣𝑞𝑟 −(𝜔𝑒 − 𝜔𝑟)

𝜔𝑏𝜓𝑑𝑟 +

𝑅𝑟𝑋𝑙𝑟

(𝜓𝑚𝑞 − 𝜓𝑞𝑟) (3.3)

𝑑𝜓𝑑𝑟𝑑𝑡

= 𝜔𝑏 𝑣𝑑𝑟 +(𝜔𝑒 − 𝜔𝑟)

𝜔𝑏𝜓𝑞𝑟 +

𝑅𝑟𝑋𝑙𝑟

(𝜓𝑚𝑑 − 𝜓𝑑𝑟) (3.4)

𝑑𝜔𝑟𝑑𝑡

= 1

2𝐻 [𝑇𝑒 − 𝑇𝐿] (3.5)

Where

𝜓𝑚𝑞 = 𝑋𝑚𝑙 𝜓𝑞𝑠𝑋𝑙𝑠

+𝜓𝑞𝑟𝑋𝑙𝑟

(3.6)

𝜓𝑚𝑑 = 𝑋𝑚𝑙 𝜓𝑑𝑠𝑋𝑙𝑠

+𝜓𝑑𝑟𝑋𝑙𝑟

(3.7)

𝑋𝑚𝑙 = 1 1𝑋𝑚

+ 1𝑋𝑙𝑠

+1𝑋𝑙𝑟

(3.8)

𝑇𝑒 = (𝜓𝑞𝑠𝑖𝑞𝑠 − 𝜓𝑑𝑠𝑖𝑑𝑠) (3.9)

𝑇𝐿 = 𝑇𝑜(𝐴𝜔𝑟2+𝐵𝜔𝑟 + 𝐶) (3.10)

The currents in equation (3.9) can be calculated as follows:

𝑖𝑞𝑠 =1𝑋𝑙𝑠

𝜓𝑞𝑠 − 𝜓𝑚𝑞 (3.11)

𝑖𝑑𝑠 =1𝑋𝑙𝑠

[𝜓𝑑𝑠 − 𝜓𝑚𝑑] (3.12)

71

The active and reactive power of the induction motor are calculated as follows:

𝑃𝐼𝑀 = 𝑣𝑞𝑠𝑖𝑞𝑠 + 𝑣𝑑𝑠𝑖𝑑𝑠 (3.13) 𝑄𝐼𝑀 = 𝑣𝑞𝑠𝑖𝑑𝑠 − 𝑣𝑑𝑠𝑖𝑞𝑠 (3.14)

Therefore, the total power consumed by the selected load model structure can be written as follows: 𝑃𝐿 = 𝛼1𝑃𝑍𝐼𝑃 + 𝛽1𝑃𝐼𝑀 (3.15)

𝑄𝐿 = 𝛼2𝑄𝑍𝐼𝑃 + 𝛽2𝑄𝐼𝑀 (3.16)

With 𝛼1 + 𝛽1 = 1 𝑎𝑛𝑑 𝛼2 + 𝛽2 = 1 Where d: direct access, q: quadratic access, s: stator variable, r: rotor variable, 𝜓𝑑𝑠 and 𝜓𝑞𝑠 : direct and quadratic access stator flux linkage, 𝜓𝑑𝑟 and 𝜓𝑞𝑟 : direct and quadratic access rotor flux linkage, 𝜓𝑚𝑑 and 𝜓𝑚𝑞 : direct and quadratic access magnetizing flux linkage, 𝑣𝑑𝑠 and 𝑣𝑞𝑠 : direct and quadratic access stator voltages, 𝑣𝑑𝑟 and 𝑣𝑞𝑟 : direct and quadratic access rotor voltages, 𝑖𝑑𝑠 and 𝑖𝑞𝑠 : direct and quadratic access stator currents, Rs and Rr : stator and rotor resistance, Xls and Xlr : stator and rotor leakage reactance, Xm: magnetizing leakage reactance, 𝜔𝑒 : stator angular electrical frequency, 𝜔𝑏 : motor angular electrical base frequency, 𝜔𝑟 : rotor angular electrical speed, H: inertia constant, Te : electrical output torque, To : initial load torque, TL: load torque, A, B and C: torque coefficients that obey the following equality A+B+C=1, PIM and QIM: active and reactive power consumed by the induction motor. PL and QL: active and reactive power consumed by the aggregate load. 𝛼1, 𝛼2, 𝛽1 and 𝛽2: static and dynamic load percentages.

72

3.3.2.2. Load Model Parameter Estimation

The purpose of parameter estimation techniques is to find a set of model

independent parameters that results in the best fit between the recorded measurement

and model output. Parameter estimation techniques are well established for linear

models, however, for nonlinear systems are still a relatively open field. In general

there are three approaches for parameter estimation of nonlinear dynamic models

[13]:

1. Analytical Based Approach: This kind of approach derives parameters

determinately and can be used for special tests such as step/staged/controlled test.

However, it is extremely sensitive to measurement error.

2. Optimization Based Approach: This kind of approach searches the best

parameters, which minimizes an error function between the measured output

variables and simulated ones. Traditional search algorithms have been applied in

this type of approach such as :

a. Statistical Search Algorithms: such as least square and gradient search, etc.

b. Heuristic Search Algorithms: such as genetic algorithm, simulated

annealing, etc.

3. Stochastic Based Approach: These approaches may be limited to the

assumptions of the error function (noise).

In search of best-fit parameters, there is an important issue to consider, that is,

the data are subject to measurement errors (noise). Therefore, typical data never

exactly fit the model that is being used, even when that model is correct [13]. The

least squares method was selected for estimating load parameters, since it is used in

many applications and easy to implement. Furthermore, it is robust, and on the

contrary, the real application of Artificial Intelligence (AI) based approaches is

limited partly due to lack of theoretical and practical guidance on selection of various

parameters that affect these algorithms [13]. The selected aggregate load model

structure was built in MATLAB Simulink for the purpose of parameter estimation as

shown in Figure A.1 (Appendix A).

73

Based on the selected load model structure, load parameters that need to be identified

are summarized in Table 3.6.

Table 3.6: Parameters to be identified for the Aggregate Load Model

Load Model Structure Parameters to be Estimated

ZIP + Induction Motor Load Composition 𝛼1, 𝛼2, 𝛽1 and 𝛽2

ZIP model 𝑝1,𝑝2,,𝑝3, 𝑞1, 𝑞2 𝑎𝑛𝑑 𝑞3

Induction Motor Model Rs, Rr, Xls, Xlr , Xm, H, A, B and C

The Objective function for parameter identification is defined as the difference

between estimated load model outputs and actual measurements. The mathematical

formulation of the objective function is as follows [43]:

𝐹𝑜𝑏𝑗(𝜃𝑃) = 𝑚𝑖𝑛1𝑛𝜀𝑘2𝑛

𝑘=1

(𝜃) = 𝑚𝑖𝑛1𝑛[𝑃𝑚(𝑘) − 𝑃𝑒(𝑘)]2 (3.17)𝑛

𝑘=1

𝐹𝑜𝑏𝑗𝜃𝑄 = 𝑚𝑖𝑛1𝑛𝜀𝑘2𝑛

𝑘=1

(𝜃) = 𝑚𝑖𝑛1𝑛[𝑄𝑚(𝑘) − 𝑄𝑒(𝑘)]2 (3.18)𝑛

𝑘=1

Where 𝑃𝑚 𝑎𝑛𝑑 𝑄𝑚 are the measured active and reactive powers, 𝑃𝑒 𝑎𝑛𝑑 𝑄𝑒 are the

estimated load model active and reactive powers and 𝜀(𝜃) represents the load model

output error.

As mentioned earlier, DEWA have installed DFRs on five selected 132/11 kV

substations at 11 kV side representing different load classes and mixes for the purpose

of load modeling and continuous updating. These DFRs are capable of continuously

recording the normal variations in frequency as well as instantaneous voltages and

currents of the three phases. The DFRs were programed to capture system

disturbances such as voltage dips and frequency transients. The available data from

the DFRs can be easily exported in (*.csv) format. This facilitates plotting curves of

interesting quantities and extracting useful information using MS EXCEL or

MATLAB. All the measured and estimated active and reactive power are converted

to positive sequence to match the quantities adopted in transient stability analysis

[45]. In order to calculate positive sequence active and reactive power, the

74

instantaneous samples of voltage and current signals are first converted into phasor

form, corresponding to their fundamental frequency, using Discrete Fourier

Transform (DFT). Then, the positive sequence voltage and currents are calculated

Using Fortescue transformation. The positive sequence active and reactive power are

calculated as:

𝑃+ + 𝑗𝑄+ = √3 .𝑉+. 𝐼+∗ (3.19)

Where V+ is the positive sequence voltage phasor and I+* is the complex conjugated

positive sequence current phasor. P+ and Q+ are the positive sequence active and

reactive power. The overall procedure of measurement based load modeling is

illustrated in Figure 3.13.

Figure 3.13: Parameter Estimation Procedures

A set of four interesting recorded voltage dip incidents have been carefully

selected for parameter estimation. Two of the selected incidents are three phase

voltage dip and the other two are single phase voltage dip. Measurement-based

approach generally assumes the load characteristics do not change during the time

interval that measurements were taken. This is because the time interval of the load

measurement data used for load parameter estimation is from 1s to 10s, which is

75

enough to capture induction motor dynamics. Therefore, for the selected set of

incidents, it is assumed that the percentages of static and dynamic load for, each

incident remain constant during the period that measurement data were recorded. A

description of each incident and the corresponding measured quantities is given

thereafter:

Incident No.1:

This incident has resulted in three phase voltage dip of 850 ms duration. Figure 3.14

shows the measured instantaneous three phase voltage and current signals and

Figure 3.15 shows the calculated positive sequence voltage and current.

Figure 3.14: Measured Instantaneous Voltage and Current Signals – Incident No.1

Figure 3.15: Calculated Line Positive Sequence Voltage and Current – Incident No.1

76

Incident No.2:

This incident has resulted also in three phase voltage dip of 800 ms duration.

Figure 3.16 shows the measured instantaneous three phase voltage and current signals

and Figure 3.17 shows the calculated positive sequence voltage and current.



77

Incident No.3:

This incident has resulted in single phase voltage dip of 250 ms duration. Figure 3.18





78

Incident No.4:

This incident has resulted in single phase voltage dip of 250 ms duration. Figure 3.20





In order to improve the estimation process, the parameter estimation task was

initiated with typical induction motor parameters. The search interval for each

parameter was selected carefully based on the physical characteristic of each

79

parameter. Our objective is to identify a set of induction motor parameters that is

capable of representing the aggregated induction motor load in the system. That

means the difference between the estimated load parameters for each incident from

the other should result from the different ZIP Load Model (static load) parameters and

the load composition parameters (percentage of induction motors), i.e. the estimated

induction motor parameters remain unchanged when the load changes. Therefore,

parameter estimation was repeated several times for the selected incidents. After

several trials, the static load parameters and the percentage of induction motor were

identified for each incident and carried out for the remaining of the parameter

estimation task. After identifying the static load parameters and the percentage of

induction motors for each incident, the induction motor parameters were identified by

starting the estimation of an incident using the induction motor parameters found from

the previous estimation trial of another incident, until the difference between the

estimated induction motor parameters for each incident became negligible. The

estimated versus measured active and reactive power for Incident No.1, Incident

No.2, Incident No.3 and Incident No.4 are shown in Figure 3.22 through Figure 3.25

respectively. Table 3.7 shows the estimated load composition and ZIP Load Model

parameters for each Incident and Table 3.8 shows the estimated unified aggregate

motor load parameters.

Figure 3.22: Measured and Estimated Active and Reactive Power – Incident No.1

80




81

Table 3.7: Estimated Load Composition and ZIP Load Model Parameters for each Incident

Estimated Parameters Incident Name

Incident No.1 Incident No.2 Incident No.3 Incident No.4

Load Composition 𝜶𝟏 0.28 0.31 0.41 0.35 𝜶𝟐 0.35 0.47 0.46 0.37

𝜷𝟏 0.72 0.69 0.59 0.65 𝜷𝟐 0.65 0.53 0.54 0.63

ZIP Load model

𝒑𝟏 0.1 0.09 0 0.01

𝒑𝟐 0.05 0.03 0 0.03 𝒑𝟑 0.85 0.88 1 0.96 𝒒𝟏 0.92 0.78 0.5 0.8 𝒒𝟐 0.04 0.2 0.20 0.03

𝒒𝟑 0.04 0.02 0.3 0.17

Table 3.8: Estimated Unified Aggregate Motor Load Parameters

Rs (Ω) Rr (Ω) Xls (Ω) Xlr (Ω) Xm (Ω) H A B C

1.1154 0.4586 0.7074 0.0253 42.0565 7.2534 1.4954 x 10-6 0.0019 0.9981

The Torque, Speed and Currents of the estimated aggregated motor parameters were

plotted for a step load torque input to ensure that these parameters represent an

aggregate motor load as shown in Figure 3.26 through Figure 3.28.

Figure 3.26: Aggregated Motor Currents

82

Figure 3.27: Aggregated Motor Torque

Figure 3.28: Aggregated Motor Speed

3.3.2.3. Validation of the Aggregate Motor Load Parameters

To check the validity of the estimated unified aggregate motor load parameters

presented in Table 3.8 for different incidents other than those used during parameter

estimation, the parameters were tested for randomly selected incident. Sample test

incident is given in Figure 3.29, which shows the measured instantaneous three phase

voltage and current signals. Figure 3.30 shows the calculated positive sequence

voltage and current. Figure 3.31 shows the measured versus simulated active and

reactive power using the estimated unified aggregate motor load parameters presented

in Table 3.8. Only the load composition and static load model parameters were

slightly changed to match the steady state (pre-incident) conditions. It is found that

the estimated unified aggregate motor load parameters have a good generalization

83

capability, i.e., that they are not incident-dependent. Therefore, these parameters can

be used to represent the aggregated motor loads for Dubai Power Grid. However, it is

recommended to test the validity and generalization capability of these parameters

periodically, for example once annually, in order to account for load variations and

network changes (especially at demand side) that take place in a year time.

Figure 3.29: Measured Instantaneous Voltage and Current Signals

Figure 3.30: Calculated Line Positive Sequence Voltage and Current

84

Figure ‎3.31: Measured and Simulated Active and Reactive Power

3.3.2.4. Identification of Load Tripping Rules

While analyzing the recorded incidents, it was observed that load tripping

takes place for positive sequence voltage as high as 90% of the nominal voltage for

dip duration of 150 ms and above. Two load tripping patterns were observed, Fast

Tripping and Slow Tripping. The two load tripping patterns were analyzed in order to

extract load tripping rules as follows:

1. Fast Tripping:

It was observed that in some of the recorded incidents, there is a reduction in the

active power immediately after the voltage recovers to its nominal value, then the

active power stabilizes at the new value indicating that portion of the load tripped in a

fast manner as illustrated in Figure 3.32 and Figure 3.33.

85

Figure 3.32: Fast Tripping Pattern-Ilustration-1

Figure 3.33: Fast Tripping Pattern-Ilustration-2

The amount of tripped load of the fast pattern was correlated to the positive voltage

dip. The dip duration was ignored, since for the majority of incidents, the dip duration

was around 300 ms. The amount of tripped load was plotted against the voltage dip,

then, three fast tripping schemes were deduced, the first is linear, the second is

exponential and the third is staircase shape as shown in Figure 3.34. Table 3.9 shows

the details of the three extracted Load Tripping Schemes of the Fast Tripping Pattern.

86

The proper tripping scheme shall be selected through the load model validation

process later in section 3.2.3.

Figure ‎3.34: Different Tripping Schemes (Fast Tripping Pattern)

Table ‎3.9: Extracted Load Tripping Scheme (Fast Tripping Pattern)

Stage

Voltage

Threshold

(%)

Sensing

Time

(ms)

Tripping

Time

(ms)

Load Tripping Fraction (%)

Linear Exponential Staircase

1 90% 50 200 3.0% 2.0% 5.0%

2 80% 50 200 7.0% 3.0% 10.0%

3 70% 50 200 11.0% 6.0% 15.0%

4 60% 50 200 15.0% 11.0% 20.0%

5 50% 50 200 19.0% 23.0% 25.0%

1. Slow Tripping:

In some of the recorded incidents another slower pattern of load tripping was

observed. There is immediate active power reduction after the voltage recovers to its

nominal value like in the previous incidents. However, the active power does not

stabilize; it rather continues to reduce in linear slow manner indicating slow form of

load tripping as illustrated in Figure 3.35 and Figure 3.36. Figure 3.37 shows the slow

87

linear tripping pattern during 10 seconds time extracted from the available

measurements with respect to the triggering voltage dip magnitude (p.u). The average

curve was implemented in this thesis.

Figure ‎3.35: Slow Tripping Pattern-Ilustration-1

Figure ‎3.36: Slow Tripping Pattern-Ilustration-2

88

Figure 3.37: Extracted Slow Tripping Pattern related to the Triggering Voltage Dip

3.3.3 Validation of the Developed Load Model

In any modeling task, irrespective of the modeling approach, a model

verification and validation should be performed to ensure its accuracy [11]. Now,

after identifying the load composition, aggregated load model parameters and fast and

slow load tripping schemes, the updated load model of Dubai Power Grid is ready for

validation. For this purpose, the model shall be validated against two recorded global

system disturbances (i.e., seen by the whole system). The first disturbance is the

aforementioned single phase to ground fault encountered at the 400 kV bus of

Mushrif (MUSH) 400/132 kV load substation on the 20th of October 2009 which

resulted in tripping 8% of the total supplied load. The second disturbance is a single

phase to ground fault at the 400 kV bus of Warsan (WRSN) 400/132 kV generation

and load substation on the 21st of November 2012 and resulted in tripping 10% of the

supplied load.

PSS/E® software package was used for validating the updated load model

against these two recorded disturbances. For each disturbance, PSS/E® load flow

model of Dubai Power Grid was tuned to reflect the recorded system conditions

during these two disturbances (including Network topology, total generation, total

load, etc). For each of the two disturbances, the system events, including fault

89

occurrence, fault clearance, tripping of power system components, etc., were

simulated dynamically in their chronological order. The simulated voltage trends

were compared with the measured ones. Also, the total amount of tripped load during

the simulation was monitored for the sake of comparison with the recorded figure.

Mushrif disturbance was simulated using the three fast tripping schemes deduced

earlier in section 3.3.1.1, namely linear, exponential and staircase tripping schemes, in

order to select the best one among the three. The selected tripping scheme was

carried out for Warsan disturbance.

3.3.3.1. Validation Dubai Power Grid Updated Model against Mushrif

Disturbance

This disturbance was initiated by a single phase to ground fault encountered at

the 400 kV bus of Mushrif (MUSH) 400/132 kV load substation. The fault was

cleared within 60 ms (which is within the normal fault clearance time criteria of

Dubai Power Grid). The fault clearance was accompanied by tripping one 400 kV

feeder circuit (out of three) at MUSH substation bus and three 400/132 kV

transformers (out of four) at MUSH substation. The disturbance had resulted in

tripping 8% of the total supplied load. This disturbance was dynamically simulated

using the updated load model and the simulated voltage trend at MUSH 132 kV bus

was compared with the recorded one as shown in Figure 3.38. It is obvious from

Figure 3.38 that the fast linear load tripping schemes give the best results for voltage

trends. The results of the fast linear load ripping scheme were compared to the load

tripping scheme of the old load model as shown in Figure 3.39. It is clear that the

new load model is much more accurate than the old load model. Figure 3.40 shows

the total tripped load during simulation of this disturbance using the fast linear load

tripping scheme is around 9% which is very close to actual figures.

90

Figure 3.38: Recorded vs. Simulated Voltage Trend at MUSH 132 kV bus – MUSH Disturbance

(Different Fast Load Tripping Scheme)

Figure 3.39: Recorded vs. Simulated Voltage Trend at MUSH 132 kV bus – MUSH Disturbance

(New and Old Load Model)

91

Figure 3.40: Simulated Total System Load during MUSH Disturbance (New Load Model)

3.3.3.2. Validation Dubai Power Grid Updated Model against Warsan Disturbance

This disturbance was initiated by a single phase to ground fault encountered at

the 400 kV bus of Warsan (WRSN) 400/132 kV load substation. The fault was

cleared within 66 ms (which is within the normal fault clearance time criteria of

Dubai Power Grid). The fault clearance was accompanied by tripping one 400 kV

feeder circuit (out of nine) at WRSN substation. The disturbance had resulted in

tripping 10% of the total supplied load. This disturbance was dynamically simulated

using the updated load model and the simulated voltage trend at MUSH 132 kV bus

was compared to the recorded one as shown in Figure 3.41 and the simulated voltage

trend at BKRA 400 kV bus was compared with the recorded one as shown in

Figure 3.42. Figure 3.43 shows that the total load lost during simulation of this

disturbance using the fast linear load tripping scheme is around 10% which is very

close to reality.

The validation effort of the updated Dubai Power Grid Load Model against the

recorded global system disturbances shows that the model is capable, with high

accuracy, of replicating the system behavior (especially voltage trends) during the

recorded system disturbances. Therefore, this model shall be used in the next chapter

to perform voltage stability assessment of Dubai Power Grid.

92

Figure 3.41: Recorded vs. Simulated Voltage Trend at MUSH 132 kV bus – WRSN Disturbance

Figure 3.42: Recorded vs. Simulated Voltage Trend at BKRA 400 kV bus - WRSN Disturbance

93

Figure 3.43: Simulated Total System Load during WRSN Disturbance

94

Chapter 4: Voltage Stability Assessment of Dubai Power Grid

4.1 Overview of Dubai Power Grid Planning Standards

The Dubai Electricity and Water Authority (DEWA) meets its responsibility to

supply electricity demanded by its customers with a high degree of reliability. This is

being done through the carefully planned development of power generating sources,

transmission, and distribution systems. A reliable supply of electricity involves two

elements – adequacy and security. Planning a reliable transmission system needs to be

done taking into account adequate reliability/continuity of supply and due

consideration to the various constraints (environmental, operational, regulatory, etc.),

all in line with the best international standards and practices. This involves several

assessment processes including evaluation of existing system capabilities and

identification of system deficiencies. Analysis of system performance should include

thermal, voltage, short circuit, stability and frequency assessments [41]. The focus of

this thesis is voltage stability assessment due to the reasons mentioned earlier in

Section 3.2.

The 400 kV Network is the backbone of the system and N-2 on-line reliability

criterion is used. This means that the sudden loss of two 400 kV circuits (even during

summer peak) should not result in system collapse nor lead to a widespread loss of

power supply to consumers. The Expanded Planning Criteria for Dubai Power Grid

was prepared based on North American Electric Reliability Council “NERC”

Planning Standard Document. These criteria define the reliability of Dubai Power

Grid using the following two terms:

• Adequacy: The ability of the electric systems to supply the aggregate electrical

demand and energy requirements of their customers at all times, taking into

account scheduled and reasonably expected unscheduled outages of system

elements.

• Security: The ability of the electric systems to withstand sudden disturbances

such as electric short circuits or unanticipated loss of system elements.

The main objective of the Expanded Planning Criteria is to maintain Dubai Power

Grid within the normal state. Examples of system conditions that could cause

departure from the normal state are: capacity deficiencies, energy deficiencies, loss of

95

generation or transmission facilities, transmission facility overloads and high or low

voltages and abnormal power system frequency. Dubai Power Grid is being planned

with a sufficient transmission capability to withstand the loss of specified,

representative and reasonably foreseeable contingencies at projected customer

demand and anticipated power transfer levels. The application of these credible

contingency planning criteria should not result in any criteria violations, or the loss of

a major portion of the system, or unintentional separation of a major portion of the

system. The planning criteria, however, recognize that extreme non-credible events

might also occur. These events are considered to be high impact low probability

(HILP) events and, therefore, require appropriate assessment [41].

Voltage stability of Dubai Power Grid shall be maintained at all times

according to the following criteria:

a. Pre-Contingency Voltage Criteria

For both normal and emergency transfers, no bus voltage shall be below its pre-

contingency low voltage limit nor be above its pre-contingency high voltage limit.

b. Post-Contingency Voltage Criteria

No bus voltage shall fall below its post-contingency low voltage limit nor rise above

its post-contingency high voltage limit.

Table 4.1 shows the steady state voltage levels for 400 kV and 132 kV levels under

normal and contingency operational conditions.

Table 4.1: Steady State Voltage Levels for 400 kV and 132 kV levels (Normal and Contingency Operational Conditions)

Nominal Voltage

Normal Operation Contingency

Min > Max< Min > Max <

~kV ~% ~kV ~% ~kV ~% ~kV ~%

400 kV 380 -5% 420 +5% 360 -10% 420 +5%

132 kV 125 -5% 138 +5% 120 -10% 145 +10%

96

4.2 Voltage Stability Assessment Methodology

The comprehensive methodology for voltage stability assessment of power

systems using modern analytical tools addressed in [8] was implemented to assess the

voltage stability of Dubai Power Grid. The methodology endorses performing both

steady state and dynamic voltage stability analyses, with high emphasis that the latter

becomes very critical especially for a power system with a significant share of motor

loads. As mentioned earlier, dynamic voltage stability analysis describes the time-

dependent voltage performance of the system by revealing the transient and/or the

longer-term voltage stability of a power system under study following system

disturbances. In this thesis, the time frame of interest is 10 seconds after the inception

of the disturbance, since this period is enough to capture the induction motor

dynamics, which are the dominant load type of Dubai Power Grid. Therefore, the use

of “dynamic voltage stability analysis” term denotes short-term or transient voltage

stability analysis. Long term voltage stability analysis is not addressed.

4.2.1 Software Tool

PSS/E® Software Package was used to conduct both steady state and dynamic

voltage stability analysis. PSS/E® Software Package is comprised of a comprehensive

suite of programs for studies of power system transmission network and generation

performance in both steady state and dynamic conditions [10].

4.2.2 Load Model Representation

For steady state voltage stability analysis, short term transients and dynamics

can be ignored; therefore, static load models (such as exponential and polynomial or

ZIP load models) are exclusively employed as their effects dominate during a voltage

disturbance under study. As explained earlier in Section (2.6.1), static load model may

consist of a combination of three load components: Constant Impedance, Constant

Current and Constant Power loads. Constant Power loads maintain a constant power

draw from the system regardless the change in voltage. Therefore, constant power

loads tend to aggravate a voltage collapse condition. On the other hand, the power

drawn by constant current and constant impedance loads decreases with voltage drop

providing load relief and hence, better voltage recovery [9]. In this thesis, for steady

97

state voltage stability analysis constant power load is assumed, at all load buses, as a

worst case scenario. For dynamic voltage stability analysis, the detailed load model

developed in Chapter-3 is implemented to incorporate the impact of the large

proportion of induction motors and reveal if there is any associated transient or quasi-

steady state voltage problems that were not captured by steady state voltage analysis.

4.2.3 Study Considerations and Scenarios

Steady state and dynamic voltage stability analyses were performed for Dubai

Power Grid considering Year 2014 Network Topology and Forecasted Peak Load

Conditions. Figure B.1 (Appendix B) shows the planned 400 kV network topology of

Dubai Power Grid for year 2014. The available generation capacity for year 2014 is

around 9650 MW and the forecasted load 6995 MW. Four main load substations

(400/132 kV level) were selected for voltage stability analysis as these substations are

most likely the weakest from the voltage stability point of view due to the following

reasons:

• Two of the four substations are currently feeding heavy load compared to

other load substations in the system. The other two are expected to feed heavy

load in the future.

• The four substations are located in highly loaded and developing area that is

Deira Side of Dubai City.

• The remoteness of these four substations from the main generation complex

in Jebel Ali. Figure B.2 (Appendix B) shows the location of the four

substations on Dubai City Map.

These substations are Nahda (NHDA) 400/132 kV substation, Mushrif (MUSH)

400/132 kV substation, Car Complex (CARX) 400/132 kV substation and Mamzar

Beach (MBCH) 400/132 kV substation. Typical 400/132 kV substation layout is

shown in Figure 4.1.

98

Figure 4.1: Typical Substation Layout a 400/132 kV Substation

Various disturbance scenarios were selected for assessment according to the

contingency criteria for credible and secured events identified in Dubai Power Grid

Expanded Planning Criteria. These disturbances cover:

• N-1 contingencies including loss of one circuit or loss of one transformer as

shown in Figure 4.2.

• N-2 contingencies including loss of two circuits, or loss of one transformer and

one circuit (equivalent to the loss of one bus section) as shown in Figure 4.3.

• N-3 contingencies including loss of two circuits and one transformer as shown in

Figure 4.4.

A comprehensive list of all the above contingency types was prepared for each

substation. The total number of contingencies of all the three categories for each

substation ranges from 13 to 21 contingencies. The comprehensive contingency list

for each substation is presented in Tables B.1, B.2, B.3 and B.4 (Appendix B) for

NHDA, MUSH, CARX and MBCH respectively. For steady state analysis, the

analysis was performed for each substation using the full contingency list. The steady

99

state analysis results were used to identify the worst contingency of each contingency

category for each substation i.e. three contingency scenarios for each substation.

Flowingly, the dynamic analysis was performed using the identified worst

contingencies for each substation.

Figure 4.2: Example of N-1 Contingencies



100

4.2.4 Steady State Voltage Analysis Results

The steady-state voltage stability analysis was performed based on the criteria

established by Western Electricity and Coordinating Council (WECC) of North

America. The document that was used as a guide for this study was approved by

WECC in May of 1998 [46]. The WECC voltage stability criteria are specified in

terms of active and reactive power margins, as shown in Table B.5 (Appendix B).

PSS/E® software package was used for performing PV and VQ analysis.

The active power transfer limit for each contingency scenario is calculated

from the nose of the corresponding PV curve. Then, for each contingency category

the active power transfer limit of the worst contingency among this category is

considered as the maximum active power transfer margin. According to WECC

voltage stability criteria, a safety margin of 5% is taken for N-0 and N-1 contingency

conditions and 2.5% for N-2 and N-3 contingency conditions. Figure 4.2 illustrates

the process of calculating the maximum active power transfer margin.

Figure 4.5: Calculating Active Power Transfer Margin from PV Curves

The Reactive Power Reserve Margin (RRM) for each contingency scenario is

calculated from the knee of the corresponding QV curve. For each contingency

scenario, the reactive power margin for the base load and a load increase of 5% is

calculated. The change in reactive power margin between base load and base load

+5% is calculated, and the contingency having the maximum margin change is

considered the worst contingency of its category. This means, under this contingency,

101

the system is very sensitive to load change, i.e. prone to voltage instability. This

process is illustrated in Figure 4.3.

Figure 4.6: Calculating Reactive Power Reserve Margin from VQ Curves

4.2.4.1 PV Analysis Results

PV curves for NHDA, MUSH, CARX and MBCH 400/132 kV substation are

plotted for intact network condition (Base Case or N-0) as well as the comprehensive

list of N-1, N-2 and N-3 contingencies addressed in Tables B.1, B.2, B.3 and B.4

respectively (Appendix B). PV analysis of NHDA 400/132 kV substation is

presented in details. For MUSH, CARX, and MBCH, the results are summarized in

this section and the remaining curves and tables are provided in Appendix C.

Figure 4.4 shows all the PV curves plotted for NHDA 400/132 kV substation

covering the Base Case (N-0) and all the studied N-1, N-2 and N-3 contingency

scenarios. For comparison purposes and in order to identify the worst contingency

among each category, Figure 4.5 segregates the PV curves plotted of all N-1

contingencies, Figure 4.6 segregates the PV curves plotted of all N-2 contingencies,

and Figure 4.7 segregates the PV curves plotted of all N-3 contingencies. The worst

contingency of each category is the one that corresponds to the lowest active power

transfer among the others. After identifying the worst contingency of each category,

the corresponding PV curves are segregated with the PV curve of Base Case (N-0) in

Figure 4.8. The normal loading point and the substation firm capacity are shown in

102

Figure 4.8. The steady state voltage limits for normal operation and contingency

conditions are also displayed.

Figure 4.7: PV Curves for NHDA 400/132 kV Substation – Base Case and All Contingencies

Figure 4.8: PV Curves for NHDA 400/132 kV Substation – All N-1 Contingencies

0.85

0.90

0.95

1.00

1.05

1.10

500 700 900 1100 1300 1500 1700

V (p.u.)

Power Transfer (MW)

BASE CASECONTINGENCY-1CONTINGENCY-2CONTINGENCY-3CONTINGENCY-4CONTINGENCY-5CONTINGENCY-6CONTINGENCY-7CONTINGENCY-8CONTINGENCY-9CONTINGENCY-10CONTINGENCY-11CONTINGENCY-12CONTINGENCY-13CONTINGENCY-14CONTINGENCY-15CONTINGENCY-16CONTINGENCY-17CONTINGENCY-18CONTINGENCY-19CONTINGENCY-20CONTINGENCY-21

0.85

0.90

0.95

1.00

1.05

1.10

500 700 900 1100 1300 1500 1700

V (p.u.)

Power Transfer (MW)

CONTINGENCY-1

CONTINGENCY-2

CONTINGENCY-3

CONTINGENCY-4

CONTINGENCY-5

103



0.85

0.90

0.95

1.00

1.05

1.10

500 700 900 1100 1300 1500 1700

V (p.u.)

Power Transfer (MW)

CONTINGENCY-6

CONTINGENCY-7

CONTINGENCY-8

CONTINGENCY-9

CONTINGENCY-10

CONTINGENCY-11

CONTINGENCY-12

CONTINGENCY-13

CONTINGENCY-14

CONTINGENCY-15

0.85

0.90

0.95

1.00

1.05

1.10

500 700 900 1100 1300 1500 1700

V (p.u.)

Power Transfer (MW)

CONTINGENCY-16

CONTINGENCY-17

CONTINGENCY-18

CONTINGENCY-19

CONTINGENCY-20

CONTINGENCY-21

104

Figure 4.11: PV Curves for NHDA 400/132 kV Substation – Worst Contingencies

It is obvious from Figure 4.8 that all PV curves for Base Case and the worst

contingencies fall within the acceptable voltage limits. It is also found that NHDA

400/132 kV substation can be loaded to near its firm capacity during the worst

contingency scenario. Table 4.2 presents the calculated maximum active power

transfer margin for the Base Case and all the contingency scenarios. The worst

contingency of each category, highlighted in yellow, is considered as the limiting

contingency of its category, and hence, the corresponding active power transfer limit

is considered the maximum active power transfer margin for this category. The

figures shown are after implementing the 5% safety margin for N-0 and N-1

contingencies, and 2.5% safety margin for N-2 and N-3 contingencies.

0.85

0.90

0.95

1.00

1.05

1.10

500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600

V (p.u.)

Power Transfer (MW)

Base Case (N-0) Worst N-1 Contingency

Worst N-2 Contingency Worst N-3 Contingency

Normal Loading (Year 2014)

Low Voltage Limit (Normal)

Low Voltage Limit

(Contingency)

High Voltage Limit (Normal &

Contingency)

Substation Firm Capacity

105

Table 4.2: Maximum Active Power Transfer Margin for NHDA 400/132 kV Substation – Base Case and All Contingencies

Contingency Identifier Category Active Power

Transfer Limit (MW)

Maximum Active Power Transfer Margin (MW)

BASE CASE N-0 1519 1443

CONTINGENCY-1

N-1

1138

1081

CONTINGENCY-2 1256

CONTINGENCY-3 1468

CONTINGENCY-4 1431

CONTINGENCY-5 1514

CONTINGENCY-6

N-2

1138

1060

CONTINGENCY-7 1181

CONTINGENCY-8 1213

CONTINGENCY-9 1425

CONTINGENCY-10 1425

CONTINGENCY-11 1425

CONTINGENCY-12 1088

CONTINGENCY-13 1131

CONTINGENCY-14 1131

CONTINGENCY-15 1125

CONTINGENCY-16

N-3

1044

1018

CONTINGENCY-17 1056

CONTINGENCY-18 1069

CONTINGENCY-19 1119

CONTINGENCY-20 1113

CONTINGENCY-21 1138

Similarly, PV analyses were performed for MUSH, CARX and MBCH

400/132 kV substation. PV Curves corresponding to the worst contingencies are

shown in Figure 4.9, Figure 4.10 and Figure 4.11 for MUSH, CARX and MBCH

400/132 respectively. Table 4.3 summarizes PV analyses results for the four

substations. It was found that NHDA and MUSH 400/132 kV substation can be

loaded to near their firm capacity during the worst contingency scenario. However,

for CARX and MBCH 400/132 kV substation the maximum active power transfer

margin of the worst contingency is approximately equivalent to the load forecast of

year 2023. This means that detailed analyses of these two substations are required

before 2023.

106

Figure 4.12: PV Curves for MUSH 400/132 kV Substation – Worst Contingencies

Figure 4.13: PV Curves for CARX 400/132 kV Substation – Worst Contingencies

0.85

0.90

0.95

1.00

1.05

1.10

500 600 700 800 900 1000 1100 1200 1300 1400 1500

V (p.u.)

Power Transfer (MW) Base Case (N-0) Worst N-1 ContingencyWorst N-2 Contingency Worst N-3 Contingency

Normal Loading (Year 2014) Substation Firm Capacity


Contingency)


Low Voltage Limit (Contingency)

0.85

0.90

0.95

1.00

1.05

1.10

200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400

V (p.u.)

Power Transfer (MW) Base Case (N-0) Worst N-1 ContingencyWorst N-2 Contingency Worst N-3 Contingency



Contingency)



107

Figure 4.14: PV Curves for MBCH 400/132 kV Substation – Worst Contingencies

Table 4.3: Maximum Active Power Transfer Margin for NHDA, MUSH, CARX and MBCH 400/132 kV Substation – Base Case and Worst Contingencies

Substation Contingency Type

Maximum Power Transfer Margin

(MW) Remarks

NHDA

Base Case (N-0) 1443

Approaching Firm Capacity Worst N-1 1081 Worst N-2 1060 Worst N-3 1018

MUSH

Base Case (N-0) 1358

Approaching Firm Capacity Worst N-1 1186 Worst N-2 1199 Worst N-3 1168

CARX

Base Case (N-0) 849

Approaching Year 2023 Forecasted Load

Worst N-1 707 Worst N-2 713 Worst N-3 695

MBCH

Base Case (N-0) 785

Approaching Year 2023 Forecasted Load

Worst N-1 642 Worst N-2 610 Worst N-3 549

0.85

0.90

0.95

1.00

1.05

1.10

200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400

V (p.u.)

Power Transfer (MW)

Base Case (N-0) Worst N-1 ContingencyWorst N-2 Contingency Worst N-3 Contingency



Contingency)



108

4.2.4.2 VQ Analysis Results

VQ curves for NHDA, MUSH, CARX and MBCH 400/132 kV substation are

plotted for intact network condition (Base Case or N-0) as well as the comprehensive

list of N-1, N-2 and N-3 contingencies addressed in Tables B.1, B.2, B.3 and B.4

respectively (Appendix B). VQ analysis of NHDA 400/132 kV substation is presented

in details. For MUSH, CARX, and MBCH, the results are summarized in this section

and the remaining curves and tables are provided in Appendix C.

Figure 4.12 shows all the QV curves plotted for NHDA 400/132 kV substation

covering the Base Case (N-0) all the studied N-1, N-2 and N-3 contingency scenarios.

For comparison purposes and to identify the worst contingency among each category,

Figure 4.13 segregates the QV curves plotted of all N-1 contingencies, Figure 4.14

segregates the QV curves plotted of all N-2 contingencies, and Figure 4.15 segregates

the VQ curves plotted of all N-3 contingencies. After identifying the worst

contingency of each category, the corresponding QV curves are segregated with the

QV curve of Base Case (N-0) in Figure 4.16.

Figure 4.15: QV Curves for NHDA 400/132 kV Substation – Base Case and All Contingencies

-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)

BASE CASECONTINGENCY-1CONTINGENCY-2CONTINGENCY-3CONTINGENCY-4CONTINGENCY-5CONTINGENCY-6CONTINGENCY-7CONTINGENCY-8CONTINGENCY-9CONTINGENCY-10CONTINGENCY-11CONTINGENCY-12CONTINGENCY-13CONTINGENCY-14CONTINGENCY-15CONTINGENCY-16CONTINGENCY-17CONTINGENCY-18CONTINGENCY-19CONTINGENCY-20CONTINGENCY-21

109

Figure 4.16: QV Curves for NHDA 400/132 kV Substation – All N-1 Contingencies



-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)

CONTINGENCY-1CONTINGENCY-2CONTINGENCY-3CONTINGENCY-4CONTINGENCY-5

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)

CONTINGENCY-16CONTINGENCY-17CONTINGENCY-18CONTINGENCY-19CONTINGENCY-20CONTINGENCY-21

-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)

CONTINGENCY-6CONTINGENCY-7CONTINGENCY-8CONTINGENCY-9CONTINGENCY-10CONTINGENCY-11CONTINGENCY-12CONTINGENCY-13CONTINGENCY-14CONTINGENCY-15

110

Figure 4.19: QV Curves for NHDA 400/132 kV Substation – Worst Contingencies

The curves are plotted for base load and a load increase of 5% in order to calculate the

reactive power reserve margin for Base Case (N-0) and all contingency scenarios as

shown in Table 4.4. The worst contingency of each category, highlighted in yellow,

is the ones with the highest change in reactive power reserve margin with respect to a

load increment of 5%. Hence, the corresponding reactive power reserve margin (for

base load +5% situations) is considered as the available reactive power margin of

each contingency category. It is found that the calculated reactive power reserve

margin for each category is compliant with WECC voltage stability criteria addressed

in Table B.5 (Appendix B).

-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)



111

Table 4.4: Available Reactive Power Reserve Margin for NHDA 400/132 kV Substation – Base Case and All Contingencies

Contingency Identifier Category

Reactive Power Reserve Margin (RRM) (MVAr) Change

in RRM (MVAr)

Available RRM

(MVAr) Base Load

Base Load +5%

BASE CASE N-0 1012 971 42 971

CONTINGENCY-1

N-1

931 865 66

865 CONTINGENCY-2 921 871 50 CONTINGENCY-3 943 901 42 CONTINGENCY-4 930 888 42 CONTINGENCY-5 981 939 42 CONTINGENCY-6

N-2

775 712 62

726

CONTINGENCY-7 759 695 64 CONTINGENCY-8 794 744 49 CONTINGENCY-9 904 862 42

CONTINGENCY-10 894 852 42 CONTINGENCY-11 844 801 43 CONTINGENCY-12 814 726 88 CONTINGENCY-13 862 797 66 CONTINGENCY-14 900 834 66 CONTINGENCY-15 849 783 66 CONTINGENCY-16

N-3

652 509 144

509

CONTINGENCY-17 637 514 122 CONTINGENCY-18 687 594 93 CONTINGENCY-19 823 757 66 CONTINGENCY-20 813 747 66 CONTINGENCY-21 763 696 67

Similarly, VQ analyses were performed for MUSH, CARX and MBCH

400/132 kV substation. VQ Curves corresponding to the worst contingencies are

shown in Figure 4.17, Figure 4.18 and Figure 4.19 for MUSH, CARX and MBCH

400/132 respectively. Table 4.5 summarizes VQ analyses results for the four

substations. It is found that the calculated reactive power reserve margin for each

contingency category is compliant with WECC voltage stability criteria addressed in

Table B.5 (Appendix B).

112

Figure 4.20: VQ Curves for MUSH 400/132 kV Substation – Worst Contingencies

Figure 4.21: VQ Curves for CARX 400/132 kV Substation – Worst Contingencies

-1500

-1000

-500

0

500

1000

1500

2000

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)



-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)



113

Figure 4.22: VQ Curves for MBCH 400/132 kV Substation – Worst Contingencies

Table 4.5: Available Reactive Power Reserve Margin for NHDA, MUSH, CARX and MBCH 400/132 kV Substation – Base Case and All Contingencies

Substation Contingency Type

Available RRM

(MVAr) WECC Criteria

NHDA

Base Case (N-0) 971 Reference RRM

Compliant Worst N-1 865 > 50% of RRM of N-0 Worst N-2 726 > 50% of RRM of N-0 Worst N-3 509 > 0

MUSH



CARX



MBCH



-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)



114

4.2.5 Dynamic Voltage Analysis Results

Transient dynamic voltage analysis was performed for each of the four

selected substations. The studied disturbance scenarios reflected the worst case

contingencies of each contingency category (N-1, N-2 and N-3) revealed from the

steady state analysis. The disturbances were simulated to reflect the worst

contingencies considering practical scenarios as illustrated in Table 4.6. The different

fault locations are illustrated in .The simulated fault is a solid single phase to ground

fault. Three phase fault is not simulated due to the very low occurrence probability.

During the past 23 years, Dubai Power Grid has not encountered any Three Phase

Fault on the high voltage network (400 and 132 kV levels). Moreover, the load model

was not validated for three phase faults due to unavailability of records of such

incidents.

Table 4.6: Details of Simulated Disturbances

Contingency Category

Simulated Disturbance Scenario

Fault Location Protection Action Tripped Equipment Total Fault Clearance

Time

N-1

One Transformer Normal Fault

Clearance

One Transformer

100ms Or Or

One 400 kV Circuit One 400 kV Circuit

N-2

Two 400 kV circuits on the same tower

Normal Fault Clearance

Two 400 kV Circuits

100 ms Or Or

Bus Section (Feeding Two Elements)

Bus Section (One 400 kV Circuit

+ One Transformer)

One Transformer Breaker Protection Failure Bus Section

(One 400 kV Circuit +

One Transformer)

280 ms Or Followed by

One 400 kV Circuit Activation of Backup Protection

N-3

Bus Section (Feeding Three Elements)

Normal Fault Clearance

Bus Section (Two 400 kV Circuit

+ One Transformer)

100ms

Two 400 kV circuits on the same tower

Breaker Protection Failure One 400 kV Circuit

280 ms Followed by and

Activation of Backup Protection

Bus Section (One 400 kV Circuit

+ One Transformer)

115

Figure 4.23: Illustration of Different Fault Locations PSS/E® software package was used for simulating these disturbances. The

simulation starts with normal system operation for 1 second duration, then the fault is

applied at t = 1.0 second. The fault is then cleared after 100 ms (for normal fault

clearance time scenarios) or 280 ms (for breaker failure scenarios) as described in

Table 4.10. After fault clearance the elements outages according to the study

scenarios take place. The simulation then is carried out for 20 seconds to monitor

system behavior following the simulated disturbance. During the simulation, the

voltage trends at the 400 kV and 132 kV buses of the substation under study were

plotted to screen any oscillatory response, overvoltage and or voltage recovery

problem resulting from the applied disturbance. The voltage trends were plotted for

20 seconds duration. The results of dynamic voltage stability analysis for each of the

selected substations are presented thereafter.

116

The disturbances presented in Table 4.6 were simulated for all the four

selected 400/132 kV substation. For all the disturbances, the voltage trends at the 400

kV and the 132 kV buses of each substation are plotted. The voltage trends for NHDA

400/132 kV substation are shown in details, however for MUSH, CARX and MBCH

400/132 kV substations the voltage trends figures are provided in Appendix C.

Figure 4.20 shows the voltage trends at NHDA 400 kV Bus for the worst

contingencies for normal fault clearance time. Figure 4.21 shows the voltage trends at

NHDA 132 kV Bus for the worst contingencies for normal fault clearance time.

Figure 4.22 shows the voltage trends at NHDA 400 kV Bus for the worst

contingencies for backup protection clearance time and Figure 4.23 shows the voltage

trends at NHDA 132 kV Bus for the worst contingencies for backup protection

clearance time.

Figure 4.24: Voltage Trends at NHDA 400 kV Bus for the Worst Contingencies (Normal Fault Clearance Time)

00.10.20.30.40.50.60.70.80.9

11.11.2

0 5 10 15 20

V(p.u.)

Time (s)

V-NHDA-400 - Worst N-1 - Normal Fault ClearaNormal Fault Clearance TimeV-NHDA-400 - Worst N-2 - Normal Fault ClearaNormal Fault Clearance TimeV-NHDA-400 - Worst N-3 - Normal Fault ClearaNormal Fault Clearance Time

117

Figure 4.25: Voltage Trends at NHDA 132 kV Bus for the Worst Contingencies

(Normal Fault Clearance Time)


(Breaker Failure- Backup Protection Time)

00.10.20.30.40.50.60.70.80.9

11.11.2

0 5 10 15 20

V(p.u.)

Time (s)

V-NHDA-132 - Worst N-1 - Normal Fault ClearaNormal Fault Clearance TimeV-NHDA-132 - Worst N-2 - Normal Fault ClearaNormal Fault Clearance TimeV-NHDA-132 - Worst N-3 - Normal Fault ClearaNormal Fault Clearance Time

00.10.20.30.40.50.60.70.80.9

11.11.2

0 5 10 15 20

V(p.u.)

Time (s)

V-NHDA-400 kV - Worst N-2 -Breaker Failure- Backup Protection TimeV-NHDA-400 kV - Worst N-3 -Breaker Failure- Backup Protection Time

118


(Breaker Failure- Backup Protection Time)

For all simulated single phase to ground faults cleared in normal fault

clearance times, there is a fast voltage recovery and a slight transient voltage rise at

NHDA 400 kV and 132 kV buses. However, the voltage returns to the acceptable

limit within 10 seconds. On the other hand, for all the simulated single phase to

ground faults cleared in backup protection time, the voltage recovery at NHDA 400

kV and 132 kV buses is relatively slow. However, the voltage returns to the

acceptable limit within maximum time duration of 10 seconds at NHDA 400 kV bus.

For NHDA 132 kV bus, although the voltage recovery is slow, the final voltage after

20 seconds is above acceptable limits for both N-2 and N-3 contingency scenarios.

The same voltage behavior is observed for MUSH, CARX and MBCH 400/132 kV

substations with slight differences.

In summary, for all the simulated contingencies resulting from single phase to

ground faults cleared in normal fault clearance times, there is a fast voltage recovery

at 400 kV and 132 kV buses. In some cases, there is a slight transient voltage rise (up

to 1.08 p.u. and 40 ms duration for the worst case). However, the voltage returns to

the acceptable limit within 10 seconds. On the other hand, for all the simulated

contingencies resulting from single phase to ground faults cleared in backup

protection time, the voltage recovery at 400 kV and 132 kV buses is relatively slow.

00.10.20.30.40.50.60.70.80.9

11.11.2

0 5 10 15 20

V(p.u.)

Time (s)

V-NHDA-132 kV - Worst N-2 -Breaker Failure- Backup Protection TimeV-NHDA-132 kV - Worst N-3 -Breaker Failure- Backup Protection Time

119

This is probably due to the extended fault duration which is sufficient to cause motor

stalling at the low voltage side. Though the voltage recovery is slow for extended

fault durations, the voltage returns to the acceptable limit within maximum time

duration of 10 seconds for 400 kV buses, and 20 seconds for few 132 kV buses. In

some cases, the final voltage after 20 seconds is above acceptable limits for both N-2

and N-3 contingency scenarios.

It is obvious that the updated load model revealed three problems related to

voltage stability, the first one, is the transient and/or quasi steady state voltage rise

that was encountered for normal cleared faults. The second problem is the slow

voltage recovery after extended fault durations and/or severe contingencies, which

was also evidenced in the slow voltage recovery during Mushrif incident in 2009. And

the third problem is the final voltage rise encountered following extended fault

durations and/or severe contingencies, this also was witnessed during Mushrif

incident in 2009. These three problems could be worsened with higher motor load

proportion, more severe contingencies, and more severe fault conditions. Moreover,

the dynamic simulation proved that steady state voltage stability analysis is not

sufficient to assess the voltage stability of a system with high proportion of motor

loads.

Based on the above, it is recommended to perform a comprehensive reactive

power assessment study for Dubai Power Grid, especially in view of the anticipated

high penetration of new load types including inverter based appliances. The study

may investigate the potential benefits of installing dynamic reactive power

compensation devices such as Static Var Compensators (SVCs).

120

Chapter 5: Conclusions, Recommendations and Future Work

5.1 Conclusions

Motivated by the increasing concern of power utilities (including Dubai

Electricity and Water Authority) in voltage stability problem and its potential adverse

impact on system reliability and security, this thesis presented the efforts performed to

assess the voltage stability of Dubai Power Grid against the increasing use of

transmission system resources, growing demand and associated stress on available

active and reactive power resources.

In order to investigate practical problems, as such, and propose appropriate

solutions, power system utilities depend on static and dynamic simulations. Power

system load characteristics have a major impact on voltage stability of power systems.

Load model uncertainty is recognized as the major source for simulation inaccuracy.

Accordingly, in any system stability study, especially voltage stability studies, it is

very important to have accurate load models capable of reflecting load behavior

during system disturbances. This enhances the ability of power system planners to

anticipate possible risks and design power systems more accurately.

The main two objectives of this thesis are:

• To update Dubai Power Grid load model, to become capable of capturing load

behavior during system disturbances and enhance the accuracy of voltage

stability studies.

• To evaluate the voltage performance of Dubai Power Grid against the

increasing use of transmission system resources, growing demand and

associated stress on available active and reactive power resources.

The first objective (Load Modeling) was accomplished through implementing a

hybrid methodology consisting of a combination of two load modeling approaches:

• Component Based Approach, which was accomplished by:

o Surveying the supplied load at all main load buses and classifying

them into classes including: Residential, Commercial, Industrial and

District Cooling Load.

121

o Identifying Load Composition for each Load Class including: Resistive

Loads, Small Motor Loads, and Large Motor Loads.

o Selecting a proper aggregate load model representing the identified

load composition. This step is the last step in component-based

approach and the first step in measurement-based approach, in which

the parameters of the aggregated load model shall be estimated using

actual system measurements.

• Measurement-Based Approach: which was achieved by:

o Collecting extensive set of monitored natural load versus voltage

variations from Digital Fault Recorders DFRs installed at five different

load buses.

o Estimating aggregate load model parameters from the collected

measurements using parameter estimation techniques.

o Extracting load tripping (self-disconnection) scheme from the collected

measurements.

The updated load model is validated against recorded system disturbances and

found capable, with high accuracy, of replicating the system behavior during the

recorded system disturbances. Several literatures have dealt with aggregate load

modeling and validating the developed load models against small voltage variations

disturbances. However, few studies investigated the validity of these models during

large voltage variation disturbances. In this thesis, the load model parameters were

estimated using actual measurements of small voltage variations disturbances and the

developed load model was validated against recorded large voltage variation

disturbances, however the availability of large voltage vibration disturbances data has

been limited.

The second objective (Voltage Stability Assessment) was accomplished via

performing comprehensive voltage stability studies for Dubai Power Grid, using the

developed load model, under normal operational conditions and various contingency

scenarios. The assessment comprised of two assessment tools:

• Static Voltage Stability Analysis using PV and VQ curves to determine the

steady state maximum power transfer limits and reactive power margin for

main load buses.

122

• Dynamic Voltage Stability Analysis using Time Domain Simulation to capture

the transient behavior of system voltages during and after disturbances.

The results of voltage stability assessment of Dubai Power Grid shows that the

maximum power transfer margins and reactive power reserve margins for the

substations under study (which represent the worst case scenario) are compliant with

WECC voltage stability criteria. On the other hand, the updated load model revealed

two problems related to voltage stability, the first one, is the transient and/or quasi

steady state voltage rise that was encountered for normal cleared faults. The second

problem is the slow voltage recovery after extended fault durations and/or severe

contingencies, which was also evidenced in the slow voltage recovery during Mushrif

incident in 2009. Both problems could be worsened with higher motor load

proportion, more severe contingencies, and more severe fault conditions. Moreover,

the dynamic simulation proved that steady state voltage stability analysis is not

sufficient to assess the voltage stability of a system with high proportion of motor

loads.

5.2 Recommendations

In view of the escalating stress on most of the power systems around the world

and the associated growing concern in voltage stability problems, it is, therefore,

recommended for all power system utilities, to develop accurate load model based on

real system data and measurements, in order to enhance the accuracy of system

stability studies, especially voltage stability. Power systems that are characterized by

high proportion of air conditioner load are exposed to short-term voltage stability, fast

voltage collapse, and delayed voltage recovery problems. Therefore, the dynamic

characteristic of air conditioning load and their protection must be adequately

represented in voltage stability studies. Furthermore, the new generation of electric

appliances that employ the advent of power electronics (such as inverter-based

appliances) are easily disconnected from the power system following voltage dips.

The self-disconnection of such loads causes a significant rise in post-disturbance

voltage, which is a major concern in voltage stability studies. Therefore, the self-

disconnection behavior of new load types need to be incorporated in power system

load models.

123

The following consideration must be taken while developing load models:

1. The load model will be mainly used for performing stability studies; therefore, it

should be easily integrated into the power system analysis tool.

2. The developed load model should have a generalization capability in order to be

valid under most scenarios. For special system studies, a special load model

should be built according to the study requirement.

3. The developed load model should have clear physical interpretations in order to be

accepted and easily implemented by the system analysts and operators.

4. The developed load model must be verified against actual system disturbances.

Bearing in mind that different load models may give completely opposing conclusions

on system voltage stability, load modeling should be considered as a continuous task.

A developed load model is never considered final and therefore, the developed load

model need to be updated as frequent as possible, every three or five years for

example.

5.3 Future Work

Extensive efforts have been made to extract load model parameters by

analyzing the load-voltage characteristic during small voltage variation disturbances.

However, there is still a lot of w to be done to analyze the load-voltage characteristic

during large voltage variation disturbances and develop a more generalized aggregate

load model. A more comprehensive study would include the analysis of different

sized voltage dips for different load compositions and / or different operating

conditions. This thesis investigated the load-voltage characteristic during short time

scales. Long term time scales were not studied. Also, load frequency sensitivity has

not been analyzed.

It has been found that the estimation of load parameters from sinusoidal signal

is a very challenging task, especially in the presence of harmonic distortion and/or

noise. The high nonlinearity of the load model, the large number of parameters to be

estimated and the huge searching space are the main sources of difficulties faced

during the load model parameter estimation task. Therefore, a great deal of work

needs to be done in this area.

124

Following the recent economic recovery and the resulting real estate business

revival, the demand side at Dubai Power Grid is expected to incur enormous

penetration of more efficient new Air Conditioning technologies based on inverter

driven motors ranging from small home Air Conditioning appliances to large District

Cooling/Chiller Plants. These load appliances are characterized by a higher

sensitivity to sudden voltage variations, and accordingly the amount of self-

disconnected loads due to sudden voltage variations will increase. Indeed, the load-

voltage characteristics of these new appliances need to be represented in the load

model. This would require gathering more information about these appliances from

their manufacturers.

This thesis has dealt with voltage stability assessment, however, voltage

stability enhancement using static and dynamic reactive power compensation such as

Static Var Systems (SVS) were not considered. It is worth to perform a

comprehensive reactive power assessment study for Dubai Power Grid, especially in

view of the anticipated high penetration of new load types including inverter based

appliances.

125

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Appendix A

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129

Appendix B

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00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-1

7Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of N

HD

A -

MB

CH

400

kV

Circ

uit

Loss

of N

HD

A -

TCG

Y 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-1

8Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of N

HD

A -

MB

CH

400

kV

Circ

uit

Loss

of N

HD

A -

WR

SN 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-1

9Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of N

HD

A -

WR

SN 4

00 k

V C

ircui

tLo

ss o

f NH

DA

- H

STA

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-2

0Lo

ss o

f One

400

/132

kV

Tran

sfor

mer

Loss

of N

HD

A -

WR

SN 4

00 k

V C

ircui

tLo

ss o

f NH

DA

- TC

GY

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-2

1Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of N

HD

A -

HST

A 4

00 k

V C

ircui

tLo

ss o

f NH

DA

- TC

GY

400

kV

Circ

uit

133

Tab

le B

.2: C

ompr

ehen

sive

Con

tinge

ncy

Lis

t for

MU

SH40

0/13

2 kV

Sub

stat

ion

Con

tinge

ncy

Typ

eL

ost E

lem

ents

Con

tinge

ncy

Iden

tifie

rC

ontin

genc

y D

escr

iptio

n

N-1

Loss

of O

ne 4

00/1

32 k

V T

rans

form

erC

ON

TIN

GEN

CY

-1

Loss

of O

ne 4

00/1

32 k

V T

rans

form

er

Loss

of O

ne 4

00 k

V C

ircui

tC

ON

TIN

GEN

CY

-2

Loss

of M

USH

- C

AR

X 4

00 k

V C

ircui

tC

ON

TIN

GEN

CY

-3

Loss

of M

USH

- W

RSN

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-4

Lo

ss o

f MU

SH -

MB

CH

400

kV

Circ

uit

N-2

Loss

of T

wo

400

kV C

ircui

t

CO

NTI

NG

ENC

Y-5

Lo

ss o

f MU

SH -

CA

RX

400

kV

Circ

uit-c

kt 1

Loss

of M

USH

-C

AR

X 4

00 k

V C

ircui

t-ckt

2

CO

NTI

NG

ENC

Y-6

Lo

ss o

f MU

SH -

CA

RX

400

kV

Circ

uit

Loss

of M

USH

-W

RSN

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-7

Lo

ss o

f MU

SH -

CA

RX

400

kV

Circ

uit

Loss

of M

USH

- M

BC

H 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-8

Lo

ss o

f MU

SH -

WR

SN 4

00 k

V C

ircui

tLo

ss o

f MU

SH -

MB

CH

400

kV

Circ

uit

Loss

of O

ne 4

00 k

V C

ircui

t +

One

400

/132

kV

Tra

nsfo

rmer

CO

NTI

NG

ENC

Y-9

Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

USH

- C

AR

X 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-1

0Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

USH

- W

RSN

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-1

1Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

USH

- M

BC

H 4

00 k

V C

ircui

t

N-3

Lo

ss o

f Tw

o 40

0 kV

Circ

uit

+ O

ne 4

00/1

32 k

V T

rans

form

er

CO

NTI

NG

ENC

Y-1

2Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

USH

- C

AR

X 4

00 k

V C

ircui

t-ckt

1Lo

ss o

f MU

SH -

CA

RX

400

kV

Circ

uit-c

kt 2

CO

NTI

NG

ENC

Y-1

3Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

USH

- C

AR

X 4

00 k

V C

ircui

tLo

ss o

f MU

SH -

WR

SN 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-1

4Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

USH

- C

AR

X 4

00 k

V C

ircui

tLo

ss o

f MU

SH -

MB

CH

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-1

5Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

USH

- W

RSN

400

kV

Circ

uit

Loss

of M

USH

- M

BC

H 4

00 k

V C

ircui

t

134

Tab

le B

.3: C

ompr

ehen

sive

Con

tinge

ncy

Lis

t for

CA

RX

400/

132

kV S

ubst

atio

n

Con

tinge

ncy

Typ

eL

ost E

lem

ents

Con

tinge

ncy

Iden

tifie

rC

ontin

genc

y D

escr

iptio

n

N-1

Loss

of O

ne 4

00/1

32 k

V T

rans

form

erC

ON

TIN

GEN

CY

-1

Loss

of O

ne 4

00/1

32 k

V T

rans

form

er

Loss

of O

ne 4

00 k

V C

ircui

tC

ON

TIN

GEN

CY

-2

Loss

of C

AR

X -

MU

SH 4

00 k

V C

ircui

tC

ON

TIN

GEN

CY

-3

Loss

of C

AR

X -

TCG

Y 4

00 k

V C

ircui

tC

ON

TIN

GEN

CY

-4

Loss

of C

AR

X -

HR

SR 4

00 k

V C

ircui

t

N-2

Loss

of T

wo

400

kV C

ircui

t

CO

NTI

NG

ENC

Y-5

Lo

ss o

f CA

RX

-M

USH

400

kV

Circ

uit-c

kt 1

Loss

of C

AR

X -

MU

SH 4

00 k

V C

ircui

t-ckt

2

CO

NTI

NG

ENC

Y-6

Lo

ss o

f CA

RX

-M

USH

400

kV

Circ

uit

Loss

of C

AR

X -

TCG

Y 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-7

Lo

ss o

f CA

RX

-M

USH

400

kV

Circ

uit

Loss

of C

AR

X -

HR

SR 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-8

Lo

ss o

f CA

RX

-TC

GY

400

kV

Circ

uit

Loss

of C

AR

X -

HR

SR 4

00 k

V C

ircui

t

Loss

of O

ne 4

00 k

V C

ircui

t +

One

400

/132

kV

Tra

nsfo

rmer

CO

NTI

NG

ENC

Y-9

Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of C

AR

X -

MU

SH 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-1

0Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of C

AR

X -

TCG

Y 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-1

1Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of C

AR

X -

HR

SR40

0 kV

Circ

uit

N-3

Lo

ss o

f Tw

o 40

0 kV

Circ

uit

+ O

ne 4

00/1

32 k

V T

rans

form

er

CO

NTI

NG

ENC

Y-1

2Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of C

AR

X -

MU

SH 4

00 k

V C

ircui

t-ckt

1Lo

ss o

f CA

RX

- M

USH

400

kV

Circ

uit-c

kt 2

CO

NTI

NG

ENC

Y-1

3Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of C

AR

X -

MU

SH 4

00 k

V C

ircui

tLo

ss o

f CA

RX

- TC

GY

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-1

4Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of C

AR

X -

MU

SH 4

00 k

V C

ircui

tLo

ss o

f CA

RX

- H

RSR

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-1

5Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of C

AR

X -

TCG

Y 4

00 k

V C

ircui

tLo

ss o

f CA

RX

- H

RSR

400

kV

Circ

uit

135

Tab

le B

.4:C

ompr

ehen

sive

Con

tinge

ncy

Lis

t for

CA

RX

400/

132

kV S

ubst

atio

n

Con

tinge

ncy

Typ

eL

ost E

lem

ents

Con

tinge

ncy

Iden

tifie

rC

ontin

genc

y D

escr

iptio

n

N-1

Loss

of O

ne 4

00/1

32 k

V T

rans

form

erC

ON

TIN

GEN

CY

-1

Loss

of O

ne 4

00/1

32 k

V T

rans

form

er

Loss

of O

ne 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-2

Lo

ss o

f MB

CH

- M

USH

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-3

Lo

ss o

f MB

CH

- N

HD

A 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-4

Lo

ss o

f MB

CH

- W

RSN

400

kV

Circ

uit

N-2

Loss

of T

wo

400

kV C

ircui

t

CO

NTI

NG

ENC

Y-5

Lo

ss o

f MB

CH

- M

USH

400

kV

Circ

uit

Loss

of M

BC

H -

NH

DA

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-6

Lo

ss o

f MB

CH

- M

USH

400

kV

Circ

uit

Loss

of M

BC

H -

WR

SN 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-7

Lo

ss o

f MB

CH

- N

HD

A 4

00 k

V C

ircui

tLo

ss o

f MB

CH

- W

RSN

400

kV

Circ

uit

Loss

of O

ne 4

00 k

V C

ircui

t +

One

400

/132

kV

Tra

nsfo

rmer

CO

NTI

NG

ENC

Y-8

Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

BC

H -

MU

SH 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-9

Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

BC

H -

NH

DA

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-1

0Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

BC

H -

WR

SN 4

00 k

V C

ircui

t

N-3

Lo

ss o

f Tw

o 40

0 kV

Circ

uit

+ O

ne 4

00/1

32 k

V T

rans

form

er

CO

NTI

NG

ENC

Y-1

1Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

BC

H -

MU

SH 4

00 k

V C

ircui

tLo

ss o

f MB

CH

-N

HD

A 4

00 k

V C

ircui

t

CO

NTI

NG

ENC

Y-1

2Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

BC

H -

MU

SH 4

00 k

V C

ircui

tLo

ss o

f MB

CH

- W

RSN

400

kV

Circ

uit

CO

NTI

NG

ENC

Y-1

3Lo

ss o

f One

400

/132

kV

Tra

nsfo

rmer

Loss

of M

BC

H -

NH

DA

400

kV

Circ

uit

Loss

of M

BC

H -

WR

SN 4

00 k

V C

ircui

t

136

WECC oltag

137

138

Appendix C

Figure C.1: PV Curves for MUSH 400/132 kV Substation – Base Case and All Contingencies

Figure C.2: PV Curves for MUSH 400/132 kV Substation – All N-1 Contingencies


0.85

0.90

0.95

1.00

1.05

1.10

500 700 900 1100 1300 1500 1700

V (p.u.)

Power Transfer (MW)

BASE CASECONTINGENCY-1CONTINGENCY-2CONTINGENCY-3CONTINGENCY-4CONTINGENCY-5CONTINGENCY-6CONTINGENCY-7CONTINGENCY-8CONTINGENCY-9CONTINGENCY-10CONTINGENCY-11CONTINGENCY-12CONTINGENCY-13CONTINGENCY-14CONTINGENCY-15

0.85

0.90

0.95

1.00

1.05

1.10

500 700 900 1100 1300 1500 1700

V (p.u.)

Power Transfer (MW)

CONTINGENCY-1CONTINGENCY-2CONTINGENCY-3CONTINGENCY-4

0.85

0.90

0.95

1.00

1.05

1.10

500 700 900 1100 1300 1500 1700

V (p.u.)

Power Transfer (MW)

CONTINGENCY-5CONTINGENCY-6CONTINGENCY-7CONTINGENCY-8CONTINGENCY-9CONTINGENCY-10CONTINGENCY-11

139


Table C.1: Maximum Power Transfer Margin for MUSH 400/132 kV Substation – Base Case and All Contingencies

Contingency Identifier CategoryActive Power

Transfer Limit (MW)


BASE CASE N-0 1429 1358

CONTINGENCY-1

N-1

1248

1186CONTINGENCY-2 1429

CONTINGENCY-3 1424

CONTINGENCY-4 1378

CONTINGENCY-5

N-2

1429

1217

CONTINGENCY-6 1419

CONTINGENCY-7 1429

CONTINGENCY-8 1368

CONTINGENCY-9 1248

CONTINGENCY-10 1242

CONTINGENCY-11 1229

CONTINGENCY-12

N-3

1229


CONTINGENCY-14 1229

CONTINGENCY-15 1198

0.85

0.90

0.95

1.00

1.05

1.10

500 700 900 1100 1300 1500 1700

V (p.u.)

Power Transfer (MW)


140

Figure C.5: QV Curves for MUSH 400/132 kV Substation – Base Case and All Contingencies

Figure C.6: QV Curves for MUSH 400/132 kV Substation – All N-1 Contingencies


-1500

-1000

-500

0

500

1000

1500

2000

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)


-1500

-1000

-500

0

500

1000

1500

2000

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)


-1500

-1000

-500

0

500

1000

1500

2000

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)


141


Table C.2: Available Reactive Power Margin for MUSH 400/132 kV Substation – Base Case and All Contingencies



in RRM(MVAr)

Available RRM

(MVAr)Base Load

Base Load +5%

BASE CASE N-0 1014 961 53 1014

CONTINGENCY-1

N-1

889 796 93

796CONTINGENCY-2 994 941 53

CONTINGENCY-3 985 932 53


CONTINGENCY-5

N-2

776 710 67

637







CONTINGENCY-12

N-3

625 564 61

517CONTINGENCY-13 832 723 109



-1000

-500

0

500

1000

1500

2000

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)


142

Figure C.9: PV Curves for CARX 400/132 kV Substation – Base Case and All Contingencies

Figure C.10: PV Curves for CARX 400/132 kV Substation – All N-1 Contingencies


0.85

0.90

0.95

1.00

1.05

1.10

200 400 600 800 1000 1200 1400

V (p.u.)

Power Transfer (MW)


0.85

0.90

0.95

1.00

1.05

1.10

200 400 600 800 1000 1200 1400

V (p.u.)

Power Transfer (MW)


0.85

0.90

0.95

1.00

1.05

1.10

200 400 600 800 1000 1200 1400

V (p.u.)

Power Transfer (MW)


143


Table C.3: Maximum Power Transfer Margin for CARX 400/132 kV Substation – Base Case and All Contingencies

Contingency Identifier CategoryActive Power

Transfer Limit (MW)


BASE CASE N-0 894 849

CONTINGENCY-1

N-1

744


CONTINGENCY-3 881

CONTINGENCY-4 856

CONTINGENCY-5

N-2

888

713

CONTINGENCY-6 894

CONTINGENCY-7 850

CONTINGENCY-8 894

CONTINGENCY-9 744

CONTINGENCY-10 738

CONTINGENCY-11 731

CONTINGENCY-12

N-3

738


CONTINGENCY-14 731

CONTINGENCY-15 713

0.85

0.90

0.95

1.00

1.05

1.10

200 400 600 800 1000 1200 1400

V (p.u.)

Power Transfer (MW)


144

Figure C.13: QV Curves for CARX 400/132 kV Substation – Base Case and All Contingencies

Figure C.14: QV Curves for CARX 400/132 kV Substation – All N-1 Contingencies


-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)


-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)


-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)


145


Table C.4: Available Reactive Power Margin for CARX 400/132 kV Substation – Base Case and All Contingencies

-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)



Reactive PowerReserve Margin (RRM) (MVAr) Change in

RRM(MVAr)

Available RRM

(MVAr)Base Load

Base Load +5%

BASE CASE N-0 1029 1002 28 1002

CONTINGENCY-1

N-1

1011 978 32

710CONTINGENCY-2 1026 998 28



CONTINGENCY-5

N-2

1029 1002 27

683







CONTINGENCY-12

N-3

1010 978 32

553CONTINGENCY-13 935 903 32



146

Figure C.17: PV Curves for MBCH 400/132 kV Substation – Base Case and All Contingencies

Figure C.18: PV Curves for MBCH 400/132 kV Substation – All N-1 Contingencies


0.85

0.90

0.95

1.00

1.05

1.10

200 400 600 800 1000 1200 1400

V (p.u.)

Power Transfer (MW)

BASE CASECONTINGENCY-1CONTINGENCY-2CONTINGENCY-3CONTINGENCY-4CONTINGENCY-5CONTINGENCY-6CONTINGENCY-7CONTINGENCY-8CONTINGENCY-9CONTINGENCY-10CONTINGENCY-11CONTINGENCY-12CONTINGENCY-13

0.85

0.90

0.95

1.00

1.05

1.10

200 400 600 800 1000 1200 1400

V (p.u.)

Power Transfer (MW)


0.85

0.90

0.95

1.00

1.05

1.10

200 400 600 800 1000 1200 1400

V (p.u.)

Power Transfer (MW)

CONTINGENCY-5CONTINGENCY-6CONTINGENCY-7CONTINGENCY-8CONTINGENCY-9CONTINGENCY-10

147


Table C.5: Maximum Power Transfer Margin for MBCH 400/132 kV Substation – Base Case and All Contingencies

Contingency Identifier CategoryMaximum Active Power Transfer

Limit (MW)


BASE CASE N-0 826 785

CONTINGENCY-1

N-1

676


CONTINGENCY-3 807

CONTINGENCY-4 807

CONTINGENCY-5

N-2

626

610

CONTINGENCY-6 701

CONTINGENCY-7 807

CONTINGENCY-8 688

CONTINGENCY-9 645

CONTINGENCY-10 645

CONTINGENCY-11

N-3

563


CONTINGENCY-13 638

0.85

0.90

0.95

1.00

1.05

1.10

200 400 600 800 1000 1200 1400

V (p.u.)

Power Transfer (MW)

CONTINGENCY-11CONTINGENCY-12CONTINGENCY-13

148

Figure C.21: QV Curves for MBCH 400/132 kV Substation – Base Case and All Contingencies

Figure C.22: QV Curves for MBCH 400/132 kV Substation – All N-1 Contingencies


-1500

-1000

-500

0

500

1000

1500

2000

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)

BASE CASECONTINGENCY-1CONTINGENCY-2CONTINGENCY-3CONTINGENCY-4CONTINGENCY-5CONTINGENCY-6CONTINGENCY-7CONTINGENCY-8CONTINGENCY-9CONTINGENCY-10CONTINGENCY-11CONTINGENCY-12CONTINGENCY-13

-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)


-1500

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)

CONTINGENCY-5

CONTINGENCY-6

CONTINGENCY-7

CONTINGENCY-8

CONTINGENCY-9

149


Table C.6: Available Reactive Power Margin for MBCH 400/132 kV Substation – Base Case and All Contingencies



in RRM(MVAr)

Available RRM

(MVAr)Base Load

Base Load +5%

BASE CASE N-0 1006 977 29 977

CONTINGENCY-1

N-1

978 941 36

769CONTINGENCY-2 802 769 33CONTINGENCY-3 949 920 29CONTINGENCY-4 923 896 28CONTINGENCY-5

N-2

641 596 45

596

CONTINGENCY-6 685 652 33CONTINGENCY-7 857 828 29CONTINGENCY-8 767 723 45CONTINGENCY-9 920 884 36

CONTINGENCY-10 895 860 35CONTINGENCY-11

N-3579 512 66

512CONTINGENCY-12 650 606 45CONTINGENCY-13 829 792 36

-1000

-500

0

500

1000

1500

0.70 0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

Q (MVAr)

V(p.u.)

CONTINGENCY-11CONTINGENCY-12CONTINGENCY-13

150

Vita

Salha Ali Al Disi was born on September 14, 1974, in Al Ain, UAE. She

started her education in local public schools, and then graduated from Al Ain

Secondary School in 1992. She was ranked 9th among the 1991/92 UAE Secondary

Students’ Batch. She then enrolled in UAE University in Al Ain, from which she

received her Bachelor Degree, with Second Class Honors, in Electrical Engineering in

1998. She had secured a GPA of 3.95/4 and was ranked the 1st among the 1997/98

UAE University Students’ Batch.

Ms. Al Disi has been working at Dubai Electricity and Water Authority since

2005. She is presently holding the position of Manager – System Analysis in Power

Transmission Planning Department. In 2010, Ms. Al Disi was awarded a scholarship

from Dubai Electricity and Water Authority to attain her Master’s Degree in Electrical

Engineering at the American University of Sharjah. She was awarded the Master of

Science Degree in Electrical Engineering in Spring 2013.

Documents

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