price based intelligent automatic generation and …

i

PRICE BASED INTELLIGENT AUTOMATIC

GENERATION AND CONTROL

A Thesis submitted To Gujarat Technological University

For the Award of

Doctor of Philosophy

In

Electrical Engineering

By

Prajapati Yogeshkumar Ramanlal

[129990909002]

Under supervision of

Supervisor

Dr. Vithal N Kamat

Co-supervisor

Dr.Jatinkumar J Patel

GUJARAT TECHNOLOGICAL UNIVERSITY

AHMEDABAD

January - 2021

ii

PRICE BASED INTELLIGENT AUTOMATIC

GENERATION AND CONTROL

A Thesis submitted To Gujarat Technological University

For the Award of

Doctor of Philosophy

In

Electrical Engineering

By


[129990909002]

Under supervision of

Supervisor

Dr. Vithal N Kamat

Co-supervisor

Dr. Jatinkumar J Patel


AHMEDABAD

January - 2021

iii

© [PRAJAPATI YOGESHKUMAR RAMANLAL]

iv

DECLARATION

I declare that the thesis entitled Price Based Intelligent Automatic Generation and

Control submitted by me for the degree of Doctor of Philosophy is the record of research

work carried out by me during the period from 2012 to 2019 under the supervision of Dr.

Vithal N Kamat and this has not formed the basis for the award of any degree, diploma,

associateship, fellowship, titles in this or any other University or other institution of

higher learning.

I further declare that the material obtained from other sources has been duly

acknowledged in the thesis. I shall be solely responsible for any plagiarism or other

irregularities, if noticed in the thesis.

Signature of the Research Scholar: Date: 27/01/2021

Name of Research Scholar: Prajapati Yogeshkumar Ramanlal

Place: Vallabh Vidyanagar

v

CERTIFICATE

I certify that the work incorporated in the thesis Price Based Intelligent Automatic

Generation and Control submitted by Shri Prajapati Yogeshkumar Ramanlal was

carried out by the candidate under my supervision / guidance. To the best of my

knowledge: (i) the candidate has not submitted the same research work to any other

institution for any degree / diploma, Associateship, Fellowship or other similar titles (ii)

the thesis submitted is a record of original research work done by the Research Scholar

during the period of study under my supervision, and (iii) the thesis represents

independent research work on the part of the Research Scholar.

Signature of Supervisor:

Date: 27/01/2021

Name of Supervisor: Dr. Vithal N Kamat

Place: Baroda Meters Limited, Vithal Udyonagar

Signature of Co-Supervisor:

Date: 27/01/2021

Name of Co-Supervisor: Dr. Jatinkumar J Patel

Place: G H Patel College of Engineering & Technology

Electrical Engineering Department,

Vallabh Vidyanagar

vi

Course-work Completion Certificate

This is to certify that Mr. Prajapati Yogeshkumar Ramanlal enrolment no.

129990909002 is a PhD scholar enrolled for PhD program in the branch Electrical

Engineering of Gujarat Technological University, Ahmedabad.

(Please tick the relevant option(s))

He has been exempted from the course-work (successfully completed during M. Phil

Course)

He has been exempted from Research Methodology Course only (successfully

completed during M. Phil Course)

He has successfully completed the PhD course work for the partial requirement for

the award of PhD Degree. His performance in the course work is as follows-

Grade Obtained in Research

Methodology

(PH001)

Grade Obtained in Self Study Course

(Core Subject)

(PH002)

BC BB


(Dr. Vithal N Kamat)


(Dr. Jatinkumar J Patel)

vii

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It is certified that PhD Thesis titled Price Based Intelligent Automatic Generation

and Control has been examined by us. We undertake the following:

a. Thesis has significant new work / knowledge as compared already published or

are under consideration to be published elsewhere. No sentence, equation,

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from time to time (i.e. permitted similarity index <=10%).

Signature of the Research Scholar: D Date: 27/01/2021


✓ Place: BVM Engineering College, Vallabh Vidyanagar.

Signature of Supervisor: Date: 27/01/2021

Name of Supervisor: Dr. Vithal N Kamat

Place: Baroda Meters Limited, Vithal Udyognagar.

Signature of Co-Supervisor: Date: 27/01/2021



Electrical Engineering Department,Vallabh Vidyanagar

x

PhD THESIS Non-Exclusive License to


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of the thesis.

f) I represent that my thesis is my original work, does not infringe any rights of

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conferred by this non-exclusive license.

g) If third party copyrighted material was included in my thesis for which, under the

terms of the Copyright Act, written permission from the copyright owners is

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h) I retain copyright ownership and moral rights in my thesis, and may deal with the

copyright in my thesis, in any way consistent with rights granted by me to my

University in this non-exclusive license.

i) I further promise to inform any person to whom I may hereafter assign or license

my copyright in my thesis of the rights granted by me to my University in this

non-exclusive license.

xi

j) I am aware of and agree to accept the conditions and regulations of PhD including

all policy matters related to authorship and plagiarism.

Signature of the Research Scholar:


Date: 27/01/2021

Place: BVM Engineering College


Name of Supervisor: Dr, Vithal N Kamar

Date: 27/01/2021.

Place: Baroda Meters Limited, Vithal Udyonagar

Seal:



Date: 27/01/2021


Electrical Engineering Department,

Vallabh Vidyanagar

xiii

ABSTRACT

In an interconnected power system, the frequency deviates due to the mismatch between

load demand and generation and as well due to the power feeding to the grid from the

renewable energy sources that are highly intermittent. At generator side, the primary and

secondary loop of Automatic Generation Control (AGC) helps regulate deviations in the

frequency in a slow manner due to governor action. Moreover, Availability Based Tariff

Mechanism (ABT) has been implemented to regulate the grid frequency in which the

Unscheduled Interchange (UI) price is also closely linked with secondary frequency

control. Even smart controllers with different techniques for AGC operation are proposed

to regulate frequency on the generator side using different practices by the industries as

well as by researchers, and satisfactory results have no achieved. Also, due to new

policies of the government, such as price based AGC that considers generation marginal

cost and UI price, as well as the introduction of Electrical Vehicles (EVs) that can also

perform as a nonlinear load during their charging time, is proposed to regulate frequency.

Here, due to slow response of generators against sudden load variation, uncontracted

load, governor dead band, generation rate constraint and fluctuating power of grid-

connected due to renewable energy sources the actual system characteristics becomes

non-linear. Also, due to the frequent operation of the generator with these non-linearities,

there is unnecessary fuel consumption, increased wear and tear on generators and an

increase in UI prices of ABT. It also causes tripping of the generators sometimes.

In the present work, for improving existing AGC mechanism, the general framework for

deriving the state-space model of two area restructured power system having 2 numbers

of Generating Companies (GENCOs) with the thermal-thermal non-reheat unit in each

area, and 2 numbers of Distribution Companies (DISCOs) with renewable energy sources

have been considered. The model for UI price of ABT mechanism (CERC, 2016) has

been used to analyze AGC performance based on real-time price linked to frequency. The

marginal cost of the generator and UI price are considered for the price based operation

of the AGC. The fleet of Electrical Vehicles (EVs) operated by bidirectional charger

followed by battery charging/discharging characteristics considered. The suggested

diversified transmission link through which Load Frequency Control (LFC) simulated,

improves the Area Control Error (ACE) due to EVs.

xiv

AGC operation for two area restructured power system incorporated with renewable

energy source have simulated in MATLAB / Simulink environment with and without

EVs. The proposed concept has been shown to cope better than the existing AGC

mechanism. By comparing the results, the effectiveness of the proposed control scheme

for the reduction in the deviations of frequency, generator power and UI price has shown.

Normally, to charge and discharge the EV battery, PI controllers are used to tracking the

load continuously. But due to nonlinearity and uncertainty of load, performance of PI

controller becomes poor. So, fuzzy-based PI controllers are used. It can track better as

compared to PI controllers to reduce the frequency deviations and settling time too. The

comparative analysis between PI and Fuzzy PI controllers are presented.

xv

Acknowledgement

I would like to express my sincere gratitude to all who have inspired and provided the

best support for completing entire research work. On this occasion, I am heartily thankful

to the management of Charutar Vidya Mandal and my parent institute Birla Vishvakarma

Mahavidyalaya.

I am highly thankful to the Principal of BVM, Dr Indrajit Patel for providing all kind of

facility during my entire PhD work. I am also obliged by Head, Electrical Engineering

Department, Dr N G Mishra for providing me infrastructural support where and when

required.

It is my sincere gratitude from the bottom of the heart to my supervisor(s) Dr. Vithal N

Kamar and Dr. Jatinkumar J Patel who have provided opportunity to work and their

continuous support to complete this task under their guidance.

I am also thankful to Gujarat Technological University (GTU) Vice-Chancellor,

Registrar and PhD section for all necessary supports during my work. My gratitude

towards to DPC members Dr. B. R Parekh and Dr. C. D. Kotwal also for their valuable

suggestions, corrections and continuous evaluations of my research work.

This research is dedicated to my family members including my parents, wife shweta, my

children Trijal and Swara, who became a source of continuous encouragement and

provided unconditional support without any voice of dissent or discomfort.

Above all, I am very much thankful to my “इष्टदेव” Lord “Swaminarayan” and

Guruhari “Shree Mahant Swami Maharaj” without whose blessings this entire

research work would have been meaningless for me.

There are several others who have played a significant role in the completion of this entire

work whose names I may have failed to mention here, I extend my sincere thanks to all

who have contributed directly or indirectly in achieving this goal.


xvi

Table of Content

Chapter 1 Introduction 1

1.1 Introduction 1

1.2 Deregulation of a power system 1

1.3 Electricity market structural design and classification 2

1.3.1 Electricity market structural design 2

1.4 Market Classification 3

1.4.1 Classification according to Power Market 3

1.4.2 Classification according to the Time of Operation 4

1.5 Ancillary Services 5

1.5.1 Types of Ancillary Services 5

1.5.1.1 Ancillary Services to control Frequency 5

1.5.1.2 Ancillary Services to control Power System

Network

6

1.5.2 Functions of Ass 6

1.5.3 Driving force for development and need of Ancillary

services for India

7

1.5.4 Issues with the growth of ancillary services 7

1.5.5 Benefits of ancillary services 8

1.6 Issues in Indian Power Sector 8

1.7 Challenges in the Indian Power Sector 9

1.8 Frequency Regulation in Indian Power System 10

1.8.1 Primary Control Loop 11

1.8.2 Secondary Control Loop 11

1.8.3 Emergency Control 11

1.9 ABT Mechanism 12

1.10 UI Modelling 14

1.11 A detail description of the state of the art of the research 14

1.12 Thesis objectives and chapter organization 17

Chapter 2 Review of Literature 20

2.1 Introduction 20

2.2 LFC in Single and Multi Area Power System 21

2.3 LFC using Artificial Intelligent Techniques 23

2.3.1 Genetic Algorithm 23

2.3.2 Neural Network 24

2.3.3 Fuzzy Logic 24

2.4 LFC using Other Soft Computing Techniques 25

2.4.1 Particle Swarm Optimization (PSO) 25

2.4.2 Bacterial Foraging Algorithm (BFOA) 25

2.4.3 Tabu Search Algorithm (TSA) 26

2.4.4 Other Optimization Control Techniques 26

2.5 LFC with AC-DC Parallel Tie line 27

2.6 LFC with Flexible AC Transmission System (FACTS) 27

2.7 LFC Based on Other theory 27

2.7.1 Different feedback theory 27

2.7.2 Hierarchical Load Frequency Control 28

xvii

2.7.3 LFC Using Internal Model Control 28

2.7.4 Load Frequency Control Using Observer 29

2.8 LFC based on by consideration of communication delay 29

2.9 LFC Based on different Learning Techniques 29

2.10 LFC with Distributed Energy Generation 30

2.10.1 LFC in Micro Grid 30

2.10.2 LFC in Smart Grid 31

2.10.3 LFC with DR 31

2.10.4 LFC with a V2G control 31

2.10.5 LFC with Hybrid System 32

2.11 LFC in Deregulated Power System 36

2.12 Electrical Energy Storage 38

2.13 Conclusion 38

Chapter 3 Price Based AGC Operation 40

3.1 Introduction 40

3.2 Grid operation in India 40

3.2.1 Scheduling and Dispatch under ABT mechanism 42

3.3 Reforms in Indian Power Systems 45

3.4 Price based AGC block diagram with the control scheme 45

3.5 Mathematical Modeling 48

3.6 Conclusion 49

Chapter 4 AGC operation in Restructured Power System 50

4.1 Introduction 50

4.2 About Restructured Power System 50

4.3 System Under Examination 51

4.3.1 Mathematical modelling of two area restructured

power system

51

4.3.2 AGC operation Under Different Market 54

4.3.2.1 Poolco Based Market 55

4.3.2.2 Bilateral Market 55

4.3.3.3 Bilateral Market with Contract Violation 55

4.3.3 Comparison Between Various Markets 56

4.3.4 Simulation and Results 56

4.4 Price based AGC operation under ABT mechanism 59

4.5 AGC operation during Peak Hours Off-Peak Hours 61

4.6 Conclusion 65

Chapter 5 Energy Storage for Frequency Regulation 66

5.1 Introduction 66

5.2 The need for Electrical Energy Storage 66

5.3 Grid Frequency Regulation Using Electrical Vehicle 66

5.4 Proposed Block Diagram for EV 68

5.5 Price Based AGC Operation in Coordination with EV 71

5.5.1 Poolco Based Transaction 71

5.5.2 Bilateral Transaction 74

5.5.3 Bilateral Transaction with Contract Violation 76

5.6 Conclusion 81

Chapter 6 AGC operation with Renewable Energy Sources (RES) 82

6.1 Introduction 82

6.2 Solar Energy System with Price Based AGC Operation 83

xviii

6.2.1 Block Diagram of Two Area Restructured Power

System with Solar Energy System

83

6.2.2 AGC Operation Under Different Market Conditions 85

6.3 Wind Energy System with Price Based AGC Operation 93

6.3.1 Description of Two Area Restructured Power System 94

6.3.2 AGC Operation Under Different Market Conditions 94

6.4 Conclusion 103

Chapter 7 Price based Automatic Generation Control Using fuzzy-based

Grid Connected Electrical Vehicles

105

7.1 Introduction 105

7.2 Description of the Model 105

7.3 EV Battery Charging Controller 106

7.4 Intelligent Price Based AGC Operation Under the Different

Market

107

7.4.1 Poolco based contract 107

7.4.2 Bilateral contract 111

7.4.3 Bilateral contract with a contract violation 115

7.5 Conclusion 120

Chapter 8 Summary, Conclusion and Future Scope 122

8.1 Original contribution by the thesis 122

8.2 Conclusion 123

8.3 Future Scope 125

List of References 126

List of Publication 156

Appendices 158

xix

List of Abbreviations

ABT Availability Based Tariff

ACE Area Control Error

AFC Alkaline Fuel Cell

AGC Automatic Generation Control

AHC Adapting Hill Climbing

AI Artificial Intelligence

APF ACE Participation Matrix

AS Ancillary Services

BCO Bee Colony Optimization

BESS Bacterial Foraging Optimization Algorithm

BFOA Bacterial Foraging Optimization Algorithm

CAES Compressed Air Energy Storage

CDLC Central Direct Load Control

CERC Central Electricity Regulatory Commission

CPP Captive Power Plants

DB Dead Band

DDC Dynamic Demand Control

DE Differential Evolution

DERs Distributed Energy Resources

DESS Distributed Energy Storage System

DFIG Doubly-Fed Induction Generator

DG Diesel Engine Generator

DGVCL Dakshin Gujarat Vij Company Limited

DISCO Distribution Companies

DMFC Direct Methanol Fuel Cell

DPM Disco Participation Matrix

DR Demand Response

DSSV Detailed Structured Singular Value

EES Electrical Energy Storage

ERC Electricity Regulatory Commission

ERCOT Electric Reliability Council of Texas

EV Electrical Vehicle

FACTS Flexible AC Transmission System

FC Fuel Cells

FESS Flywheel Energy Storage System

FG Fuzzy Logic

FGMO Free Governor Mode Of Operation

FGMO Free Governor Mode of Operation

FO Foraging Optimization

G2V Grid to Vehicle

GA Genetic Algorithm

GALMI Genetic Algorithm Linear Matrix Inequalities

GCE Generation Control Error

GCE Generation Control Error

GDC Generalized Droop Control

xx

GENCO Generation Companies

GHG Green House Gas

GoI Government of India

GRC Generation Rate Constraint

GUVNL Gujarat Urja Vikas Nigam Limited

HTTES High-Temperature Thermal Energy Storage

IEGC Indian Electricity Grid Code

ILMI Iterative Linear Matrix Inequalities

IPP Independent Power Producer

IPS Interconnected Power System

ISGSs Inter-State Generating Stations

ISO Independent System Operator

LCOA lozi Map-Based Chaotic Algorithm

LFC Load Frequency Control

LMI Linear Matrix Inequalities

LMIs Linear Matrix Inequalities

LTTES Low-Temperature Thermal Energy Storage

M Medium

MCFC Molten Carbonate Fuel Cell

MES Mechanical Energy Storage

MGPC Multivariable Generalized Predictive Control

MGVCL Madya Gujarat Vij Company Limited

MOO Minimal Order Observer

MPC Model Predictive Control

NB Negative Big

NLDC National Load Dispatch Centers

NN Neural Network

NS Negative Small

PAFC Phosphoric Acid Fuel Cell

PB Positive Big

PEM Proton Exchange Membrane

PEMFC Polymer Electrolyte Membrane Fuel Cell

PGVCL Pachim Gujarat Vij Company Limited

PHEV Hybrid Electrical Vehicle

PHS Pumped Hydro Storage

PI Proportional Integral

PS Positive Small

PSASRAI Power System Ancillary Service Requirement Assessment Indices

PSO Particle Swarm Optimization

PVs Photovoltaic Systems

PX Power Exchange

Rescos Retail Energy Services Companies

RESs Renewable Energy Sources

RFC Regenerative Fuel Cells

RL Reinforcement Learning

RLDC Regional Load Dispatch Centers

S Small

SERC State Electricity Regulatory Commission

SLDC State Load Dispatch Centers

xxi

SMES Super Magnetic Energy Storage

SOC State of Charge

SOEC Solid Oxide Electrolysis Cells

SOFC Solid Oxide Fuel Cell

SSPS Solid-State Phase Shifter

SVC Static Var Compensators

TATRIPS Two Area Thermal Reheat Interconnected Power System

TCPS Thyristor Controlled Phase Shifter

TES Thermal Energy Storage

TRANSCO Transmission Companies

TSA Tabu Search Algorithm

TSO Transmission System Operator

UC Unit Commitment

UGVCL Uttar Gujarat Vij Company Limited

UI Unscheduled Interchange

V2G Vehicle to Grid

VB Very Big

VIU Vertically Integrated Utility

VIU Vertically Integrated Utility

WTG Wind Turbine Generators

Z Zero

xxii

List of Symbols

b1,b2,b3,b4 Frequency Bias Factor (MW/Hz)

𝑐𝑝𝑓𝑖𝑗 Contract Participation Factor

D Damping factor represents the frequency dependency on load in

MW/Hz

D1,D2 Damping Factor

F Frequency (Hz)

𝛥𝑓 Frequency deviations

H Inertia constant in MWs

H1,H2 Inetria Constant

Ki Integral gain constant

Kmax Maximum EV Gain, Kw/Hz

𝐾𝐸𝑉 EV gain

Kp1,Kp2 Power system gain

KPV

Ratio of change in power (ΔPPV) to the change in radiation

𝑃𝑟1, 𝑃𝑟2 Rated Power (MW)

𝛥𝑃𝑔𝑜𝑣 Governor Output signal

𝛥𝑃𝑒 Setting command for speed changer

𝛥𝑃𝑉 Hydraulic valve output power

𝛥𝑃𝑔 Output power of turbine-generator

𝛥𝑃𝐿 Load Perturbation

PL1, PL2 Local Load (MW)

PPV Output power of the PV (KW)

𝛥𝑃𝑡𝑖𝑒1−2,𝑒𝑟𝑟𝑜𝑟 Tie Line Error (MW)

𝛥𝑃𝑡𝑖𝑒1−2,𝑎𝑐𝑡𝑢𝑎𝑙 Tie line actual power (MW)

𝛥𝑃𝑡𝑖𝑒1−2,𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝑑 (Demand of Disco in Area II from GENCOs in Area1 – Demand of

DISCOs in Area1 from GENCOs in Area II)

𝑃𝑚 Maximum EV power, KW

𝑃𝑔0𝑖 Marginal cost of a generator

𝛥𝑃𝑔𝑖 Power contracted to supply by ith GENCO

R Slope of the governor regulation / Droop Characteristics

R1, R2 Frequency Regulation Factor Hz/MW

S1 Deviations in the frequency signal

S2 UI price signal

S3 Schedule generation (𝑃𝑔0) MW - Changes in the generator power

(𝛥𝑃𝑔𝑖) MW

S4 Incremental cost signal

𝑆 Photovoltaic (PV) array area in m2

S5 Generation Control Error (GEC) Rs/MWh

𝑇𝑎 Ambient temperature in𝐶∘

xxiii

𝑇ℎ Hydraulic valve time constant

𝑇𝑡 Time constant of a turbine

T12 Tie-Line time constant (s)

Tg1,Tg2,Tg3,Tg4 Governor Time Constant (s)

Tp1,Tp2 Power system time constant (s)

Tt1,Tt2,Tt3,Tt 4 Turbine Time Constant (s)

Tw Wind Turbine time constant (s)

𝜌𝑖 UI price signal

𝛾𝑖 Marginal Cost signal

𝜂 Conversion efficiency

𝜑 Solar insolation in (kw/m2)

xxiv

List of Figures

Fig. No Title Page No.

1.1 Deregulation in Power System 3

1.2 Automatic Generation Control (AGC) Process 10

1.3 Operation of AGC loops

12

1.4 UI price vs frequency chart (CERC, 2016) 14

3.1 National Grid in India 41

3.2 Hierarchical Structure of Power Grid in India 42

3.3 Function of Indian Power System Entities 42

3.4 Process of Schedule and Dispatch Under ABT Mechanism 44

3.5 ABT based AGC loop 45

3.6 Flow Chart for GCE Calculation 47

3.7 Control Scheme for price based AGC 47

4.1 Block Diagram of Two Area Restructured Power System 54

4.2 (a) GENCO1 power deviations of Area1 (MW) 56

4.2 (b) GENCO2 power deviations of Area1 (MW), (c) GENCO3

power deviations of Area1 (MW), (d) GENCO4 power

deviations of Area2 (MW)

57

4.2 (e) Area1 Frequency deviations (Hz), (f) Area2 Frequency

deviations (Hz), (g) Tie Line Power deviations (MW)

58

4.3 UI price vs frequency chart (CERC, 2016) 59

4.4 (a) Area1 UI Price deviations (Rs/MWh), (b) Area2 UI Price

deviations (Rs/MWh)

69

4.5 (a) GENCOs power (Mw), (b) Area1 and Area2 Frequency

Deviations (Hz)

63

4.5 (c) Area1 UI price (Rs/Mwh), (d) Area2 UI price (Rs/Mwh),

(e) Tie line power (MW)

64

5.1 Proposed block diagram for the grid frequency regulation 68

5.2 EV battery operating characteristics to Charge and Discharge 70

5.3 (a) Load deviation response of generated power of Area 1, (b)

Load deviation response of generated power of Area 2, (c)

Frequency deviation response of Area 1 and Area 2

72

5.3 (d) Tie line power deviation, (e) Area1 UI Price (Rs/Mwh), (f)

UI Price (Rs/Mwh)

73

5.4 (a) Load deviation response of generated power of Area 1 74

5.4 (b) Load deviation response of generated power of Area 2, (c)

Frequency deviation response of Area 1 and Area 2, (d) Tie line

power deviation

75

5.4 (e) Area1 UI Price (Rs/Mwh), (f) UI Price (Rs/Mwh) 76

5.5 (a) Load deviation response of generated power of Area 1, (b)

Load deviation response of generated power of Area 2

77

5.5 (c) Frequency deviation response of Area 1 and Area 2, (d) Tie

line power deviation, (e) Area1 UI Price (Rs/Mwh)

78

5.5 (f) UI Price (Rs/Mwh) 79

6.1 Model for output fluctuation of (PV) solar power 83

6.2 Two area restructured power system 84

xxv

6.3 Impact of Solar Power on (a) GENCO1 Power Deviation, (b)

GENCO2 Power Deviation, (c) GENCO3 Power Deviation

86

6.3 Impact of Solar Power on (d) GENCO4 Power Deviation, (e)

Area1 Frequency Deviation, (f) Area2 Frequency Deviation

87

6.3 Impact of Solar Power on (g) Tie Line Power Deviation, (h)

Area1 UI Price (Rs/Mwh), (i) Area2 UI Price (Rs/Mwh)

88

6.4 Impact of a Fleet of EVs on (a) GENCO1 Power Deviation, (b)

GENCO2 Power Deviation, (c) GENCO3 Power Deviation

86

6.4 Impact of a Fleet of EVs on (d) GENCO4 Power Deviation, (e)


87

6.4 Impact of a Fleet of EVs on (g) Tie Line Power Deviation, (h)


88


GENCO2 Power Deviation

89

6.5 Impact of Solar Power on (c) GENCO3 Power Deviation, (d)

GENCO4 Power Deviation, (e) Area1 Frequency Deviation

90

6.5 Impact of Solar Power on (f) Area2 Frequency Deviation, (g)

Tie Line Power Deviation, (h) Area1 UI Price (Rs/Mwh),

91

6.5 Impact of Solar Power on (i) Area2 UI Price (Rs/Mwh) 92



89

6.6 Impact of Solar Power on (c) GENCO3 Power Deviation, (d)


90

6.6 Impact of Solar Power on (f) Area2 Frequency Deviation, (g)


91

6.6 Impact of Solar Power on (i) Area2 UI Price (Rs/Mwh) 92

6.7 Block diagram representation two area restructured power

system with Wind Turbine (WT)

95

6.8 Block diagram of Wind Power Plant 95

6.9 Impact of Wind Power on (a) GENCO1 Power Deviation, on

(b) GENCO2 Power Deviation, (c) GENCO3 Power Deviation

96

6.9 Impact of Wind Power, (d) GENCO4 Power Deviation, (e)


97

6.9 Impact of Wind Power on (g) Tie Line Power Deviation, (h)


98

6.10 Impact of Wind Power on (a) GENCO1 Power Deviation, on

(b) GENCO2 Power Deviation, (c) GENCO3 Power Deviation

96

6.10 Impact of Wind Power, (d) GENCO4 Power Deviation, (e)


97

6.10 Impact of Wind Power on (g) Tie Line Power Deviation, (h)


98

6.11 Impact of Wind Power on (a) GENCO1 Power Deviation, (b)


99

6.11 Impact of Wind Power on (c) GENCO3 Power Deviation, (d)


100

6.11 Impact of Wind Power on (f) Area2 Frequency Deviation, (g)


101

6.11 (i) Area2 UI Price (Rs/Mwh) 102

6.12 Impact of Wind Power on (a) GENCO1 Power Deviation, (b)


99

xxvi

6.12 Impact of Wind Power on (c) GENCO3 Power Deviation, (d)


100

6.12 Impact of Wind Power on (f) Area2 Frequency Deviation, (g)


101

6.12 (i) Area2 UI Price (Rs/Mwh) 102

7.1 Block diagram of a fuzzy-based tuned PI controller 106

7.2 Comparative results of PI and Fuzzy PI controller in Poolco

Based Transaction for (a) GENCO1 Power Deviations of Area1,

(b) GENCO2 Power Deviations of Area2

108


Based Transaction for (c) GENCO2 Power Deviations of Area2,

(d) GENCO2 Power Deviations of Area1, (e) Frequency

Deviations of Area1.

109


Based Transaction for (f) Frequency Deviations of Area2, (g) UI

Price Deviations of Area1, (h) UI Price Deviations of Area2

110


Based Transaction for (i) Tie-Line Flow

111

7.3 Comparative results of PI and Fuzzy PI controller in Bilateral

Transaction for (a) GENCO1 Power Deviations of Area1, (b)

GENCO2 Power Deviations of Area1. (c) GENCO2 Power

Deviations of Area2

112


Transaction for (d) GENCO2 Power Deviations of Area2, (e)

Frequency Deviations of Area1, (f) Frequency Deviations of

Area2.

113


Transaction for (g) UI Price Deviations of Area1, (h) UI Price

Deviations of Area2. (i) Tie-Line Flow.

114


Transaction with contract violation for (a) GENCO1 Power

Deviations of Area1

115


Transaction with contract violation for (b) GENCO2 Power

Deviations of Area1, (c) GENCO2 Power Deviations of Area2,

(d) GENCO2 Power Deviations of Area2

116


Transaction with contract violation for (e) Frequency

Deviations of Area1, (f) Frequency Deviations of Area2, (g) UI

Price Deviations of Area1

117


Transaction with contract violation for (i) Tie-Line Flow

(MW), (h) UI Price Deviations of Area2

118

xxvii

List of Table

Table No Title Page No.

4.1 DPM Matrix 52

4.2 Calculated Parameters for all cases 56

4.3 Two Area Power System (GUVNL) 61

4.4 Area1 (MGVCL,DGVCL) 62

4.5 Area2 (UGVCL,PGVCL) 62

5.1 Calculated Power At GENCOs And Tie Line (Poolco Based

Transaction)

72

5.2 Calculated Power At GENCOs And Tie Line (Bilateral

Transaction)

74

5.3 Calculated Power At GENCOs And Tie Line 77

5.4 Summary of Comparison with EV and Without EV 79

6.1 Calculated Power At GENCOs and Tie Line (Poolco Based

Transaction)

85


Transaction)

89

6.3 Comparative Analysis of PV and EV integration 92


Transaction)

96


Transaction)

99

6.6 Comparative Analysis of WT and EV integration 102

7.1 Fuzzy logic Rules 107


Transaction)

108


Transaction)

112

7.4 Calculated Power At GENCOs And Tie Line (Contract

Violation)

115

7.5 Comparative Analysis of PI and FGPI integration 118

xxviii

List of Appendices

Title Page No.

Appendix A Challenges in the Indian Power Sector 158

Appendix B Mathematical modelling of AGC 163

Appendix C System Data 165

Appendix D Classification of Energy Storage (ES) 166

Appendix E Fuzzy Logic 172

1

CHAPTER 1

Introduction

1.1 Introduction

There are tremendous changes in electricity to generate, transmit and distribute in past

days. In the various parts around the world, an electrical grid is differentiated concerning

their design, ownership, operating process and pattern. An electricity utility market is a

monopoly under their control. These utilities are private or government-owned and

regulated by the government. The Indian government has started the “Power for All"

mission to give power supply to all the people. India has one “National Grid” with an

installed power generation capacity of 360.788 GW as on 31st August 2019. To operate

and control power in a large grid is centralized, secure, reliable and cost-effective. Since

a long time, economists of the electricity area had started thinking and questioning for

the market discipline-oriented operation. Presently, the electricity market is the open-

access market. For the transmission owning utilities, it is necessary to provide equal

access to third parties. It has made the competition possible.

1.2 Deregulation of A Power System

The process of changing the electricity rules and regulation of electricity is known as the

deregulation of a power system. It controls the electric industry and gives consumers the

option of power suppliers. So, the customers will be benefited to get electrical power with

a lower rate due to the price competition between electric industries. It improves the

process of use of electricity and economic efficiency. The common structural design of

the deregulation is presented in Fig 1.

The main objectives behind deregulation are:

➢ To supply power for all realistic load.

Introduction

2

➢ To encourage competition between generation and supply.

➢ To give quality service and continuous power supply.

The advantages associated with deregulation are:

➢ Efficient utilization of electric network capacity

➢ To optimize the supply of energy

➢ It clears the electricity rates to consumers.

➢ Reduction in electricity rates.

➢ Use of efficient technology by replacing old and bad technologies.

➢ Restructuring in price signal has improved the efficiency of usage.

➢ Improvement in the choice of the consumer to purchase electricity.

➢ Power will supply to a shortage area

➢ Ancillary services prices reduced.

1.3 Electricity Market Structural Design And Classification

1.3.1 Electricity Market Structural Design

There are four players such as GENCO, TRANSCO, DISCO & ISO in the electricity

market [237]. The market architecture of an IPS is presented in Fig. 1.1.

A. GENCOs: It is an independent entity and runs one or more generators. Generator

running schedule depends on the power bids in a competitive market. It can also

negotiate for power generation from others if their generation is not available.

B. TRANSCOs: These companies are the owners of the transmission line and

operated by themselves. It provides transmission line and electricity transport

from the generator to all customer. On behalf of their services, they seek a

transmission tariff.

C. DISCOs: The DISCOs operate the local electrical distribution system in their own

area. They purchase wholesale power from the spot market. Also, they purchase

power from the direct contracts between GENCOs and DISCOs.

D. ISO: It is taking care of reliable and harmless operation of IPS. It will not take

part in electricity market trading and run independently.

CHAPTER 1

3

E. Customers: The customers have various options to purchase power and use in

deregulation.

F. Retailers: It is the electric power retailer.

Generation

Transmission

Distribution

Costomer

GENCOs

TRANSCOs

DISCOs

Costomer

PX

ISO

Rescos

Before After

Deregulation

Deregulation

Deregulation

FIGURE 1.1 Deregulation in Power System.

1.4 Market Classification

The markets are classified in two ways according to Power Market and the Time of

Operation.

1.4.1 Classification According to Power Market

The classification of the market is as follows.

Energy market: A market in which electricity trading is competitive is known

as an energy market. This centralizes mechanism makes energy trading easier

between buyers and suppliers. After the submission of bids by sellers and buyers,

the electricity market clearing price will determine. Electrical energy rates are

reliable for market participants as well as consumers.

Introduction

4

Transmission market: In this market, there is a competition among power

suppliers, the demand of distributors and large users. In the market, traded

commodity is the right of the transmission system operator. The rights to extract

and inject are given to its holder.

Ancillary Services Market: For system security and reliability, these services

are required. There are two-parts in the bid of ancillary services such as capacity

bid and ancillary bid [182].

1.4.1 Classification According to the Time of Operation

Forward Market: Bilateral contracts exist in this market operation. It is

classified into two types. One is a day-ahead type market. In which, sellers bid

different prices for the schedule to supply. Here, on hourly basis prices are

determined. The sellers will give the resources for the sold power and buyers

will give the delivery point to purchase the power. Second is the hourly type

market which is same as the day-ahead type market.

Spot Market: It is a real-time based market which is generally day-ahead. The

equilibrium between supply and load must maintain during real-time for the

reliability of the power supply. Energy and Ancillary services both can offer a

trade-in this market.

Wholesale Electricity Market: It creates a competitive environment. Due to the

interaction between demand and supply, the rates are determined in the

wholesale market.

Retail Electricity Market: In the retail market, the entire consumers have

directly or via the retailer to access opposing generators. The generation and

retailing are separated in this way.

Real-time market: In real-time, generation-demand balance must maintain. The

real-time market is required because of different values of power generation,

transmission and load as compared to the spot market and forward market.

CHAPTER 1

5

Pool Market: In this market, mainly customer (buyer) and supplier (seller) are

participating. Both will submit bids and prices of energy into the pool. System

Operator (SO) will determine locational marginal prices.

Bilateral Market: In this market, buyer and seller both will participate in

delivering and receipt of power. This process may be direct or through some

broker. The role of SO is only to verify the availability of sufficient transmission

capacity for the execution of the transaction.

1.5 Ancillary Services (ASs)

Ancillary services help to support the interconnected grid to transmit reliable, quality and

secure power [182]. On 10th April 2013 CERC has introduced different ASs for the

country power market. It is designed for supplementing efforts to keep power quality,

reliability and safety of the interconnected grid. Worldwide, the ASs becomes most

popular in the electricity supply system. Also, in India, along with the grid restructuring,

ancillary services such as frequency stability, voltage stability, generation storages,

scheduling, and dispatch are introduced and developed. An introduction of the ASs in an

effective way is discussed in [267]. Ancillary services are classified as follows.

1.5.1 Types of ASs.

1. Ancillary Services to control Frequency

2. Ancillary Services to regulate the grid Network

1.5.1.1 Ancillary Services to Control Frequency

It maintains the frequency in the preferred range. The generation and load should be

balanced in real-time. Generators are running under the AGC loop help to normalize the

frequency. AGC loop works in mainly three levels of control, (a) Primary loop of

frequency control with a reaction period from 5 to 10 seconds, (b) Secondary loop of

frequency control with a reaction period from 10 seconds to 10 minutes and (c)

Emergency / Tertiary loop of frequency control with a reaction period from 10 to 30

Introduction

6

minutes. The governors by AGC and demand-side management both help to regulate

frequency.

1.5.1.2 Ancillary Services to Control Power System Network

The power system network parameter in a prescribed range is maintained. The

Generators, Synchronous Condensers, FACTS devices, Capacitors, Inductors and DERs

help to provide network-controlled services. The network-controlled ancillary service is

also distributed for the following purpose.

a) Power flow control: - This service use in the IPS for power flow control.

b) Voltage control: - This service use to keep the voltage within a specified range,

the primary, secondary and tertiary loop helps to maintain voltage.

c) System restart: - These types of services are hold for emergency conditions. It

helps to restart the system from blackout situations. System backup capacity is

required to return the system into normal operating mode, after a partial or major

blackout.

Based on the requirement of the service, ancillary services are further classified as

follows.

• Services essential to do a daily operation.

• Services essential to stop unfortunate accidents.

• Services essential to restore all systems after the blackout.

1.5.2 Functions of ASs

The functions of Ass are listed below.

i) Regulation: - To keep generation-demand balance minute to minute.

ii) Load following: - To do load-generation equilibrium at the end of the scheduling

period.

iii) Operating reserve: To provide the unloaded generators interconnected with grid

immediately in case of transmission or generation outages.

iv) Provide support to regulate voltage and reactive power: Regulation of voltage by

managing the reactive power with the help of capacitor banks and generator.

v) System black start capability:- Generation unit can restart the generation due to

blackout without any help from the grid side. Also, to help another unit to start.

CHAPTER 1

7

vi) Network stability services: - Give support from generation sources for the stability

of the transmission line by using fast responding equipment.

1.5.3 Driving Force for Development and Need of Ass for India

The following are the driving force for the existence of ASs in the Indian power

market.

i) Various buyers as well as sellers of power in the electrical power market.

ii) Restructuring of a power system.

iii) Shortage of enough reserves.

iv) Frequency controller.

v) The slow action of AGC loops such as primary, secondary, and tertiary.

vi) Sudden load shedding.

vii) Power shortage during peak hours.

1.5.4 Issues with the Growth of AS

1. Power cuts in power system:- Power cuts are due to the reasons such as inadequate

infrastructure, variable load condition and the inability of DISCOs to purchase

expensive power, etc.

2. Fluctuating nature of renewable power:- Due to the variable nature of renewable

power, it affects scheduling and pricing. Also, the prediction of renewable power

is difficult, which increases more challenges to regulate the frequency.

3. Power requirement during peak hour:- Because of the uncertain rise in the demand

during peak hours, the generation-demand imbalance creates. So, the stored

power is required.

4. Network issues: - Damage in the network due to thermal overload, instability of

frequency and voltage.

5. Identify ancillary service for effective solution:- Identification required ancillary

service to maintain system reliability. Also, provision of payment against

ancillary services.

Introduction

8

1.5.5 Benefits of Ancillary Services

The ancillary services help to fulfill the customer electricity requirement, gives financial

inspiration and utilization of distributed sources potential, thus use undispatched

generation. It helps to stabilize the grid more stable. The performance of ancillary

services discussed in detail [75]. The following are the steps to control the issues faced

in providing AS.

1. To make regulatory policy to design and procure market mechanism.

2. To prepare the framework of AS providers.

3. The framework of policy to provide ancillary services, the rule framework for the

provision of AS.

4. Guideline of tariffs for power procurement aspects so that price can be affordable

to the consumer.

5. FGMO helps to regulate frequency. Distributed generation sources can also

participate in frequency regulation as an ancillary service.

Because of assorted nature and requirements of the Indian power sector, it is a

requirement to increase the reliability and quality of power system network.

1.6 Issues in Indian Power Sector

Worldwide India is third in electricity production and fourth in consumption [297]. India

has 319.60 GW installed generating capacity [298] and 360.788 GW as on 31st August

2019. The following issues are noted in the Indian power system.

Power Generation

In India, NTPC is the biggest power generation company from coal. It has an

installed capacity of 55126 MW, declared in the annual audit report of May 2019.

Up to 2032, the capacity will expand 128000 MW [300]. Because of poor coal

quality and insufficient stock, the plant load factor is low [299]. Also, India lacks

private investment in the coal sector. There is a monopoly in the production of

coal by Coal Indian limited. Because of these reasons, the utilization rate of

operational plants will reduce the generated capacity and results in poor financial

growth.

CHAPTER 1

9

Power Transmission

India’s largest power transmission utility is the Power Grid Corporation of India

(PGCI) [303]. It has a transmission capacity of 90650 MW as on February 2019.

It manages the grid through National Load Dispatch Centre (NLDC) at a national

and regional level grid. The issues at this level are the design of tariff rates,

Investment and to allot space and media for the transmission of the electric power

[302].

Power Distribution

As per electricity Act 2003 in Open Access market customer and DISCOs can

buy electric power directly from the generating companies. But awareness at

costumer level is required. One of the salient features in the open-access market

is the competition between the utilities. Major issues in the power distribution are

the reduction in the technical and commercial losses, power quality, availability

of independent feeder, automation for power distribution and availability based

tariff [301].

1.7 Challenges in the Indian Power Sector

In India, the total generating capacity was 157 GW on 31st March 2010. It increased to

360 GW in 2019. There is a drastic increase in demand, and the power shortage will be

there in the near future [47].

A. Demand supply gap

India has increased peak demand recorded from 119 GW in 2009 to 183 GW in 2019.

The demand has continuously increased, as shown in Fig. A.5 (Appendix A). In 2009

the peak demand and the met peak demand were 119 GW and 104 GW. The deficits were

-12.7%. In 2019 the peak demand was 183 GW and met peak demand was 182 GW with

the deficits -0.7%. Day by day, the generation has to increase against the demand.

Presently, the deficits are reducing, as shown in Fig. A.6 (Appendix A).

Introduction

10

B. Huge Transmission & Distribution Losses

Transmission and Distribution Losses during the year 2012-13 was 23.04 %. Every year

the losses are reduced. In the year of 2016-17 losses were 21.42%. Statistics of

Transmission and Distribution Losses is presented in Fig. A.9 (Appendix A).

1.8 Frequency Regulation in Indian Power System

In an interconnected power system, unexpected demand variation causes generation-

demand imbalance. It results in the fluctuations of frequency from its fundamental value.

It affects power system operation and control. Also, affects the reliability and security of

the grid. It reduces the efficiency of the network by damage in the equipment, reduces

the load efficiency, and increases the overloading of overhead lines and triggering the

protective systems. The grid frequency can control by controlling the speed of the

generator. So, it can maintain by regulating the speed of the generator. The governor

mechanism regulates the speed. The real-time speed of the machine is sense and changes

in speed in the form of error signal are generated. Following the error signal, an integral

controller helps to control the turbine input by moving the steam valve.

In the AGC operation, generator runs constantly to follow the load. An expense will be

less in AGC operation. The AGC stabilize the fluctuations in frequency as well as tie-

line flow. In the AGC process, there are mainly two frequency loops as shown in Fig. 1.2

A first control loop is primary control loop operates in a self-regulating mode to regulate

frequency error. This loop attends normal frequency deviations. But, the frequency

cannot be regulated fully due to the governor slow response [261][125]. After a few

seconds, the secondary control loop will operate for the control of remaining fluctuations

in frequency and power. In this loop, the controller with the integral control helps to

minimize the error. It is also defined as a supplementary control loop or LFC loop. Due

to some reasons such as loss of generation, load shedding or some fault, there is a major

inequity between generation and load. It creates rapid changes in frequency. The

emergency control loop will operate to establish frequency. The mathematical modeling

for the AGC operation is given in Appendix B.

CHAPTER 1

11

Controller

Speed

Changer

Motor

Speed

governor

Hydraulic

Amplifier

Frequency

sensor Load

GeneratorTurbine

Steam / Water

Primary Control Loop

Secondary Control Loop

f

mP

cP

tieP

FIGURE 1.2 Automatic Generation Control (AGC) Process.

1.8.1 Primary Control Loop

Primary control loop makes initial coarse adjustment of frequency. It reacts between 2 to

20 seconds (depending on the type of turbine) to adjust turbine mechanical torque against

load variation. The governor senses the variation in the speed. Following the changes in

the speed, the hydraulic amplifier generates necessary mechanical forces for the

movement of the main valve. The mechanical power regulated by moving (open or close)

the valve. By varying the turbine power, the frequency can regulate. The input (steam or

water) of the prime mover is regulated constantly against the frequent changes in the

frequency. In India, the speed governors of generators were allowed to respond with

frequency ranges from 47.5 to 51.5 Hz under the AGC operation to regulate frequency

using tie-line bias control [35]. Purpose of AGC to control megawatt is discussed in

[203].

1.8.2 Secondary Control Loop

It is also known as LFC which has a key role during AGC operation. This loop is

performing its function via frequency deviation. The resulting signal generated from the

dynamic controller helps to change the speed of the speed changer. The speed changer

generates an error signal to operate the governor. The error signal is used to restore the

frequency.

1.8.3 Emergency Control

During the events such as load shedding, emergency control action should perform to

avoid the risk of generation loss or system shutdown. Due to the fall in the frequency

Introduction

12

below 50 Hz, the load shedding would be used. It is an emergency control action by

curtailing system load to maintain system reliability.

In Fig. 1.3 operation of primary, secondary and emergency control loops are presented.

All generators will react quickly for the event 1. Then the system frequency normalizes

with a fixed value, but due to the droop in the generator, the frequency will settle down

on different value. The value of set frequency is proportionate to the changes. Due to the

different value of set frequency, the power will flow on tie-line. It will differ from

scheduled value. In event 1, fall out in the frequency is not very fast, and the AGC system

will get enough time to regain the frequency by balancing the power. As observed from

event 2, the fallout in the frequency is very fast, and if it crosses the permissible limit,

then emergency control actions such as load shedding will initiate. Emergency control is

another level of control in AGC operation. In case of insufficient secondary reserves,

sometimes there will meet a requirement to trip the power, redistribute the output of the

generator or control from the demand side.

FIGURE 1.3 Operation of AGC loops.

1.9 ABT Mechanism

In July 2002 the ABT introduced [29]. There is a total of three charges such as Capacity

charge, Energy charge and UI charge. The scheme named availability because the

generating company will be paid higher if the average real availability of the plant is

higher than real availability and payment will lower if an average actual availability

achieved is lower.

CHAPTER 1

13

A. Capacity Charge (Fixed Charge):

It is associated with the plant accessibility such as its ability to transport power on a daily

basis.

The yearly payment to the generating company is depending on the plant availability.

B. Energy Charge (Variable Charges):

In the power plant, the cost of fuel is a variable for power generation. It is according to

the scheduled generation as a substitute of actual generation. So its name is a variable

charge.

C. Unscheduled Interchange (UI) Charge:

For the frequency greater than 50 Hz, the UI rates are low and frequency less than 50 Hz

UI rates are high. In each 15-minute time block deviations has been recorded by ABT

meters. User has to pay UI charge in case of over drawl. During the low-frequency

condition high UI charge applied, which controls the excess drawl and low throughout

high-frequency situation means excess energy in the grid.

Advantages of ABT Mechanism

It plays a role dramatically in the operation of Indian regional grid.

1. ABT mechanism improves grid discipline by controlling the grid parameters.

2. It limits the high-frequency period.

3. Provides better grid discipline by controlling the grid parameters.

4. In real-time, it is possible to make the load and generation balance.

5. It provides the facility of over drawls / under drawls within the frequency band.

A state will be benefited by overdrawing from the grid during high frequency.

6. It allows the bilateral transaction among the states.

7. Utilization of hydel resources during the high cost of power due to frequency-

based UI price and incremental cost.

8. Intra-regional trading of power is possible.

9. In balancing the generation and load.

Introduction

14

10. Improve the output capability of the plant by providing incentives during peak

hours.

11. Fixed charges are applicable based on the availability, and the variable charge

will be applicable during off-peak hours.

12. Lowering in the frequency is due to the over drawl during the peak hours. By the

high rate of UI charges during peak hours low-frequency problem is solved.

1.10 UI modelling

In India, ABT mechanism is applied by CERC for pricing bulk power. Out of all three

components, the third component UI Charge is linked to frequency. UI rates will be low

when the frequency is more that 50 Hz and UI rates are higher for the frequency below

50 Hz. ABT meters record deviations from the schedule in 15-minute time blocks. For

price based operation, data of UI curve from CERC, 2016 are used (see Fig. 1.4), where

the set frequency ranges between 49.7 and 50.05 Hz. For the frequency range between

50.05 Hz to 50.00 Hz, the charges for each 0.01 Hz step is equivalent to 35.60 paisa /

kWh and 20.84 paisa / kWh in the frequency range below 50 Hz to below 49.7 Hz.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

UI C

har

ges

(IN

R/K

wh

)

Frequency (Hz)

FIGURE 1.4 UI price vs frequency chart (CERC, 2016).

1.11 A Detail Description of the State of the Art of the Research

Worldwide electric power utilities had adopted the deregulated market scenario and the

restructuring process is undergoing [233]. The GENCOs, TRANSCOs, DISCOs as well

as ISOs are the individual entities in the restructured power system. In the current state

DISCOs having the authorization to do a deal with the same area GENCOs. It is known

CHAPTER 1

15

as “Poolco Based Transaction” and also with any other control areas which are known as

“Bilateral Transaction”. The entire contract will in the supervision of ISO. For reliability

of power supply, many ancillary services such as reactive power control, voltage stability,

and minimization of transmission losses, load balancing, scheduling, dispatch and energy

balancing have been provided by system operators. In such a situation either ISO or TSO

has been entrusted with the deal of procurement of these services. It has been observed

that AGC, also known as “LFC” is widely accepted for ancillary service in practice

[289][41]. The various problems of LFC after deregulation has been discussed [224].

Operation of Price based AGC in a deregulated environment been encouraged as the

future business environment for electric utility [162] and the case study with different

load following contracts have been represented in [59]. Price based AGC outline is

reported in [212] by doing operational changes in the AGC for Bilateral as well as Poolco

based market. In a competitive electricity market, frequency regulation linked to price is

professionally used by investigators [291]. Under the ABT mechanism, the controller

purpose is to reduce the frequency variations and reschedule generating unit for the

economical operation. So, the frequency linked UI price with the generation marginal

cost can be included in the system running under a conventional AGC mechanism to

satisfy the objective. In this direction, Tyagi and Srivastava [287] have developed a

mathematical framework. In which the frequency is transformed into the UI price signal

in the secondary loop, which is known as ‘ABT loop’. Then UI price is matched with the

generator marginal cost, which produces an error signal ‘GCE’. This GEC helps to nullify

the frequency deviations followed to the reference power setting by the generation

control. But due to the UI price and marginal cost mismatch problem in running the

model. Modified GEC scheme has been proposed in [49] to over the above problem with

the GCE generation. Here, GEC is generated by comparing the UI cost, the marginal cost

and the reference UI price. This model has been checked under CERC 2009 regulations.

The GCE is the difference of either UI price and marginal cost or UI price and reference

UI price or sometimes even zero. If the frequency fluctuates from the rated value by

considering the fixed value of the UI prices with system marginal cost near to or more

than the reference UI price then, it can conclude that the control method is highly sensitive

for the variable values of the UI price and marginal cost. ABT based LFC loop for an IPS

presented in [112]. Also, in the multi-area network, the price based LFC is applied in

[94], but it not successful to normalize the frequency. It is concluded that by making the

Introduction

16

generation-load balance, the frequency deviations can nullify during the loading-

unloading condition. Big penalties to be paid by GENCO for inactive participation to

control frequency against load variation. During the day, for each 15 min duration, an

average frequency is monitored and the UI charges will be calculated against frequency

value. To minimize UI charges GENCO set the generation. By following this issue an

effort has been made to build up a new control design to reduce the UI prices and

generation fuel expenditure to normalize the frequency against deviations.

Still, the process is hard for the synchronous generator to track the load deviation

completely due to slow dynamic response [112]. Also, because of frequent power

changes with the integration of renewable energy sources, it has been difficult to regulate

frequency due to insufficient regulation reserve capacity and rapid power imbalance

[226][234]. Thermal and hydro units can be used to regulate frequency. Due to limited

regulation capacity, slow response according to the power demand changes and frequent

operation of generator decreases its life as well as efficiency [310]. Finally increases

generation cost, labor cost and maintenance cost due to wear and tear of generators [61].

Under these circumstances, a usual frequency control by the governor for rapid load

changes due to its slow response becomes very difficult. Also, sometimes either voltage

or frequency will fall out or the generator fails.

Based on recent literature so far, AGC has been reformulated in restructured environment

by implementing various methods like PSO [243], BFOA [141], artificial intelligence

methods like genetic algorithm [219], NN [27], fuzzy logic [142], distribution energy

storage technologies like BESS [252], Flywheel [307], super capacitor [186], LFC with

SMES [247], battery swapping station [15] and many more. Among this variety of

feasible solutions, BESS is an extensive choice and recent technological aspect now a

day due to quick and fast action for lessening overshoots of frequency deviations in the

restructuring of power system [85]. Electrical vehicles are large distributed battery energy

storage and will use broadly in upcoming transportation system [104] and has the

potential to provide the distributed spinning reserve to the power system. The Customer

side Electrical vehicles (EV) will use widely due to the limited life of petroleum products,

less battery charging cost and for the reduction in greenhouse gasses [182]. In [204]

potential to overcome load frequency control problem with an electric vehicle, An EV

swapping station [105] and energy storage system (BESS) with appropriate results [216]

CHAPTER 1

17

has been discussed. In present smart grid power system, renewable power is intermittent

and there is a need for controllable energy storage. The battery of EV can be used to

combine as a buffer and energy storage against the energy generated by renewable plants

[85]. The V2G and G2V schedule can be managed by vehicle users [293] followed by

battery SOC condition to suppress frequency fluctuation in two area IPS [182]. To

enhance both performance and robustness for an operation of V2G and G2V mode a

bidirectional controller with scheduled charging is required. For the bidirectional

operation to track the load changes constantly bidirectional charge controllers are used in

practice.

In this work, the strategy for EV bidirectional operation of charger for load frequency

control by participating in AGC operation has been proposed and results with and without

an EV integration to load frequency control have been simulated.

1.12 Thesis Objectives and Chapter Organization

The main objective of present work as per the issues discussed and concern literature are

as follows.

Frequency deviations caused by the uncertain load variation and changes in power

pumped by RES into the grid require to be investigated. For this to come up with the

solutions for the various issues such as minimization of settling time and peak overshoots

in frequency deviation and also UI price will be the main focus of my research. To

regulate the deviations in frequency, generator power and tie-line flow we plan to devise

new control mechanism.

To achieve these objectives, the scope of work includes:

1. To develop a mathematical model of 2 area restructured IPS with a thermal-thermal

non-reheat unit that can identify the deviations in the frequency as well as power.

Also, to simulate the same in MATLAB / Simulink environment.

2. Using the model developed, to observe the deviations in frequency and power during

AGC operation under Poolco based contract and Bilateral contract conditions of

GENCOs and DISCOs.

Introduction

18

3. To devise a new control strategy for bidirectional charging / discharging of EVs that

can help aggregated EVs to participate in minimizing the frequency and power

fluctuations.

4. To analyze the results of the new control mechanism for verification of its

effectiveness in the AGC operation.

The thesis has to organize in eight chapters. The present chapter introduces the

deregulated power system with different types of competitive electricity markets and real-

time markets. Also, the types and need of ancillary services are discussed. Moreover, the

ABT mechanism with its importance is discussed. In ABT the modeling of UI curve is

presented. In the section of a brief description of the state of the art of the research, the

potential of EV to participate in the AGC operation under different market conditions is

presented. .

Chapter 2 represents the review of literatures of different techniques used for AGC

operation to regulate frequency. The techniques discussed are LFC using different soft

computing methods such as PSO, BFOA, the LFC based on AI techniques such as FG,

NN and GA, LFC in smart grid and micro grid, and application of distributed energy

storage systems such as BESS, Flywheel, Super capacitor, SMES and Battery swapping

station to regulate the frequency. Finally, the potential of EV is presented to regulate

frequency.

Chapter 3 describes the issues as well as challenges in power generation, its transmission

and distribution in the grid. Also, issue of frequency regulation and its solution by ABT

mechanism is presented. By using the UI component of ABT mechanism the AGC has

been discussed.

In Chapter 4, the mathematical model of two area restructured power system for AGC

operation is presented and simulated. The results of deviations in the frequency, generator

power, tie-line flow and UI charges are presented during the AGC operation. Moreover,

the AGC operation during peak hour and off-peak hour is presented.

Chapter 5 highlights the different types and needs of ESS for grid frequency regulation.

From these storage technologies, EV has been focused due to its quick response. An EV

model with bidirectional charging / discharging characteristics is presented with the

diversified transmission link for the participation in AGC. The comparative results of the

CHAPTER 1

19

deviations in GENCO power, tie line flow, and frequency and UI price with EV and

without EV are presented and discussed.

In Chapter 6 describes an impact of solar energy and wind energy on frequency as well

as UI price during the AGC operation. The solution to reduce the deviation range of

frequency and UI price is presented under the different market with comparative results.

Chapter 7 presents the fuzzy logic application. The AGC is presented by considering the

UI price. The comparative results with PI and FGPI controllers are presented and

discussed.

In Chapter 8 discussions of the original contributions, conclusion and the future scopes

are presented.

.

20

CHAPTER 2

Review of Literature

Now days, LFC is very popular ancillary service in interconnected power system

functioning as reduction in the Generation Control Error (GCE). This presented chapter

over here refers worldwide history of various types of controllers and applied smart

control strategies in generation, transmission and distribution areas of IPS. Additionally,

in deregulated power system different types of energy storage at distribution area are

reviewed and highlighted for upcoming research work. It has been observed from

rigorous review of different research papers that in a smart-grid due to the RESs nature

and unpredictable daily as well as seasonal load variations an imbalance between supply

and demand has been created, which results in deviations in the frequency. After the

review of proposed paper, researchers can able to understand issues related to energy

storage at distribution end in the context of LFC.

2.1 Introduction

Today, the grid is a smart-grid. In a large grid, many types of power plants are connected

to supply power to the load. It becomes essential to maintain fluctuations in the frequency

as well as tie-line flow against load disturbance when more than one power systems are

connected. The frequency of network deviates, because of the unexpected load change.

The nature of RESs is uneven, which causes frequency and power deviations too. Much

research work has been carried on LFC issue of conventional power system as well as a

deregulated power system. Study based on LFC problem with different soft computing

techniques such as GA, NN, FG, PSO, BFP, TSA has been carried out. Also, many

researchers have tried to solve LFC problem using AC-DC tie line, FACTS, considering

communication delay, different learning techniques and different types of DESS. Further

study on communication delay, RESs penetration and need for electrical storage with

different types of storages have also been carried out. Storage potential in BESS as

CHAPTER 2

21

centralized and decentralized distributed electrical vehicles has found tremendous

potential to regulated frequency. Presently, in the competition of the deregulated power

system, different companies sell unbundled power with lower rates. Also, a lot of

ancillary service is introduced like voltage and frequency regulation, load balancing, peak

power savings, etc. LFC as ancillary services is becoming most important today. Here,

the LFC techniques with various control strategies are outlined.

2.2 LFC in Single and Multi Area Power System

The different techniques in single area network are presented. The LFC issue of single

area network with delay margin by using Lyapounuv theory with the dependent criterion

and LMIs techniques is represented [144]. Based on the comparative analysis the results

for single area network by consideration of GRC without the controller, full state

controller and with optimal output feedback are represented [264]. Lili Dong has

presented LFC in single area thermal system. It has reheat, non-reheat and hydraulic unit

considered. The design was based on active disturbance rejection control [69]. Other

approach of AGC with output vector control feedback theory with the inclusion of AC /

DC-link is presented in [126]. The LFC problem in a single area having hydro unit with

the multi-pipe scheme is presented [242]. Decentralize LFC for multi-area power system

also discussed [197].

The different techniques in multi area LFC are presented by authors. Conventional

controller using PID controller based application is presented for LFC [5] and PI based

LFC in a decentralized mode is also proposed by M. Aldeen [175]. Different approach

using sliding Mode is use to regulate frequency for a multi-area network [151]. In multi-

area network impact of time delay [117] during LFC operation and real-time

implementation are also important. Similarly, an impact due to communication delay is

very important, which is presented with results in [232] for three area thermal system.

There are lot of smart technologies are used for LFC such as AI. In this context in the

three area power system, GA based PI controller performance is presented by Fatemeh

Daneshfar [86]. Similarly, in this multi area power system intelligent approach using GA

based RF learning algorithm applied for LFC. Here, area participation factor is used to

from the participation of GENCOs [82]. An author of [36] has discussed LFC issue in

two area IPS considering with GRC and DB and the solution is proposed using the hybrid

evolutionary fuzzy PI controller. This fuzzy logic operated by GA is applied and analyzed


22

for multi-area LFC problem with better performance [308]. The GA superiority is

presented by comparative study between GA and LMI technique [71].

Application of various distributed energy storage also presented for LFC. The redox flow

battery storage application for LFC operation with results is presented in [129]. Another

storage application in Multi-area is presented by three different controllers such as fuzzy-

based SMES, PI base SMES and without SMES for LFC [25]. By in synchronizing SMES

with SSPS, regulation of the frequency is possible [134]. In [33] Tripathi had represented

adaptive AGC control with SMES. The application of SMES as an adaptive controller

during LFC operation can reduce the deviations in the frequency [239]. The BESS helps

to suppress the deviations in the frequency [57].

AGC with generalized approach for multi-area system [204] and in a discreet mode with

ACE [184] is discussed. By this discreet controller the possibilities in LFC are presented

in [6]. Similarly, an application of variable structural controller for AGC operation can

be used [309], which is presented for hydro-thermal unit. In another way, two area IPS

in a decentralized control mode [4] with structural singular value is also presented [277].

The design during the operation of decentralized LFC in a multi-area IPS is presented in

[273]. In [100] decentralize LFC with Governor and Voltage controls were proposed. In

[150] Two area IPS with decentralized approach for frequency control is presented. In

two area LFC problem is solved in [223] with excitation control method and voltage

perturbation method. In two-area IPS, the LFC based on tie-line control with the

nonlinearity is addressed in [32], in this context the GRC [274] and GDB [246] are also

considered to optimize the LFC parameter. With this non linearity and by considering the

stochastic nature of the load, AGC operation for LFC in two area system is addressed by

Dorais wami [227]. Manual operation of AGC is also reported, where regulation error is

reduced by tuning the controller parameter with human experience [22]. But in the

manual operation requires large experience. The conventional AGC operation in a

thermal power plant having reheat unit is reported by Nanda [222] with stability and

optimal setting point of view. Moreover, the frequency instability [209] study is also

presented. The step response of the governor in hydro unit during AGC operation is

discussed [279].

CHAPTER 2

23

The LFC operation based on the Lyapunov method [99] is presented. Similarly, a bang-

bang LFC strategy based on the second method of Lyapunov for two area IPS is reported

in [98]. In [198] Two area power systems with trajectory sensitivity design for controller

gain tuning had been proposed. Multi-area power system with linear regulator theory

[194] had been discussed for load frequency control. Autonomous controller with single

phase inverter to control active as well as reactive power due to large disturbances has

been presented in [11]. Hierarchical optimal robust LFC is represented in [166]. An

author of [155] had presented a robust decentralized multi-area LFC problem. Chun-Feng

Lu had presented LFC operation in multi-area power system. The two areas IPS with

prediction of area generation are discussed in [103]. The mathematical model for

Megawatt control is presented in [205].

Due to the non-linear property of the grid-connected load, governor DB, and GRC, the

actual characteristic becomes non-linear from the system response. So, there is a

mismatch between the linear line bias characteristic and the response characteristic of

actual system. It increases in unwanted fuel expenditure and increased wear and tear on

generators.

2.3 LFC using Artificial Intelligent Techniques

2.3.1 Genetic Algorithm

GA is an optimization tool with the process of natural genetics. GA is mostly useful for

solving complex nonlinear optimization problems such as AGC. Normally, the GA tool

is used by researcher to optimize the gain value. GA tool with different techniques used

by different researchers is summarized below.

A comparative study between GA as an optimization tool and conventional Matrix-

Riccati based optimal control is reported by Ghosal [253]. Here, the successful results for

the optimized gain value are achieved using the GA tool. Similarly, GA based FG is used

for frequency regulation with multi-machine system [53-54]. The gain scheduling of PI

controller using GA based fuzzy logic rule-based system in a multi-area IPS reported

again by C. S. Chang [37]. This tool is also used to tuned PI controller gains [71], where

the robust LFC design method based on H∞ control with LMI techniques and GA tuned

PI controller are proposed. Here, the result revealed that robustness of performance of


24

GALMI is almost the same as that of the robust H∞ controllers. In another paper [207]

GA based PI controller is applied for the EVs battery incorporation with the grid-

connected RES. A comparative study based on a hybrid FGPI controller and hybrid GA-

PSO optimization techniques is presented in [42]. Some researchers had work in hydro

unit also. Aditya has discussed the design of GA for two area hydropower system model

[14]. In the hydro-thermal unit, a continuous-discrete mode for AGC framework is

presented and also in the hydro unit digital computer, the GA application is identified

[55]. Least square algorithm using GA for achieving real-time parameters for the optimal

value of gain for LFC is reported in [164]. Author of [171] had presented the control

parameter optimization using the GA technique for robust decentralized frequency

stability.

2.3.2 Neural Network

NN works on prediction based theory and corrects nonlinear relationship between input

and output.

The NN is investigated as an intelligent controller in conjunction with standard adaptive

LFC [88]. Automatic LFC using NN is developed to control the steam and water flow

[7], where the flow controller regulates the speed of the generator, which helps to regulate

frequency and generator output power. During the flow controller operation

nonlinearities affect in the AGC performance. The impact of nonlinearities such as DB

and reheat in a two area system reported in [63]. The robust decentralized design for LFC

is presented in [107]. This training based controller for better performance of the adaptive

controller then the NN based adaptive scheme is presented in [278].

2.3.3 Fuzzy Logic

Fuzzy logic can solve LFC problem based on knowledge and experience. Many

researchers have presented the superiority of FG controller. The superiority of FG than

the classical integral controller is presented [142]. With the nonlinearity such as DB and

DRC, LFC problem is simulated in four area networks [38] and better results are obtained

using FGPI controller. Similarly, the fuzzy rule-based PI controller gain scheduling is

presented by Denna et al [43]. In AGC operation of two area thermal unit [80], the fuzzy

controller application to regulate frequency is reported in [81]. For AGC with three area

CHAPTER 2

25

power system, the PSO tuned FG controller used, which is based on GA [243]. Chia and

Chun, has given an idea of gain tuning using GA based FG controller for a two-area

having thermal unit [54]. Here, the non-linearities as governor DB and GRC were

considered for better performance of FG. The different study with robust decentralized

control scheme based on C-Means clustering discussed [269] with the nonlinearity as

GRC was considered.

2.4 LFC using other Soft Computing Techniques

2.4.1 Particle Swarm Optimization (PSO)

PSO can be used on the concept of swarm intelligence. Highlights on PSO techniques

used by different researchers for better LFC operation are given below.

Aqeel S. Jaber has presented the PSO technique for LFC issue solution by tuning the

fuzzy logic input and output parameter [23]. For a hydropower plant, PSO and FG with

sliding mode control are presented in [229]. PSO technique is used by considering

parallel connected AC and HVDC tie line [254]. Author of [219] has presented hybrid

PSO for the tuning of PID controller gain value in the deregulated four-area system.

Based on the adaptive neuro-fuzzy inference system and PSO control scheme, LFC

dynamic response is simulated [251]. For two area IPS, the LFC using adaptive weighted

PSO and multi-objective PID controller is solved in [9].

2.4.2 Bacterial Foraging Algorithm (BFOA)

The BFOA theory is motivated by nature and operated in mainly four steps such as

swarming, duplication, removal and distribution [74]. Bacterial foraging is another

modern meta-heuristic method used to get solution of the complex problem. In context

to LFC, a comparative study of various integer order (IO) controllers are performed [257].

This integral controller and FOPID controller are compared for AGC operation. Here, the

thermal reheat unit and GRC are taken in multi-area for AGC performance. In [141]

Optimization for the governor speed regulation parameter (Ri) and frequency bias factor

(Bi) is evaluated. Moreover, the superiority of BFO against GA and classical optimization

techniques by gain tuning for the integral controller is presented. A similar approach of

using BFO algorithm to tune controller gain was also presented by many authors with the


26

advantage of the proposed controller against conventional area controller and GA

optimized controller.

2.4.3 Tasu Search Algorithm (TSA)

The TSA initializes with some primary possible solution and tries to get a good result

based on hill-climbing algorithm. TSA uses past step to create a better solution. Saravuth

Pothiya has shown an innovative optimization method of an FGPI controller with the

multiple TSA [259]. The progress in learning of fuzzy rule-based system with the help of

heuristic symbolic is observed.

2.4.4 Other Optimization Control Techniques

The techniques such as Differential Evolution, mixed H2 / H∞ and LMI are reported by

various researchers listed below. By considering active power flow in [165] an FG rule-

based system was created for the selection of the finest controllers, its size of steps and

movements. In [177] M. Farahani S presented the method of optimized PID gains by the

LCOA to solve the LFC problem. LFC problem solutions with DE algorithm, which

optimize the PID gain [231]. It is noted that the controllers discussed are robust. The

parameters of the system and its operating range should wide from its nominal values.

Umesh Kumar Rout, have reported the PI controller performance analysis for AGC. The

operating of the PI controller is based on DE algorithm [288]. Similarly, Banaja Mohanty

controller parameters tuning of the DE algorithm highlighted and the comparative

analysis between DE, I, PI and PID with result is presented [31]. In thermal unit of single

area, for LFC a robust controller made by the Riccati-equation is used [101]. This Robust

controller using an m-synthesis approach, LFC in a deregulated IPS with thermal unit is

proposed [116]. A design of decentralized H∞ method for damping control of frequency

is discussed [30], which is in the LMI framework with the mixed-sensitivity formulation.

In deregulation with three areas, the LFC solution is suggested [119], where the mixed

H2 / H∞ robust control method is proposed. The design of the PI controller with H∞

static output feedback control theory is described for the LFC problem. The solution is

presented with the iterative LMI technique [294]. Robust controller design for LFC is

also reported [118][123].

CHAPTER 2

27

2.5 LFC with AC-DC Parallel Tie line

Researchers have highlighted an application of parallel AC-DC link for LFC [314]. In

which the HVDC link by using the centre of inertia (COI) signals is introduced, which

can keep a system frequency stable. In a deregulated multi-area system, parallel AC-

HVDC link considered to analyze the network dynamic behaviors. The performance of

LFC with parallel HVDC was found better than without HVDC. In [173] authors have

presented a wind turbine generator model delivered power via HVDC link controlled

through line commutated converter. Effectiveness of the inclusion of HVDC link for LFC

solution presented in [149][89]. The two and three area system with various subsystems

connected by asynchronous tie-lines is also reported [199].

2.6 LFC with Flexible AC Transmission System (FACTS)

The FACTS controllers help to enhance the transmission line capacity. The FACTS

controllers also help to regulate frequency. Some reported literatures are listed as follows.

A comparative analysis of SMES-SMES, TCPS-SMES and SSSC – SMES in AGC in a

IPS consists of hydro-hydro unit to regulate frequency is reported by P.Bhatt[220]. The

SSSC can regulate LFC in deregulated IPS [64]. Moreover P. Bhatt has investigated the

LFC for multi-unit hydropower system and thermal-hydro mixed unit [218]. Coordinated

control between TCPS and SMES has kept. Using craziness based PSO the integral

controller gains as well as parameters of TCPS/SMES being optimized. For the

restructured power system with three area [106], the LFC problem solution is provided

by the use of TCPS. The AGC regulator is operated by SVC, which is reported in [2].

Also, the TCPS is proposed for the tie-line flow regulation. In [128] presents the design

based GA for the decentralized controller with and without redox flow batteries together

with TCPS.

2.7 LFC Based on Other theory

2.7.1 Different Feedback Theory

For the solution of LFC for hydro, thermal and gas turbine units by controlling the

governor speed with PI state feedback controller [160], which regulates the parameter


28

(R) and participation factor in AGC. Shoorangiz Shams have presented Quantitative

feedback theory (QFT) method for LFC [260]. Here, the comparative study of the QFT

based proposed controllers and the conventional controller is given to prove the

superiority of QFT. Rakshani et al. had designed a linear quadratic regulator for

frequency regulation [73]. The proposed regulator results with, full state feedback and

state observer methods are carried out. In a deregulated environment a robust

decentralized LFC is reported [180], where the solved is provided using H2/H∞ control

technique.

2.7.2 Hierarchical Load Frequency Control

The two level of hierarchical control discussed in [180]. Here, the first level of control

for each area was obtained after incorporating local information along with the others

coming from the other areas. In the second control level, a global solution based on

coordination strategy and a robust controller was obtained. The structure for AGC is

presented [131] with the effect of the nonlinear filter on frequency deviation harmonics

in Spanish. One more hierarchical AGC operation was presented by Marinovici et al.

[141], where for primary frequency control, the robust controller in a decentralized

manner was proposed to eliminate the frequency fluctuations as well as tie-line

fluctuations against disturbances in a IPS. A special type of study of the data integrity

attack on AGC performance with the market of electricity operation was reported [263].

In which the capability to maintain frequency by control algorithm by detecting the attack

and mitigation is presented. It helps to remove attacks due to low false ramp. For the

regulation of tie-line power and frequency [97] pluralistic LFC method presented with

two control area such as real control area (RCA) and a virtual control area (VCA). By

considering AGC operation in three area power system both schemes of pluralistic and

hierarchical control were presented [28] by the simulated study.

2.7.3 LFC Using Internal Model Control

To reduce the problem complexity in AGC, this method is used. The power system grid

is a complex network, so the internal model control (IMC) scheme is applied in LFC to

regulate frequency fluctuations. Internal model control (IMC) was successfully applied

for AGC. Application of IMC based on two degrees of liberty concept in LFC problem

is presented [295] and in [255] second-order plus dead time (SOPDT) is used instead of

CHAPTER 2

29

the full order system. Another scheme of Model Predictive Controller (MPC) for AGC

operation is reported [26], which followed by a NN model predictive controller is

reported [15].

2.7.4 LFC Using Observer

In an LFC, a lot of component of the power system is monitored which are connected

directly or indirectly. Hence, the observer plays a vital role in LFC and observes minute

to minute deviations condition of every components to remain within the limit. The

advantages of observer concept were successfully used to solve load frequency control

problems [77]. Observer also helps in monitoring, in this context the problem of

measuring and monitoring all the state variables at all time, a reduced observer controller

is designed [77]. Multi-area LFC issue in a large IPS was solved by of reduced-order

observer [121]. Also, dynamic responses were enhanced in the deregulated system. LFC

in two area system using a PI feedback controller based on quasi – decentralized

functional observers (QDFOs) theory. Proposed QDFOs theory reduces the complexity

of the system [312], where LFC solution is presented using decentralized sliding mode

control in IPS.

2.8 LFC Based on by Consideration of Communication Delay

Open access strategy in restructured power system makes the LFC problem more

complex. To overcome this open communication infrastructure is required. The time

delay is not fixed in an open communication network as compared to a traditional closed

communication network. Hence, more accuracy can be achieved in the AGC operation

by considering the communication delay. AGC with communication delays is also

presented in [304][200]. In [30] ILMI algorithm followed by H2/H∞ control method with

communication delay is used for the LFC problem. The Lyaponuv – theory combined

with LMIs method for LFC is discussed in [163] with delay and results of the dynamic

analysis of the LFC and delay boundary was achieved. A robust method to achieve a PID

based LFC scheme with communication delays is reported in [56].

2.9 LFC based on Different Learning Techniques


30

For machine learning, dynamic programming is used in the RF environment. With a

multi-agent RF approach [275], PI controllers gain is tuned for LFC in multi-area IPS

[82]. During AGC operation to solve the issues to dispatch generation command

hierarchical reinforcement learning technique can be used [282]. Presently, for

restructured power system operating under the bilateral contract, complete classifier

method by continuous-valued inputs for LFC is reported [87] to regulate frequency

deviations.

2.10 LFC with Distributed Energy Generation

Renewable Energy Sources (RES) are predominantly used as distributed energy sources

in a micro-grid and smart grid. On the other hand, a smart grid employs analog as well as

digital communication technologies. LFC problem has been discussed by various authors

as follows.

2.10.1 LFC in Micro Grid

A micro-grid is a group of generation from RES, energy storage, and loads. The detailed

study on LFC in micro-grid [70] and the for the smart-grid [124] are reported. From the

study the types of LFC in the micro grid are as follows: 1. LFC using analysis of small

signal and 2. LFC scheme based on tuning techniques of parameters.

The micro grid with WTG, PVs, DG, FC, and aqua-electrolyzer and energy storage

systems are developed for AGC in [160-161]. In smart grid, applications of the different

controllers such as conventional as well as smart controllers are reported by many

researchers. The Ziegler-Nichols for the fine-tuning of gain is used [91], where small

signal analysis using battery and flywheel is described based on parameter tuning. An

application of AI for LFC in micro-grid [90] is discussed. The FGPID as a AI controller

is superior then non-FGPID controller [91]. Another control strategy of an optimal

FGPID controller followed by the BCO technique for governor operation of diesel

generator and blade pitch angle control of wind turbine is presented [271]. For LFC

problems using smart techniques such as AI, FO, PSO and GA are also reported

simultaneously. A fuzzy neural control method of AI is used for the indication of the

SOC status during forecasting of load and generation data [70]. Here, in the micro grid

with the wind, solar, storage and load are considered. The operation of the combined

CHAPTER 2

31

sources in micro-grid creates the instability in voltage as well as frequency. For that in a

micro grid, a GDC scheme proposed [114] to overcome the problem. LFC solution for a

micro grid with a PV system is presented by MOO. In this, a load deviation index was

calculated using estimated load power. A low pass filter of second-order used to produce

base power from PV unit.

2.10.2 LFC in Smart Grid

In a smart grid, LFC is classified in two ways, LFC with the incorporation of (DR) and

LFC with a V2G control. LFC with the incorporation of DR and V2G is presented.

2.10.3 LFC with DR

The DR control strategy consists of four main participants such as the balancing

authority, a DR aggregator and the distribution utility with customers. The DR control

strategy which is mentioned to as CDLC. It includes three operating modes such as

normal operation, frequency deviation range followed by AHC control, frequency and

returned to its normal value followed by step by step load manipulation strategy.

The power balance during AGC operation by DDC is reported by Devika Jay [66], where

the PI controller parameters were optimized using Lyapunov technique. Another DP

response application [169] with the coordination of different spinning reserve constraints

is reported. This real-time DR is proposed [159] to increase the RES penetration level for

LFC. In a smart grid, a comprehensive central DR algorithm was developed for LFC

[238]. For the frequency, sensitive load frequency deviations are investigated using the

DR scheme under smart grid environment [111]. In another study, followed by DR all

the distributed energy sources participated uniformly.

2.10.4 LFC with a Vehicle to Grid (V2G) Control

Many researchers have reported V2G and G2V technology for LFC. The V2G control

has three state conditions such as vehicles charging, idle and discharging conditions.

Secondary frequency control using PHEVs presented. Here, the duty cycle coordinated

scheme is described with the controllable thermal load in the household [193]. In this

scheme, a decentralized combined unit of heat-power generation considered with the


32

MPC strategy. The BESS is used to solve the LFC problem due to RESs integration [280].

With the integration of Megawatt (MW) class distributed PV systems, a numerical

solution is presented [187] with the study of EVs An author of [145] has proposed the

multiple model predictive control (MMPC) method applied for PHEV for charging/

discharging battery and SOC control, which helps to stabilize the frequency. EV requires

the charging controller, for that the smart charging control scheme is reported [310] for

distributed EVs for Load frequency control. For commercial purpose, a fleet of EV in

coordination with an aggregator is proposed commercially to generate revenues [189]. In

a micro-grid including EVs and DG, their coordination based on MGPC technique for

LFC is suggested [147]. The LFC with grid-connected 50,000 PEVs for V2G operation

with a smart charging algorithm in real-time is considered and economic analysis is

studied [281]. The study to manage energy between wind and PEV has been carried out

[262] because of the dynamically quick action of an EV battery. It shows the capability

to balance supply and demand. Moreover, V2G coordinated control method studied for

the smart grid having WT [265]. For the EV charger AI applications are also reported by

researchers. In this context the fuzzy logic

based charging / discharging control approach for the LFC due to the presence of PV

and DG in the smart grid [65]. Dual benefits such as energy harvesting during peak period

and greenhouse gas reduction using EVs in the benefit of the environment are discussed

[58].

2.10.5 LFC with Hybrid System

Minute- Minute Wind Variation

Wind speed creates more disturbances in LFC problem. So, it is required to maintain

frequency control against minute to minute variations in wind speed.

The dynamic performance with wind penetration to regulate frequency has been carried

out [50][109], where different wind events such as die put, wind rise, wind lulls, wind

gusts and sudden loss of wind farm are observed minute to minute. Similarly, dynamic

analysis of AGC based on the ERCOT model reported by Chavez et al [119], where a

stochastic model was analyzed to reduce frequency deviations.

CHAPTER 2

33

BESS

The BESS has great potential to regulate frequency. Different types of battery

applications are reported in LFC. The RF battery is used [283] for the quick response

characteristic, where the RF battery capacity should be ten times more than fossil fuel

operated plant. D. Kottick has presented a single area LFC with 30 MW batteries for LFC

in the Israeli isolated network [60]. Various work for LFC in two area network using GA

based controller parameter tuning [241] is reported, where the BESS helps to nullify the

frequency as well as tie-line fluctuations [252]. Observer method is also reported. Using

a disturbance observer method with the estimation of the load by generalized predictive

control method [16] better performance can achieved. BESS was considered in parallel

with the wind farm. The frequency domain analysis is micro-grid [70] by considering

WTG, DEG, two FCs, PV and the EESs consist of a BESS and a flywheel is presented.

Here, an aqua- electrolyzer absorbs some part of generated energy from PV or WTG for

the generation of hydrogen during frequency control. The LFC based on MPC [179]

technique with real-time data measurement from an existing grid is proposed. Liang

Liang has proposed the LFC of two area network with wind power and large scale BESS

[172]. This BESS battery based on droop characteristic proposed [272], which can solve

issue in LFC during its parallel operation. From the operation and economic point of

view, this battery is presented as best option after two years of test operation in berline in

1987 [108]. The BESS also helps in the presence of the GRC and DB, in this context Lu

and Lui had presented LFC solution using BESS [57].

Flywheel

The Flywheel has tremendous potential to store the energy. An optimal sizing model for

the Flywheel ESS by considering the initial cost, maintenance and operation cost is

reported in [307]. Here, the control strategy is developed to regulate the SOC of FESS.

In a restructured IPS, FESS performance to regulate grid frequency incorporation with

wind turbine generator is studied [256] and reported. Here, the GA tuned PID controller

to control generator output power is used.

Super Capacitor

The super capacitor bank for the improvement of LFC problem is used with the FG

controller is highlighted in [186].


34

SMES

Similar to other energy storage devices SMES has an extraordinary potential to store

energy and quick response. This stored can be used during the AGC operation to regulate

the frequency. The LFC issue in multi-area power system a comparative study with and

without SMES impact is reported [268] with time-domain analysis [236]. In two area IPS

for better frequency regulation, the SMES operated by fuzzy logic is presented [247].

Similarly, fuzzy gain scheduling for SMES is also performed well [53][185].

This SMES control strategy by using the IGBT converter technology is presented by

Tripathi and Juengst [245], which improves the results. In multi area IPS Other

sophisticated control with solid-state phase shifter for SMES is used [135] for

improvement in the LFC. In the presence of non-linearity such as GDB in thermal-

thermal reheat unit, the SMES performed better in LFC operation. An impact of

distributed small sized SMES unit application for LFC improvement is reported by S.

Banerjee [240], where an Adaptive AGC with storage as SMES is considered and the

comparative study of adaptive and non-adaptive has been presented against load

disturbances.

LFC without Storage

Various methods of without storage during LFC operation are reported which are

summarized as follows.

In the LFC problem for IPS containing thermal, WT and solar unit using standard data of

39-bus and 24-bus test system were analyzed [51]. Here, the fluctuations because of the

load, solar and wind power were considered. In other technique, to steady the frequency

fluctuations, a two-tier structure for a multi-machine system was proposed [102]. In one

more study of [191] power and frequency oscillations, a comparative study between

conventionally operated full-power converter wind plants and the newly proposed

frequency controlling wind plants have been studied. Newly proposed frequency

controller gave an optimistic result. Effect of WT farms on the dynamic behavior of the

grid is reported by Mats Wang-Hansen [192] that WT increases the fluctuations in the

frequency and it needs the storage to regulate the frequency. AGC in multi-area IPS was

tested on the 10 machines New England system [113]. For testing, an agent-based

intelligent control scheme of Bayesian Networks (BNs) used. One more study based on

CHAPTER 2

35

wind energy integration with distributed agent-based control architecture using NERC

reliability management rules was presented [190]. From the above study it is realize that

power from RESs increases in the deviations of power and frequency, so there is a need

of storage in the smart grid. By using participation of wind farm, LFC solution is

proposed set-point controlling scheme [168], but due to the uneven property of wind

velocity frequency deviates [96]. By taking the IEEE 14 bus system, AGC for fixed and

variable speed wind machine is presented using the Newton-Raphson algorithm [174]

and in [188] the DFIG with a conventional generator for LFC is presented. The power

delivering ability of DFIG of reduces the speed and release the stored mechanical energy

during frequency regulation is presented [228][140][201]. More discussion on LFC using

participation of DFIG is also presented with its impact on LFC [62][230][241]. To reduce

the deviation due to WT, the solution using pitch angle control method [211][235] and

with HVDC [84] are reported. Solar integration is also increases, so the LFC problem

due to integration of PV power feeding into the grid is reported [122]. Fuel cell

application is also reported [20] with an impact on LFC with the presence of BESS and

SMES units. In a smart-grid to coordinate the different sources the coordinator is

required. In this context the FG for WT [115] and in an isolated power system including

solar and diesel power plant [170] for better gain is reported. In [225] authors have

proposed to do changes in UC, economic dispatch, regulation of LFC when wind

generation capacity level is important. Another approach with frequency linked price in

a two-area network including DFIG based wind turbine LFC problem is discussed

[8][311].

Other Distributed Generation

In [270] LFC problem is presented for a small hydropower plant by controlling dump

load, where the rating of the load is approximately same to the rated output of the

generator. In [161] author has point out an impact of solid oxide fuel cells in the

distributed generation. In [83] Katiraei et al. have focused on real-reactive power

management strategies in a micro-grid consists of electronically interfaced DG units. An

author of [210] has proposed the small-signal analysis in independent hybrid DG system.

For the AGC analysis unexpected load variation and uneven wind speed are considered

in addition to solar radiation. Also, the hybrid systems have different RESs such as WT,

PV, FC and DG and need storage. For the storage purpose, the EES such as battery and

flywheel units are considered. In [18] an author has addressed the conventional AGC


36

structure with its drawbacks. To eliminate the drawbacks of old AGE, the new control

scheme for AGC with cyber architecture has been proposed. It contains DES in a smart

grid. In [127] the LFC for a wind-hydro unit as an independent micro-grid is presented.

The control technique of DC power feeding system in cooperation with power maker and

distributors is presented in [221]. The LFC problem solution by GA based PI control

strategy for an independent hybrid system is presented in [292].

2.11 LFC in Deregulated Power System

In this section, the LFC problem solution by different methods in the restructured network

discussed. The LFC in a multi-area network is addressed in [196][287] and solution using

ANN-based PI controller is discussed. In deregulation AGC one ancillary service. AGC

operation in deregulated IPS with various contracts such as POOLCO based, Bilateral

and Bilateral contract with contract violation is presented [219][233][289]. Jose Luis and

Martınez Ramos have proposed an AGC in a decentralized manner, which is running at

the Spanish system and compared to the actual Spanish power system [146]. O. P. Rahi

have presented different ASs in restructured power system such as frequency regulation,

energy balance, spinning reserve, supplemental services, dynamic scheduling, reactive

power management, voltage regulation, transmission losses minimization and black start

capability [202]. The load following service in a bilateral contract in a competitive mode

is presented [33][78][143] as ASs.

Different control strategy using smart controllers are reported by various authors, which

are listed below. The AGC operation is presented in the multi-area thermal unit [133] and

there is issue due to nonlinearity as a GRC [257] in LFC operation. Here, the comparative

study between different integer order controller (IC) and fraction order PID controller

was evaluated. AI is also applied for LFC. In this context an application of GA tuned

FGPID controller [21] for LFC issue in IPS is reported. Similarly, ANN based operation

of AGC design in multi-area IPS by considering GRC and DB [256] is presented. Here,

comparative study was evaluated between ANN and GA based controller. Another

controller performance for LFC, where gain tuning is based on reduced-order observer

method is discussed [154]. Ajay of [208] has presented AGC of thermal-hydro and

thermal-thermal system with GRC in a deregulated atmosphere with the comparative

performance of PI and PID controller. Application of with and without thyristor

controlled phase shifter using PSO [106] and thyristerised controller switched-capacitor

CHAPTER 2

37

[17] is reported. In this, a design of the PI controller linear matrix inequalities (LMI)

algorithm is used. Finally is compared with an H2/H1 control technique. Daniele Menniti

has presented LFC study based on flexible AC transmission line controller [64]. Young-

Hyun Moon has presented a two-level methodology for AGC operation [313], where the

upper-level AGC deals for frequency regulation and generation distribution to IPPs with

respect to the value of ACE and the lower level AGC deals with only optimal generation

allocation.

To reduce the frequency fluctuations in deregulated power system method based on u-

synthesis is discussed [116], where the inefficiency of classical AGC based on ACE is

presented and proposed the Ramp following controller for LFC. The Ziegler Nicholas

controller is proposed for an individual system to tune Kp and Ki of the PI controller. By

considering the LFC problem [258] a bilateral market transaction a fractional order (OF)

controller is placed for all the areas. The optimal parameters of OF controller are chosen

by flower pollination algorithm. T. Anil Kumar has presented dynamic performance of

LFC solution using parallel AC-DC [276]. An adaptive controller named as feedback

error learning (FEL) is projected for AGC operation by comparing the performance of

the controller’s dynamically [158]. S. Farook of [249] has presented AGC with PID

controller parameter optimization using an evolutionary real coded genetic algorithm

(RCGA) in deregulated multi-area hydro-thermal and hydro-gas power system. The

controllers presented here have degraded performance in deregulated environment, which

can be realize from comparative study for AGC operation [217].

In deregulated power system, storage technologies are also highlighted by many authors.

Kalyan Chatterjee has presented the effect of BESS for LFC in a deregulated electricity

market scenario by considering GRC and DB [157]. LFC using different types of storage

technologies is discussed in the following section.

2.12 Electrical Energy Storage System

The frequency and power fluctuations occur because of sudden changes in the load and

RES power. So, energy storage demand increases day by day. The different storages are

categorized by following the function, response times, and time duration of suitable

storage [306]. It can give fast and active response for power is categorized in the different

types. One type is the MES such as pumped hydro storage, compressed air and flywheel.


38

Similarly, FC and hydrogen are TESS. The electrochemical storages are classified as Li-

on, lead-acid and redox flow and NaS batteries. The chemical energy storage is hydrogen,

the electrical energy as a capacitor, super capacitor and SMES. Tremendous potential in

physical storage worldwide had been discussed [46]. The solution of LFC problem using

electrical energy storage technologies is addressed [152][296] and an ideal solution for a

extended lifetime with fewer expenses, more energy and power density is presented. The

LFC problem with penetration of RES is also addressed [61] with the discussion on wear

and tear of a generator during AGC operation. The solution using a hybrid electrical

vehicle is proposed in the power system [214], where an author had proposed PHEVs /

PEVs technology with higher energy efficiency, fewer carbon emissions,

environmentally friendly and easily available. An impact of V2G technique on the

demand side with utility interconnection has been discussed by many researchers.

The different controllers and controller strategies are also used for the frequency

regulation during AGC operation. In this context an EV by proposed diversified (AC/DC)

transmission link is discussed [1]. An application of fractional order PID controller is

presented for the coordination between EV and conventional plant. EV has been

addressed to regulate LFC in a micro-grid. Gains of the PID controller is tuned by PSO

based ANN technique for the stable results of frequency deviations [19]. Grey Wolf

Optimization technique for quick operation of distributed generator as an EV is proposed,

which results in a reduction in the deviations with settlement time [24]. The author of

[266] has addressed the requirement of the controller for the LFC problem when

renewable energy is integrated into the grid. The Grey Wolf Optimization method is use

with the integration of RES. Reviews on traditional controller, combined and artificial

controllers addressed [67] and there is a need for storage technology. In [195] a smart

grid with the integration of EV and RES, LFC problem is addressed. The PI controller

design based on AI techniques such as FG, FOPID followed by fuzzy-Model Predictive

Control (MPC) is presented. In [156] author has pointed out the problem of time-varying

delay during the involvement of EV agent for LFC. Delay dependent stability criteria are

proposed based on the Lyapunov theory and LMIA.

2.13 Conclusion

An effort has been put for a critical literature survey on the LFC problem. A review of

LFC under conventional as well as restructured IPS with and without grid-connected style

https://www.sciencedirect.com/topics/engineering/model-predictive-control

https://www.sciencedirect.com/topics/engineering/model-predictive-control

CHAPTER 2

39

is carried out. The repeated AGC operation of the governor will increase wear and tear.

Also, the dynamic response of the governor is slow. Moreover, the conventional and

smart controllers have degraded performance against the disturbances. So, a lot of

research potential exists in energy storage technologies. We have made an attempt to tap

this potential in chapter 5, chapter 6 and chapter 7.

40

CHAPTER 3

Price Based AGC Operation

3.1 Introduction

In chapter 2 different LFC techniques of AGC are presented. In India, ABT was

implemented for frequency regulation. Before the ABT implementation in 2003, there

were uncertain frequency fluctuations. So, ABT was implemented for grid operation and

control as well frequency regulation. In AGC the secondary loop is closely linked with

UI price of ABT. It makes price based AGC operation in a real-time.

India is the fast-growing economies as compared to other countries in the world. To

provide enough electricity supply is one of the top priorities of the GoI. The power

generation capacity increased since last few years, but gradually the demand is growing

more as compared to supply, which has created a shortage of 10.6 to 12.1 per cent energy

over the years. The problems such as high transmission and distribution losses, issues in

the supply of fuel and poor economic condition of utilities created due to the shortage of

power supply. To enhance the electrical network capacity of the generation, transmission

and distribution, GoI has initiated many steps. The Electricity Act 2003 was introduced

to encouraging the competition and trading activities in the Indian Power Industries.

In this chapter, the Indian grid operation is presented. The ABT mechanism, with its

advantages and scheduling-dispatch process under ABT mechanism, is presented.

3.2 Grid Operation in India

Grid power operation and control is a critically important function of the nation to deliver

power for the end consumer. During the electrical grid operation control, the security,

reliability, economy and efficiency should be taken care of. In 1991, the first

interconnection between North Eastern and Eastern grids was established in India. Then

Western Grid was interconnected in March 2003 with the above grid. In 2006, the

CHAPTER 3

41

Northern grid was connected. The southern Grid was alone pending to be interconnected

with central Grid. After the installation of the 765 kV Raichur-Solapur transmission line,

the grid was synchronized on 31st December 2013 [206] [132].

Power System Operation Corporation Ltd (POSOCO) is given the responsibility for the

Indian grid. POSOCO will facilitate for the inter-state power transmission across India

through its NLDC and five Regional Load Dispatch Centers. There are total five regional

grids in India are there: Northern Regional (NR) Grid, Southern Regional (SR) Grid,

Eastern Regional (ER) Grid, Western Regional (WR) Grid, and North- East Regional

(NER) grid. The Indian structure of transmission network is represented in Fig. 3.1.

POSOCO is also an administrator of the wholesale electricity market of India and making

the balance between demand and generation every 15 minutes in line with the regulations

of the CERC.

The Indian transmission network is organized in a hierarchical manner, which is shown

in Fig. 3.2. At the state level, the grids (state grid) consist of a transmission line below

400 VK. All state grids consist of the State Generating Stations (SGSs), small IPPs and

CPPs in states are grid-connected. The state grids are connected with the regional grid.

The regional grid mainly comprises of transmission lines of 400 kV and above. Up to the

year 2002, each region operated as a separate AC system, with interregional links. There

was an exchange of power between different regions through HVDC links. Presently,

there is a single grid in India, and all regional grids are synchronously connected.

The Function of Indian IPS Entities are presented in Fig. 3.3. At the regional level, the

RLDCs are responsible for the grid operation. Similarly, in the SLDCs are responsible

for grid operation. The CERC had given a guideline for interstate power transfer and the

SERC has to give guideline to transfer Intra-state power. In the IEGC the various utility

roles and responsibility, the guideline to schedule and dispatch, the planning code,

responsibilities and need in real-time operation are defined. CERC issues this IEGC.

Similarly, state grid codes are issued by SERCs.


42

National Grid

Northern Regional

(NR) Grid

Southern Regional

(SR) Grid

Western Regional

(WR) Grid

Eastern Regional

(ER) Grid

North-Eastern

Regional (NER) Grid

Delhi

Hariyana

Himachal Pradesh

Hammu Kashmir

Punjab

Rajashtan

Uttar Pradesh

Uttaranchal

Goa

Diu Daman

Gujarat

Madhya Pradesh

Chattisgarh

Andhra Pradesh

Karnataka

Kerala

Tamil Nadu

Rondichery

Maharastra

Dadra Nagar Haveli

Bihar

Jharkhand

West Bengal

Orissa

Sikkim

Assam

Arunachal Pradesh

Meghalaya

Tripura

Manipur

Nagaland

Mizoram

FIGURE 3.1 National Grid in India.

3.2.1 Scheduling and Dispatch under ABT mechanism

The various states in the various regions in the country have ISGSs. These ISGSs have

their bulk consumers and beneficiaries. In the region, ISGSs will declare its next day

expected output generating capacity to Regional Load Dispatch Centers (RLDC). RLDC

will convey the information of the output capacity of generating stations to the State Load

Dispatch Centers (SLDCs). The SLDCs look after the best ways to fulfill the load demand

of their customers throughout 24 hours. To meet the load demand, SLDC collects

generation data from its own generating stations and ISGSs. SLDCs also take care of the

CHAPTER 3

43

power supply in the irrigation area. These SLDCs convey the scheduling information to

the RLDCs. Then RLDCs aggregate all the demands and declare the delivery schedules

from the ISGSs and draw the schedule for the beneficiaries.

NATIONAL

GRID

RIGIONAL GRID RIGIONAL GRID RIGIONAL GRID

STATE GRID STATE GRID STATE GRID

SEB/DISCOM

ISGS IPP

IPPSGS CPP

FIGURE 3.2 Hierarchical Structure of Power Grid in India.

NRLDC

RLDC

SLDC

CERC

SERC

CTU

STU

IEGC

SEGC

Schedule

and

Dispatch RegulatorTransmission

Operator Grid Code

National Level

Regional Level

State Level

FIGURE 3.3 Functions of Indian Power System Entities.


44

ISGSs can revise the output capability in case of contingencies; beneficiaries can revise

their requisition. The RLDCs will be rescheduled accordingly. In Fig. 3.4 the scheduling

and dispatch process is presented under the ABT mechanism.

The generation and load are regulated by following the schedule. The deviations are

allowed within limits, so that they do not affect the system security. These deviations are

determined over every 15-minute interval. For the recording purpose, ABT meter is used.

The UI price depends on frequency. The recorded energy consumption is matched with

the scheduled energy in this 15-minute duration. The difference in scheduled energy and

recorded energy is UI energy. By averaging the frequency of each 15-minute interval

through the day, the UI rates get finalized. This scheduled energy and actual energy are

metered for each ISGSs and each geographical State for every 15-minute time block.

FIGURE 3.4 Process of Schedule and Dispatch Under ABT Mechanism.

I

S

G

S

R

L

D

C

S

L

D

C

Declared

Availabilty

Injection

Schedule

Entitlments

DC Revision

Drawl Schedule

Requisitions and

Agreements

Final Injection

Schedule

Final Drawl

Schedule

D-1 Day

D Day

00:00 To

24:00

Hrs

10:00 hrs

15:00 hrs

18:00 hrs

22:00 hrs

08:00 hrs

Revisions During

Current Delay

Revision in

Requisition

Revisions During

Current Delay

CHAPTER 3

45

3.3 Reforms in Indian Power Systems

Under the Electricity Act 2003, freedom is given to increase the competition of power

trading [285]. Because of the weak economic condition of the SEBs, the GOI has started

to attract the private sector industry for energy generation by making changes in the IE

Act 2010 and EC ACT 1948. Simultaneously, steps for privatization of the generating,

transmitting and distributing companies of the SEBs were initiated. After the

implementation of the ERC act in 1998, the rules of the Central Electricity Regulatory

(CERC) were laid down.

3.4 Price based AGC Operation

The mathematical model for AGC operation including UI price and marginal cost is

represented in Figure 3.5. In real-time operation of the generator, the generating unit will

automatically respond to UI price signal(𝜌𝑖). As per the control mechanism, the UI price

signal (𝜌𝑖) is observed by each generator. Now, (𝜌𝑖) is compared with the marginal cost

of a generator(𝑃𝑔0𝑖). The difference of (𝜌𝑖) and(𝑃𝑔0𝑖), represented as Generation Control

Error (GCE).

Frequency

to Price

1

1

+sTgi 1

1

+sTti

Power Plant

Generation to

Marginal Cost

iR

1

-+

++

-+

++

UI Price

ifgiP

-+

LiP

1+sT

K

pi

pi

Step Load Change

refP

)( i

)( i

Marginal Cost

Fundamental Frequency )( 0f

Scheduled Generation

ControlleriGCE

)(0 igP

FIGURE 3.5 Control Scheme for price based AGC.

The GCE is a signal is generated from the GCE logic block. Now, the controller reacts

to run the generator following the error signal. If (𝜌𝑖) > (𝑃𝑔𝑜𝑖) then, the GCE signal is


46

positive. It means generators can profit by increasing more generation. On the other hand,

if the GCE signal is negative, the generators can be profitable by decreasing the power

generation.

The mathematical block diagram of Fig. 3.5 is represented with the primary loop and

secondary control loops in Fig. 3.6. Here, the secondary control loop is represented by

ABT based loop. In Primary frequency control loop, which is under the FGMO,

generators respond against changes in the frequency. In the secondary frequency control

loop UI price signal is used.

Generator Power System

Fundamental

Frequency

UI Price

Schedule

Generation

Marginal Cost

Controller

Droop

-+

+

+

+

Changes in Local Load

Changes in tie line Power

f

LiP

0f

giP

giP0gP

Primary Frequency

ControltieP

Secondary

Frequency Control

GCE

1S

2S

+

3S

4S

5S

FIGURE 3.6 ABT based AGC loop.

This price-based AGC scheme is represented in Fig. 3.7, where the change in the

frequency is represented by the signal S1. The signal S2 is frequency to UI price signal

according to CERC (2016). Signal S3 represents the addition of the scheduled generation

CHAPTER 3

47

and the changes at the generator output. The calculated marginal cost is represented by

signal S4. Generation Control Error (GCE) is represented by signal S5, which is the

difference in the marginal cost and UI price signals.

04 )( S

YesNo

)()()( 425 SSGCES −=

Yes

No

02 )( S

Yes

No

025 )()( −= SGCES

No

0)(5 =GCES

Yes

)()( 42 SS

)()( 42 SS

Yes

Read ,

FIGURE 3.7 Flow Chart for GCE Calculation.

Here, the positive value of GEC represents generators earn by accelerating the generation

level and the negative value of GEC represents the generators that profit by reducing the

generation level. The mathematical modelling of Price based AGC represented in the

following section.


48

3.5 Mathematical Modeling of Price Based AGC

The frequency deviates by 𝛥𝑓 Hz, during the step changes of the load MW occur in the

power system, the. The deviations in the frequency 𝛥𝑓 are added to the fundamental

frequency f0 to generate signal S1 (3.1). The signal S2 is calculated as per the UI charges

based on the frequency norms published by the respective year. Now, S2 is calculated as

per the following logic as shown in the flow chart (Fig. 3.6) to generate the GEC S5

Rs/MWh.

𝑆1 = 𝛥𝑓 + 𝑓0, (3.1)

Where 𝑓0is the fundamental frequency in Hz

If S1<=49.7 Hz ; S2=8032 Rs/MWH

elseif S1<=50 Hz ; S2=1780+20856*(50- S1) Rs/MWH

elseif S2<=50.05 Hz ; S2=35600*(50.05- S1) Rs/MWH

else S2=0 Rs/MWH.

S3 is the difference between the scheduled generation (𝑃𝑔0) MW and the changes in the

generator power (𝛥𝑃𝑔𝑖) MW as given by (3.2).

𝑆3 = 𝑃𝑔0 + 𝛥𝑃𝑔𝑖 (3.2)

Now, in the signal UI S2 is compared with the S4. (Incremental cost signal of the

generator) and the controller generates GEC signal S5. (3.3) gives the signal S4 for each

generator.

𝑆4 = 2 ∗ 𝑐𝑖 ∗ 𝑆3 + 𝑏𝑖 Rs/MWh (3.3)

Here, the positive value of GEC represents generators earn by accelerating the generation

level and the negative value of GEC represents the generators that profit by reducing the

generation level.

CHAPTER 3

49

3.6 Conclusion

Today, it is one of the top priorities of generating companies to provide uninterrupted

power supply. From the data given in Appendix-A one can observe that, the demand is

increasing at a rate higher than that of power generation creating an energy shortage of

about 10.6 to 12.1 per cent. Due to the issues in the fuel supply, there is a shortage of

energy. Also, high transmission and distribution losses exist in the electrical network. All

these affect the financial health of utilities. To enhance the generation, transmission and

distribution capacity GoI has initiated many steps, amongst which implementation of

ABT in 2003 was one, which helps in controlling the frequency.

In this chapter grid operation in India is presented. The process of scheduling and dispatch

after implementation of ABT mechanism is discussed. The ABT mechanism creates real-

time operation of market. Here, UI rates can be derived by measuring the frequency. The

secondary loop of AGC is closely linked with the UI price component of the ABT, which

make AGC price based. By sensing the UI price signal and marginal cost, the GCE can

be controlled.

The positive GCE value indicates the earning opportunity for generators by accelerating

the generation level. The negative GCE value indicates that the generators can profit by

reducing the generation level. AGC operation is covered in chapter 4.

50

CHAPTER 4

Automatic Generation Control in Restructured

Power System

4.1 Introduction

This chapter highlights the AGC operation. Due to the uncertain load fluctuations the

mismatch is created between the generation and demand. This results in the deviations of

tie-line flow as well as frequency. To nullify the tie-line flow, generators running under

the AGC loop respond. During this AGC operation the generator output, frequency and

tie-line flow deviates.

The previous chapter 3 deals with the ABT loop/secondary control loop of AGC. In this

chapter the study of conventional AGC under restructured market environment is

presented. Dynamic study of GENCO power deviations, frequency deviations and UI

price deviation by load fluctuation under Poolco based contract, Bilateral contracts and

contract violation is simulated. For the simulation DISCO participation has been

considered throughout the work. Also, power, frequency and UI price are observed during

peak-hours and off-peak hours.

4.2 About Restructured Power System

Worldwide electric power utilities had adopted the deregulated market scenario and the

restructuring process is undergoing [92].GENCOs, TRANSCOs and DISCOs, as well as

ISOs, are the individual body in the restructured IPS. Presently, the DISCOs have the

authorization to do an agreement with the same area GENCOs. It is known as "Poolco

Based Transaction". If they do an agreement with the outside area, then it is called as

"Bilateral Transaction". Both the contracts are supervised by ISO. For the reliability of

power supply, many ancillary services such as reactive power management, voltage

CHAPTER 4

51

control, loss minimization, scheduling-dispatch as well as energy balancing have been

provided by system operators. In such a situation either ISO or TSO has been entrusted

with the deal of procurement of these services. It has been observed that AGC / LFC is

widely accepted as an ancillary service in practice [40]. The various issue of LFC during

the operation and control of IPS after deregulation has been discussed [224]. The

operation of price based AGC in a newly restructured power system was inspired as a

future business environment for electric utility [284] and the case study with different

load following contracts have also been represented [18]. Here, a customer can contract

alone with a different utility.

Though, it is hard for the synchronous generator to track the demand completely because

electricity characteristics have a limitation on the instantaneous procedure between

generation and utilization due to slow dynamic response [65]. Also, because of frequent

power changes by RES like solar and wind power generation in an existing grid, it

becomes difficult to regulate frequency due to insufficient regulation reserve capacity

and rapid power imbalance [10]. Thermal and hydro units can be used to regulate

frequency. Due to limited regulation capacity, slow response according to the power

demand changes and frequent operation of generator decreases the life of the governor as

well as turbine [4]. Finally increases generation cost, labour cost and repairs cost due to

wear and tear [218]. Under these circumstances, a usual frequency control by the time-

consuming action of the governor will not capable to follow the rapid load variation,

which fallout into either voltage or frequency deviation or even tripping of generator [4].

4.3 System Under Examination

An isolated two area restructured IPS is considered for AGC operation, which is shown

in Fig.1.

4.3.1 Mathematical modelling of two area restructured power system

For simplicity and better understanding of contracts, the DPM is considered [289]. Table

I represents the DPM matrix of the power system with two area exposed in Fig. 4.1. The

pairs of GENCO1 (Thermal Power plant1) - DISCO1, GENCO2 (Hydro Power Plant2) -

DISCO2 in Area1 and GENCO3 (Thermal Power plant3) - DISCO3, GENCO4 (Thermal

Power plant4) - DISCO4 in Area2 is considered. In the table, the number of rows stands

Automatic Generation Control in Restructured Power System

52

for the GENCOs (G) number and the column stands for the DISCOs (D) number. This

matrix represents the agreement of DISCOs with GENCOs. It is known as “DPM”. The

equivalent DPM is given as follows.

TABLE 4.1 DPM Matrix

Area1 Area2

DISCO1 DISCO2 DISCO3 DISCO4

Area1 GENCO1 G1-D1 G1-D2 G1-D3 G1-D4

GENCO2 G2-D1 G2-D2 G2-D3 G2-D4

Area2 GENCO3 G3-D1 G3-D2 G3-D3 G3-D4

GENCO4 G4-D1 G4-D2 G4-D3 G4-D4

G=GENCO, D=DISCO

𝐷𝑃𝑀 = [

𝑐𝑝𝑓11 𝑐𝑝𝑓12 𝑐𝑝𝑓13 𝑐𝑝𝑓14𝑐𝑝𝑓21 𝑐𝑝𝑓22 𝑐𝑝𝑓23 𝑐𝑝𝑓24𝑐𝑝𝑓31 𝑐𝑝𝑓32 𝑐𝑝𝑓33 𝑐𝑝𝑓34𝑐𝑝𝑓41 𝑐𝑝𝑓42 𝑐𝑝𝑓43 𝑐𝑝𝑓44

] (1)

From the above matrix “Contract Participation Factor (cpf)”, brings the contract

information to track the power of GECNO and load from DISCO. The cpf can be

calculated from (2). The diagonal elements give information of agreement among the

GENCOs and DISCOs of the same area. The off-diagonal elements give information

about an agreement among the GENCOs of one area and DISCOs of outside area.

Assume the DISCO3 demands 150 MW powers, out of which 30 MW is demanded from

DISCO1, 45 MW from DISCO2, 60 MW from DISCO3 and 15 MW from DISCO4. The

variables of DPM of (1) are defined as by (2). The changes in the GENCO power can

calculate from (3). The following mathematical formulas are used for the analysis of the

two area restructured IPS [289].

∑𝑐𝑝𝑓𝑖𝑗 = 1

𝑖

(2)

𝑐𝑝𝑓13 =30

150=⥂ 0.2, 𝑐𝑝𝑓23 =

45

150= 0.3,

𝑐𝑝𝑓33 =60

150= 0.4, 𝑐𝑝𝑓43 =

15

150= 0.1.

The power contracted to supply by ith GENCO is given by,

𝛥𝑃𝑔𝑖 = ∑ 𝑐𝑝𝑓𝑖𝑗𝛥𝑃𝐿𝑗

𝐷𝐼𝑆𝐶𝑂4

𝑗=1

(3)

Where 𝛥𝑃𝐿𝑗represents DISCOj total load demand as presented in (4).

𝛥𝑃𝐿1,𝐿𝑂𝐶 = 𝛥𝑃1 + 𝛥𝑃2, ⥂⥂⥂ (4)

CHAPTER 4

53

𝛥𝑃𝐿2,𝐿𝑂𝐶 = 𝛥𝑃31 + 𝛥𝑃4

Due to the changes in the load, the tie line scheduled power is given by (5) and (6).

𝛥𝑃𝑡𝑖𝑒1−2,𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝑑 = (𝑑𝑒𝑛𝑎𝑑𝑜𝑓𝐷𝐼𝑆𝐶𝑂𝑠𝑖𝑛𝑎𝑟𝑒𝑎𝐼𝐼𝑓𝑟𝑜𝑚𝐺𝐸𝑁𝐶𝑂𝑠𝑖𝑛𝑎𝑟𝑒𝑎1) − (𝑑𝑒𝑛𝑎𝑛𝑑𝑜𝑓𝐷𝐼𝑆𝐶𝑂𝑠𝑖𝑛𝑎𝑟𝑒𝑎𝐼𝑓𝑟𝑜𝑚𝐺𝐸𝑁𝐶𝑂𝑠𝑖𝑛𝑎𝑟𝑒𝑎𝐼𝐼)

(5)

𝛥𝑃𝑡𝑖𝑒,12,𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒 =∑∑𝑐𝑝𝑓𝑖𝑗𝛥𝑃𝐿𝑗

4

𝑗=3

2

𝑖=1

−∑∑𝑐𝑝𝑓𝑖𝑗𝛥𝑃𝐿𝑗

2

𝑖=1

4

𝑗=3

(6)

The tie line actual power is given by (5).

𝛥𝑃𝑡𝑖𝑒1−2,𝑎𝑐𝑡𝑢𝑎𝑙 = (2𝜋𝑇12𝑠

) (𝛥𝐹1 − 𝛥𝐹2) (5)

𝛥𝑃𝑡𝑖𝑒1−2,𝑒𝑟𝑟𝑜𝑟 = 𝛥𝑃𝑡𝑖𝑒1−2,𝑎𝑐𝑡𝑢𝑎𝑙 − 𝛥𝑃𝑡𝑖𝑒1−2,𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝑑 (6)

The error in the tie line by (6) helps to generate ACE in the respective area as in the

modern power system [289]. This ACE can be calculated by (7).

𝐴𝐶𝐸1 = 𝐵1𝛥𝑓1 + 𝛥𝑃𝑡𝑖𝑒1−2,𝑒𝑟𝑟𝑜𝑟 𝐴𝐶𝐸2 = 𝐵2𝛥𝑓2 + 𝑎12𝛥𝑃𝑡𝑖𝑒1−2,𝑒𝑟𝑟𝑜𝑟

(7)

𝑎12 = −𝑃𝑟1𝑃𝑟2

(8)

Where, 𝑃𝑟1 is area 1 rated capacity and 𝑃𝑟2is area2 rated capacity.

The DPM matrix helps GENCO for giving power to respective DISCO. The effect on

power system dynamics because of the sudden load fluctuation has been applied by the

input𝛥𝑃𝐿,𝐿𝑂𝐶. Now, the LFC signal generated from ACE due to unbalance between load

and supply. Dispatching of the LFC signal will through ACE participation matrix to the

GENCOs are considered i.e. 𝑎𝑝𝑓11, 𝑎𝑝𝑓12, 𝑎𝑝𝑓21 and 𝑎𝑝𝑓22 . The controller does a

significant function in the generation of an Error signal, which is distributed for GENCOs

according to apfs. The gain of the controller is tuned integral control law as given in (9).

𝑈𝑖 = −𝐾1𝑖∫𝐴𝐶𝐸𝑖𝑑𝑡 (9)


54

Local

Load1

DIS

CO

-1

DIS

CO

-2

apf1

apf2

+

cpf11

cpf12

cpf13

cpf14

cpf21

cpf22

cpf23

cpf24

-

-

+

-

AREA1

1

1

R2

1

R

3

1

R 4

1

R

-

+

1

1

1 +sTg 1

1

1 +sTt

1

1

3 +sTg

Thermal Power Plant1


Local

Load1

apf3

apf4

+-

-

Thermal Power Plant 3


1

1

4 +sTg

1

1

3 +sTt

1

1

4 +sTt

11

1

+sT

K

p

p

12

2

+sT

K

p

p+

-

s

T12

+

-

+

+

-

--

--

--

++

+

+

++

+

+

DIS

CO

-3

DIS

CO

-4

cpf31

cpf32

cpf33

cpf34

cpf41

cpf42

cpf43

cpf44

++

+

+

++

+

+

+-+

+

+

++

AREA2

+-+

Demand of

DISCO2 in Area1

to GENCOs in

Area2

Demand of

DISCO2 in Area1

to GENCOs in

Area2

Controller1

Controller2

1b

2b

12a

12a

ACE1

ACE2

1

1

2 +sTt1

1

2 +sTg

1F

2F

1F

2F

FIGURE 4.1 Block Diagram of Two Area Restructured Power System.

4.3.2 AGC Operation Under Market

The AGC operation under different market conditions has been carried out. The different

contracts taken into consideration are as follows.

CHAPTER 4

55

1. POOLCO based contract

2. Bilateral contract

3. Bilateral contract with a contract violation.

The two area system with Base MVA of 2000 MVA and the rated capacity of 2000 MW

is considered [289]. The system data of the isolated network are shown in Table C.1 of

Appendix C. For the three markets the AGC operation is given below.

4.3.2.1 Case1: Poolco Based Market

In this market, the load change of 10% (200 MW) in area1 is considered. Participation of

GECNOs is presented by apfs, where apf1 = apf2 = 0.5 and apf3 = apf4 = 0.5. In this, only

DISCO1 and DISCO2 demanding the load. DISCO3 and DISOC4 do not claim the

exchange of power from any generating company. The DPM for the Poolco based market

is presented in (18).

𝑫𝑷𝑴 = [

𝟎. 𝟓 𝟎. 𝟓 𝟎 𝟎𝟎. 𝟓 𝟎. 𝟓 𝟎 𝟎𝟎 𝟎 𝟎 𝟎𝟎 𝟎 𝟎 𝟎

] (18)

4.3.2.2 Case2: Bilateral Market

In bilateral market, contract exists among GENCOs and DISCOs of the same area as well

as different area. The DPM is given in (19). Here, it is considered that each DISCO is

demanding 10% power from GENCOs. The participation of each GENCO in AGC will

be followed by𝑎𝑝𝑓𝑠, which is given as apf1= 0.75, apf2 = 0.25, apf3 =0.5 and apf4=0.5.

The DPM for the bilateral market is given by (19).

𝐷𝑃𝑀 = [

0.5 0.25 0 0.30.2 0.25 0 00 0.25 1 0.70.3 0.25 0 0

] (19)

4.3.2.3 Case3: Bilateral Market with Contract Violation

The contract may violate by DISCO due to excess power demand. This excess power is

uncontracted with any GENCO. The GENCO have to supply for the uncontracted power

of the same area. For visualization of a contract violation, excess power of 10% (200


56

MW) demanded by DISCO in area2 is considered. It is replicated in the area as a local

load. It is not the contract demand, so there is no change in DPM elements.

4.3.3 Comparison between the Various Markets

The following Table 4.2 represents the different calculated values of the power for the

above three cases.

TABLE 4.2 Calculated Parameters for all Cases

Parameters Case1 Case2 Case3

GENCO1 power deviations of Area1 (MW) 100 105 180

GENCO2 power deviations of Area1 (MW) 100 45 70

GENCO3 power deviations of Area1

(MW),

0 195 195

GENCO4 power deviations of Area1

(MW),

0 55 55

Tie Line Power deviations (MW) 0 -50 -50

4.3.4 Simulation and Result Analysis

Due to the sudden load variation, the dynamic behavior with unexpected load changes

has been represented in Fig. 4.2 (a) to (g) for the different parameters. Fig. 4.2 (a) to (d)

represents the changes in all GENCO power. Similarly, Fig. 4.2 (e) and (f) represents the

changes in the frequency in Area1 and Area2. Due to the contract exist in both the areas,

the deviations in the tie line is presented in Fig. 4.2 (g).

FIGURE 4.2 (a) GENCO1 power deviations of Area1 (MW).)

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

300

Time(s)

GE

NC

O1(

MW

)

Case1

Case2

Case3

CHAPTER 4

57

(b)

(c)

(d)

FIGURE 4.2 (b) GENCO2 power deviations of Area1 (MW), (c) GENCO3 power deviations of Area1

(MW), (d) GENCO4 power deviations of Area2 (MW)

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

180

Time(s)

GEN

CO2(

MW

)

Case1

Case2

Case3

0 5 10 15 20 25 30 35 40 45 50-50

0

50

100

150

200

250

Time(s)

GEN

CO3(

MW

)

Case1

Case2

Case3

0 5 10 15 20 25 30 35 40 45 50-40

-20

0

20

40

60

80

100

120

140

Time(s)

GEN

CO4(

MW

)

Case1

Case2

Case3


58

(e)

(f)

(g)

FIGURE 4.2 (e) Area1 Frequency deviations (Hz), (f) Area2 Frequency deviations (Hz), (g) Tie Line

Power deviations (MW).

The above results represent the deviations in the power, frequency and tie-line flow

during the AGC operation.

0 5 10 15 20 25 30 35 40 45 5049.5

49.6

49.7

49.8

49.9

50

50.1

50.2

50.3

Time(s)

Area

1 Fr

eque

ncy(

Hz)

Case1

Case2

Case3

0 5 10 15 20 25 30 35 40 45 5049.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

50.25

Time(s)

Area

2 Fr

eque

ncy(

Hz)

Case1

Case2

Case3

0 5 10 15 20 25 30 35 40 45 50-80

-60

-40

-20

0

20

40

Time(s)

Tie

Line

Pow

er (M

W)

Case1

Case2

Case3

CHAPTER 4

59

4.4 Price Based AGC Operation under ABT Mechanism

The ABT mechanism implemented in July 2002 [12], which consists of 3 numbers of

mechanisms: (a) Capacity Charge (b) Energy Charge (c) Unscheduled Interchange (UI)

Charge. This scheme named availability because generating company will pay higher if

the average real accessibility of the plant is higher than real availability and payment will

lower if an average actual availability achieved is lower.

The UI curve was introduced in 2000. The changes are done in the UI rates by CERC. At

an initial level, the frequency band for UI prices was from 49.0 to 50.5 Hz. In 2016,

CERC has come up with the latest regulations (CERC, 2016 as shown in Fig. 4.3) which

adjust the frequency band between 49.7 and 50.05 Hz [48]. The rates for the deviation of

each 0.01 Hz step are equal to 35.60 Paisa/Kwh for frequency band from 50.05 to 50.00

Hz. For the frequency from 50 Hz to 49.7 Hz and below, 20.84 paisa/Kwh is considered.

The UI curve is presented in Fig. 4.3.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

UI C

har

ges

(IN

R/K

wh

)

Frequency (Hz)

FIGURE 4.3 UI price vs frequency chart (CERC, 2016).

The inverse relation between price and frequency deviation has been implemented using

an embedded MATLAB function block in MATLAB/Simulink. The MATLAB code for

UI price is shown below.

if frequency<=49.7

Price=8032;

elseif frequency<=50

Price=1780+20856*(50-frequency);

elseif frequency<=50.05


60

Price=35600*(50.05-frequency);

else

Price=0;

End

In Fig. 4.4 (a) and (b) the simulated results of UI price against load deviations for the

Poolco based market, bilateral market and bilateral market with contract violations are

presented.

(a)

(b)

FIGURE 4.4 (a) Area1 UI Price deviations (Rs/MWh), (b) Area2 UI Price deviations (Rs/MWh).

The above results represent the deviations in the UI charges during the AGC operation.

UI charge helps to regulate the frequency. Presently, the governor in the AGC loop helps

to stabilize deviations.

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

9000

Time(s)

Are

a1 U

I P

rice (

Rs/M

wh))

Case1

Case2

Case3

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time(s)

Are

a2 U

I P

rice

(Rs/

Mw

h)

Case1

Case2

Case3

CHAPTER 4

61

4.5 AGC Operation during Peak Hours and Off-Peak Hours

Price Based Operation of AGC under Peak Hours-Off Hours is simulated in

MATLAB/Simulink under the bilateral market transaction. Here, the load variation

during peak- hour and off-peak hour is considered and simulated for 24 hours under the

availability based (ABT) tariff mechanism. Unscheduled Interchange (UI) charges of

ABT have calculated against frequency deviations. The charges of peak hours are high

as compared to off-peak hours. The results of tie line error, frequency deviations, and

changes in power deviations and UI charges vs. frequency are represented. For the

analysis in MATLAB/ Simulink the data used for area1 and area2 (GUVNL, 2016)

presented in Table 4.3, Table 4.4 and Table 4.5.

TABLE 4.3 Two Area Power System (GUVNL)

Sr.No Parameter Unit Area-1 Area-2

1 Thermal-hydro Thermal-Thermal

2 Pr MW 3000 2600

3 Base MVA MVA 3400 2600

4 PL MW 3000 2000

5 f Hz 50 50

6 H S 5 5

7 D pu/Hz 0.02 0.013

8 Tg S 0.08 0.08

9 Tt S 0.3 0.3

10 Tr S 10 10

11 Kr S 0.5 0.5

12 B puMW/Hz 0.425 0.425

13 R puMW/Hz 2.4 2.4

14 T12 0.545 0.545

15 Kd 2.4 2.4

16 Kp 2.4 2.4

17 Ki 4.5 4.5

18 Tw 1 1

19 Tp S 10 15

20 Kp puMW/Hz 50 75

21 % load variation MW 300 200

22 % load variation

Case-1 puMW 0.2 (300) 0

23 % load variation

Case-2 puMW 0.2 (300) 0.2 (200)

24 Uncontracted load puMW 0 260

25 % load variation

Case-3 puMW 0.2 (300) 0.3


62

The simulation for 24 hours of operation with peak hours (morning 6 to 11 and evening

6 to 11) and off-peak hours (11 am to 18 pm and 11 pm to 6 am) for bilateral transaction

is carried out. Here, all the DISCOs do the agreement with the GENCOs for power, which

is followed by the DPM as shown in (19). It is assumed that DISCOs in area1 demands

3000 MW power and DISCOs in area2 demands 2000 MW power of respective

GENCOs. The “𝑐𝑝𝑓𝑠” and 𝑎𝑝𝑓𝑠 guide the GENCO to participation in AGC operation.

The result of GENCO power deviations, area frequency deviations, UI prices deviations

and flow on the tie-line are presented in Fig. 4.5 (a) to (e). The load variation

TABLE 4.4 Area1 (MGVCL and DGVCL)

Sr.No Plant Location Capacity Total MW

1

Thermal

Ukai TPS 850

2820 2 Wanakbori I to VI 1,260

3 Wanakbori VII 210

4 Ukai Expansion 6 500

5 Hydro

Ukai Hydro 305 547

6 Kadana Hydro 242

7

Gas

Utran Gas Based 135

2600

8 Dhuvaran Gas Based - Stage-I 107

9 Dhuvaran Gas Based - Stage-II 112

10 Utran Extension 375

11 GPEC,surat 655

12 GIPCL II (165) 165

13 GIPCL-SLPP 250

14 GSEG 156

15 GIPCL - I (145) 145

16 GMDC - Akrimota 250

17 GIPCL, Expansion 250

18

Solar

Mithapur 50

71 19 Kambhar 15

20 Baroda 5

21 Sanand 1

22 Total 6038

TABLE 4.5 Area2 (UGVCL and PGVCL)

Sr.No Plant Location Capacity Total MW

1

Thermal

Gandhinagar I to IV 660

1700

2 Gandhinagar V 210

3 Sikka TPS 240

4 Kutch Lignite I to III 215

5 Kutch Lignite IV 75

6 ESSAR 300

7 Solar Charankha 850 850

8 Total 2550

CHAPTER 4

63

in the generation of both areas is considered during 24 Hrs of operation. During the peak

hour operation of power system DISCOs in area1 which are MGVCL and DGVCL

demands 225 MW each and DISCOs of area2, which are UGCVL and PGVCL demands

for 125 MW each and during an off-peak hour operation of power system DISCOs in

area1 which are MGVCL and DGVCL demands 150 MW each and DISCOs of area2

which are UGCVL and PGVCL demands for 50 MW each. For peak hours from 6 am to

11 am and from 6 pm to 11 pm, the 15% (450 MW in area1 and 150 MW in area2) load

changes considered in both areas. Similarly, during off-peak hours, the 10% (300 MW in

area1 and 200 MW in area2) load changes considered in both areas. An area participation

factors (apfs) and DPM matrix are assumed as follows. The changes in generated power

are calculated on an average basis for both area for peak- hours and off-peak hours of

operations of the power system are given as bellow. The area participation factors are

given as follows and DPM from (19) is considered.

𝑎𝑝𝑓1 = 0.75, 𝑎𝑝𝑓2 = 1 − 𝑎𝑝𝑓1 = 0.25, 𝑎𝑝𝑓3 = 0.5, 𝑎𝑝𝑓4 = 1 − 𝑎𝑝𝑓3 = 0.5.

(a)

(b)

FIGURE 4.5 (a) GENCOs power (Mw), (b) Area1 and Area2 Frequency Deviations (Hz).

0 5 10 15 20 24-200

-100

0

100

200

300

400

Time (Hour)

GE

NC

O P

ow

er

(MW

)

GENCO1 (MW)

GENCO2 (MW)

GENCO3 (MW)

GENCO4 (MW)

0 5 10 15 20 2449.6

49.7

49.8

49.9

50

50.1

50.2

50.3

50.4

Time (Hour)

Fre

quen

cy (

Hz)

Area1 Frequency (Hz)



64

(c)

(d)

(e)

FIGURE 4.5 (c) Area1 UI price (Rs/Mwh), (d) Area2 UI price (Rs/Mwh), (e) Tie line power (MW).

The above results represent the AGC operation during peak and off-peak hours. During

the AGC operation in peak hours the deviations in the frequency and UI price are more

as compared to off-peak hours. The deviations are for short time due to sudden changes

in the load, which can compensate by some quick responding device.

49.6 49.7 49.8 49.9 50 50.1 50.2 50.3 50.40

1000

2000

3000

4000

5000

6000

7000

8000

9000


Are

a1 U

I P

rice

(Rs/

Mw

h)

49.8 49.85 49.9 49.95 50 50.05 50.1 50.15 50.20

1000

2000

3000

4000

5000

6000


Are

a2 U

I P

rice

(Rs/

Mw

h)

0 5 10 15 20 24-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

Time (Hour)

Tie

Lin

e (M

W)

CHAPTER 4

65

4.6 Conclusion

A Bilateral market as well as Poolco based market exists among the DISCOs and the

GENCOs in control areas. The “DPM” guides the AGC operation under the contracts.

Frequency linked UI price based operation of the generators running under AGC mode

is observed under the restructured power network. We can conclude that the generating

stations under AGC loop respond to the price signals manually as well as automatically.

The repeated operation of governor during the AGC increases repairs expense because of

wear and tear. Also, UI charges increases against the changes in the frequency. From the

result analysis, one can conclude that the frequent operation of the generator is not healthy

for them. The frequency can be regulated from the load side. The frequency fluctuations

are for a short time and occur frequently. So, the EES can help to give a quick response

against the frequency fluctuation. From the critical literature review, we can observe that

there is a tremendous potential in BESS as storage and to regulate frequency. BESS as a

load and source can participate in AGC for LFC, which is covered in chapter 5.

66

CHAPTER 5

Energy Storage for Frequency Regulation

5.1 Introduction

An energy storage device can help to regulate frequency. There is a tremendous potential

in EESS applications in power system. The different types of EESS are presented in

Appendix D. Among all the storage technologies, EVs as a distributed BESS can give

better, quick and fast service in frequency regulation [40][85]. Here, a diversified

transmission link is proposed. It distributes the LFC signal to all EVs through aggregators

for the participation in AGC. According to the ACE value an EV can charge or discharge

its battery.

5.2 The need for Electrical Energy Storage

In India, electricity requirement has increased after the announcement of the "Power for

All" target to give 24x7 hours of electricity by 2019. It also has announced plans to

increase 40% of electricity generation from RES by 2030. Due to uncertain renewable

power the grid is facing the problem of frequency fluctuations. There is a requirement of

fast responding ancillary services for frequency stability. BESS has a huge prospective

to regulate the frequency. Applications of BESS play a vital role to enhance system

frequency stability.

5.3 Grid Frequency Regulation Using Electrical Vehicle

As it is mentioned in the introduction, the grid frequency can be controlled by balancing

load against generation. To keep frequency near to 50 Hz, it is required to reserve a

definite amount of real power for frequency regulation. The service of EV to regulate the

frequency is called as vehicle to grid (V2G) and grid service to vehicle (G2V) service.

CHAPTER 5

67

There are three necessities for V2G and G2V operation [248].

1. Grid Connection for power flow.

2. Communication infrastructure is required to deal with aggregators.

3. Metering and control requirement for Vehicle to Grid and Grid to Vehicle

operation.

To put V2G as an ancillary service, the requirement of both TSO and vehicle operator

must be satisfied. The TSO wants the reliability of supply from the V2G service, and

vehicle owner gets remuneration. These two objectives are taken into consideration for

frequency regulation [72]. Worldwide, the numbers of study are presented by various

authors for centralizing or decentralized EV operation. There are two types of control

architecture are 1. Centralize Control Architecture, 2. Aggregative Architecture.

A. Centralize Control Architecture

In this control mechanism, all the information collected centrally. It can control the

vehicles individually. There is a direct communication line between the TSO and

vehicles. The vehicle owners are allowed to bid for V2G and G2V operation

independently at charging station.

B. Aggregative Architecture

The aggregator plays an important role between TSO and vehicle owners. It provides the

ancillary service for V2G and G2V operation [215]. The aggregator will receive the

information from the TSO and distribute among the contracted vehicles. The aggregator

can bid any time during the absence or presence of EV.

Out of the above two types, the aggregative architecture is more suitable due to the large

value of minimum bid for a single EV owner. In this model, the aggregators will get the

AGC signal from TSO. An aggregators will have all the details about availability of EVs.

After getting AGC signal aggregators will send the signal to all the EVs. Following the

signal sent by the aggregator, all EV will consume or give the power. Aggregators play

an intermediate role between AGC and EV. Infrastructure for the communication is

required to transmit and receive the signals.


68

5.4 Proposed Block Diagram for EV

Fig. 5.1 represents the mathematical model of N-area IPS. The following additions are

made to the model: thermal plant with non-reheat unit, wind turbine model and dynamic

model for the discharging of an EV battery. From the control centre, the System Operator

(SO) disperses the ACE signal to GENCOs and LFC signal to the aggregators through

Area Participation Factor (APF). The LFC signal is dispersed from the aggregators to all

the EVs. The bidirectional charger allows the EV to feed power into the grid.

FIGURE 5.1 Proposed block diagram for the grid frequency regulation.

A. EV Modelling

The conventional control techniques are not able to regulate the frequency variations

absolutely. To maintain the generation-demand, the generating unit changes the

generation according to the ACE. As per the actual Megawatts capacity of EVs in the

area, the LFC generated from ACE is dispatched to all EVs via local dispatch centre by

the TSO. By dispatching LFC signal to all EVs from TSO of that control area, it is

possible to set the rated value of frequency [250]. Also, the flow on tie-line can control

in the given rated value [181]. EV power can be calculated from (12). The way of

controlling the frequency by LFC signal is equivalent to the AGC operation performed

by the governor of thermal or hydro unit. From LFC signal, the charge as well as

discharge procedure of EV battery can be controlled as shown in Fig. 5.2. In figure 5.1

the function for time delay (𝑇𝐸𝑉) for EV fleet is represented. For that the first-order

transfer function is used. It is in series with the gain (𝐾𝐸𝑉) of EV [265]. The power output

of EV control methodology based on ACE shown in (5.1) and droop characteristics

(W/Hz) of Electrical Vehicle (EV) presented in fig. 2. It is presented as a ratio of changes

CHAPTER 5

69

in power to the changes in frequency. Frequency range from 49.7 to 50.04 has been

considered as per CEA, India in 2016.

𝐴𝐶𝐸𝑖 = 𝛽𝛥𝑓𝑖 + 𝛥𝑃𝑖𝑗 (5.1)

Where, 𝛥𝑃𝑖𝑗 indicate from area II to an area I tie-line power flow.

The output of charging power (5.2) can be measured with the help of droop

characteristics𝐾𝐸𝑉,

𝑃𝐸𝑉1 = {𝐾𝐸𝑉1𝛥𝑓(|𝐾𝐸𝑉1𝛥𝑓|) ≤ 𝑃𝑚𝑃𝑚(𝑃𝑚 < |𝐾𝐸𝑉1𝛥𝑓|)

(5.2)

Where, 𝐾𝐸𝑉1 stands for the EV gain responsible for the power exchange for V2G

followed by the SOC deviation range. 𝑃𝑚is the maximum EV power.

Discharging power𝑃𝐸𝑉2 is calculated by (5.3). Load Frequency Control (LFC) signal is

calculated from (5.4), which is the ratio of ACE and characteristics constant.

𝑃𝐸𝑉2 = {

𝐾𝑚𝛥𝑓(0 < 𝐾𝑚𝛥𝑓 ≤ 𝑃𝑚)

𝑃𝑚(𝑃𝑚 < 𝐾𝑚𝛥𝑓)

0(𝐾𝑚𝛥𝑓 < 0) (5.3)

𝐿𝐹𝐶 = −𝐴𝐶𝐸𝑖

𝐾𝐸𝑉𝑖 (5.4)

𝐾𝐸𝑉𝑖 = The change in battery charging power divided by the change in frequency. LFC

signal is dispersed to all EVs for controlling the charging power.

Linear characteristics of frequency between 49.7 to 50.04 (CERC, 2016) Hz is considered

as presented in Fig. 5.3 [138]. The dispatched value of ACE is proportional to the degree

of a gradient. For positive ACE the LFC signal will negative, which increases the EV

battery charging power. The negative value of ACE increases in the EV battery

discharging power.

B. EV Charge-Discharge Control

For the controlling of charging and discharging power against frequency deviations

𝑃𝐸𝑉1and𝑃𝐸𝑉2 respectively LFC based PI controller can be used, which will use to adjust


70

Frequency (Hz)

50

Dis

char

ging

Cha

rgin

g

mP

mf

V2G

-G2V

Pow

er (

W)

50.01 50.02 50.03 50.0449.9449.8749.7 49.79

ACE<0

ACE>0

mK

FIGURE 5.2 EV battery operating characteristics to Charge and Discharge.

the value of gain𝐾𝐸𝑉. The PI controller will track variations in the value of frequency.

The value of 𝐾𝐸𝑉 is subject to the upper and lower boundary of a SOC (SOC=Actual

capacity/ Rated capacity) value. The maximum and minimum values assumed for SOC

are 90% and 30% respectively. To track the SOC deviations of the battery, an Integral

Absolute Error (IAE) method application is used in both areas. IAE I of area1 and area2

are represented by (5.5) and (5.6) respectively.

IAE of 𝛥𝑆𝑂𝐶𝐴𝑟𝑒𝑎1 = ∫ |𝛥𝑆𝑂𝐶𝐴𝑟𝑒𝑎1|∞

0𝑑𝑡

(5.5)

IAE of 𝛥𝑆𝑂𝐶𝐴𝑟𝑒𝑎2 = ∫ |𝛥𝑆𝑂𝐶𝐴𝑟𝑒𝑎2|∞

0𝑑𝑡 (5.6)

IAE values of the SOC deviations can be calculated from (5.5) and (5.6) by multiplying

the number of EVs [44]. The IAE value is subjected to a minimum and maximum gain

value, which is presented in (5.7) and (5.8).

𝐾𝑝𝑖𝑝𝑖,𝑚𝑎𝑥𝑝𝑖,𝑚𝑖𝑛 (5.7)

𝐾𝐼𝑖𝐼𝑖,𝑚𝑎𝑥𝐼𝑖,𝑚𝑖𝑛 (5.8)

Where, i= area1 and area2, 𝐾𝑝𝑖,𝑚𝑖𝑛and𝐾𝐼𝑖,𝑚𝑖𝑛are minimum gain value PI controller.

Similarly, 𝐾𝑝𝑖,𝑚𝑎𝑥and𝐾𝐼𝑖,𝑚𝑎𝑥 are the maximum gain values PI controller.

CHAPTER 5

71

5.5 Price Based AGC Operation In Coordination with EV

The AGC operation under different market conditions has been performed. The different

cases of the contracts are as follows.

1. POOLCO based contract

2. Bilateral contract

The operation AGC is simulated by considering isolated two areas IPS as shown in Fig.

4.1. The Base MVA of 2000 MVA and capacity of 2000 MW is considered [289]. The

system parameters of are given in Appendix-C. The AGC function is observed under the

following three markets. For the analysis data as shown in Appendix-B are used.

5.5.1 POOLCO based contract

In this contract, the changes in a load of area 1 only are considered. Participation of

GECNOs is presented by apf, where apf1 = apf2 = 0.5 and apf3 = apf4 = 0.5. In this case of the

poolco based transaction only DISCO1 and DISCO2 are demanding the load. DISCO3

and DISOC4 do not claim the exchange of power from any generating company. The

DPM presented as,

𝑫𝑷𝑴 = [

𝟎. 𝟓 𝟎. 𝟓 𝟎 𝟎𝟎. 𝟓 𝟎. 𝟓 𝟎 𝟎𝟎 𝟎 𝟎 𝟎𝟎 𝟎 𝟎 𝟎

] (5.9)

Sudden load fluctuation of 200 MW in area1 has been only considered for the

visualization of AGC operation and dynamics. Dynamic behaviour with unexpected load

changes with and without EVs is represented in Fig. 5.3 (a) to (f). The Fig. 5.3 (a) to (b)

represents the GENCO power. The Fig. 5.3 (c) represents the frequency fluctuation and

Fig. 5.3 (d) shows the fluctuation in the tile line flow. The area1 UI price (Rs/Mwh) and

Area2 UI price (Rs/Mwh) deviations are presented in Fig. 5.3 (e) to (f). As only area1

have nonzero load demand, so momentary rise and fall of frequency in area1 are larger

than in area2. Due to no contract exists between GENCOs of and DISCOs of the different

area; the tie-line power is zero. Calculated values of the GENCO power are presented in

Table 5.1.


72

TABLE 5.1 Calculated Power At GENCOs And Tie Line (Poolco Based Transaction)

Change in Generation Tie

Line Unit G-1 G-2 G-3 G-4

MW 100 100 0 0 0

(a)

(b)

(c)

FIGURE 5.3 (a) Load deviation response of generated power of Area1, (b) Load deviation response of

generated power of Area2, (c) Frequency deviation response of Area1 and Area2.

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

Time (s)

Pow

er

(MW

)

GENCO 1 Without EV

GENCO 2 Without EV

GENCO 1 With EV

GENCO 2 With EV

0 5 10 15 20 25 30 35 40 45 50-30

-20

-10

0

10

20

30

Time (s)

Pow

er

(MW

)

GENCO 3 Without EV

GENCO 4 Without EV

GENCO 3 With EV

GENCO 4 With EV

0 5 10 15 20 25 30 35 40 45 5049.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

50.25

Time (s)

Fre

quency (

Hz)

Area 1 Freq (Hz) Without EV

Area 2 Frwq (Hz) Without EV

Area 1 Freq (Hz) With EV


CHAPTER 5

73

(d)

(e)

(f)

FIGURE 5.3 (d) Tie line power deviation, (e) Area1 UI Price (Rs/Mwh), (f) Area2 UI Price

(Rs/Mwh).

From the results it is observed that, the peak overshoots and deviations in the power, tie

line, frequency and UI price has been reduced with grid-connected EVs.

0 5 10 15 20 25 30 35 40 45 50-50

-40

-30

-20

-10

0

10

20

30

Time (s)

Pow

er

(MW

)

Tie Line Without EV

Tie Line With EV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time(s)

Are

a1 U

I P

rice (

Rs/M

wh))

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 501200

1400

1600

1800

2000

2200

2400

2600

2800

Time(s)

Are

a2 U

I P

rice

(Rs/

Mw

h))

Without EV

With EV


74

5.5.2 Bilateral Transaction

In this, a contract exists among the GENCOs and the DISCOs of the same and different

areas. The DPM is given in (19). It is assumed that each DISCO is demanding 10% (100

MW) power. To supply the power the GENCO participation is given by 𝑎𝑝𝑓𝑠 where,

𝑎𝑝𝑓1 = 0.75, 𝑎𝑝𝑓2 = 1 − 𝑎𝑝𝑓1 = 0.25, ,𝑎𝑝𝑓3 = 0.5, 𝑎𝑝𝑓4 = 1 − 𝑎𝑝𝑓3 = 0.5

𝐷𝑃𝑀 = [

0.5 0.25 0 0.30.2 0.25 0 00 0.25 1 0.70.3 0.25 0 0

] (5.10)

Here, the total load changes of 200 MW in area1 and area2 have been considered. Impact

with EV and without EVs is represented in Fig. 5.4 (a) to (f). The GENCOs power is

presented in Fig. 5.4 (a) and (b). Fig. 5.4 (c) shows the results of frequency deviations

and Fig. 5.4 (d) shows the tie line flow. The deviations in the UI price (Rs/Mwh) are

presented in Fig. 5.4 (e) and (f). The calculated values of GENCO power are shown in

Table 5.2.

TABLE 5.2 Calculated Power At GENCOs And Tie Line (Bilateral Transaction)



MW 105 45 195 55 -50

(a)

FIGURE 5.4 (a) Load deviation response of generated power of Area 1.

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

180

Time (s)

Pow

er

(MW

)

GENCO 1 Without EV

GENCO 2 Without EV

GENCO 1 With EV

GENCO 2 With EV

CHAPTER 5

75

(b)

(c)

(d)

FIGURE 5.4 (b) Load deviation response of generated power of Area 2, (c) Frequency deviation

response of Area 1 and Area 2, (d) Tie line power deviation.

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

TIme (s)

Pow

er

(MW

)

GENCO 3 Without EV

GENCO 4 Without EV

GENCO 3 With EV

GENCO 4 With EV

0 5 10 15 20 25 30 35 40 45 5049.7

49.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

Time (s)

Fre

quen

cy (

Hz)





0 5 10 15 20 25 30 35 40 45 50-70

-60

-50

-40

-30

-20

-10

0

Time (s)

Pow

er

(MW

)

Tie Line Without EV

Tie Line With EV


76

(e)

(f)

FIGURE 5.4 (e) Area1 UI Price (Rs/Mwh) ,(f) Area2 UI Price (Rs/Mwh).

From the above results it is noted that, the peak overshoots and deviations in the power,

tie line, frequency and UI price has been reduced with grid-connected EVs.

5.5.3 Bilateral Transaction with Contract Violation



of the same area. For visualization of a contract violation, case 2 is considered with excess

power of 100 MW demanded by DISCO in area2. It is replicated in the area as a local

load, not as the contract demand, so there is no change in DPM elements. An overall load

deviation in the area is (𝛥𝑃𝐿1,𝐿𝑂𝐶) = (DISCO1+ DISCO2) Total Load = (100+100)+100

= 300 MW. Likewise, in area II the total local load = (DISCO3 + DISCO4)load = 200

MW.

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

Time(s)

Are

a1 U

I P

rice (

Rs/M

wh))

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 500

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Time(s)

Are

a2 U

I P

rice

(Rs/

Mw

h))

Without EV

With EV

CHAPTER 5

77

Comparative results of the different parameters with and without EVs are shown in Fig.

5.5 (a) to (f). Fig. 5.5 (a) to (b) shows the simulated results of the GENCOs power. The

Fig.5.5 (c) shows the results of frequency deviations and Fig. 5.5 (d) shows the tie-line

flow. The deviations in the UI price (Rs/Mwh) are presented in Fig. 5.5 (e) and Fig. 5.5

(f). The calculated values of GENCOs power are presented Table 5.3.

Table 5.3 Calculated Powers at GENCOs And Tie Line



MW 180 70 195 55 -50

(a)

(b)

FIGURE 5.5 (a) Load deviation response of generated power of Area1, (b) Load deviation response of

generated power of Area 2

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

300

Time(s)

Pow

er

(MW

)

GENCO 1 Without EV

GENCO 2 Without EV

GENCO 1 WIth EV

GENCO 2 With EV

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Time (s)

Pow

er

(MW

)

GENCO 3 Without EV

GENCO 4 Without EV

GENCO 3 With EV

GENCO 4 With EV


78

(c)

(d)

(e)

FIGURE 5.5 (c) Frequency deviation response of Area 1 and Area 2, (d) Tie line power deviation,

(e) Area1 UI Price (Rs/Mwh),

0 5 10 15 20 25 30 35 40 45 5049.5

49.6

49.7

49.8

49.9

50

50.1

50.2

50.3

Time (s)

Fre

quen

cy (

Hz)

Area1 Freq (Hz) Without EV

Area2 Freq (Hz) Without EV

Area1 Freq (Hz) With EV

Area2 Freq (Hz) With EV

0 5 10 15 20 25 30 35 40 45 50-120

-100

-80

-60

-40

-20

0

Time (s)

Pow

er

(MW

)

Tie Line Without EV

Tie Line WIth EV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

9000

Time(s)

Are

a1 U

I P

rice

(Rs/

Mw

h)

Without EV

With EV

CHAPTER 5

79

(f)

FIGURE 5.5 (f) UI Price (Rs/Mwh)

In Table 5.4 represents the results of frequency and time for without and with EVs

integration in a grid. Frequency stability can be achieved with EV.

TABLE 5.4 Summary of Comparison with EV and Without EV

Case Controller

Settling Time (s) Peak Overshoots No. of

Oscillations >50 Hz <50 Hz

Area1 Area2 Area1 Area2 Area1 Area2 Area1 Area2

F UI F UI F UI F UI F UI F UI F UI F UI

1

Without

EV 12 12 12 12 50.2 0 50.09 0 49.77 6500 49.92 2600 11 11 10 10

With EV 5 5 5 5 50.08 0 50.02 0 49.85 5000 49.98 2250 5 5 6 6

2

Without

EV 13 13 13 13 50.16 0 50.2 0 49.73 7200 49.77 4900 8 8 8 8

With EV 10 10 10 10 50.05 0 50.1 0 49.84 5200 49.85 4500 5 5 5 5

3

Without

EV 11 11 11 11 50.25 0 50.12 0 49.56 8000 49.78 6500 8 8 8 8

With EV 9 9 9 9 50.1 0 50.03 0 49.57 7900 49.95 4900 6 6 6 6

The above results are discussed in the following section.

POOLCO based Market

In case of the power system operating under Poolco based market with EV, the Area1

frequency operating range is 50.2 - 49.77 Hz (Fig.5.3(c)) , the UI price operating range

is 0 - 6500 Rs/Mwh (Fig. 5.3 (e)), no of oscillations in the frequency are 11

(Fig.5.4(c))and frequency settling time is 12 s (Fig.5.3(c)). In case of grid connected EV

the frequency operating range reduces to 50.08 - 49.85 Hz (Fig.5.3 (e)), the UI price

operating range reduces to 0 - 5000 Rs/Mwh (Fig.5.3 (f)), the no of oscillation in the

frequency are reduces 5 and frequency settling time reduces to 5 s (Fig.5.3(c)).

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time(s)

Are

a2 U

I P

rice

(Rs/

Mw

h)

Without EV

With EV


80

Similarly, in Area2 without EV the frequency operating range 50.09 - 49.92Hz

(Fig.5.3(c)), the UI price operating range 0 - 2600 Rs/Mwh (Fig.5.3(e)), no of oscillations

are 10 and frequency settling time is 12 s (Fig.5.3(c)). In case of grid connected EV the

frequency operating range reduces to 50.2 - 49.98 Hz (Fig.5.3(c)), the UI price reduces

to 0 - 2200 Rs/Mwh (Fig.5.3 (f)), the no of oscillations are reduces to 6 (Fig.5.3(c)) and

frequency settling time reduces to 5 s (Fig.5.3(c)).

By comparing the results of with and without EV from the simulation and Table 5.4,

better results achieved with EV for reduction in an operating range of frequency,

operating range of UI charges, frequency settlement time and frequency oscillation.

Bilateral Market

In case of the power system operating under Bilateral market with EV, the Area1

frequency operating range is 50.16 - 49.73 Hz (Fig.5.4(c)) , the UI price operating range

is 0 - 7200 Rs/Mwh (Fig.5.4(e)), no of oscillations in the frequency are 8 (Fig.5.3(c)) and

frequency settling time is 13 s (Fig.5.4(c)). In case of grid connected EV the frequency

operating range reduces to 50.05 - 49.84 Hz (Fig.5.4(c)), the UI price operating range

reduces to 0 - 5200 Rs/Mwh (Fig.5.4 (f)), the no of oscillation in the frequency are

reduces 5 (Fig.5.4(c)) and frequency settling time reduces to 10 s (Fig.5.4(c)).

Similarly, in Area2 without EV the frequency operating range 50.2 - 49.77Hz

(Fig.5.4(c)), the UI price operating range 0 - 4900 Rs/Mwh (Fig.5.4(e)), no of oscillations

are 8 and frequency settling time is 10 s (Fig.5.4(c)). In case of grid connected EV the

frequency operating range reduces to 50.1 - 49.85 Hz (Fig.5.4(c)) (Fig.5.4(c)), the UI

price reduces to 0 - 4500 Rs/Mwh (Fig.5.4(f)), the no of oscillations are reduces to 5

(Fig.5.4(c)) and frequency settling time reduces to 10 s (Fig.5.4(c)).

By comparing the results of with and without EV from the simulation and Table 5.4,

better results are achieved with EV for reduction in an operating range of frequency,

operating range of UI charges, frequency settlement time and frequency oscillation.

Bilateral Market with Contract Violation

In case of the power system operating Bilateral market with contract violation with EV,

the Area1 frequency operating range is 50.25 - 49.57 Hz (Fig.5.5(c)) , the UI price

operating range is 0 - 8000 Rs/Mwh (Fig.5.5(e)), no of oscillations in the frequency are

8 (Fig.5.5(c)) and frequency settling time is 11 s (Fig.5.5(c)). In case of grid connected

CHAPTER 5

81

EV the frequency operating range reduces to 50.01 - 49.57 Hz (Fig.5.5(c)), the UI price

operating range reduces to 0 - 7900 Rs/Mwh (Fig.5.5 (e)), the no of oscillation in the

frequency are reduces 6 (Fig.5.5(c)) and frequency settling time reduces to 9 s

(Fig.5.5(c)). Similarly, in Area2 without EV the frequency operating range 50.12 - 49.78

Hz (Fig.5.5(c)), the UI price operating range 0 - 6500 Rs/Mwh (Fig.5.5(f)), no of

oscillations are 8 and frequency settling time is 11 s (Fig.5.5(c)). In case of grid connected

EV the frequency operating range reduces to 50.03 - 49.95 Hz (Fig.5.5(c)), the UI price

reduces to 0 - 4900 Rs/Mwh (Fig.5.5 (f)), the no of oscillations are reduces to 6

(Fig.5.5(c)) and frequency settling time reduces to 9 s (Fig.5.5(c)).

By comparing the results of with without EV from the simulation and Table 5.4, better

results achieved with EV for reduction in an operating range of frequency, operating

range of UI charges, frequency settlement time and frequency oscillation.

5.6 Conclusion

From the previous section it can be observed that the proposed fleet of aggregated EVs

can control load frequency by participating in AGC. From the results, it has been realized

that the time to settle the normal frequency value, its oscillations peak overshoots are

reduced when EVs are grid-connected. Also, deviations in UI charges, its peak overshoots

and settling time are reduced during the charging/discharging operating mode of EVs.

So, distributed EVs perform a key function in the network which is to reduce the burden

on governor during AGC operation of synchronous generators.

82

CHAPTER 6

AGC Operation with Renewable Energy Sources

6.1 Introduction

Now a day’s electric grid is fast getting transformed into a smart grid and AGC operation

has become complex. With an integration of RESs the deviations in the frequency are

increasing. The percentage of RESs is steadily increasing day by day. As on 31st March

2018, India has 22 GW solar and 34 GW wind installed capacity. After the generation of

power from a renewable source, feeding that power into the grid is a sensitive point. Even

though India is encouraging the initiatives for green energy integration into the grid, it is

still a big challenge to develop infrastructure for the same. Installation of the renewable

projects takes 12-18 months, while transmission line takes up to five years. All of these

create a big challenge. For example, in India because of the variation in wind power, 50%

of the wind power generation is backed down by the Tamil Nadu State Load Dispatch

Centre (CERC, 2016). It causes financial loses for wind generators.

In Chapter 4, AGC operation without the grid connected RESs is discussed. Day by day

an integration of RESs increases [224]. The generated power from RESs is intermittent

in nature. Also, distance is a major constraint for the transmission of renewable power.

Moreover, India lacks a storage facility. Due to nature of RES, the deviations in the

frequency and UI price increases. Also, the AGC operation becomes complex [305].

In this chapter deviation in the frequency, GENCO power, Tie line flow and UI rates are

presented when RES power is fed into the grid. The above electrical parameters are highly

fluctuating due to both load variation and renewable power. A solution using storage as

a grid-connected fleet of electrical vehicles/ hybrid electrical vehicles is presented. It

helps to lessen the peak overshoots and time period of the oscillations in the load

frequency, power and UI rates.

CHAPTER 6

83

6.2 Solar Energy System with Price Based AGC Operation

6.2.1 Block Diagram of Two area Restructure Power System with Solar System

In this section two areas restructure power system with thermal-hydro and the thermal-

thermal unit has been considered for the AGC operation, shown in Fig. 6.2. Area1 has

GENCO1 (Thermal Unit) and GENCO2 (Hydro unit) with two distribution companies

MGVCL as DISCO1 and DGVCL as DISOC2. Similarly, Area2 has GENCO3 (Thermal

Unit) and GENCO4 (Thermal Unit) with two distribution companies UGVCL as

DISCO3 and PGVCL as DISCO4.The contract of DISCO and GENCO is monitored by

ISO followed by DPM [289].

The following solar model is assumed to understand an impact of the solar plant. (6.1)

represents the power output [65] of the PV system.

𝑃𝑃𝑉 = 𝜂𝑆𝜑{1 − 0.005(𝑇𝑎 + 25} (6.1)

The conversion efficiency is ranging from 8 to 15 %. The 𝜂and𝑆 has a constant value.

The output power of PV depends only on 𝑇𝑎 and 𝜑.

The PV model dynamic equation is presented in (6.2).

𝐾𝑃𝑉 =𝐺𝑃𝑉

1 + 𝑠𝑇𝑃𝑉=𝛥𝑃𝑃𝑉𝛥𝜑

(6.2)

Where KPV = Ratio of change in power (ΔPPV) to the change in radiation.



The transfer function for the PV model is represented as given below in Fig. 6.1 [265].

FIGURE 6.1 Model for output fluctuation of (PV) solar power


84

FIGURE 6.2 Two area restructured power system

CHAPTER 6

85

6.2.2 AGC Operation under Different Market Condition

The operation AGC is simulated by considering isolated two area power system. The

Base MVA of 3400 MVA (Thermal-Hydro Unit) in Area1 and 2600 MVA (Thermal-

Thermal Unit) in Area2 with a rated capacity of 3000 MW and 2600 MW is considered.

The system parameters are shown in the Table 4.3, Table 4.4 and Table 4.5. The AGC

operation is observed under the following three markets.

1. POOLCO based Market

2. Bilateral Transaction

For the analysis in MATLAB/ Simulink, data for two area IPS used from Gujarat Urja

Vikas Nigam Limited ((GUVNL), 2016 are presented in Table 4.2 for Area1 and Table

4.3 for Area2.

6.2.2.1 Poolco Based Market

Load variation of 20% (300 MW) in area1 considered. Fig. 6.3 (a) to (i) represents the

results of fluctuations in power, frequency, tie line flow and UI price because of the

fluctuating temperament of solar power. In this, the power deviation of 300 MW of area1

is represented by 150 MW by GENCO1 and 150 MW by GENCO2. In this transaction,

GENCO3 and GENCO4 of area2 will not participate. For the reduction in the deviations,

the grid-connected fleet of EV is considered. EV helps to reduce the deviations, which

are presented in Fig. 6.4 (a) to (i). Fig. 6.4 (a) to (d) represents the of GENCO power

deviations, Fig. 6.4 (e) to (f) represents the results of frequency deviations, Fig. 6.4 (g)

represents the result of tie-line flow and Fig. 6.4 (h) and (i) represents the results of UI

prices. The calculated results of GENCO power and tie-line flow are presented in Table

6.1.

TABLE 6.1 Calculated Power at GENCOs And Tie Line (Poolco Based Transaction)



MW 150 150 0 0 0

6.2.2.2 Bilateral Market

Load variation of 20% (300 MW) in area1 and 20% (300 MW) in area2 considered. Fig.

6.5 (a) to (i) represents the results of fluctuations in power, frequency, tie line flow and

UI price because of the fluctuating temperament of solar power.


86

(a) (a)

(b) (b)

(c) (c)

FIGURE 6.3 Impact of Solar Power on

(a) GENCO1 Power Deviation, (b) GENCO2 Power

Deviation, (c) GENCO3 Power Deviation.

FIGURE 6.4 Impact if a Fleet of EVs on

(a) GENCO1 Power Deviation, (b) GENCO2 Power Deviation, (c) GENCO3 Power Deviation.

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

300

350

Time(S)

GE

NC

O1(M

W)

Without PV Without EV

With PV

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

300

350

Time(S)

GE

NC

O1(M

W)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 50-300

-250

-200

-150

-100

-50

0

50

100

150

200

Time(S)

GE

NC

O2(M

W)


With PV

0 5 10 15 20 25 30 35 40 45 50-300

-250

-200

-150

-100

-50

0

50

100

150

200

Time(S)

GE

NC

O2(M

W)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 50-20

-10

0

10

20

30

40

50

Time(S)

GE

NC

O3(M

W)


With PV

0 5 10 15 20 25 30 35 40 45 50-20

-10

0

10

20

30

40

50

Time(S)

GE

NC

O3(M

W)

With PV

With EV

CHAPTER 6

87

(d) (d)

(e) (e)

(f) (f)


(d) GENCO4 Power Deviation, (e) Area1 Frequency

Deviation, (f) Area2 Frequency Deviation.



Deviation, (f) Area2 Frequency Deviation.

0 5 10 15 20 25 30 35 40 45 50-20

-10

0

10

20

30

40

50

Time(S)

GE

NC

O4(M

W)


With PV

0 5 10 15 20 25 30 35 40 45 50-20

-10

0

10

20

30

40

50

Time(S)

GE

NC

O4(M

W)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 5049.6

49.7

49.8

49.9

50

50.1

50.2

50.3

Time(S)

Fre

quency A

rea1(H

z)


With PV

0 5 10 15 20 25 30 35 40 45 5049.6

49.7

49.8

49.9

50

50.1

50.2

50.3

Time(S)

Fre

quency A

rea1(H

z)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 5049.8

49.85

49.9

49.95

50

50.05

50.1

Time(S)

Fre

quency A

rea2(H

z)


With PV

0 5 10 15 20 25 30 35 40 45 5049.8

49.85

49.9

49.95

50

50.05

50.1

Time(S)

Fre

quency A

rea2(H

z)

With PV

With EV


88

(g) (g)

(h) (h)

(i) (i)


(g) Tie Line Power Deviation, (h) Area1 UI Price

(Rs/Mwh), (i) Area2 UI Price (Rs/Mwh).

FIGURE 6.4 Impact of a Fleet of EVs on



The power deviation of 300 MW of area1 is represented by 157.5 MW by GENCO1 and

67.5 MW by GENCO2. The power deviation of 300 MW of area2 is represented by 195

MW by GENCO1 and 55 MW by GENCO2. In this transaction, GENCO3 and GENCO4

0 5 10 15 20 25 30 35 40 45 50-100

-80

-60

-40

-20

0

20

40

Time(S)

Tie

Lin

e P

ow

er

(MW

)


With PV

0 5 10 15 20 25 30 35 40 45 50-100

-80

-60

-40

-20

0

20

40

Time(S)

Tie

Lin

e P

ow

er

(MW

)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

9000

Time(S)

UI

Price A

rea1(R

s/M

wh)


With PV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

9000

Time(S)

UI

Price A

rea1(R

s/M

wh)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

Time(S)

UI

Price A

rea2(R

s/M

wh)


With PV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

Time(S)

UI

Price A

rea2(R

s/M

wh)

With PV

With EV

CHAPTER 6

89

of area2 will not participate. For the reduction in the deviations, the grid-connected fleet

of EV is considered. EV helps to reduce the deviations, which are presented in Fig. 6.6

(a) to (i). Fig. 6.6 (e) to (f) represents the results of frequency deviations, Fig. 6.6 (g)

represents the result of tie-line flow and Fig. 6.6 (h) and (i) represents the results of UI

prices. The calculated results of GENCO power including the tie-line flow are presented

in Table 6.2.

TABLE 6.2 Calculated Power At GENCO And Tie Line (Bilateral Transaction)



MW 157.5 66 195 55 -50

(a) (a)

(b) (b)



Deviation.



Deviation.

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

300

350

400

Time(S)

GE

NC

O1(M

W)


With PV

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

300

350

400

Time(S)

GE

NC

O1(M

W)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 50-120

-100

-80

-60

-40

-20

0

20

40

60

80

Time(S)

GE

NC

O2(M

W)


With PV

0 5 10 15 20 25 30 35 40 45 50-120

-100

-80

-60

-40

-20

0

20

40

60

80

Time(S)

GE

NC

O2(M

W)

With PV

With EV


90

(c) (c)

(d) (d)

(e) (e)


(c) GENCO3 Power Deviation, (d) GENCO4 Power

Deviation, (e) Area1 Frequency Deviation.


(c) GENCO3 Power Deviation, (d) GENCO4 Power

Deviation, (e) Area1 Frequency Deviation.

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

Time(S)

GE

NC

O4(M

W)


With PV

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Time(S)

GE

NC

O3(M

W)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

Time(S)

GE

NC

O4(M

W)


With PV

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

Time(S)

GE

NC

O4(M

W)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 5049.6

49.7

49.8

49.9

50

50.1

50.2

50.3

50.4

Time(S)

Fre

quency A

rea1(H

z)


With PV

0 5 10 15 20 25 30 35 40 45 5049.6

49.7

49.8

49.9

50

50.1

50.2

50.3

50.4

Time(S)

Fre

quency A

rea1(H

z)

With PV

With EV

CHAPTER 6

91

(f) (f)

(g) (g)

(h) (h)


(f) Area2 Frequency Deviation, (g) Tie Line Power

Deviation, (h) Area1 UI Price (Rs/Mwh).




0 5 10 15 20 25 30 35 40 45 5049.8

49.85

49.9

49.95

50

50.05

50.1

Time(S)

Fre

quency A

rea2(H

z)


With PV

0 5 10 15 20 25 30 35 40 45 5049.8

49.85

49.9

49.95

50

50.05

50.1

Time(S)

Fre

quency A

rea2(H

z)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 50-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

Time(S)

Tie

Lin

e P

ow

er

(MW

)


With PV

0 5 10 15 20 25 30 35 40 45 50-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

Time(S)

Tie

Lin

e P

ow

er

(MW

)

With PV

With EV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

9000

Time(S)

UI

Price A

rea1(R

s/M

wh)


With PV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

9000

Time(S)

UI

Price A

rea1(R

s/M

wh)

With PV

With EV


92

(i) (i)


(i) Area2 UI Price (Rs/Mwh).



The results of settling time, peak overshoots and oscillations of frequency as well as UI

price are given in the Table 6.3.

TABLE 6.3 Comparative Analysis of PV and EV integration

Case Condition

Frequency Settling

Time (s)

Peak Overshoots No. of

Oscillations in

Frequency >50 Hz <50 Hz



Case

1

Without PV

14 14 15 15 50.14 0 50.04 0 49.65 8000 49.84 5300 8 8 8 8

With PV - - - - 50.15 0 50.05 0 49.65 8000 49.84 5300 25 25 25 25

With EV 6 6 14 14 50.04 0 50.03 0 49.79 6300 49.97 2500 12 12 10 10

Case

2

Without PV

10 10 18 18 50.23 0 50.07 0 49.63 8000 49.83 5700 7 7 9 9

With PV - - - - 50.24 0 50.08 0 49.62 8000 49.82 5800 Continuous

oscillations With EV 10 10 15 15 50.03 0 50.07 0 49.8 6000 49.87 4500


Poolco Based Market

In case of the power system operating under Poolco based market with PV, the Area1

frequency operating range is 50.15 - 49.65 Hz (Fig. 6.4 (e)) , the UI price operating range

is 0 - 8000 Rs/Mwh (Fig. 6.4 (h)) respectively and the oscillations in the frequency are

continuous (Fig. 6.4 (e)). In case of grid connected EV the frequency operating range

reduces to 50.04 - 49.79 Hz (Fig. 6.4 (e)), the UI price operating range reduces to 0 -

6300 Rs/Mwh (Fig. 6.4 (h)) and the oscillation in the frequency are continuous (Fig. 6.4

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

Time(S)

UI

Price A

rea2(R

s/M

wh)


With PV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

Time(S)

UI

Price A

rea2(R

s/M

wh)

With PV

With EV

CHAPTER 6

93

(e)). Similarly, in Area2 with WT the frequency operating range 50.24 - 49.62 Hz (Fig.

6.4 (f)), the UI price operating range 0 - 8000 Rs/Mwh (Fig. 6.4 (i)) and the oscillations

are continuous (Fig. 6.4 (f)). In case of grid connected EV the frequency operating range

reduces to 50.03 - 49.8 Hz (Fig. 6.4 (f)), the UI price reduces to 0 - 6000 Rs/Mwh (Fig.

6.4 (i)), the no of oscillations are continuous (Fig. 6.4 (f)). By comparing the results of

with PV and with EV from the simulation and Table 6.3, better results achieved with EV

for reduction in an operating range of frequency as well as operating range of UI charges.

The frequency oscillations are continuous in both the cases, but the peak overshoot are

less in with EV.

Bilateral Market

In case of the power system operating under Bilateral market with PV, the Area1

frequency operating range is 50.24 - 49.62 Hz (Fig. 6.6 (e)) , the UI price operating range

is 0 - 8000 Rs/Mwh (Fig. 6.6 (h)) and the oscillations in the frequency are continuous

(Fig. 6.6 (e)). In case of grid connected EV the frequency operating range reduces to

50.03 - 49.8 Hz (Fig. 6.6 (e)) , the UI price operating range reduces to 0 - 6000 Rs/Mwh

(Fig. 6.6 (h)) and the oscillation in the frequency are reduces continuous (Fig. 6.6 (e)).

Similarly, in Area2 with WT the frequency operating range 50.08 - 49.82 Hz (Fig. 6.6

(f)), the UI price operating range 0 - 5800 Rs/Mwh (Fig. 6.6 (i)) and the oscillations are

continuous (Fig. 6.6 (f)). In case of grid connected EV the frequency operating range

reduces to 50.07 - 49.87 Hz (Fig. 6.6 (f)), the UI price reduces to 0 - 4500 Rs/Mwh (Fig.

6.6 (i)) and the oscillations are continuous (Fig. 6.6 (f)). By comparing the results of with

PV and with EV from the simulation and Table 6.3, better results achieved with EV for

reduction in an operating range of frequency as well as operating range of UI charges.

The frequency oscillations are continuous in both the cases, but the peak overshoot are

less in with EV.

6.3 Wind Energy System with Price Based AGC Operation

The nature of wind is unpredictable under weather conditions. In this section two area

restructured IPS with the thermal-thermal non-reheat unit has been considered with and

without wind power. The local load variations of 20% with and without wind power

fluctuations are considered.


94

6.3.1 Description of Two Area Restructured Power System

The network assumed for the analysis is presented in Fig.6.1. The power purchase

contract agreement of GENCOs and DISCOs is represented by DPM. The contract is

monitored by ISO. For the visualization of bilateral contract, the IPS of Gujarat has been

assumed and synthetically divided into Area1 and Area2 as shown in Fig. 6.7.

In Area1 and Area2 contains two numbers of GENCOs in their own area. Similarly, two

numbers of DISCOs are there in both areas. The power system is considered under

deregulated environment. The DPM represents the contract between GENCO and

DISCO. The wind turbine is considered to be connected in both area. Fig. 6.8 shows the

wind turbine model [286] with a hydraulic pitch actuator, and data fit pitch response. A

first-order system describes the dynamics of the wind power generating unit.

6.3.2 AGC Operation under Different Market Condition



1. POOLCO based Market

2. Bilateral Market

The operation of AGC is simulated by considering isolated two area power system. The

Base MVA of 2000 MVA and rated capacity of 2000 MW has been considered. The

system parameters of the isolated network are shown in Appendix-C. The Electrical

Vehicle parameters considered from Appendix A. The parameters for the wind

considered are as follows.

Wind Power Unit:

TP1= TP2=0.41 S, KP1=1.25, KP2=1.

6.3.2.1 Poolco Based Market

The load variation of 10% (200 MW) in area1 considered. The load deviation of 200 MW

in area1 is represented by 100 MW by GENCO1 and 100 MW by GENCO2. In this

transaction, GENCO3 and GENCO4 of area2 will not participate. It is represented

CHAPTER 6

95

GENCO-1

GENCO-2

GENCO-3

GENCO-4

Electric Vehicles

Electric Vehicles

Local

Load1

Power System1

Power System2

DIS

CO

1

DIS

CO

2

DIS

CO

3

DIS

CO

4

apf1

apf2

apf3

apf4

+

cpf11

cpf12

cpf13

cpf14

cpf21

cpf22

cpf23

cpf24

cpf31

cpf32

cpf33

cpf34

-

-

+

+

+

-

-

-

-

-

-

cpf31

cpf32

cpf33

cpf34

+

+

+

+

+

+

+

++

+ +

+ +

+ +

Wind Power

Local

Load1

+-

-

AREA1

AREA2

++

++

-

++

++

+

-

Wind Power

FIGURE 6.7 Block diagram representation two area restructured power system with wind turbine.

)1(

3

S

K P

+21

1

PST+ S

STK PP

+

+

1

)1(12

Hydraulic pitch actuator Data fit pitch

response

+

-

CONTROLLER

DROOP

FIGURE 6.8 Block diagram of Wind Power Plant.

by Fig. 6.9 (a) to (i) represents the results of fluctuations in power, frequency, tie line

flow and UI price because of the fluctuating temperament of solar power. For reduction

in the deviations, the grid-connected fleet of EV is considered. An EV helps to reduce the

deviations, which are presented in Fig. 6.10 (a) to (i). The calculated power deviation

data are presented in Table 6.4. Fig. 6.10 (e) to (f) represents the results of frequency

deviations, Fig. 6.10 (g) shows the result of tie-line flow and Fig. 6.10 (h) and (i) represent

the results of UI prices.


96




MW 100 100 0 0 0

(a) (a)

(b) (b)

(c) (c)

FIGURE 6.9 Impact of Wind Power on






0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

Time (S)

del P

g1 (

MW

)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

50

100

150

Time (S)

del P

g1 (

MW

))

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

Time (S)

del P

g2 (

MW

)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

50

100

150

Time (S)

del P

g2 (

MW

))

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 50-30

-20

-10

0

10

20

30

Time (S)

del P

g3 (

MW

)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 50-25

-20

-15

-10

-5

0

5

10

15

20

Time (S)

del P

g3 (

MW

))

Without EV

With EV

CHAPTER 6

97

(d) (d)

(e)

(e)

(f) (f)



Deviation,(f) Area2 Frequency Deviation.


(d)GENCO4 Power Deviation, (e) Area1 Frequency

Deviation,(f) Area2 Frequency Deviation.

0 5 10 15 20 25 30 35 40 45 50-30

-20

-10

0

10

20

30

Time (S)

del P

g4 (

MW

)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 50-25

-20

-15

-10

-5

0

5

10

15

20

Time (S)

del P

g4 (

MW

))

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 5049.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

50.25

Time (S)

Fre

quency A

rea1 (

Hz)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 5049.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

50.25

Time (S)

Fre

quency A

rea1 (

Hz)

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 5049.85

49.9

49.95

50

50.05

50.1

Time (S)

Fre

quency A

rea2 (

Hz)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 5049.85

49.9

49.95

50

50.05

50.1

Time (S)

Fre

quency A

rea2 (

Hz)

Without EV

With EV


98

(g) (g)

(h) (h)

(i) (i)







6.3.2.2 Bilateral Market

In this contract 20% (200 MW) load variation in area1 and area2 are considered. Fig.

6.11(a) to (i) represents the results of fluctuations in power, frequency, tie line flow and

0 5 10 15 20 25 30 35 40 45 50-50

-40

-30

-20

-10

0

10

20

30

Time (S)

Tie

Lin

e P

ow

er

(MW

)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 50-40

-30

-20

-10

0

10

20

30

Time (S)

Tie

Lin

e P

ow

er

(MW

)

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time (S)

UI

Price A

rea1 (

Rs/M

wh))

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time (S)

UI

Price A

rea1 (

Rs/M

wh)

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 500

500

1000

1500

2000

2500

3000

3500

4000

Time (S)

UI

Price A

rea2 (

Rs/M

wh))

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

500

1000

1500

2000

2500

3000

3500

4000

Time (S)

UI

Price A

rea2 (

Rs/M

wh)

Without EV

With EV

CHAPTER 6

99

UI price because of the fluctuating characteristics of the WT. The load deviation of 300

MW of area1 is represented by 105 MW by GENCO1 and 45 MW by GENCO2. The

power deviation of 300 MW of area2 is represented by 195 MW by GENCO1 and 55

MW by GENCO2. For the reduction in the deviations, the grid-connected fleet of EV is

considered. EV helps to reduce the deviations, which are presented in Fig. 6.12 (a) to (i).

The calculated power deviations values are presented in Table 6.5. Fig. 6.12 (e) to (f)

represents the results of frequency deviations, Fig. 6.12 (g) represents the result of tie-

line flow and Fig. 6.12 (h) and (i) represents the results of UI prices.

TABLE 6.5 Calculated Power At GENCO And Tie Line (Bilateral Transaction)



MW 105 45 195 55 -50

(a) (a)

(b) (b)


(a) GENCO1 Power Deviation, (b) GENCO2

Power Deviation


(a) GENCO1 Power Deviation, (b) GENCO2

Power Deviation.

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

180

Time (S)

del P

g1 (

MW

))

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

180

Time (S)

del P

g1 (

MW

))

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

Time (S)

del P

g2 (

MW

))

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

Time (S)

del P

g2 (

MW

))

Without EV

With EV


100

(c) (c)

(d) (d)

(e) (e)


(c) GENCO3 Power Deviation, (d) GENCO4

Power Deviation, (e) Area1 Frequency Deviation.


(c) GENCO3 Power Deviation, (d) GENCO4

Power Deviation, (e) Area1 Frequency Deviation.

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Time (S)

del P

g3 (

MW

))

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Time (S)

del P

g3 (

MW

))

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

Time (S)

del P

g4 (

MW

))

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

Time (S)

del P

g4 (

MW

))

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 5049.7

49.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

Time (S)

Fre

quency A

rea1 (

Hz))

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 5049.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

Time (S)

Frequency A

rea1 (H

z))

Without EV

With EV

CHAPTER 6

101

(f) (f)

(g) (g)

(h) (h)





(f) Area2 Frequency Deviation, g) Tie Line Power


0 5 10 15 20 25 30 35 40 45 5049.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

50.25

Time (S)

Fre

quency A

rea2 (

Hz)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 5049.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

50.25

Time (S)

Fre

quency A

rea2 (

Hz)

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 50-80

-70

-60

-50

-40

-30

-20

-10

0

Time (S)

Tie

Lin

e P

ow

er

(MW

)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 50-80

-70

-60

-50

-40

-30

-20

-10

0

Time (S)

Tie

Lin

e P

ow

er

(MW

)

Without EV

With EV

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

Time (S)

UI

Price A

rea1 (

Rs/M

wh)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time (S)

UI

Price A

rea1 (

Rs/M

wh)

Without EV

With EV


102

(i) (i)





TABLE 6.6 Comparative Analysis of WT and EV integration

Case

Condition





1

Without WT

17 17 15 15 50.2 0 50.09 0 49.77 6500 49.93 3400 13 13 11 11

With WT 25 25 20 20 50.23 0 50.09 0 49.77 6500 49.92 3600 16 16 16 16

With EV 8 8 8 8 50.1 0 50.02 0 49.84 4900 49.98 2200 6 6 6 6

2

Without WT

11 11 13 13 50.15 0 50.2 0 49.75 7400 49.8 6300 9 9 9 9

With WT 15 15 15 15 50.14 0 50.24 0 49.74 7300 49.8 6300 12 12 11 11

With EV 10 10 11 11 50.05 0 50.1 0 49.86 5000 49.84 4900 5 5 5 5


Poolco based Market

In case of the power system operating under Poolco based market with WT, the Area1

frequency operating range is 50.23 - 49.77 Hz , the UI price operating range is 0 - 6500

Rs/Mwh respectively , no of oscillations in the frequency are 16 and frequency settling

time is 25 s. In case of grid connected EV the frequency operating range reduces to

50.01 - 49.84 Hz, the UI price operating range reduces to 0 - 4900 Rs/Mwh, the no of

oscillation in the frequency are reduces 6 and frequency settling time reduces to 8 s.

Similarly, in Area2 with WT the frequency operating range 50.09 - 49.92 Hz, the UI price

operating range 0 - 3600 Rs/Mwh, no of oscillations are 16 and frequency settling time

is 20 s. In case of grid connected EV the frequency operating range reduces to 50.02 -

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time (S)

UI

Price A

rea2 (

Rs/M

wh)

Without WT

With WT

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time (S)

UI

Price A

rea2 (

Rs/M

wh)

Without EV

With EV

CHAPTER 6

103

49.98 Hz, the UI price reduces to 0 - 2200 Rs/Mwh, the no of oscillations are reduces to

8 and frequency settling time reduces to 6 s. By comparing the results of with WT and

with EV from the simulation and Table 6.6, better results achieved with EV for reduction

in an operating range of frequency, operating range of UI charges, frequency settlement

time and frequency oscillation.

Bilateral Market

In case of the power system operating under Bilateral market with WT, the Area1

frequency operating range is 50.14 - 49.74 Hz , the UI price operating range is 0 - 7300

Rs/Mwh, no of oscillations in the frequency are 12 and frequency settling time is 15 s. In

case of grid connected EV the frequency operating range reduces to 50.01 - 49.86 Hz,

the UI price operating range reduces to 0 - 5000 Rs/Mwh, the no of oscillation in the

frequency are reduces to 5 and frequency settling time reduces to 10 s. Similarly, in Area2

with WT the frequency operating range 50.24 - 49.8 Hz, the UI price operating range 0 -

6300 Rs/Mwh, no of oscillations are 11 and frequency settling time is 15 s. In case of

grid connected EV the frequency operating range reduces to 50.1 - 49.84 Hz, the UI price

reduces to 0 - 4900 Rs/Mwh, the no of oscillations are reduces to 5 and frequency settling

time reduces to 11 s. By comparing the results of with WT and with EV from the

simulation and Table 6.6, better results achieved with EV for reduction in an operating

range of frequency, operating range of UI charges, frequency settlement time and

frequency oscillation.

6.4 Conclusion

In the restructured power network, a number of generators running with AGC loop to

balance generation-load in all conditions. In the bilateral as well as poolco based market,

generators balance the frequency by complex AGC loop. Presently, in the smart-grid, the

power feeding by RESs such as wind turbine is uneven. It makes unbalance between

supply and demand. So, uneven load as well as RESs power creates more fluctuations

into the frequency and power. To short-out the problem of fluctuations in the frequency

and power, the quick responding storage is required. The RES can participate in day

ahead electricity market by adopting stationary energy source such as battery. In poolco

based market and bilateral market, the frequency settling time as well as numbers of


104

oscillation are less in case of grid connected EV as compared to without EV. So, the fast-

responding storage as EV battery is a best option.

The frequency, generator power, tie-line flow and UI charges deviations because of

sudden local load deviation, PV power fluctuations, as well as WT power fluctuation, are

presented in the simulation under POOLCO based and bilateral contract market. From

the study, it is realized that EVs can settle the deviations in the frequency, power as well

as UI price. The grid-connected fleet of electrical vehicles, presented in the simulation

helps to decrease the peak magnitude of the overshoots and time duration of frequency

oscillations, power deviations and UI rates.

105

CHAPTER 7

Price Based Automatic Generation Control Using

Fuzzy-Based Grid Connected EVs

7.1 Introduction

The grid connected fleet of EV is a better option as a fast responding device against

frequency deviations. Normally, to charge and discharge the EV battery, PI controllers

are used to tracking the load continuously. But due to nonlinearity and uncertainty of

load, performance of PI controller becomes poor. So, fuzzy-based PI controller can be

used. It can track better as compared to PI controller to reduce the frequency deviations

and settling time too.

The fuzzy logic theory depends on the experience from which people can take decisions.

The details of the fuzzy logic are given Appendix E. An application of FG controller to

adjust the PI controller gain is presented in this chapter. The restructured power system

in isolated mode with the thermal- hydro and thermal-thermal non-reheat system is

considered for AGC operation. The DPM has been used for making the contract between

DISCOs and GENCOs under the restructured environment. Dynamic study of frequency

deviations in the power system by load fluctuation under Poolco based market as well as

Bilateral markets with its violation conditions has been carried out and simulated. Finally,

the fleet of a electrical vehicle (EVs) as large battery energy storage has been proposed

due to its fast response against load variation like a generator-turbine unit of the thermal

power plant.

7.2 Description of the Model

Two area Automatic generation control (AGC) strategy using an electric vehicle as grid-

connected distributed energy storage has been proposed under the deregulated power

system along with a solar as grid-connected renewable energy source (Fig.6.1). The DPM

Price Based Automatic Generation Control Using Fuzzy-Based Grid Connected EVs

106

is used for AGC operation under Poolco based and bilateral contract. The uncertain nature

of RES and load increases the fluctuations in the frequency. An EV model is proposed.

The operation for bidirectional charging controller followed by the frequency

characteristic has been simulated using the PI controller and FGPI controller.

7.3 EV Battery Charging Controller

PI controllers can track the changes to get the optimal performances. But, a PI controller

cannot provide the expected results against change in the operating conditions. Intrinsic

complication or nonlinearity or improbability of load, which is unexpectedly in the power

system, causes frequency deviations. The PI controllers cannot provide the preferred

presentation, so due to the flexible and simple operation of Fuzzy logic; the gains of PI

controllers have been tuned. The logic of FGPI controller is presented in Fig. 7.1.

Fuzzy

Rules

ACE

dACE

pK

iK

NB NS Z PB PS

NB NS Z PB PS

S M B

S M B

FIGURE 7.1 Block diagram of a fuzzy-based tuned PI controller

The FG rules and MF of fuzzy control are shown in Table 7.1. The “Triangular” MF is

used. There are two inputs and two outputs. The ACE and 𝛥ACE as inputs and PI

controller gain KP and KI as outputs. To map the inputs as ACE and 𝛥ACE, a total of 25

numbers of fuzzy rules are defined as shown in Table 7.1.

Rule: If the first input, ACE is NB and 𝛥ACE is NB then output is S.

Table 7.1 presents the Rules of fuzzy logic to tune KP and KI.

The conventional controller is used for secondary frequency control. Based on pre-

specified values, the PI controller is normally used in practice. This controller can follow

the load constantly and give optimal performance. The PI cannot provide the expected

results against change in the operating conditions [59]. So, for tuning of PI controller

gain such as 𝐾𝑝 and𝐾𝐼, an intelligent fuzzy logic is suitable [3][5] as shown in Fig 7.1.

TABLE 7.1 Fuzzy logic Rules

CHAPTER 7

107

Input1 ACE

Input2 NB NS Z PS PB

𝜟𝑨𝑪𝑬

NB S S M M B

NS S M M B VB

Z M B B VB VB

PS B B VB VB VB

PB B VB VB VVB VVB

7.4 Intelligent Price Based AGC Operation under The Different

Market

The operation AGC is simulated under three different contract as a case study by

considering isolated two area power system of Base MVA of 2000 MVA and capacity

of 2000 MW. The parameters of the isolated network are presented in Appendix-B.

Case 1: POOLCO based Market

Case 2: Bilateral Market

Case 3: Bilateral Market with a contract violation

7.4.1 Poolco Based Market

In this market, the load change in area 1 only considered. Participation of GECNOs is

presented by apf as follows. Here, the apf1=0.5, apf2=0.5, apf3=0.5 and apf4=0.5 are

considered. Here, only DISCO1 and DISCO2 demanding the load. DISCO3 and DISOC4

do not claim the exchange of power from any generating company. The DPM presented

in (7.1) used for the simulation.

𝑫𝑷𝑴 = [

𝟎. 𝟓 𝟎. 𝟓 𝟎 𝟎𝟎. 𝟓 𝟎. 𝟓 𝟎 𝟎𝟎 𝟎 𝟎 𝟎𝟎 𝟎 𝟎 𝟎

]

(7.1)

Sudden load variation of 200 MW in area1 has only been considered for the visualization

of AGC operation and dynamics. Dynamic behaviour in case of unexpected load changes

by a comparative analysis of the PI controller and Fuzzy PI Controller (FPC) of EV

bidirectional charger represented in Fig. 7.4 (a) to (i). The peak overshoots and deviations

in the power, tie line, frequency and UI price has been reduced by FPC of grid-connected

EV. In Fig. 7.2 (a) to (d) deviations in the power of GENCOs are shown. In Fig. 7.2 (e)

and (f), since area1 have nonzero load demand, the momentary rise and fall of area1


108

frequency is larger than in area2. The deviations in the UI price (Rs/Mwh) are presented

in Fig. 7.2 (g) and (h). The tie-line power is zero due to no contract exists among the

GENCOs and the DISCOs of the different area. It is presented in Fig.7.2 (i). The

calculated values of generator power have been shown in Table 7.2.




MW 100 100 0 0 0

(a)

(b)

FIGURE 7.2 Comparative results of PI and Fuzzy PI controller in Poolco Based Transaction for (a)

GENCO1 Power Deviations of Area1, (b) GENCO2 Power Deviations of Area2

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

Time(s)

GE

NC

O1 P

ow

er

(MW

))

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

Time(s)

GE

NC

O2

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller

CHAPTER 7

109

(c)

(d)

(e)

FIGURE 7.2 Comparative results of PI and Fuzzy PI controller in Poolco Based Transaction for (c)

GENCO2 Power Deviations of Area2, (d) GENCO2 Power Deviations of Area1, (e) Frequency


0 5 10 15 20 25 30 35 40 45 50-15

-10

-5

0

5

10

Time(s)

GE

NC

O3

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 50-15

-10

-5

0

5

10

Time(s)

GE

NC

O4

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 5049.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

50.25

Time(s)

Are

a1 F

requ

ency

(H

z))

PI Controller

Fuzzy PI Controller


110

(f)

(g)

(h)

FIGURE 7.2 Comparative results of PI and Fuzzy PI controller in Poolco Based Transaction for

(f) Frequency Deviations of Area2, (g) UI Price Deviations of Area1, (h) UI Price Deviations of Area2.

0 5 10 15 20 25 30 35 40 45 5049.95

49.96

49.97

49.98

49.99

50

50.01

50.02

50.03

Time(s)

Are

a2 F

requ

ency

(H

z))

PI Controler

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time(s)

Are

a1 U

I P

rice

(Rs/

Mw

h))

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 501200

1400

1600

1800

2000

2200

2400

2600

2800

Time(s)

Are

a2 U

I P

rice

(Rs/

Mw

h))

PI Controller

Fuzzy PI Controller

CHAPTER 7

111

(i)

FIGURE 7.2 Comparative results of PI and Fuzzy PI controller in Poolco Based Transaction for (i) Tie-

Line Flow.

7.4.2 Bilateral Market

The contract may get violated by a DISCO due to excess power demand. This excess

power is uncontracted with any GENCO. The GENCO has to supply the uncontracted

power of the same area. For visualization of a contract violation, an excess power of 10%

of demand by DISCO in area2 is considered. It is replicated in the area as a local load. It

is not the contract demand. So, there is no change in DPM elements. For the bilateral

transaction, the DPM is presented in (7.2). Here, the apf1=0.5, apf2=0.5, apf3=0.5 and

apf4=0.5 are considered.

𝐷𝑃𝑀 = [

0.5 0.25 0 0.30.2 0.25 0 00 0.25 1 0.70.3 0.25 0 0

] (7.2)

The load changes of 200 MW occurring in area1 and area2 is asuimed. Dynamic

behaviour in case of unexpected load changes by a comparative analysis of the PI

controller and Fuzzy PI Controller (FPC) of EV bidirectional charger is represented in

Fig. 7.3 (a) to (i). In Fig. 7.3 (a) to (d) the deviations in the GENCOs are shown. In Fig.

7.3 (e) and (f), since area1 have nonzero load demand, so momentary rise and fall of

frequency in area1 are larger than in area2. The deviations in the UI price (Rs/Mwh) are

presented in Fig. 7.3 (g) and (h). The tie-line flow is presented in Fig.7.3 (i). The

calculated values of generator power are presented in Table 7.3.

0 5 10 15 20 25 30 35 40 45 50-25

-20

-15

-10

-5

0

5

10

Time (s)

Tie

Lin

e F

low

(M

W)

PI Controller

Fuzzy PI Controller


112

TABLE 7.3 Calculated Power At GENCOs And Tie Line (Bilateral Transaction)



MW 105 45 195 55 -50

(a)

(b)

(c)

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

Time(s)

GE

NC

O1

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 500

10

20

30

40

50

60

70

80

Time(s)

GE

NC

O2

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Time(s)

GE

NC

O3

Pow

er (

MW

))

CHAPTER 7

113

FIGURE 7.3 Comparative results of PI and Fuzzy PI controller in Bilateral Transaction for (a) GENCO1

Power Deviations of Area1, (b) GENCO2 Power Deviations of Area1. (c) GENCO2 Power Deviations

of Area2.

(d)

(e)

(f)

FIGURE 7.3 Comparative results of PI and Fuzzy PI controller in Bilateral Transaction for (d)

GENCO2 Power Deviations of Area2, (e) Frequency Deviations of Area1, (f) Frequency Deviations of

Area2.

0 5 10 15 20 25 30 35 40 45 500

10

20

30

40

50

60

70

80

Time(s)

GE

NC

O4

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 5049.8

49.85

49.9

49.95

50

50.05

Time(s)

Are

a1 F

requ

ency

(Hz)

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 5049.8

49.85

49.9

49.95

50

50.05

50.1

50.15

50.2

Time(s)

Are

a2 F

requ

ency

(Hz)

PI Controller

Fuzzy PI Controller


114

(g)

(h)

(i)

FIGURE 7.3 Comparative results of PI and Fuzzy PI controller in Bilateral Transaction for (g) UI Price

Deviations of Area1, (h) UI Price Deviations of Area2. (i) Tie-Line Flow.

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

Time(s)

Are

a1 U

I P

rice(

Rs/

Mw

h)

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

Time(s)

Are

a2 U

I P

rice(

Rs/

Mw

h)

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 50-70

-60

-50

-40

-30

-20

-10

0

Time (s)

Tie

Lin

e F

low

(M

W)

PI Controller

Fuzzy PI Controller

CHAPTER 7

115

7.4.3 Bilateral Transaction with a Contract Violation



of the same area. For visualization of a contract violation, Case 2 is considered with

excess power of 100 MW demanded by DISCO in Area2. It is indicated as a local load.

It is not the contract demand. So, there is no change in DPM elements. An overall load

deviation in the area I,(𝛥𝑃𝐿1,𝐿𝑂𝐶) = Load of DISCO1+ load of DISCO2= (100+100)+100

= 300 MW. Likewise, the total local load in area II = (DISCO3 + DISCO4) = 200 MW.

For EV bidirectional charger PI and FGPI controllers are considered for comparative

analysis. The comparative analysis by applying unexpected load is presented in Fig. 7.4(a)

to (i). Fig. 7.4 (a) to (d) presents the deviations in the GENCOs power. In Fig. 7.4 (e) and

(f), only area1 have nonzero load demand, so momentary rise and fall in area1 frequency

are larger than in area2. The deviations in the UI price (Rs/Mwh) are presented in Fig.

7.4 (g) and (h). There is no contract among the GENCOs and the DISCOs of the different

areas. So, the tie-line power is zero. The actual tie-line power is presented in Fig.7.4 (i).

Calculated values of generator power have been shown in Table 7.4.

Table 7.4 Calculated Power At GENCOs And Tie Line (Contract Violation)


Line

Unit G-1 G-2 G-3 G-4

MW 180 70 195 55 -50

(a)

FIGURE 7.4 Comparative results of PI and Fuzzy PI controller in Bilateral Transaction with contract

violation for (a) GENCO1 Power Deviations of Area1.

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Time(s)

GE

NC

O1

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller


116

(b)

(c)

(d)


violation for , (b) GENCO2 Power Deviations of Area1, (c) GENCO2 Power Deviations of Area2, (d)

GENCO2 Power Deviations of Area2.

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

160

180

Time(s)

GE

NC

O2

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Time(s)

GE

NC

O3

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

140

Time(s)

GE

NC

O4

Pow

er (

MW

))

PI Controller

Fuzzy PI Controller

CHAPTER 7

117

(e)

(f)

(g)


violation for (e) Frequency Deviations of Area1, (f) Frequency Deviations of Area2, (g) UI Price


0 5 10 15 20 25 30 35 40 45 5049.5

49.6

49.7

49.8

49.9

50

50.1

50.2

50.3

Time(s)

Are

a1 F

requ

ency

(Hz)

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 5049.75

49.8

49.85

49.9

49.95

50

50.05

50.1

50.15

Time(s)

Are

a2 F

requ

ency

(Hz)

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

8000

9000

Time(s)

Are

a1 U

I P

rice(

Rs/

Mw

h)

PI Controller

Fuzzy PI Controller


118

(h)

(i)


violation for (h) UI Price Deviations of Area2, (i) Tie-Line Flow (MW).

TABLE 7.5 Comparative Analysis of PI and FGPI integration

Cas

e

Controlle

r



Area1 Area2 Area1 Area2 Area1 Area2 Area

1

Area

2

F U

I F

U

I F

U

I F

U

I F UI F UI F UI F UI

1

PI 11 11 11 11 50.2

4 0

50.00

7 0

49.7

7

650

0

49.9

6

260

0 7 7 7 7

FGPI 5 5 10 10 50.0

8 0

50.00

1 0

49.8

5

500

0

49.9

8

220

0 5 5 6 6

2

PI 13 13 14 14 50.0

6 0 50.11 0

49.8

3

530

0

49.8

4

530

0 6 6 5 5

FGPI 10 10 10 10 50.0

5 0 50.05 0

49.8

3

530

0

49.8

6

500

0 5 5 4 4

3

PI 14 14 15 15 50.2 0 50.12 0 49.5

8 800

0 49.7

7 650

0 8 8 7 7

FGPI 12 12 10 10 50.0

5 0 50.07 0 49.7

780

0

49.8

5

490

0 8 8 4 4

0 5 10 15 20 25 30 35 40 45 500

1000

2000

3000

4000

5000

6000

7000

Time(s)

Are

a2 U

I P

rice(

Rs/

Mw

h)

PI Controller

Fuzzy PI Controller

0 5 10 15 20 25 30 35 40 45 50-80

-70

-60

-50

-40

-30

-20

-10

0

Time (s)

Tie

Lin

e F

low

(M

W)

PI Controller

Fuzzy PI Controller

CHAPTER 7

119


Poolco Based Market

In case of PI controller for the power system operating under poolco based market, the

Area1 frequency operating range is 50.24 - 49.77 Hz (Fig. 7.2(e)) , the UI price operating

range is 0 - 6500 Rs/Mwh (Fig. 7.2(g)), no of oscillations in the frequency are 7 (Fig.

7.2(e)) and frequency settling time is 11 s (Fig. 7.2(e)). By using FGPI controller the

frequency operating range reduces to 50.08 - 49.98 Hz (Fig. 7.2(e)), the UI price

operating range reduces to 0 - 5000 Rs/Mwh (Fig. 7.2(g)), the no of oscillation in the

frequency are reduces 5 and frequency settling time reduces to 5 s (Fig. 7.2(e)). Similarly,

in Area2 with PI controller the frequency operating range 50.007 - 49.96 Hz (Fig. 7.2(f)),

the UI price operating range 0 - 2600 Rs/Mwh (Fig. 7.2(h)), no of oscillations are 7 (Fig.

7.2(f)) and frequency settling time is 11 s (Fig. 7.2(f)). By using FGPI controller the

frequency operating range reduces to 50.001 - 49.98 Hz (Fig. 7.2(f)), the UI price reduces

to 0 - 2200 Rs/Mwh (Fig. 7.2(h)), the no of oscillations are reduces to 10 (Fig. 7.2(f))

and frequency settling time reduces to 6 s (Fig.7.2(f)). By comparing the results of with

WT and with EV from the simulation and Table 7.5, better results achieved with EV for

reduction in an operating range of frequency, operating range of UI charges, frequency

settlement time and frequency oscillation.

Bilateral Market

In case of PI controller for the power system operating under bilateral market the Area1

frequency operating range is 50.06 - 49.83 Hz (Fig. 7.3(e)), the UI price operating range

is 0 - 5300 Rs/Mwh (Fig. 7.2(g)), no of oscillations are 13 (Fig. 7.3(e)) and frequency

settling time is 6 s (Fig. 7.3(e)). By using FGPI controller the frequency operating range

reduces to 50.04 - 49.86 Hz (Fig. 7.3(e)), the UI price operating range reduces 0 - 5000

Rs/Mwh (Fig. 7.3(g)), the no of oscillation in the frequency are reduces to 10 (Fig. 7.3(e))

and frequency settling time reduces to 5 s (Fig. 7.3(e)). Similarly, in Area2 with PI

controller the frequency operating range 50.007 - 49.96 Hz (Fig. 7.3(f)), the UI price

operating range 0 - 2600 Rs/Mwh (Fig. 7.3(h)), no of oscillations are 7 (Fig. 7.3(f)) and

frequency settling time is 11 s (Fig. 7.3(f)). By using FGPI controller the frequency

operating range reduces to 50.001 - 49.98 Hz (Fig. 7.3(f)), the UI price reduces to 0 -

2200 Rs/Mwh (Fig. 7.3(h)), the frequency settling time reduces to 10 (Fig. 7.3(f)) and


120

frequency settling time reduces to 6 s (Fig. 7.3(f)). By comparing the results of with WT

and with EV from the simulation and Table 7.5, better results achieved with EV for

reduction in an operating range of frequency, operating range of UI charges, frequency

settlement time and frequency oscillation.

Bilateral Market with Contract Violation

In case of PI controller for the power system operating under bilateral market with

contract violation, the Area1 frequency operating range is 50.4 - 49.58 Hz (Fig. 7.4(e)),

the UI price operating range is 0 – 8000 Rs/Mwh (Fig. 7.4(g)), no of oscillations are 8

(Fig. 7.4(e)) and frequency settling time is 14 s (Fig. 7.4(e)). By using FGPI controller

frequency operating range reduces to 50.02 - 49.58 Hz (Fig. 7.4(e)), the UI price

operating range reduces 0 - 7800 Rs/Mwh (Fig. 7.4(g)), the no of oscillation in the

frequency are reduces to 8 (Fig. 7.4(e)) and frequency settling time reduces to 5 s (Fig.

7.4(e)). Similarly, in Area2 with PI controller the frequency operating range 50.12 - 49.77

Hz (Fig. 7.4(f)), the UI price operating range 0 - 6500 Rs/Mwh (Fig. 7.4(h)), no of

oscillations are 7 (Fig. 7.4(f)) and frequency settling time is 15 s (Fig. 7.4(f)). By using

FGPI controller the frequency operating range reduces to 50.07 - 49.85 Hz (Fig. 7.4(f)),

the UI price reduces to 0 - 4900 Rs/Mwh (Fig. 7.4(h)), the no of oscillations are 4 (Fig.

7.4(f)) and frequency settling time reduces to 10 s (Fig. 7.4(f)). By comparing the results

of with WT and with EV from the simulation and Table 7.5, better results achieved with

EV for reduction in an operating range of frequency, operating range of UI charges,

frequency settlement time and frequency oscillation.

7.5 Conclusion

The bidirectional contract exists in restructured power system among the DISCOs and

GENCOs of different control areas. In the present IPS, the nature of RES is intermittent

in nature. It produces power fluctuations that add to the deviations caused by the load.

The deviations in the load result in frequency becoming unstable. From the study of

different kinds of literature, the power system needs storage for the stability of frequency.

The design of bidirectional charging control for EV to stabilize the frequency fluctuation

is proposed. Also, grid-connected RES is considered. By sending the LFC signal to all

EVs, the operator is able to manage the charging operation by taking care of the battery

CHAPTER 7

121

SOC level. EV can normalize the deviations in the frequency, generator power, tie-line

flow and UI price. An EV has tremendous advantage that it can work as a load and source.

For the operation of bidirectional charger, PI controllers are used. The PI controllers can

track the system changes. But against various working conditions of the grid, PI

controllers are not able to give optimal performance. Further efforts have been put to tune

PI parameters using fuzzy logic and comparative results have been obtained with PI and

with the fuzzy PI controller. From the results it is concluded that FGPI controller provided

better performance as compared to the PI controller.

122

CHAPTER 8

Summary, Conclusion and Future Scope

This chapter summarizes the work reported in this report, the major contribution and

some important conclusions. The scope for future work is also presented at the end of the

chapter.

8.1 The Original Contribution of the Thesis

The main contribution of the thesis is as follows,

1. Developed a mathematical model of two area restructured IPS by considering

thermal-thermal (non-reheat) and grid-connected wind turbine unit. It is simulated

under the different contracts between generating companies and distribution

companies, such as POOLCO based contract and Bilateral contract. The effect of 10

% load variation has also been studied.

2. Developed frequency to UI price block which will convert the frequency deviation

into the price. From the UI price calculated and from the marginal cost of generation,

GCE is calculated. Based on GCE, price based AGC) is performed.

3. Developed a mathematical model of grid-connected Electrical Vehicles (EVs) with

bidirectional charger. The bidirectional charger is applied to reduce the deviations in

frequency, amount of generator power and UI price. Moreover, the concept of LFC

signal is proposed to disperse all EVs by diversified transmission link through

aggregators followed by Area Requirement (AR).

4. The proposed method of aggregated EVs to regulate frequency and power deviation

is tested under Poolco based contract and Bilateral contract for peak hours and off-

peak hours. This work has shown that using this technique frequency and power

deviations are minimized.

CHAPTER 8

123

8.2 Conclusion

Based on the research work carried out it is observed that, sudden load variations and

variation in renewable power generation produce power imbalances in demand and

supply. This results in frequency deviations. This research work has resulted in a

unique solution that has minimized the deviation in the frequency as well as UI price

by load variation and renewable power generation. The dynamic storage (EV)

mechanism suggested in this research has given better solution to normalize the

frequency fluctuations in restructured IPS with the help of bidirectional charge

controller. For this analysis, a fleet of EVs has been considered under POOLCO based

contract and Bilateral contract during peak hours and off peak hours.

In the power system without RESs (Chapter 5), the improvement in the frequency

and UI price deviations due to a fleet of EVs are presented in Fig. 8.1.

FIGURE 8.1 Improved Results in Case of Without RESs.

In the power system with presence of PV system (Chapter 6), the improvement in the

frequency and UI price deviations due to a fleet of EVs are presented in Fig. 8.2

46.51

23.08

77.78

13.46

51.16

27.78

41.86

8.16

22.06

0

76.47

24.62

0

10

20

30

40

50

60

70

80

90

Frequency UI Charges Frequency UI Charges

Area 1 Area 2

Imp

rove

me

nt

in %

Pooclo Based Market

Bilateral Market

Bilateral Market withContract Violation

Summary, Conclusion and Future Scope

124

FIGURE 8.2 Improved Results in Case of With PV system.

In the power system with presence of WT (Chapter 6), the improvement in the

frequency and UI price deviations due to a fleet of EVs are presented in Fig. 8.3

FIGURE 8.3 Improved Results in Case of With WT.

In the power system without RESs (Chapter 7), the improvement in the frequency

and UI price deviations due to a fleet of EVs with FGPI controller are presented in

Fig. 8.4.

50

21.25

71.43

52.83

62.9

25 23.07 22.41

0

10

20

30

40

50

60

70

80


Area 1 Area 2

Imp

rove

me

nt

in %

Pooclo Based Market

Bilateral Market

43.48

24.62

76.47

38.89

52.5

31.51

40.91

22.22

0

10

20

30

40

50

60

70

80

90


Area 1 Area 2

Imp

rove

me

nt

in %

Pooclo Based Market

Bilateral Market

CHAPTER 8

125

FIGURE 8.4 Improved Results using FGPI Controller

Thus it has been found that the technique developed here lowers down the frequency

and power deviations as well as fluctuations in UI price.

8.3 Future Scope

In this thesis different method at generation, transmission and distribution area are

represented. These methods regulate the frequency to operate near 50 Hz and try to

minimize deviations and generation cost due to sudden load variation as well as power

of RESs. Some of the scopes for further investigations are as follows.

1. The research work has considered of the EV bidirectional charger development

for V2G and G2V operation in India. By developing the Battery Management

System (BMS), the bidirectional power transfer is possible after following the

battery SOC condition. The BMS also help to coordinate the number of EVs.

2. Because of high tariff rates in peak hours and low tariff rates during off-peak

hours, one can perform the financial analysis on charging and discharging of EV

with infrastructure development.

51.06

23.08

55.32

15.38

4.350

29.63

5.66

60.98

2.5

37.14

24.62

0

10

20

30

40

50

60

70


Area 1 Area 2

Imp

rove

me

nt

in %

Pooclo Based Market

Bilateral Market

Bilateral Market withContract Violation

126

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List of Publications

A. Publications based on research work done so far are as follows :

1. YR Prajapati and V N Kamat, “Secondary frequency regulation / Automatic

Generation Control under Deregulated Power System along with renewable

energy sources using Electric Vehicle/ Distributed Energy Storage Systems”,

IEEE International Conference on Electrical, Electronics, and Optimization

Techniques (ICEEOT),2016.

2. Y R Prajapati, V N Kamat and J J Patel “Impact of Grid Connected Solar Power

on Load Frequency Control In Restructured Power System”, i-PACT IEEE intern.

Conf.2017 at VIT, Vellore University, Tamilnadu, 2017.

3. YR Prajapati, V N Kamat and J J Patel “Price Based Automatic Generation

Control (AGC) in a restructured power system by considering peak hours and off-

peak”, Wulfenia Journal, Klagenfurt, Austria, ISSN- 1561 882X, vol. 25, No.3,

March 2018.

4. YR Prajapati, V N Kamat and J J Patel “Impact of grid-connected wind power on

frequency regulation in restructured power system”, International Conference

APGRES 2019, Banswara, RTU, Available on ELSEVIER-SSRN, 2019.

5. Yogesh R Prajapati, Vithal N Kamat and Jatinkumar J Patel, “Automatic

Generation Control in a Smart Grid using Electrical vehicle as a Battery Energy

Storage System”, IJRTE, vol.8 ISS.4 pp. 2390-2395, November 2019.

6. Yogesh R Prajapati, Vithal N Kamat, Jatinkumar J Patel and Rahul Kher,” A

Comprehensive Survey on Use of Soft Computing and Optimization Techniques

for Load Frequency Control”, JEEE, vol.8(2), pp.64-70, 2020.

7. Yogesh R Prajapati, Vithal N Kamat and Jatinkumar J Patel, “ Load Frequency

Control Under Restructured Power System Using Electrical Vehicle as

Distributed Energy Source”, IE(I) Series B, vol.101, pp.379–387, 2020.

8. Yogesh R Prajapati, Jatinkumar J Patel and Vithal N Kamat, “Automatic

Generation Control (AGC) operation in a restructured power system under

List of Publications

157

Availability Based Tariff (ABT) mechanism by considering peak hours and off-

peak hours”, IJST, vol.13(33), pp.3400-3408, 2020.

List of Appendices

158

Appendix-A

Challenges in the Indian Power Sector

Fig. A.1 shows the total generation capacity within India from the central, state and private

sectors. The power generating ability of the private sector is more as compared to the state

and central sector. The sharing of power between state and central sector are near to each

other.

FIGURE A.1 Total Generation within India (as on 31st August 2019)

Fig. A.2 presents the sharing of power generation from different fossil fuels. Major power

generation from coal consumption is more as compared to other sources which are around

54.3%. Because of limited water reservoir and storage, the hydro generation is limited.

Nowadays the government is encouraged to enhance the use of RES due to limited stock of

fossil fuels and environmental issues such as global warming and climate changes. The

0

20,000

40,000

60,000

80,000

1,00,000

1,20,000

1,40,000

1,60,000

1,80,000

Central Sector State Sector Private Sector

MW

Total Generation 3,60,456 MW

28.5%

46.5%

25%

List of Appendices

159

following Fig.A.3 represents the increase in the consumption of coal in India from 281 MT

in 2005 to 608 mT in 2018.

FIGURE A.2 Power Generation from Different Sources (as on 31st August 2019)

FIGURE A.3 Coal Consumption in India (as on 31st August 2019)

0

50000

100000

150000

200000

250000

Coal Lignite Gas Diesel Hydro Nuclear RES

Po

we

r G

en

era

tio

n (

MW

)54.3%

1.7%6.9%

0.2%

12.6%

1.9%

22.0%

0

100

200

300

400

500

600

700

Co

al C

on

sum

pti

on

(M

T)

Year

List of Appendices

160

There is a drastic increase in demand as shown in Fig. A.4. So, the per capita energy

consumption increased from 631.4 kWh in 2005 to 1149 kWh in 2018.

FIGURE A.4 Per Capita Energy Consumption, Kwh (as on 31st August 2019)

0

200

400

600

800

1000

1200

1400P

er

Cap

ita

Ene

rgy

Co

nsu

mp

tio

n (

kWh

)

Year

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

Pe

ak D

em

and

(M

W)

Year

Peak Demand

Peak Met

List of Appendices

161

FIGURE A.5 Peak Demand in India, MW (as on 31st August 2019)

FIGURE A.6 Peak Demand Deficits in India, (as on 31st August 2019)

Similarly, Energy requirement and availability are increased from 2009 to 2019 which is

shown in Fig.A.7. Also, because of development in the area of power generation, the deficits

reduce, as shown in Fig.A.8.

-14

-12

-10

-8

-6

-4

-2

0P

eak

De

man

d D

efi

cts

%

Year

Deficts

Deficts

0

200000

400000

600000

800000

1000000

1200000

1400000

Ene

rgy

(MU

)

Year

Requirement

Availability

List of Appendices

162

FIGURE A.7 Energy requirement and Availability in India (MU)

FIGURE A.8 Energy Deficits in India (%)

FIGURE A.9 Transmission and Distribution Losses

The graphical view of transmission and distribution losses is presented in Fig.A.9. It can be

observed that the losses are reduced every year.

-12

-10

-8

-6

-4

-2

0

Ene

rgy

(MU

%)

Year

Deficts

23.0422.84 22.77

21.81

21.42

20.5

21

21.5

22

22.5

23

23.5

2012-13 2013-14 2014-15 2015-16 2016-17

T &

D L

oss

es

(%)

Year

List of Appendices

163

Appendix-B

Mathematical modelling of AGC

The mathematical modelling for the primary and secondary loops of AGC is represented in

Fig. B.1

1

1

+sTgi 1

1

+sTti

-+

1

1

R

1+sT

K

pi

pi f

s

K i govP gP

LP

eP vP +-f

f

Governor Hydraulics GeneratorTurbine− SystemPower

LoopControlimaryPr

LoopControl

Secondary

Figure B.1 Mathematical modelling of AGC loops

1. Primary Control Loop

In Fig.B.1 the primary loop to control frequency is presented. It contains the governor

transfer functions, hydraulic valve and turbine-generator unit with the dynamics of the power

system.

The governor dynamics is represented by (B.1)

𝛥𝑃𝑔𝑜𝑣(𝑠) = 𝛥𝑃𝑒(𝑠) −1

𝑅𝛥𝑓(𝑠) (B.1)

The action taken by the hydraulic valve is presented by (B.2)

𝛥𝑃𝑉(𝑠) =1

1 + 𝑠𝑇ℎ𝛥𝑃𝑔𝑜𝑣(𝑠)

(B.2)

The turbine-generator response is represented by (B.3)

𝛥𝑃𝑔(𝑠) =1

1 + 𝑠𝑇𝑡𝛥𝑃𝑉(𝑠)

(B.3)

The deviations in the frequency due to unbalance between generation and demand is

represented by (B.4)

List of Appendices

164

𝛥𝑓(𝑠) =𝐾𝑝

1 + 𝑠𝑇𝑝[𝛥𝑃𝑔(𝑠) − 𝛥𝑃𝐿(𝑠)]

(B.4)

Where 𝐾𝑝 =1

𝐷 and𝑇𝑝 =

2𝐻

𝑓0𝐷.

The primary loop response for frequency control by considering the transfer function is as

follows (B.5). It represents the change in the power system frequency against small load

perturbation.

𝛥𝑓(𝑠) =𝐺𝑝

1 + (1𝑅)𝐺𝑝𝐺ℎ𝐺𝑡

𝛥𝑃𝐿(𝑠)

(B.5)

Where, 𝐺𝑝(𝑠) =𝐾𝑝

1+𝑠𝑇𝑝 , 𝐺𝑡(𝑠) =

𝐾𝑡

1+𝑠𝑇𝑡, 𝐺ℎ(𝑠) =

1

1+𝑠𝑇ℎ

For a step load change (S), the change in demand is represented by 𝛥𝑃𝐿(𝑠) =𝑆

𝑠

The steady-state value of frequency is represented by (B.6).

𝛥𝑓𝑠𝑠 = 𝑙𝑖𝑚𝑠→0

[𝑠𝛥𝑓(𝑠)] = −𝐾𝑝

1 +𝐾𝑝𝑅

𝑆 = −𝑆

𝐷 +1𝑅

𝐻𝑧

(B.6)

2. Secondary Control Loop

The action of the secondary loop for frequency control is governed by (B.7), which is closely

linked with the Unscheduled Interchange (UI) price of Availability Based Tariff (ABT)

mechanism.

𝛥𝑃𝑒 = −𝐾𝑖∫𝛥𝑓 𝑑𝑡

(B.7)

By taking the Laplace transform of (B.8)

𝛥𝑃𝑒 = −𝐾𝑖𝑠𝛥𝑓(𝑠)

(B.8)

List of Appendices

165

Appendix-C

System Data

TABLE C.1 Two area System Parameters

Parameter Unit Value

Base MVA MVA 2000

Pr1, Pr2 MW 2000

PL1, PL2 MW 200

H1,H2 S 5

D1,D2 pu/Hz 0.008

Tg1,Tg2,Tg3,Tg4 S 0.08

Tt1,Tt2, Tt3,Tt4 S 0.3

b1,b2, b3,b4 pu/Hz 0.425

R1,R2, R3,R4 pu/Hz 2.4

T12 pu /Hz 0.55

Pd1 pu 0.01

Kev Kw/Hz 12

Tev S 2

Tp S 24

Kp 120

F Hz 50

TABLE C.2 Battery Model Parameters

Parameter Area 1 Area 2

Nominal Voltage Vrms, V 400 400

Nominal Capacity, Ah 50 50

Energy Capacity, Kwh 16 16

The efficiency of charge and discharge 1 1

Internal Resistance, Ohm 0.352 0.352

TABLE C.3 EV Control Parameters

Parameter Area 1 Area 2

Max. EV Power Pm (kW) 5 5

Kmax, (Kw/Hz) 200 200

SOCmin, SOCmax 30,90 30,90

Delay Time (s) TEV 1 1

No. of EVs 60000 45000

List of Appendices

166

Appendix-D

Advantages of Energy Storage and it’s

Classification

In India, energy storage capacity increased to 23 GWh in 2018. Also, the battery energy

storage demand increased up to 56%. As per the survey carried out in India, the potential

found near to 290 GWh capacity of energy storage at grid-side applications. Electrical

vehicles had consumed batteries of 5 GWh in 2018. By 2025 it may increase up to 36 GWh.

Electrical Vehicle industries have forecasted the demand of the batteries by 110 GWh by

2019-2015.

A. Advantages of Electrical Vehicles (EVs) as Storage

Plug-in Hybrid Electrical Vehicle or Electrical Vehicle is adopted worldwide due to issues

such as environmental and climate changes, fossil fuel reserves, energy security and energy

cost [137][176]. The biggest consumer of oil products is the transportation sector. Green

House Gas (GHG) emission increases due to its consumption. An Electrical Vehicle (EV) or

Hybrid Electrical Vehicle can work as either as loads or as a large distributed power source.

In this way, it can reduce the overall cost and emission of GHG.

Environmental Advantages

The V2G concept is studied in 12 regions of the U.S [178]. It analyses the 27 % reduction in

GHG and 31 % in nitrogen oxide emission reduction. In California using PEV, current

emissions reduced by one third and in future it will reduce by one quarter energy scenario as

compared to Internal Combustion (IC) engine based vehicles [136].

List of Appendices

167

EV Service as an Ancillary Service

In restructured power system ASs are launched for the reliable and safe operation of the

network, to balance generation and load. Bidirectional service of V2G and G2V operation

can provide the ASs such as frequency stability, voltage stability, load balancing and peak

power-saving [39]. With the help of aggregators, it is possible to communicate between the

grid and vehicles.

Voltage and Frequency Regulation

For reactive power, voltage is to be regulated between supply and demand. A voltage

controller can be inbuilt in the battery charger. An inductive and capacitive reactive power

can manage by setting the phase angle [44]. An EV charging can stop during low voltage

condition of the grid and charging can start during high voltage condition of the grid [153].

For frequency regulation among the grid, three loops of AGC are defined such as primary,

secondary and tertiary frequency control [183]. For the primary control loop, grid frequency

is regulated by charging the battery during the high-frequency period and discharging the

battery during the low-frequency period [139]. For secondary and tertiary frequency control

action using EV is possible by distributing the Area Control Error (ACE) signal via

aggregators. EV owner will be in benefit when the vehicle can charge at lower tariff rates

and discharge at higher tariff rates. During the high-frequency period vehicle need to charge,

so the lower tariff rate should activate and for the lower frequency period vehicle need to

discharge, so the higher tariff rate should be activated.

Load Leveling and Peak Power

An EV can balance the energy during the V2G operation by discharging EVs batteries in

peak hours and during the G2V operation, EVs can charge their batteries in off-peak hours.

In [148] smart charging control strategy is described at the local level and globally. The peak

load can be reduced by coordinating the EV battery charger with grid for

charging/discharging activity. It needs development of the bidirectional charge/discharge

controller.

List of Appendices

168

Renewable Energy Supporting and Balancing

The nature of RESs such as solar and wind is intermittent [153]. Among all these RESs wind

power is highly complex. The PHEVs helps to power generated by RESs. The wind speed is

unpredictable, which makes it highly intermittent [13]. If the injection of power is by RESs

is high, the thermal plants or other plants must decrease the power generation. In this

situation, the vehicle battery by charging or discharging can help to match the demand and

supply. So there is no need to change the generated output power of thermal or any other

power plants. The stored energy in the battery can use for driving the vehicle.

Technical issues arise when RES and EVs are connected with grid:

The existing distribution grid is not designed for bidirectional flow. Requirement of Electric

grid infrastructure, communication and control for reliable two-way communication between

aggregators and large EVs, Battery high initial cost, battery degradation, Overloading of

transformer, underground cables and feeders which produce Voltage drop problem,

Harmonics issue.

B. Classification of Energy Storage

The various types of EES are suggested by peoples worldwide are respired in Fig. D.1. The

storage technologies are classified into their functions, time of response and duration of

storage [52] [76] [34].

FIGURE D.1 Types of Electrical Energy Storage Technologies

Types of Storage

Technologies

Mechanical Energy Storage

Pumped Hydro

Storage

Compressed Air Energy

StorageFlywheels

Electrical Energy Storage

CapacitorsSuper

Capacitors

Superconducting Magnetic

Energy Storage

Themal Energy Storage

Low Temperature

Thermal Storage

Aquifers low-temperature

ThermalEnergy Storage

Cryogenic ThermalEnergy Storage

High Temperature

Thermal Storage

Sensible Heat Storage

Latent Heat Storage

Electrochemical Energy

Storage

List of Appendices

169

A. MES

It stores the kinetic energy such as Pumped Hydro Storage (PHS), Compressed Air Energy

Storage (CAES) or Flywheels. The followings are the types of MES.

1. In PHS water is pumped from the bottom to higher location reservoir. The water is

collected during off-peak hours (low demand period) due to the lower rate of

electricity tariff. During the peak-hours (high demand period), the collected water

will release from the high location reservoir to run the hydro turbine to generate

electricity. Worldwide, approximately 127 GW hydro storage plants are there in

operation, which has average efficiency is between 70-80 % [79]. But, in India, due

to the land issue, big storage is difficult to install.

2. In CAES, an air compressor is used to generate pressurized air. During off-peak hours

or low demand period, the generated air is stored in an airtight underground tank with

a pressure ranging between 4 to 8 MPa [110]. The pressurized air used to rotate the

high-pressure turbine by heating or expansion mode during the peak hours or high

demand period. The CAES gives quick response in the generation and compression

mode against significant load variation. The disadvantage of CUS is that if air stored

for a long period, it is converted into water. Also, compressed air is very costly. So,

in the Indian power system, operation of CAES will be costly.

3. The flywheel is an electrochemical system in which rotating cylinder coupled with

magnetic bearings. The mechanical energy of the rotating cylinder converted in

electrical energy through the electrical machine [68].

B. EES

EES classified into electrostatic and magnetic energy storage. The conventional capacitors

and super capacitors stored electrostatic energy and SMES device stored magnetic energy.

The followings are the types of EES.

1. The capacitor stored energy between two electrodes separated by a dielectric

medium. Capacitors can be charged quickly and also, charge or discharge for

List of Appendices

170

several cycles more than the battery [110]. It can be used in short term storage

application due to fast response against sudden load variation. It has the limitation

of capacity and energy density.

2. The super capacitor has a good energy density compared to other devices due to

electrodes made from porous carbon material. These devices can respond quickly

against load changes in a fraction of seconds. The disadvantage is that it can store

energy for a short time and stored energy lost due to self-discharging property

[290].

3. There are two types of super magnetic coils due to operating temperature used in

SMES such as High and Low-temperature coils.

C. TES

Energy is stored using thermal insulation. It is the form of heat either at high or low

temperature.

1. Low-Temperature Thermal Energy Storage (LTTES):

It is sub-classified into two types, such as Aquifers ALTES and Cryogenic TES. In

ALTES, in off-peak chilled water is stored using thermal storage, which is cooled

down by coolant. The chilled water is used during peak hours, which is used to cool

down the air. The hot water returned by the heat exchanger. This type of application

often found in commercial buildings for space cooling, which helps to reduce the

energy cost of an air conditioner [110]. In the United States, Ice Bear Energy is a

famous option to save electricity cost during peak hours [130].

2. High-Temperature Thermal Energy Storage (HTTES):

HTTES is also classified into two types, such as the sensible and latent heat storage

systems. If the temperature of specific material rises or drops, it will absorbs or

releases the heat. Some materials like concrete and ceramics are the solid type of

storage, while molten salt is a liquid type of storage [95].

List of Appendices

171

D. Electro-Chemical Energy Storage

The following are the different techniques of Electro-Chemical Energy Storage.

1. Lead Acid Battery (PbA): This rechargeable battery is much matured, and cost per

unit is cheap [167]. The basic components of the PbA battery are Anode and Cathode

made from Lead Oxide. The aqueous sulfuric acid is used as an electrolyte. It has low

specific energy nearby 25 Wh/kg [110]. Because of the short life cycle and lower

energy density, its application is less suitable in some application. The lead-acid flow

battery with soluble lead, which dissolves in an aqueous methane-sulfuric acid

electrolyte, has the potential to solve [213].

2. Sodium-based high-temperature battery: It is classified as sodium sulphur (NaS),

sodium nickel chloride (NaNiCl2) and ZEBRA batteries. In NaS battery, liquid

sodium (Na) and sulfur (S) material are used for anode and cathode, respectively. It

can work in the temperature range between 300 to 350 0C [7]. Opposite to NaS

battery, ZEBRA battery is used solid metal chloride as a cathode, while anode and

electrolyte are same. ZEBRA battery has less corrosion on the cathode surface as

compared to NaS battery, and also, explosion issue due to metallic sodium can avoid.

Only, the disadvantage is that the power and energy density is low. Li-ion battery is

more popular due to the high range of specific energy from 75 to 125 Wh/kg [7]. But

in the case of overcharge, there is s rise in the temperature, which causes leakage of

chemical from the battery and sometimes explosion. Technically, batteries are very

well developed, but still there is a research scope in the reduction of cost. Other

batteries line Radox Flow Batteries (RFB), where the power and energy components

can independently determine due to its external storage. There are two types of RFB

available such as Vanadium Redox Battery (VRB) as well as Zinc-Bromine Battery

(ZnBr). There are low power and energy density in the RFB, which is a research area.

3. Hydrogen-based Energy Storage: The electricity is converted into hydrogen

through electrolyzers and hydrogen is stored in a metal tank. Electrolyzer technology

is further classified as Alkaline, PEM and SOEC [93]. The stored energy is converted

into electricity by RFC. The various types of fuel cells are there such as PEMFC,

List of Appendices

172

AFC, MCFC, PAFC, DMFC and SOFC. Its life is the long and cheap cost of

manufacturing [244]. The hydrogen is only the by-product [45].

List of Appendices

173

Appendix-E

Fuzzy Logic

The “Fuzzy Logic” was commenced by Lotfi Zadeh in 1965. He had introduced the theory

of fuzzy set. The fuzzy logic theory depends on the experience from which people can take

decisions. The vagueness and inaccurate information are represented by a mathematical

model known as a fuzzy set. In FG, the truth value exists between absolutely true and

absolutely false. The truth values of variables lie in 0 and 1, while in Boolean logic, the truth

value of variables will be number values either 0 or 1.

The mapping process is there between input and output in FG. The primary concept of using

fuzzy logic is making the if-then sentences called the rules. The rules are parallel and its

order is insignificant. The rules define the variables and adjectives. The rules made are set in

a natural way.

If the light is red if my speed is

high

and if the light is close then I brake hard.

If the light is red if my speed is low and if the light is far then I maintain my

speed.

If the light is

orange

if my speed is

average

and if the light is far then I brake gently.

If the light is green if my speed is low and if the light is close then I accelerate.

Also, these models can identify, present, manipulate, interpret vague data and information

and utilize it. From this example, it can be realized that the input variables are appreciated

by the brain. The FG is all about the virtual significance of accuracy.

Some findings from the fuzzy logic are given below.

• It is simple to understand its mathematical models.

• Easy to design any system because of flexibility.

• Most things are imprecise. The data which are not clear are tolerable by fuzzy logic.

• Non-linear complex functions can model by fuzzy logic.

https://en.wikipedia.org/wiki/Lotfi_A._Zadeh

https://en.wikipedia.org/wiki/Fuzzy_set_theory

List of Appendices

174

• It can model from the experience of the people.

• The use of fuzzy logic is easy because it is made by the qualitative explanation used

in every day.

Fuzzy logic starts from a fuzzy set. It defined the boundaries and known as a set without a

crisp. It consists of elements having a degree of membership. It is a classical set theory based

on FG set theory [315]. The different shapes of the membership function are such as sigmoid,

hyperbolic, tangent, exponential, Gaussian are used.

Fuzzy Logic System (FLS) is a system that is used to map the non-linear input data to scalar

output data. The Fuzzy Logic System represented in Fig E.1. It consists of the main four

components as follows.

1. Fuzzyfier

2. Rules

3. Inference Engine

4. Defuzifier

Fuzzifier

Inference

Defuzzifier

Rules

Fuzzy Input Fuzzy Output

FIGURE E.1 Fuzzy Logic System

Execution of the FG is as follows.

1. Fuzzification: The input as a crisp set is gathered and converted into a fuzzy set. Here,

the membership values of fuzzy variables are generated using linguistic variables,

membership functions and fuzzy linguistic.

2. Inference: A set of rule plays an important role in the fuzzy inference system.

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175

3. Defuzzification: Here, the fuzzy output of a fuzzy inference system converts into the

original form of crisp out using the membership functions.

A. Fuzzification

It is the method of assigning the arithmetical input of a system to fuzzy sets with some degree

of membership within the interval [0, 1].

If it is 0 then the value does not belong to the given fuzzy set and if it is 1 then the value

completely belongs within the fuzzy set. Any value from 0 to 1 indicates the degree of

improbability.

B. Fuzzy Inference System

It is a procedure to map the known input by FG into output. From the mapping process, one

can take the decision or provide examples. Inference systems of FG are successfully applied

in different areas such as power system, automatic control system, computer engineering, for

data categorization, result investigation, and expert systems. It is also known as fuzzy rule-

based controllers.

The procedure of fuzzy inference contains the Membership Functions (MF), Rules and

Logical Operators.

C. Membership function

It is one type of curve which defines the membership value from 0 to 1 by mapping the input

at each point. This input space is known by “Universe of Discourse”. There is only one clause

is that the MF must be laying in 0 to 1. The various types of MFs are presented in Fig E.2.

1. Triangular.

jar:file:///C:/Program%20Files/MATLAB/R2009b/help/toolbox/fuzzy/help.jar%21/bp78l6_-1.html#bp78l70-2

jar:file:///C:/Program%20Files/MATLAB/R2009b/help/toolbox/fuzzy/help.jar%21/bp78l6_-1.html#bp78l70-5

List of Appendices

176

2. Trapezoidal.

3. Singleton.

4. Gaussian.

5. Piecewise linear.

FIGURE E.2 Types of Membership Functions

D. Linguistic variables

In mathematics, numerical values are considered, where as in fuzzy logic, non-numerical

values are considered. These values are presented by linguistic variables such as young, old

etc. The variables are in the form of rules and facts. Using linguistic variables during

fuzzification operations mapping of arithmetic input into fuzzy MFs is possible. During the

de-fuzzifying operations, it maps the fuzzy output into a "crisp" value. It is helpful in taking

the decision.

List of Appendices

177

E. Fuzzy logic operators

Fuzzy logic operators work like Boolean logic operator. The FG reasoning is a superset of

Boolean logic. The replacement for basic operators AND, OR and NOT are shown below.

The FG operators are called the Zadeh operators.

Boolean

Operators

Fuzzy operators

AND(x,y) MIN(x,y)

OR(x,y) MAX(x,y)

NOT(x) 1 – x

Fuzzy operators give the same result like Boolean expression such as for TRUE it is 1, and

for FALSE it is 0.

F. Fuzzy Rules

In fuzzy logic systems, linguistic variables use fuzzy rules.

IF-THEN rules:

Fuzzy sets are the subjects and fuzzy operators are the verbs of FG theory. For making the

conditional statement of FG, "if-then" rules are used. Formation of a simple fuzzy if-then

rule is given below.

Example: if a is A then b is B.

Here A and B are linguistic values given by fuzzy sets on the ranges (universes of discourse)

a and b, respectively. The if-part of the rule "a is A" is called the premise and the then-part

of the rule "b is B" is called the conclusion. Some examples are given below.

If the temperature is very cold then the fan speed is stopped.

If the temperature is cold, then the fan speed is slow.

If the temperature is warm, then the fan speed is moderate.

If the temperature is hot, then the fan speed is high.

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178

G. Defuzzification

The conversion from a fuzzy set to crisp output is called Defuzzification. In the

Defuzzification the performance will vary according to the output value of the membership

function. The process of Defuzzification is not a part of 'mathematical fuzzy logic'. Several

Defuzzification methods are presented in [120], namely centroid method, the centre of sums

and mean of maxima. The various fuzzy applications in power system are listed below.

1. Contingency analysis

2. Diagnosis/monitoring

3. Distribution planning

4. Load frequency control

5. Generator maintenance scheduling

6. Generation dispatch

7. Load flow computations

8. Load forecasting

9. Load management

10. Reactive power/voltage control

11. Security assessment

12. Stabilization control (PSS)

13. Unit commitment

price based intelligent automatic generation and …

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