Faculty of Electrical Engineering
OPTIMUM DISTRIBUTED GENERATION ALLOCATION USING
MODIFIED PARTICLE SWARM OPTIMIZATION
Taha Jabbar Sahib
Master of Electrical Engineering (Industrial Power)
OPTIMUM DISTRIBUTED GENERATION ALLOCATION USING MODIFIED PARTICLE SWARM OPTIMIZATION
TAHA JABBAR SAHIB
A dissertation submitted
in fulfilment of the requirements for the degree of Master of Electrical Engineering (Industrial Power)
Faculty of Electrical Engineering
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
DECLARATION
I declare that this dissertation entitled “Optimum Distributed Generation Allocation using Modified Particle Swarm Optimization” is the result of my own research except as cited in the references. The thesis has not been accepted for any degree and is not concurrently submitted in the candidature of any other degree.
Signature : ………. Name : TAHA JABBAR SAHIB. Date : ……….
APPROVAL
I hereby declare that I have read this dissertation and in my opinion this thesis is sufficient in terms of scope and quality for the award of Master of Electrical Engineering (Industrial Power).
Signature : ………...………..…………
Supervisor Name : Datuk Prof. Dr. Mohd Ruddin Bin Ab Ghani Date : ………...………..…………
DEDICATION
i ABSTRACT
The expansion of the Distribution Generation (DG) is consistent with its increasing popularity as a sustainable part of electric power systems. DG, being small scale generation sources placed either at or near the load centre, is normally installed in the power system network. DG optimum allocation brings a number of positive impacts- voltage improvement, environmental friendliness, delay in the system upgrade, more reliable and reduced transmission and distribution network congestion, also lower network upgrading cost are the positive points of DG integration to the system. The integration of DG into distribution might be caused several issues such as voltage rise and power losses increment. In this dissertation, the voltage improvement and power losses reduction have become the main objective of this study. The impact brought on by the DG is positive or negative on the system, depending mostly on the DG location and size. The optimal allocation of DG has been considered as main problem that can be solved using Modified Particle Swarm Optimization (MPSO) method. This dissertation has the aim of developing a successful technique that can assess the consequence of distributed generation on a distribution power system and to adopt the MPSO technique to find DG units optimal placement and size with improved system reliability by mitigating the total power losses, and improving the voltage profile. This dissertation also use the Open Distribution System Simulator (OpenDSS) and MATLAB. The MPSO technique solved the optimal DG sizing and placement problem for the IEEE 13-Node, 37-Node and 123-Node Test Cases successfully. Each case is divided into three scenarios and each scenario deals with different constraints for the size and number of DG to integrate with the test case. The test cases and its scenarios have been utilized to show the effectiveness of the MPSO method to find the optimum DG allocation and demonstrate how this optimum DG allocation makes the voltage profile better and minimizes the total power losses of the test cases study.
ii
ABSTRAK
Perkembangan penjana pengagihan (DG) adalah konsisten dengan popularitinya yang semakin meningkat sebagai satu bahagian sistem kuasa elektrik yang mapan. DG, sebagai satu sumber penjana berskala kecil ditempatkan sama ada di pusat beban atau berdekatan dengan pusat beban, biasanya dipasang dalam jaringan sistem kuasa. Penempatan optima DG memberi beberapa impak positif- voltan tinggi, mesra alam, penangguhan penaiktarafan sistem, kesesakan transmisi dan pengagihan yang lebih boleh dipercayai dan kos dikurangkan, adalah kebaikan-kebaikan integrasi DG kepada sistem. Dalam disertasi ini, penambahbaikan voltan dan pengurangan kehilangan kuasa telah menjadi satu aspek penting dalam kajian ini. Impak yang dibawa oleh DG adalah positif atau negatif ke atas sistem, bergantung kepada lokasi dan saiz DG. Disertasi ini bertujuan membangunkan satu teknik yang berjaya yang boleh menilai impak penjanaan pengagihan ke atas satu sistem kuasa pengagihan dan menggunakan teknik Modified Particle Swarm Optimaization (MPSO) untuk memastikan penempatan dan saiz unit DG yang optima dengan kebolehpercayaan sistem yang lebih baik dengan cara mengurangkan kemerosotan kuasa, dan memperbaiki profil voltan. Disertasi ini juga menggunakan Open Distribution System Simulator (OpenDSS) dan MATLAB untuk membantu menyelesaikan masalah penempatan DG yang optima. Teknik MPSO menyelesaikan isu saiz dan penempatan DG untuk IEEE 13-nod, 37-nod dan 123 nod dengan jayanya. Setiap satu kes terbahagi kepada tiga senario dan setiap senario berdepan dengan pelbagai kekangan yang berbeza untuk saiz dan nombor DG untuk digabungkan dengan kes ujian. Kes-kes ujian dan senarionya telah digunakan untuk menunjukkan keberkesanan kaedah MPSO untuk mencari penempatan DG yang optima dan menunjukkan bagaimana penempatan optima DG ini menambahbaik profil voltan dan mengurangkan kekurangan kuasa mutlak kajian kes ujian berkenaan.
iii
ACKNOWLEDGEMENTS
First and foremost, all Glory and thank to Allah the Beneficent, most Merciful, who grant me life and the ability to finish this dissertation
I would like to take this opportunity to express my sincere acknowledgement to my supervisor Datuk Prof. Dr. Mohd. Ruddin Bin Ab.Ghani from the Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka (UTeM) for his essential supervision, support and encouragement towards the completion of this dissertation.
A very special acknowledgment goes to my family, for their endless love, and for giving me all of the encouragement, advice and support I could need. I am grateful to my father Jabbar Sahib, my mother Lamyaa Aziz, my brothers Hayder, Ibrahem, and Ammar, my sisters Ola, Jna and lina. This accomplishment would not have been possible without them. Thank you.
Special thanks to my friends Hazim Imad, Mohammed Rasheed and Hayder Malik for their helps and support during my years of study.
Finally, and most importantly, thank you to my best friend and beloved wife, Sally Sohaib, for always being with me. Arriving at this point would not have been possible without the support and love she provided me, and thus, I will be forever grateful to her.
iv TABLE OF CONTENTS PAGE DECLARATION i APPROVAL ii DEDICATION iii ABSTRACT i ABSTRAK ii ACKNOWLEDGEMENTS iii TABLE OF CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES ix
LIST OF APPENDICES xii
LIST OF ABREVIATIONS xiii
LIST OF SYMBOLS xiv
LIST OF PUBLICATIONS xv
CHAPTER
1. INTRODUCTION 1
1.1 Introduction 1
1.2 Distribution Generation 4
1.2.1 Distributed Generation Applications 6
1.3 Problem Statement 7 1.4 Research Objective 8 1.5 Research Contribution 9 1.6 Scope of Research 9 1.7 Dissertation Outline 10 2. LITERATURE REVIEW 11 2.1 Introduction 11 2.2 DG Integration Impact 12
2.2.1 DG Optimization Using Conventional Methods 13 2.2.2 DG Optimization Using Artificial Intelligence Methods 19 2.3 Impacts of Distributed Generators 30
2.3.1 DG Impacte on the Voltage Stability 31 2.3.2 DG Impact on the Distribution Losses 32 2.4 Chapter Summary 33
v
3.1 Introduction 34
3.2 Software and Tools 36
3.2.1 MATLAB Software 36
3.2.2 Open DSS Optimization Engine 37
3.3 Network Element Modelling 41
3.3.1 Buses 41
3.3.2 Voltage Source 42
3.3.3 Transformer 44
3.3.4 Generator 45
3.3.5 Capacitor 46
3.3.6 Conductor and Cable Modelling 48
3.3.7 Load 50
3.4 OpenDSS Sample Scripts 52 3.5 Particle Swarm Optimization (PSO) 53
3.5.1 Input data initialization 58
3.5.2 Calculate the objective function 58
3.5.3 Determine Pbest 58
3.5.4 Determine Gbest 58
3.5.5 Update velocity and position 59
3.6 Impacts of DG on Distribution Networks 60
3.7 Objective Function Formulation and Case Study Assessment 64
3.8 Proposed Methodology using Modified-PSO 66
3.8. Part 1: Model Initialization 69
3.8.2 Part 2: Initial Population (General PSO) 69
3.8.3 Part 3: MPSO Main Loop 70
3.9 Summary 70
4. RESULTS AND DISCUSSION 71
4.1 Introduction 71
4.2 Result and Discussion of Proposed Methodology 72
4.2.1 Case Study: IEEE 13 Node 72
4.2.2 Case Study: IEEE 37 Node 94
4.2.3 Case Study: IEEE 123 Node 115
vi
5. CONCLUSION AND RECOMMENDATIONS 134 FOR FUTUER RESEARCH
5.1 Introduction 134
5.2 Attainment of Research Objectives 134
5.3 Significance of Research Outputs 135
5.4 Recommendations for Future Work 136
REFERENCES 138
vii
LIST OF TABLES
TABLE TITLE PAGE
2.1 Literature review summary 28
3.1 Voltage source properties 43
3.2 Transformer properties 44
3.3 Generator properties 46
3.4 Capacitor properties 47
3.5 Line code properties 49
3.6 Load properties 51
4.1 First scenario result comparison with standard IEEE 13 node test case 75 4.2 OpenDSS file for the DG (Generator.dss) of IEEE 13 nodes 79
(first scenario)
4.3 Second scenario result comparison with standard IEEE 13 node test case 81 4.4 Open DSS file for the DG of IEEE 13 nodes (second scenario) 85 4.5 Third scenario result comparison with standard IEEE 13 node test case 88 4.6 OpenDSS file for the DG (Generator.dss) of IEEE 13 nodes 92
(third scenario)
4.7 Comparison of the standard case and the three scenarios 93 (IEEE13 Nodes)
viii
4.9 Open DSS file for the DG (Generator.dss) of IEEE 37 nodes 101 (first scenario)
4.10 Second scenario result comparison with standard IEEE 37 node test 103 case
4.11 Open DSS file for the DG of IEEE 37 nodes (second scenario) 107 4.12 Third scenario result comparison with standard IEEE 37 node test case 109 4.13 Open DSS file for the DG of IEEE 37 nodes (third scenario) 113 4.14 Comparison of the standard case and the three scenarios 114
(IEEE37 Nodes)
4.15 First scenario result comparison with standard IEEE 123 node 118 test case
4.16 OpenDSS file for the DG of IEEE 123 nodes (first scenario) 121 4.17 Second scenario result comparison with standard IEEE 123 node 123
test case
4.18 OpenDSS file for the DG of IEEE 123 nodes (second scenario) 126 4.19 Third scenario result comparison with standard IEEE 123 node 128
test case
4.20 OpenDSS file for the DG of IEEE 123 nodes (third scenario) 131 4.21 Comparison of the standard case and the three scenarios 132
ix
LIST OF FIGURES
FIGURE TITLE PAGE
Figure 1.1: Various countries losses in percentage of output 2
Figure 2.1: Basic Artificial Bee Colony flowchart 24
Figure 2.2: Basic Genetic Algorithm flow chart 26
Figure 3.1: Software and tools structure 36
Figure 3.2: OpenDSS Structure 39
Figure 3.3: Bus definition 42
Figure 3.4: Front view of OpenDSS EXE standalone application 52
Figure 3.5: OpenDSS script communicated with MATLAB 53
Figure 3.7: PSO particle movement principle 54
Figure 3.8: General PSO flowchart 56
Figure 3.9: Two-bus networks. 61
Figure 3.10: Three phase voltage drop in distance after 100% DG penetration 63 Figure 3.11: Three phase voltage drop in distance after 50% DG penetration 63 Figure 3.12: Three phase voltage drop in distance for standard case (before add DG) 64 Figure 3.13: Proposed algorithm for optimum DG allocation problem 68
Figure 4.1: IEEE 13 Node Test Case 73
x
Figure 4.3: Three phase voltage drop in distance after add DG IEEE 13 node 76 Figure 4.4: Voltage comparison for each phase before and after add DG 77 Figure 4.5: Losses minimization of optimum DG allocation of IEEE 13 nodes 78
Figure 4.6: Losses comparison before and after add DG 78
Figure 4.7: Three phase voltage drop in distance before add DG 82 Figure 4.8: Three phase voltage drop in distance after add DG 82 Figure 4.9: Voltage comparison for each phase before and after adds DG 83 Figure 4.10: Losses minimization of optimum DG allocation of IEEE 13 nodes 84
Figure 4.11: Losses comparison before and after add DG 85
Figure 4.12: Three phase voltage drop in distance before add DG 89 Figure 4.13: Three phase voltage drop in distance after add DG 89 Figure 4.14: The voltage comparison for each phase before and after add DG 90 Figure 4.15: Losses minimization of optimum DG allocation of IEEE 13 nodes 91
Figure 4.16: Losses comparison before and after add DG 91
Figure 4.17: IEEE 37 node test case 94
Figure 4.18 : Three phase voltage drop in distance before add DG 98 Figure 4.19: Three phase voltage drop in distance after add DG 98 Figure 4.20: Voltage comparison before and after DG integration for three phases 99 Figure 4.21: Losses minimization of optimum DG allocation of IEEE 37 nodes 100
Figure 4.22: Losses comparison before and after add DG 100
Figure 4.23: Three phase voltage drop in distance before add DG 104 Figure 4.24: Three phase voltage drop in distance after add DG 104 Figure 4.25: Voltage comparison for each phase before and after add DG 105 Figure 4.26: Losses minimization of optimum DG allocation of IEEE 37 nodes 106
xi
Figure 4.27: Losses comparison before and after add DG 106
Figure 4.28: Three phase voltage drop in distance before add DG 110 Figure 4.29: Three phase voltage drop in distance after add DG 110
Figure 4.30: Voltage comparison between phases 111
Figure 4.31: Losses minimization of optimum DG allocation of IEEE 37 nodes 112
Figure 4.32: Losses comparison before and after add DG 113
Figure 4.33: IEEE 123 Node Test case 116
Figure 4.34: Three phase voltage drop in distance before add DG 119 Figure 4.35 : Three phase voltage drop n distanc after add DG. 119 Figure 4.36: Losses minimization of optimum DG allocation of IEEE 123 nodes 120 Figure 4.37 : Three phase voltage drop in distance before add DG 124 Figure 4.38: Three phase voltage drop in distance after add DG 124 Figure 4.39: Losses minimization of optimum DG allocation of IEEE 123 nodes 125 Figure 4.40: Three phase voltage drop in distance before add DG 129 Figure 4.41: Three phase voltage drop in distance after add DG 129 Figure 4.42: Losses minimization of optimum DG allocation of IEEE 123 nodes 130
xii
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Matlab coding 158
xiii
LIST OF ABREVIATIONS
DG Distributed generation
PUHCA Public utilities holding company act PURPA Public utilities regulatory policy act DS Distribution system
ICLES International council on large electric systems CHP Combined heat and power
ACO Ant colony optimization AI Artificial intelligent GA Genetic algorithm
OpenDSS Open distribution system simulator PSO Particle swarm optimization Pbest Best personal position Gbest Best global position
OF Objective function
X/R Reactance divided by resistance NR Newton raphson
xiv
LIST OF SYMBOLS
t i
X Current position of particle
Pbest
X Best personal position of particle Gbest
X Best global position of particle t
i
V Velocity of particle i towards previous vector
1
t i
V Velocity of particle i towards next position
w
Inertia weight factor α & β Acceleration coefficientC1 Personal learning coefficient
C2 Global learning coefficient 2
& 1 r
r Uniformly distributed random numbers
ω1 Coefficient of total power losses violation
ω2 Coefficient of voltage violation
ω3 Balancing coefficient of the total number of integrated DG
xv
LIST OF PUBLICATIONS
Journal:
1. Taha Jabbar Sahib,Mohd Ruddin Ab Ghani, Zanariah Jano and Imad Hazim Mohamed (2017), “Optimum Allocation of Distributed Generation using PSO: IEEE Test Case Studies Evaluation”, International Journal of Applied
1 CHAPTER 1
INTRODUCTION
1.1 Introduction
Electric power system networks are composed typically of four major sub systems: generation, transmission, distribution, and utilizations. Distribution networks link the generated power to the end user. Transmission and distribution networks share similar functionality; both transfer electric energy at different levels from one point to another; however their network topologies and characteristics are quite different. Distribution networks are well-known for their low X/R ratio and significant voltage drop that could cause substantial power losses along the feeders. Ng et al. (2000) estimated that as much as 13% of the total power generation is lost in the distribution networks. Mendes et al. (2005) show that the percentage of the distribution losses from the total electric power system real power losses is approximately about 70%. In an effort towards manifesting the seriousness of such losses, Azim and Swarup (2005) reported that 23% of the total generated power in the Republic of India is lost in the form of losses in transmission and distribution. The higher power losses in the transmission and distribution system also lead to reducing efficiency of the existing system. Figure 1.1 shows the total transmission and distribution losses (in percentage of total power output) for different countries of the world for 2012, including pilferage as giving by World bank development indicator, (2015). According to world bank development indicator (2015) annual electricity transmission and distribution losses world wide average is about 8.12% of the electricity that is transmitted. Electrical
2
transmission and distribution losses as high as 55.39% have been reported for Haiti.This non negligible amount of losses has adirect impact on the financial results and the overall efficiency of the system.Thus the major focus of present-day research is on effectively utilizing the existing infrastructure with better planning and use of smart technologies.
Figure 1.1: Various Countries Losses in Percentage of Output
Distribution systems usually encompass distribution feeders configured radially and exclusively fed by a utility substation. Integrating Distribution Generation (DG) within the distribution level has an overall positive impact towards reducing the losses as well as improving the network voltage profiles. Due to advances in small generation technologies, electric utilities have begun to change their electric infrastructure and have started adapting on-site, multiple, small, and dispersed DG. In order to maximize the benefits obtained by integrating DGs within the distribution system, careful attention has to be paid to their placement, as well as to the appropriate amount of power that is injected by the utilized
3
DGs. In other words, to achieve the best results of DG deployments, the DG should be both optimally placed and sized in the corresponding distribution network. During the first third of the twentieth century, there were no restrictions on how many utility companies could be owned by financial corporations known as utility holding companies. By 1929, 80% of US electricity was controlled by 16 holding companies, and three of those corporations controlled 36% of the nation’s electricity market as reported by Masters (2004). During the Great Depression most of these utility holding companies went bankrupt. As a result, the US Congress Public Utilities Holding Company Act (PUHCA) of 1935 regulated the gas and electric industries and restricted holding companies to the ownership of a single integrated utility. PUHCA indirectly discouraged wholesale wheeling of power between different states, provinces or even countries. The Public Utilities Regulatory Policy Act (PURPA) of 1978 allowed grid interconnection and required electric utilities to buy electricity from non-utility-owned entities called Qualifying Facilities (QF) at each utility's avoided cost. The term QF refers to nonutility- owned (independent) power generators. The term 'at each utility's avoided cost' is interpreted to mean that the utility shall buy the generated electricity at a price equivalent to what it would cost the utility itself if had generated the same amount of power in its own facility or if it had purchased the power from an open electricity market, what the utility saves by not generating the same amount of power. This act heralded the dawn of the DG industry era, which paved the way to generate electricity, arguably, at a lower cost compared to that of traditional utility companies and consequently have it delivered to the end user at lower rates. The English Policy Act of 1992 (EPA) intensified competition in the wholesale electricity market by opening the transmission system for access by utilities and non-utilities electricity producers as founded by Philipson (2006).
4
1.2 Distribution Generation
Distributed generation (DG), also called on-site generation, dispersed generation, embedded generation (EG), decentralized generation, decentralized energy, site generation or distributed energy, generates electricity from many small energy sources. A large number of terms and definitions are used in relation to distributed generation.
ICLES has a working group that devotes efforts to DG. They defines DG as all generation units with a maximum capacity of 50 to 100 MW, usually connected to the distribution network and neither centrally planned nor dispatched as clarify by Massimo et al. (2003). Clearly, this latter part of their definition implies that DG units are beyond the control of the transmission grid operator. Thus, generation units built by the transmission grid operator as a substitute for grid expansion and that have measures implemented for dispatching is not considered to be DG according to this philosophy.
The IEEE defines DG as the generation of electricity by facilities that are sufficiently smaller than central generating plants so as to allow interconnection at nearly any point in a power system.
On the basis of the definitions surveyed, Dondi et al. (2002) has defined DG as a small source of electric power generation or storage (typically ranging from less than a kW to tens of MW) not part of a large central power system and located close to the load. Storage facilities are also included in the definition of DG, which is not conventional. Furthermore, this definition emphasizes the relatively small scale of the generation units as opposed to ICLES.
K.Purchala et al. (2006) also defined DG as relatively small generation units of 30 MW or less. These units are placed at or close consumers to meet specific needs, to support
