Optimal location and sizing of Distribution Generation (DG) units in radial distribution networks is critical to achieving an improvement in stability, reliability and reduction of power losses in the network. This dissertation presents a hybridized solution which is a combination of the analytical method and the firefly algorithm for optimal placement and sizing of DGs. The conventional analytical method for optimal DG placement and sizing was first modelled. The standard firefly algorithm was then modelled and integrated with the analytical method to form the proposed hybridized solution. The conventional analytical method and the proposed method were applied on standard IEEE 33 and 69 test buses for optimal DG location and sizing and the results obtained were compared to the base case scenario without DG. For the 33-bus, the analytical method found the optimal location and size of the DG to be bus 30 and 548kW respectively while the proposed method found the optimal location and size to be bus 2 and 133kW respectively. These results obtained for 33-buscaused 26.87% reduction in system losses and16.44% improvement in voltage profile for the analytical method while the proposed model‟s result caused 28.36% reduction in losses and 33.54% improvement in voltage profile when compared with the base case. For the 69-bus network, the analytical method and the hybrid model obtained the same results which was bus 63 for the optimal location and 590kW for the optimal size thereby resulting in 10.29% reduction in losses and 45.35% improvement in voltage profile. Furthermore, the proposed method had a 90.06% reduction in simulation time for the 33-bus and 76.28% for the 69-bus as compared with the analytical method. The proposed hybrid model was validated by comparing the results obtained for the 33-bus system with the published results by Viral and Khatod, (2015).Thiscomparison showed that the proposed hybrid model is valid for solving optimal DG allocation problems as the voltage profile follows the same trend with a better performance than results published by Viral and Khatod, (2015). This is an indication that hybridization of two methods would provide an optimal result faster than stand-alone methods.
TABLE OF CONTENTS
COVER PAGE i
TITLE PAGE ii
TABLE OF CONTENTS vii
LIST OF FIGURES xi
LIST OF ABBREVIATIONS xiii
CHAPTER ONE: INTRODUCTION 1
1.1 General Background 1
1.2 Aim and Objectives 3
1.3 Statement of Problem 3
1.4 Motivation 4
1.5 Scope and Limitation 4
1.6 Methodology 4
1.9 Justification of Research 5
CHAPTER TWO: LITERATURE REVIEW 7
2.1 Introduction 7
2.2 Review of Fundamental Concepts 7
2.2.1 Distributed generation (DG) 7
2.2.2 Radial distribution network power flow 10
2.2.3 Distributed generator model types 13
184.108.40.206 DG modelled as a PV type 13
220.127.116.11 DG modelled as a PQ type 13
2.2.4 The standard IEEE test benchmarks 14
18.104.22.168 The standard IEEE-33 bus system 14
22.214.171.124 The standard IEEE-69 bus system 15
2.2.5 Methods for optimal DG placement and sizing 16
2.2.6 Analytical method for DG placement and sizing 17
2.2.7 The firefly algorithm 20
2.3 Review of Similar Works 25
CHAPTER THREE: MATERIALS AND METHODS 31
3.1 Introduction 31
3.2 Distributed Generator Model 31
3.3 Objective Function 31
3.4 Analytical Method for Optimal Placement and Sizing of DGs 32
3.5 The Firefly Algorithm 34
3.6 The Proposed Method 34
3.7 Standard Test Systems 38
3.7.1 The IEEE33 bus system 38
3.7.2 The IEEE 69 bus system 38
CHAPTER FOUR: RESULTS ANALYSIS AND DISCUSSIONS 40
4.1 Introduction 40
4.2 The IEEE 33 Bus Test System 40
4.2.1 Base case total system loss 40
4.2.2 Effect of DG allocation using analytical method 41
4.2.3 Voltage profile after DG allocation using analytical method 42
4.2.4 Effect of DG placement using the hybrid algorithm 43
4.2.5 Voltage profile after DG allocation using hybrid algorithm 45
4.2.6 Comparison of results obtained for the standard IEEE 33 bus system 46
4.3 The IEEE 69 Bus Test System 47
4.3.1 Base case total system losses 48
4.3.2 Effect of DG placement after using analytical method 50
4.3.3 Voltage profile after DG allocation using analytical method 52
4.3.4 Effect of DG placement after using hybrid algorithm 53
4.3.5 Voltage profile after DG allocation using hybrid algorithm 56
4.3.6 Comparison of results obtained for the standard IEEE 69 bus system 57
4.4 Summary of Results 58
4.5 Validation 59
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION 61
5.1 Introduction 61
5.2 Conclusion 61
5.3 Significant Contributions 62
5.4 Recommendations 62
Appendix A1: Matlab Code for the Firefly Algorithm 66
Appendix A2: Matlab Code for DG Placement and Sizing Using Analytical Method 67
Appendix B1: Matlab Code of the Firefly Algorithm for Optimal DG Location 72
Appendix B2: Matlab Code of the Hybrid Algorithm for Optimal DG Location and Sizing 74
Appendix C1: Line and Bus Data of Standard IEEE 33-Bus System 80
Appendix C2: Line and Bus Data of Standard IEEE 69-Bus System 81
1.1 General Background
Due to continuous economic growth and development, load demand in distribution networks are susceptible to sharp increment. Hence, the distribution networks in most developing nations like Nigeria, are operating very close to the voltage instability boundaries. The decline of voltage stability margin is one of the important factors which restricts increment in loads served by distribution companies (Jain et al., 2014).
The rapidly increasing need for electrical power and difficulties in providing required capacity using traditional solutions, such as transmission network expansions and substation upgrades, provide a motivation to select Distributed Generation (DG) option. DG can be integrated into distribution systems to improve voltage profiles, power quality and the system generally (Muttaqi et al., 2014). These DG units when integrated into distribution networks provide ancillary services such as spinning reserve, reactive power support, loss compensation, and frequency control. On the other hand, poorly planned and improperly operated DG units can lead to reverse power flows, excessive power losses and subsequent feeder overloads(Atwa et al., 2010).
The DG solution may be more economical as it provides the system with a higher supply capacity and an additional power reserve. It provides an alternative source that can help fulfill requirements of growing power demands, improve reliability and efficiency of power supply as well as reduce the cost of electricity during peak hours (Leite da Silva et al., 2012). According toGeorgilakis and Hatziargyriou (2013), DG placement impacts critically on the operation of the distribution network. Inappropriate DG placement may increase system losses, network capital and operating costs. On the contrary, optimal DG placement (ODGP)
can improve network performance in terms of voltage profile, reduce power flows and system losses along the distribution lines, and improve power quality and reliability of supply.
Soudi in his work (Soudi, 2013), was able to show that the greatest benefit of DG application in distribution system depends on the determining the optimal site and size of DG. The results of his work showed that the optimal application of DG could reduce the losses in the system up to 47%, cost of power purchase up to 92% and cost of energy not supplied by 40%. The voltage profile of the distribution system was also siginificantly improved.
Since the integration of DGs into a distribution network could either improve the network performance or cause an adverse effect on the network, distribution companies require methods to quantify the capacity and location of these new DGs that may be connected to the distribution networks. This task has attracted a siginificant interest with a wide range of methods, objectives and constraints (Gomez-Gonzalez et al., 2012).
Several methods, objectives and constraints have been introduced by different researchers. Methods used include the classical or numerical method as presented by Atwa et al., (2010), Ochoa and Harrison (2011) and Rau and Wan (1994); the analytical approach as presented by Wang and Nehrir, (2004), Acahrya et al., (2006), Gözel and Hocoaglu, (2009), Hung et al., (2010), Hung et al., (2013) and Hung et al., (2014). Another method used is the heuristic approach as proposed in the works of Abou El-Ela et al., (2010), Soroudi and Ehsan (2011), Akorede et al., (2011), Vinothkumar and Selvan (2011), and Vinothkumar and Selvan (2012). Some reasearchers have also used combined solution methods which involve using more than one approach as shown by Afzalan and Taghikhani (2012) and Moradi and Abedini (2012). These methods have also presented different kinds of objective functions varying from single to multiple objectives and different types of constraints have also been addresed. This dissertation presents a hybridized method which integrates the analytical
method into a meta-heuristic algorithm so as to optimally locate and size DGs in radial distribution networks.
1.2 Aim and Objectives
The aim of this research work is to develop a hybridized method for optimal location and sizing of DGs in radial distribution networks with the intent of minimising total real power loss and improving on the voltage profile of the network. The objectives are:
1. Development of a standard analytical method for optimal location and sizing of a DG.
2. Development of a firefly algorithm for optimal location of a DG.
3. Hybridization of objetives 1 and 2 in order to incorporate the analytical solution for sizing only before searching for the optimal location of the DG using the metaheuristic approach.
4. Validation of the hybridized solution method on standard IEEE-33 and 69 radial test buses.
1.3 Statement of Problem
The problem widely faced when integrating DGs into a distribution system is how to integrate them in order to achieve an improved performance of the distribution system. The search for an optimal location and size of DGs to be placed can be quite challenging. In most cases, the main methods used for locating and sizing a DG in a distribution network are analytical and heuristic methods. For the problem of optimal DG allocation, the analytical method may seem easy to implement and execute, but it is computationally exhaustive and time consuming while most meta-heuristic methods on the other hand, are seemingly robust but hardly produce optimal solution.In order to solve the shortcomings of these two methods (i.e. the computational exhaustion and time consumption of analytical method and non-optimality of results obtained by meta-heuristic methods), an integration of the analytical concept into the meta-heuristic algorithm would guide the algorithm to an optimal time-
saving solution. This will take advantage of both the precision of the analytical method and the flexibility and robustness of the meta-heuristic algorithm.
A sharp increase in demand for energy has caused suppliers of energy to search for a quicker and relatively less expensive means of improving the declining reliability and stability of power distribution networks. This has therefore brought about the motivation to choose the option of Distributed Generation as an emergency approach to solve this problem because it is less expensive compared to the conventional transmission expansion or network reconfiguration. The next problem was then how to effectively integrate these DGs into distribution network to achieve the aim of improving distribution network performance rather than the reverse. This served as a motivation for this research.
1.5 Scope and Limitation
The limitations of this work are as follows:
1. All network assessments i.e. load flow, line losses, were done based on offline study of radial distribution networks.
2. System control and practical implementation of DG connection was not considered
3. The implementation was only applied to standard IEEE 33 and 69-busradial distribution test systems.
The following steps which comprises the methodology adopted for this research are as follows:
1. Selection of a DG model that is suitable for voltage profile improvement and real power loss reduction;
2. Developing the analytical method for determining the optimal DG location and sizing using MATLAB;
3. Developing the Firefly Algorithm for determination of the optimal DG location using MATLAB;
4. Integrating the developed Firefly Algorithm into the analytical method to form a hybrid method on MATLAB platform;
5. Application of the developed analytical method on the standard test buses for selection of optimal DG location and size on MATLAB platform;
6. Application of the developed hybrid method on the standard test buses for selection of optimal DG location and size on MATLAB platform;
7. Comparing the results obtained from the analytical and hybrid method(i.e. power loss and voltage profile)
8. Validation by comparing results obtained with a previously published work.
1.9 Justification of Research
The results obtained from this research have justified that optimal location and sizing of Distribution Generators in radial distribution is very critical to achieving an improvement in the voltage profile and a reduction to the total losses of the network. These results are as follows:
1. Development of a firefly algorithm based analytical method for optimal location and sizing of Distribution Generation in radial distribution networkswhich caused an overall improvement of 33.54% in the voltage profile of the standard IEEE 33-bus system when compared to the base case scenario.
2. The developed method also resulted in 28.36% reduction in the total losses of the standard IEEE 33-bus system when compared to the base case scenario.