DEVELOPMENT OF A FIREFLY ALGORITHM BASED ANALYTICAL METHOD FOR OPTIMAL LOCATION AND SIZING OF DISTRIBUTED GENERATION IN RADIAL DISTRIBUTION NETWORKS


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TABLE OF CONTENTS

TITLE PAGE
TABLE OF CONTENTS
LIST OF ABBREVIATIONS
ABSTRACT

CHAPTER ONE: INTRODUCTION
1.1       General Background
1.2       Aim and Objectives
1.3       Statement of Problem
1.4       Motivation
1.5       Scope and Limitation
1.6       Methodology
1.9       Justification of Research

CHAPTER TWO: LITERATURE REVIEW
2.1       Introduction
2.2       Review of Fundamental Concepts
2.2.1    Distributed generation (DG)
2.2.2Radial distribution network power flow
2.2.3Distributed generator model types
2.2.3.1   DG modelled as a PV type
2.2.3.2   DG modelled as a PQ type
2.2.4The standard IEEE test benchmarks
2.2.4.1   The standard IEEE-33 bus system
2.2.4.2   The standard IEEE-69 bus system
2.2.5Methods for optimal DG placement and sizing
2.2.6Analytical method for DG placement and sizing
2.2.7    The firefly algorithm
2.3       Review of Similar Works

CHAPTER THREE: MATERIALS AND METHODS
3.1       Introduction
3.2       Distributed Generator Model
3.3       Objective Function
3.4       Analytical Method for Optimal Placement and Sizing of DGs
3.5       The Firefly Algorithm
3.6       The Proposed Method
3.7       Standard Test Systems
3.7.1    The IEEE33 bus system
3.7.2    The IEEE 69 bus system

CHAPTER FOUR: RESULTS ANALYSIS AND DISCUSSIONS
4.1       Introduction
4.2       The IEEE 33 Bus Test System
4.2.1    Base case total system loss
4.2.2    Effect of DG allocation using analytical method
4.2.3    Voltage profile after DG allocation using analytical method
4.2.4    Effect of DG placement using the hybrid algorithm
4.2.5    Voltage profile after DG allocation using hybrid algorithm
4.2.6    Comparison of results obtained for the standard IEEE 33 bus system
4.3       The IEEE 69 Bus Test System
4.3.1    Base case total system losses
4.3.2    Effect of DG placement after using analytical method
4.3.3    Voltage profile after DG allocation using analytical method
4.3.4    Effect of DG placement after using hybrid algorithm
4.3.5    Voltage profile after DG allocation using hybrid algorithm
4.3.6    Comparison of results obtained for the standard IEEE 69 bus system
4.4       Summary of Results
4.5       Validation

CHAPTER FIVE: CONCLUSION AND RECOMMENDATION
5.1       Introduction
5.2       Conclusion
5.3       Significant Contributions
5.4       Recommendations
References
Appendix



ABSTRACT

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.




CHAPTER ONE


INTRODUCTION

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).....


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