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CHAPTER - 1
INTRODUCTION
1.1 INTRODUCTION
The major challenge for power system engineers is to meet the ever increasing load
demand with available generating capacities. Creating additional generation capacity involves
huge capital investments and hence it is imperative to operate the existing power system
network with optimal utilization. This requires systematic methods of planning and should
employ suitable control strategies to reduce the energy losses and to improve the power
quality supplied to the consumers. The major components of a power system are Generating
stations interconnected through high voltage transmission network and low voltage
distribution network to the different points of utilization of electrical energy. The planning,
design and operation of Generating systems and Transmission systems has been
systematically analyzed and suitable control strategies to optimize the performance have been
put into practice. Especially in the systematic planning and design of distribution systems
much attention is needed to improve the power quality supplied to the consumers.
The distribution system is generally characterized into Primary distribution network
and Secondary distribution network [15]. A primary distribution network delivers power at
higher than utilization voltages from the substation to the point where the voltages are further
stepped down to the value at which the energy is utilized by the consumers. The secondary
distribution network supplies power to the consumer premises at levels of utilization
voltages. Based on the scheme of connection the primary distribution system may be a Radial
distribution system or a Mesh system.
Most of the primary distribution systems are designed as radial distribution systems
having exclusively one path between consumer and substation and if it is interrupted results
in complete outage of power to the consumers. The main advantages of radial system are
simplicity of analysis, simpler protection schemes, lower cost and easy predictability of
performance.
A mesh system has two paths between substation and every consumer and it is more
complicated in design and requires complex protection schemes which involves higher
investment than in radial systems. The radial distribution systems are inherently less reliable
than mesh systems but reliability can be improved with good design.
Recently many researchers have suggested different strategies to effectively reduce
the energy losses in the distribution network and maintain a good voltage profile at various
buses in the network. Basically, the researchers have suggested various reactive
compensation methods to reduce the reactive component of currents in the feeders thereby
reduce the energy losses, kVA demand on the feeders and improve the voltage profile in the
distribution system. The different methods suggested to optimize the performance of
distribution system are optimal sizing and placement of Capacitors, reconductoring of
feeders, employing Voltage Regulators at proper locations and Distributed Generators at
suitable locations. In investigating the above strategies to improve the overall performance of
the distribution network, an efficient and robust load flow technique suitable for distribution
systems is required.
The conventional load flow methods used for power system networks such as Newton
– Raphson and Fast Decoupled load flow methods cannot provide solution for the distribution
system because of high R/X ratio (ill conditioned systems). Hence an efficient load flow
method of solution for solving distribution systems with balanced and unbalanced load
configuration is required. To ensure good quality of supply to consumers it is necessary to
limit the voltage drops and reduce power losses by proper choice of compensation techniques
such as capacitor placement, voltage regulators, distributed generators, network
reconfiguration and grading of conductors.
In radial distribution systems it is common to employ uniform conductor over the
entire length of the feeder. From the consideration of current carrying capacity it is not
necessary to employ uniform conductors in radial distribution systems and hence the size of
conductor should reduce as we proceed from substation to tail end of the feeder. In planning
radial distribution systems grading of conductors is employed which will reduce the losses,
kVA demand and cost of the conductor besides improving the voltage profile.
It is apparent that it will be economical if transmission lines are used to transfer only
active power and the reactive power requirements of the loads are met in the distribution
system itself either at the consumer premises or at the substation level. Series and shunt
capacitors are employed in power system to improve system stability, power factor and
voltage profile thereby enhancing the system capacity and reducing the losses. In distribution
systems it requires careful planning to meet the reactive power requirements by suitably
placing the shunt capacitors. The benefits that can be derived from shunt capacitor
installation in the distribution systems are
Reduces lagging component of current
Increases voltage level at the load
Improves voltage regulation if the capacitor units are properly switched
Reduces power losses in the system because of reduction in current
Reduces kVA demand where power is purchased
Reduces investment in system facilities per kW of load supplied
The extent of these benefits depends on the location, size, type and number of
capacitors as well as on their control settings. The capacitor placement problem is formulated
as an optimization problem for determining the location and size of the capacitor with an
objective to maximize the net savings.
Another method of improving the voltage profile in distribution systems is to employ
required number of voltage regulators with suitable tap settings at proper locations.
Installation of voltage regulators on distribution network will help in reducing the energy
losses, peak demand losses and in addition improves system stability and power factor. To
achieve these objectives, the problem is formulated as an optimization problem to reduce the
losses and hence maximize the net savings with suitable constraints.
In planning distribution systems, the present trend is installation of distributed (or
local) generators other than central generating stations closer to consumer premises
preferably at high load density locations. Distributed Generators (DGs) are small modular
resources such as photo voltaic cells, fuel cells, wind generators, solar cells. Such locally
distributed generation has several merits from the point of environmental restrictions and
location limitations.
The main reason for increasingly wide spread deployment of DG can be summarized
as follows:
DG units are closer to customers so that Transmission and Distribution costs are
reduced or avoided.
Reduced line losses
Voltage profile improvement and power quality improvement
Enhanced system reliability and security
It is easier to find sites for small generators.
Reduced fuel cost due to increased overall efficiency
Usually DG plants require shorter installation times and the investment risk is not
high.
DG plants yield fairly good efficiencies especially in cogeneration and in
combined cycles (larger plants).
The liberalization of the electricity market contributes to create opportunities for
new utilities in the power generation sector.
The costs of Transmission and Distribution costs have increased while costs of
DG have reduced and hence, the overall costs are reduced with the installation of
DGs.
In fact, the distribution systems are not planned to support installations of Distributed
Generators at various locations. Installation of DGs on one hand improves the overall
performance of the distribution system where as on the other hand poses new problems. To
mention few problems with DGs are their grid connections, pricing and change in protection
schemes. To achieve maximum benefit from installation of DGs, it is formulated as an
optimization problem to locate and fix the size of DGs with the constraint on total injection of
installed DGs in a radial distribution system.
Recently, a large number of Artificial Intelligent techniques have been employed in
power systems. Nowadays, Fuzzy logic is used to solve the problem of distribution systems
more efficiently. Fuzzy logic is a powerful tool in meeting challenging problems in power
systems. This is so because fuzzy logic is the only technique, which can handle imprecise,
‘vague or fuzzy’ information. The benefits of such fuzzification include greater generality,
higher expressive power, an enhanced ability to model real world problems and a
methodology for exploiting the tolerance for imprecision. Hence, Fuzzy logic can help to
achieve tractability, robustness, and lower solution cost.
1.2 LITERATURE SURVEY
A power system consists of power stations of various types which are interconnected
by transmission lines, sub-transmission lines and distribution networks to supply different
types of loads to different consumers. A distribution system is that part of electrical power
system with sole objective of delivering electrical energy to the end user. There has been
significant scientific development in planning of power generation and transmission systems
but little interest is shown in systematic planning of distribution systems.
Distribution system failures have severely affected the power supply to the
consumers. In practice the distribution lines has been stretched too long unscientifically
resulting in poor voltage regulation and high energy losses especially at peak load conditions.
Indeed the need of the hour is to deliver a better quality of power to the consumers by
overcoming the various deficiencies.
Hence, of late much attention has been paid on electrical distribution systems by
suggesting the improved methods for optimizing the performance namely
i. Optimal conductor selection for RDS
ii. Optimal voltage regulator placement
iii. Optimal capacitor placement
iv. Distributed generator placement
v. Network reconfiguration
The modern power distribution network is constantly being faced with an ever-
growing load demand. Distribution networks experience distinct change from a low to high
load level conditions every day. In order to evaluate the performance of a distribution system
and to examine the effectiveness of proposed modifications to a system in the planning stage,
it is essential that a load flow analysis of the system is to be repeated for different operating
conditions. Certain applications, particularly in the distribution automation (i.e., VAr
planning, state estimation, etc.) require repeated load flow solutions. It basically gives the
steady state operating condition of a distribution system corresponding to a specified load on
the system.
Any load flow method must be able to model the special features of the distribution
systems in sufficient detail. The traditional load flow methods used in transmission systems,
such as Newton – Raphson and Gauss – Seidel techniques, failed to meet the requirements in
both convergence and robustness aspects for the distribution system applications. Generally
distribution networks have high R/X ratio. Due to this reason, popularly used Newton –
Raphson [2] and Fast Decoupled load flow algorithms [3] may provide inaccurate results and
may not converge. Many researchers have suggested modified versions of the conventional
load flow methods for solving distribution networks with high R/X ratio [39, 54, 104].
Kersting [6, 11] has developed a load flow method for solving radial distribution networks by
converting distribution networks based on ladder network theory into a working algorithm. In
this method, update currents and voltages during the forward sweep and backward sweep
give directly voltage correction.
Baran and Wu [18] have proposed a method based on iterative solution of three
fundamental equations representing voltage, real and reactive power. They have computed
system Jacobian matrix using chain rule. They have also proposed decoupled and fast
decoupled distribution load flow algorithms. Chiang [21, 22] has proposed decoupled load
flow method for distribution networks and also proposed the effect of convergence criteria
for the solution of distribution systems. In fact, the decoupled and fast decoupled methods
proposed by Chiang [22] are similar to that of Baran and Wu [18]. However, very fast
decoupled distribution load flow proposed by Chiang is very attractive because it does not
require any Jacobean matrix construction and factorization but more computations are
involved because it solves three fundamental equations in terms of active power, reactive
power and voltage magnitude.
Das et al. [32, 38] have proposed load flow technique for solving radial distribution
networks, in which they have proposed a unique bus, branch and lateral numbering scheme
which help to evaluate exact real and reactive power loads fed through any bus and to obtain
bus voltages. These methods involve only the evaluation of simple algebraic expression of
receiving end voltage which does not involve any trigonometric terms, as in the case of
conventional load flow method. Haque [41] has proposed load flow solution of radial
distribution systems for different load models which are dependent on voltage magnitude.
Rajicic and Taleski [48] have proposed two load flow methods for the radial and weakly
meshed distribution networks. The first is an admittance method and the second is an
extension of the current summation method. Both are developed under an assumption that
network branches are ordered according to the known rules.
Ghosh and Das [52] have presented another method for solving radial distribution
networks by evaluating a simple algebraic expression of receiving end voltages. Ranjan et al.
[71] have proposed a new load flow method using power convergence characteristic. This
method can easily accommodate the composite load modeling if the composition of load is
known. Venkatesh and Ranjan [72] have proposed load flow method which uses concept of
data structure to define the topology of distribution system. In this, dynamic data structure for
distribution system is defined to obtain a computationally efficient solution.
Aravindhababu [74] has proposed a fast decoupled power flow method for
distribution systems based on equivalent current injections. In this method, the assumptions
on voltage magnitudes, angles and R/X ratios necessary for decoupling the network as in the
conventional FDLF are eliminated. Ranjan et al. [76, 77] have proposed load flow method for
radial distribution system and has extended it to different load models to analyze voltage
stability of the system. Sathish Kumar et al. [131] have proposed a new technique which uses
modified forward substitution method to solve radial distribution systems.
Chen et al. [24, 25] have proposed three phase power flow method for unbalanced
distribution system using YBus and also proposed detailed models of three phase generator
and transformer to incorporate in load flow method for analyzing unbalanced distribution
systems. Chen and Chang [26] have proposed Open wye/open delta and open delta/open delta
transformer models which are commonly used in analysis of three phase unbalanced
distribution systems. Zimmerman and Chiang [39] have proposed fast decoupled load flow
technique for unbalanced radial distribution systems. Thukaram et al. [50] have proposed
robust three phase power flow algorithm for unbalanced radial distribution networks. This
method uses forward and backward propagation to calculate branch currents and bus
voltages. Garcia et al. [53] have proposed three phase current injection method (TCIM) to
solve unbalanced radial distribution system. TCIM is based on the current injection equations
written in rectangular coordinates and is a full Newton method, which gives quadratic
convergence to obtain the solution.
Lin and Teng [54] have proposed fast decoupled load flow method to solve
unbalanced radial distribution systems. In this, constant G – matrix has been developed based
on equivalent current injections which needs to be inverted only once to obtain the solution.
Teng [55] has presented a network topology method to obtain the solution of unbalanced
distribution system. In this method, two matrices have been developed; one is Bus Injection
to Branch Current (BIBC) and second is Branch Current to Bus Voltage (BCBV) matrix to
find the solution. Kersting [56] has proposed modeling of transformer and other components
of distribution systems. Jen-Hao Teng [78] has proposed direct method of load flow solution
of unbalanced radial distribution networks. Garcia et al. [79] have presented a procedure to
implement PV buses in three phase distribution load flow of unbalanced radial distribution
systems.
Peng Xiao et al. [96] have proposed unbalanced distribution load flow method to
solve unbalanced distribution networks. In this, the unified three phase transformer model has
been developed to obtain the solution of unbalanced distribution systems. Subrahmanyam
[100, 132] has proposed three phase load flow method to solve three phase unbalanced radial
distribution system. This algorithm uses basic principles of circuit theory and solves simple
algebraic recursive expression of voltage magnitude to obtain the solution. Many researchers
[78, 93, 114, 127] have proposed different methods to solve unbalanced radial distribution
systems. Literature survey shows that good amount of work has been carried out for the load
flow solution of balanced and unbalanced radial distribution systems.
Attention was given to the problem of optimal conductor selection as early as 1950’s
[1]. Funkhouser and Huber [1] have proposed a method for determining economical
Aluminum Conductor Steel Reinforced (ACSR) conductor sizes for distribution systems.
Adams and Laughton [4, 5] have proposed a method based on mixed integer programming
for optimal planning of radial distribution systems. The selection of type of conductor is
based on the current carrying capacity of the optimal feeder configuration. Wall et al. [7]
have proposed a method which goes one step further, i.e. based on the need for feeder voltage
support as well as the current carrying capacity requirement. Kiran and Alder [9] have
proposed a dynamic model for the development of primary and secondary circuits supplying
a residential area. Features of this model, which support optimal conductor sizing and the
evaluation of annual reserve requirements associated with capital requirement and energy
losses.
Ponnavaikko and Prakasa Rao [8, 10] have proposed a model (PPR model) for
optimal conductor grading for radial distribution feeders. This model is flexible and can
handle the variations in the load growth rate, load factor, and cost of energy over the planned
period. The PPR model considers the conductor-grading problem as optimization problem of
minimizing the sum of the cost of the feeder and the cost of the feeder energy losses.
However, major drawback of this method is that it cannot handle the lateral branches. Tram
and Wall [17] have developed a practical computer algorithm for optimal selection of
conductors of radial distribution feeders. Many researches [23, 29, 43, 133] have formulated
the optimal conductor selection as a planning problem using Genetic, Evolutionary
programming and Differential Evolution algorithm.
Zhuding Wang et al. [58] have proposed a new approach to find the selection of
optimal conductor size for radial distribution system. In this a multisection, branching feeder
model with non-uniform load distribution has been considered to obtain the best solution.
Sivanagaraju et al. [65] have proposed a method for selecting the optimal size of conductor
for radial distribution networks, and the conductor selected by this method will satisfy power
quality constraints. Rakesh Ranjan et al. [69] have presented a method using voltage
deviation index and power quality index for selecting optimal branch conductor of radial
distribution feeders based on evolutionary programming.
Mandal and Pahwa [70] have presented a systematic approach for selection of an
optimal conductor set, by considering several financial and technical factors in the solution,
which will be the most economical when both capital and operating costs are considered.
Prasad et al. [103] have proposed an algorithm for selecting the optimal branch conductor of
radial distribution systems using Genetic Algorithm. Rama Rao and Sivanagaraju [116] have
proposed a method to select optimal branch conductor for radial distribution systems to
minimize the losses, using Plant Growth Simulation Algorithm. Sivanagaraju and Viswanatha
Rao [128] have proposed to select optimal conductor of radial distribution system by using
discrete particle swarm optimization technique. It can be observed that good amount of work
has been carried out for the selection of optimal branch conductor of radial distribution
systems.
The use of shunt capacitors in electrical distribution system is a common practice and
has been investigated by many authors in the past. A review of the literature on reactive
power compensation in distribution feeders indicates that the problem of capacitor allocation
for loss reduction in electric distribution systems has been extensively researched over the
past several decades.
Reactive currents in a distribution system produce losses and result in increased
ratings for various distribution components. Shunt capacitors can be installed in a distribution
system to reduce energy and peak demand losses, release the kVA capacities of distribution
apparatus, which improves power factor and the system voltage profile [33]. Thus, the
problem of optimal capacitor placement consists of determining the locations, sizes and
number of capacitors to be installed in a distribution system such that the maximum benefits
are achieved while operational constraints are satisfied.
Erstern and Tudor [16] proposed a method to determine the optimal size of capacitor
using non-linear dynamic programming. A nonlinear model has been developed based on the
bus impedance reference frame, and takes into account the uncertainty of the load. Pattern
recognition technique and sensitivity analysis are used to minimize the number of variables
of the model. Salama and Chikani [27, 28] have developed a method for the control of
reactive power in distribution systems for fixed and varying load conditions, giving
generalized equations for calculating the peak power and energy loss reductions, the optimal
locations and rating of the capacitors.
Abdul-Salam et al. [31] have proposed a heuristic technique, which brings about the
identification of the sensitive buses that have very large impact on reducing the losses in the
distribution systems. This method is relatively fast, very effective and gives considerable
saving in energy and in net saving when the cost of the capacitors and their installations are
taken into account. Sundharajan and Pahwa [35] have proposed a method to select optimal
size of capacitors using genetic approach and a sensitivity analysis based method is used to
find the sensitive locations for installing the capacitors. Laframboise al. [36] and
Ananthapadmanabha et al. [42] have proposed a method based on expert system to identify
the bus in distribution system to place capacitor and thereby reduce the losses in the system.
In these methods, approximate reasoning using fuzzy set theory is used for placement of
capacitor in a radial distribution system.
Chis et al. [45] proposed a heuristic method in which only a number of critical buses,
named as sensitive buses, are selected for installing capacitors in order to achieve a large
overall loss reduction in the system combined with optimum savings. The sensitive buses are
selected based on the losses caused in the system by the reactive components of the load
currents. Lee [46] has proposed a method to find the optimal size of a capacitor based on
evolutionary programming. A comparative study has been carried out with genetic algorithm
and linear programming. Haque [51] has suggested an analytical method for capacitor
placement for loss reduction in radial distribution systems. Levitin et al. [57] formulated the
capacitor allocation problem as complicated combinatorial problem and solved using Genetic
Algorithm.
Bhaskar et al. [60] presented a system approach to the problem of capacitor allocation
on radial distribution feeders. A genetic algorithm was used to determine the optimal
placement and control of capacitors, so that the economic benefits achieved from system
capacity release, overall peak load power and energy losses reductions are maximized.
Calovic and Saric [64] have proposed a new integrated fuzzy concept for multi-objective
solution of the optimal capacitor placement and compensation planning problem in
distribution networks. Sivanagaraju et al. [68, 102] presented a method of minimizing the
cost of loss associated with the reactive component of branch currents by placing optimal
capacitors at proper locations. Mekhamer et al. [73] have proposed a method for reactive
power compensation using fuzzy logic in RDSs. Khodr et al. [124] have presented
computationally efficient methodology for the optimal location and sizing of static and
switched capacitors in radial distribution systems. The optimization problem has been
formulated as mixed integer linear programming problem.
However, few papers reported capacitor allocation problem in combination with
network reconfiguration or voltage regulator placement in the system to improve the voltage
profile [19, 20, 30, 40, 95, 111]. In view of this, to include uncertainties of the system data a
fuzzy logic approach is suggested to determine the optimal location of capacitor in radial
distribution systems.
One of the other methods to reduce losses and to improve voltage profile is by placing
voltage regulators at suitable locations in a distribution system. It is a device that keeps a
predetermined voltage in distribution networks in spite of load variations within its rating.
Voltage regulators are mainly employed in extensive and loaded feeders, where the reactive
compensation does not have a satisfactory effect. The optimization problem involves the
determination of the number and optimal locations of VRs and their tap positions, in order to
minimize the peak power and energy losses and provide a smooth voltage profile along a
distribution network with lateral branches.
Grainger and Civanlar [12-14] have proposed an integrated method for the reactive
power and voltage control of radial distribution system using combination of capacitors and
voltage regulators. In these papers, they decouple the capacitor problem from the VR
problem and propose VR’s for a network completely compensated with capacitors. Also the
network voltage is the criterion for the selection of the optimal number of VRs and their
locations and tap positions. Salis and Safigianni [37, 59] have proposed a method to locate
voltage regulator and its tap setting in a radial distribution system based on voltage drop
criterion. Gu and Dizy [44] have presented a method using neural networks for combined
control of capacitor banks and voltage regulators in distribution systems. In this method, loss
equation is considered as objective function with voltage inequalities as constraints to obtain
the optimal number, location and tap position of VR. Kagan et al. [83] have proposed for
integral control of Volt / VAr using capacitors and voltage regulators in radial distribution
system.
Souza et al. [84, 85] have proposed a method using genetic algorithm for optimal
location of voltage regulators in distribution networks. Augugliaro et al. [86] have suggested
a method using evolutionary programming to fix the tap positions of VR’s to minimize the
losses and to improve the voltage regulation. Mendoza et al. [88] have presented a method
using genetic algorithm to define the number of voltage regulators and their optimal position
in radial networks based on the minimization of energy losses. The proposed method
separates the original problem into two parts, the first part consists in determining the optimal
position of VRs in the system, by solving a multi-objective optimization problem and second
part, consists in choosing the number of VR’s, by using the benefits index as the decision
making process.
Lopez et al. [101] have presented the problem of voltage regulation and minimization
of power loss for radial distribution systems with new approach of micro-genetic algorithm.
Rao and Sivanagaraju [113] have proposed Discrete Particle Swarm Optimization technique
for the voltage regulator placement problem in radial distribution systems with an objective
of maximizing the net savings. Pereia and Castro [125] have proposed an analytical method
to find optimal placement of voltage regulator in distribution systems. Ganesh and
Sivanagaraju [126] have proposed a method for placement of voltage regulators in
unbalanced radial distribution system using genetic algorithm. Rama Rao and Sivanagaraju
[129] have proposed a method, which deals with initial location of voltage regulator buses by
using Power Loss Indices (PLI) and Plant Growth Simulation Algorithm (PGSA) is used for
determining optimal number and location along with their tap setting, which provides a
smooth voltage profile along the network. The main objective of this method is to minimize
the number of voltage regulators which in turn reduces the overall cost. From the above
discussion it can be observed that good amount of work has been carried out for the selection
of optimal number and location of VRs on radial distribution systems. However, literature
survey on voltage regulator clearly shows that hardly any attempt is made to find optimal
location and number of VRs directly without sequential or recursive algorithm.
Due to the increasing interest on renewable sources in recent times, the studies on
integration of distributed generation to the power grid have rapidly increased. In order to
minimize line losses of power systems, it is very important to determine the optimal size and
location of local generation in radial distribution system. The benefits of DG are numerous
and the reasons for implementing DGs are rational use of energy, deregulation policy,
diversification of energy sources, ease of finding sites for smaller generators, shorter
construction times and lower capital costs of smaller plants and proximity of the generation
plant to heavy loads. Many researchers have proposed different methods to evaluate the
benefits from DGs in a distribution system in the form of loss reduction and reduction in
loading level [90, 97].
Kim et al. [47] have presented power flow algorithm to find the optimum DG size at
each load bus assuming every load bus can have DG source. Such methods are, however,
inefficient due to a large number of load flow computations. Celli and Pilo [62] have
proposed method to find sizing and placing of DG using GA, in order to achieve a good
compromise between costs of network upgrading and power losses. A method is presented to
identify the optimal location and size of DG based on Tabu search algorithm [63, 75]. Nara et
al. [63] have proposed a method using Tabu search algorithm to identify optimal location and
number of DG. The size of DG is calculated using analytical method. Rosehart and Nowicki
[66] have proposed Lagrangian based method to determine optimal location for placing DG,
considering economic limits and stability limits.
Genetic algorithm (GA) based distributed generator placement techniques to reduce
overall power loss in distribution system are presented in [61, 67, 82, 87, 105, 107-108, 112,
119, 120] but the problems with GA are that it is computationally intensive and suffers from
excessive convergence time and premature convergence.
Wang and Nehrir [80] have proposed analytical methods to find the optimal location
and size of DG to place in radial as well as meshed systems with respect to the reduction of
power losses. This technique is basically concerned with finding the optimal location but not
the optimal size. EI-Khattam et al. [81, 91] have proposed an optimization model to solve the
distribution system planning problem and determine optimal sizing and placing of DG.
Harrison and Wallace [89] have proposed repeated power flow method to find optimal size of
DG in continuous functions of capacity. Celli et al. [90] have proposed a method based on
multi evolutionary algorithm in terms of pre-specified sizes of DG at the best locations to
reduce losses in the distribution system.
Khattam et al. [91] have developed an optimization model to solve distribution
planning problem and determine optimal sizing and placing of DG. Iyer et al. [92] have
proposed an analytical method to improve voltage profile of radial distribution system by
optimally placing DG. Mallikarjuna and Mitra [94] have proposed a method for
determination of optimal size and location of a distributed generator for microgrid system.
Khoa et al. [98] have proposed an algorithm using the Primal dual interior point method for
solving nonlinear optimal power flow problem. The main aim is to optimize location and
sizing of DG in distribution systems for line loss reduction. Le et al. [99] have proposed a
method to find placement of optimal Distributed Generator to reduce losses in radial
distribution systems. Lakshmi Devi and Subramanyam [106, 109] have presented a case
study to minimize the loss associated with the absolute value of branch currents by placing
DG operating with any power factor at suitable locations using Fuzzy logic and the size of
DG at any power factor is calculated by analytical method.
Harrison et al. [108] have proposed a hybrid approach to find optimal number and
size of DG. In this method, GA is used to search a large range of combinations of locations,
then employing optimal power flow to define available capacity for each combination. Raj et
al. [110] have proposed particle swarm optimization technique to identify the optimum
generation capacity of the DG and its location based on indices to provide maximum power
quality improvement. Alemi and Gharehpetian [115] have proposed an analytical method
which is based on sensitivity factors for optimal allocation and sizing of DG units in order to
minimize losses and to improve the voltage profile in distribution systems. The sensitivity
factor method helps to reduce the search space by the linearization of nonlinear equations
around the initial point.
Ahmadigorji et al. [117] have incorporated the benefits of cost of DG in their method
to find the optimal location and size of DG and also have considered constraints on voltage
limits and operational limits of DG in the calculation of objective function. Firouzi et al.
[118] have proposed Ant Colony Optimization Based Algorithm for finding optimal location
and size of DG in distribution networks. Shayeghi and Mohammadi [121] have proposed
probabilistic model for optimal location and sizing of DG for loss reduction and voltage
profile improvement in power distribution networks. Lee and Park [122] have proposed a
method to select the optimal locations of multiple DGs by considering the power loss in
steady state operation. Thereafter, their optimal sizes are determined by using Kalman Filter
Algorithm.
Padma Lalitha et al. [130] have proposed new technique known as Artificial Bee
Colony (ABC) algorithm to find the optimal size of DG by taking number and location of DG
as inputs. The location of DG is identified by single DG placement method [123], which is an
analytical method. The advantages of ABC method for determination of locations of DGs are
improved convergence characteristics and less computation time with voltage and thermal
constraints being considered. From the above discussion, it can be seen that lot of work has
been carried out on DG placement to reduce the real power losses of the system. But in most
of the methods analytical approach has been adopted to find the optimal location of DG and
hardly any attempt is made to find optimal location and number by using Fuzzy Expert
System (FES).
The recent trends in power system studies emphasize the importance of FES [34,
49].Due to the advantage of FES, the optimal location and number of voltage regulating
devices that are to be employed to reduce the system losses and to improve the voltage profile
in RDS can be directly determined instead of using recursive or sequential approaches. In
addition the variation in bus voltages and power losses are simultaneously taken into
consideration for determining the optimal locations.
1.3 OBJECTIVE OF THE THESIS
The objective of the thesis is to reduce power losses and to improve the voltage
profile of radial distribution systems by using conductor grading and other voltage regulating
devices. The following methods are suggested to minimize the losses and hence maximize the
objective function, which consists of net savings in terms of cost of conductor or voltage
regulating equipment and cost of energy losses.
i. Conductor grading of RDS
ii. Optimal capacitor placement and its sizing
iii. Optimal voltage regulator placement and its tap position
iv. Optimal distributed generator placement and its rating
The above proposed methods are investigated with 15, 33, 69 bus radial distribution
systems. A load flow technique using data structures is developed for balanced and
unbalanced radial distribution systems and is employed in the above studies.
In the above optimization studies, to find optimal conductor grading or optimal
placement of voltage regulating device a fuzzy logic approach is employed.
1.4 SCOPE OF THE THESIS
The various features of distribution systems in general and survey of the past work
concerning Load flow methods for distribution networks, grading of conductors in
distribution system, optimal capacitor size with its location, optimal voltage regulator
location with its tap position and optimal size of distributed generator with its location, are
presented in Chapter 1. The objectives and motivations of the research work presented in the
thesis are discussed further in subsequent chapters.
A simple method is presented for obtaining the load flow solution of balanced and
unbalanced radial distribution systems in Chapters 2 and 3 respectively. The proposed
method involves the solution of algebraic equation of receiving end voltages and also makes
use of Data Structure to identify the structure of distribution system. This method can handle
system data with any random bus and line numbering scheme except the slack bus being
numbered as 1. In this method a Bus Incidence Matrix (BIM) is constructed and is then
processed to reflect the structure of the RDS. The proposed method is demonstrated through
different balanced and unbalanced radial distribution systems and the results are presented.
There is a need to reduce the system losses and improve the voltage profile by
suitable methods in distribution systems. Some of these methods to improve the overall
performance of the distribution systems are presented in subsequent chapters.
A method is proposed using fuzzy expert system for obtaining the optimal branch
conductor type in Chapter 4. The conductor, which is determined by this method, will satisfy
the maximum current carrying capacity and simultaneously maintains voltage levels of the
distribution system within the acceptable limits. The above method also determines the
period for which the optimal conductor selected will be suitable even taking the annual load
growth into consideration. The proposed method is tested with two different practical radial
distribution systems and results are presented.
A simple method for minimizing the loss associated with the reactive component of
branch currents by placing capacitors using fuzzy expert system is presented in Chapter 5. In
this method, first identify the suitable locations to place capacitors using fuzzy logic and the
size of the capacitors is found using an analytical method. The efficacy of the proposed
method is tested with different examples of radial distribution systems and the results are
presented.
The automatic voltage regulators help to reduce energy losses and to improve the
power quality of electric utilities, compensating the voltage drops in distribution systems. In
Chapter 6, two different methods are presented to determine optimal number of voltage
regulators and their optimal tap position. In the first method, the suitable location and tap
position of voltage regulator is determined by using Back Tracking Algorithm. In the second
method, the suitable location is determined by using fuzzy logic and its tap position is
obtained using analytical method. The two proposed methods are demonstrated through
different radial distribution systems and the comparison of results obtained from both these
methods is presented.
In Chapter 7, a method is proposed for loss reduction by injecting power locally at
load centers. In this method, optimally located distributed generator is used to minimize
losses and to improve the voltage profile of the radial distribution system. The optimal
locations of distributed generators are determined with the help of fuzzy logic and its size is
calculated using an analytical method. The proposed method is tested with different radial
distribution systems and results are presented.
The significant contributions of the entire work and the scope for future research in
this area are presented in Chapter 8.