The (or the reduction of peak loads), load balancing,

The
regulatory requirements of maintaining acceptable voltage levels in distribution
system and minimum energy loss are a challenging task in the highly stressed
distribution systems. In the competitive electricity markets, distribution
network operator need to design efficient, reliable, and cost effective power
networks. Distribution systems shall be planned to accommodate various energy
sources to grid, quality and reliable electricity access to every consumer for
present and future demands.  In the smart
distribution systems, opportunities to make measurements and perform
calculations that allow loss reduction shall be implemented to design
efficient, reliable, and cost effective power networks. Several methods have
been proposed in the literature to reduce distribution system losses: system
reconfiguration, reactive power compensation, distribution generation (DG), distribution
automation, load management (or the reduction of peak loads), load balancing,
voltage regulators, optimal cable selection, usage of energy efficient
transformers and induction motors etc.

The International Conference on Large
High Voltage Electric Systems (CIGRE), de?nes DG size of the order of 50–100 MW
1. International Energy Agency (IEA) de?nes distributed generation (DG) as
generating plant serving a customer on-site or providing support to a
distribution network, connected to the grid at distributed level voltages.
Renewable energy based DG is developing fast all over the world in recent years
due to its promising potential to reduce the portion of fossil energy
consumption in electric power generation and mitigate power losses and harmful
carbon emissions. The impact of DG on radial distribution network i.e. voltage
support, loss reduction, and distribution capacity release, power quality
issues and environmental bene?ts is explained in 3,4. Literature survey
regarding DG placement 31-40, capacitor placement 21-30, simultaneous
placement of DG, capacitor 8-11,13-14,16-20, capacitor with reconfiguration
15,42,46 and DG, capacitor with reconfiguration 7,12 is presented in this
section. Based on the literature, it is observed that many researchers solved
above problem in two stage manner 11,13,14,21,22,23,25,27,28,31,32,34 i.e.,
in the first stage optimal locations were identified using loss sensitivity
factors/power loss index/fuzzy expert system/stability indexes. Thereafter, an
optimization technique applied to determine rating of DG/capacitor.      

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A hybrid heuristic search
algorithm presented (HS-PABC) 7 comprises of Harmony Search Algorithm (HAS)
and PSO embedded Arti?cial Bee Colony algorithm (PABC). Authors used HS-PABC
algorithm for optimal placement of DG and capacitors along with reconfiguration
in distribution systems for power loss minimization. A multi-objective
evolutionary algorithm based on decomposition (MOEA/D) was applied in 8 to
determine optimal sizes and locations of DGs and SCs with an objective of
minimizing real and reactive power losses in the system. In 9, a new
optimization algorithm called intersect mutation differential evolution (IMDE)
applied identify optimal location and rating of DGs and capacitors in
distribution network. Objective function considered in their problem as
minimize the power loss and cost of energy loss. A new heuristic algorithm
known as Back Tracking Search Algorithm (BSA) was introduced in 10 for
optimal sizing and placement of DG, capacitors and thyristor-controlled series
compensator in distribution systems. Authors in 11, have presented optimal DG
and capacitor placement to minimize system power losses in two phases. In the first
phase, potential locations identified based on loss sensitivity factors.
Thereafter, Harmony Search Algorithm (HSA) and Particle Arti?cial Bee Colony
algorithm (PABC) utilized to determine sizes of DG and capacitors. The drawback
of this approach was candidate locations identified using loss sensitivity
factors may not be global optima. Improved swarm/evolutionary based
optimization algorithms such as Improved Genetic Algorithm (IGA), Improved
Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO)
proposed in 12 for optimal placing Capacitors DGs. Further, impact of
reconfiguration also studied on the system performance along with DG and
capacitors. Two-stage approach was presented in 13 to solve DG placement and
sizing problem. Loss sensitivity factors with fuzzy sets employed to identify
optimal location for DG and Backtracking Search Optimization Algorithm (BSOA)
used for DG sizing. Authors in 14, have solved simultaneous DG and capacitor
placement using heuristic and swarm based algorithms in two phase manner. In
first phase, Binary Collective Animal (BCA) algorithm applied to determine optimal
locations and Binary PSO (BPSO) used to find out optimal ratings to optimize
total power loss and voltage deviation. Binary PSO 15 algorithm was used to
obtain optimal recon?guration and capacitor placement are used to reduce power
losses. PSO algorithm applied 16 simultaneous allocation of DG and capacitors
with an objective of loss minimization. In 17, loss sensitivity factors
utilized to identify the optimal candidate locations for DG and capacitor
placement, and the quadratic curve ?tting technique employed to determine their
optimal ratings. Binary PSO based DG and capacitor placement presented in 18,
for power loss minimization, reliability and voltage improvement. In 19,
coordinate control of OLTC, DG and capacitors was addressed for voltage
regulation. Initially, genetic algorithm applied to find optimal setting of
OLTC. Thereafter, DG and capacitors installed. PSO algorithm used in 20 for
simultaneous finding of optimum DG and shunt capacitor bank location and
size. 

In 21, 22, fuzzy logic
system applied to find optimal location of capacitors and GA used for sizing of
capacitor units. Optimal location and rating of capacitors are determined using
loss sensitivity factors and PSO respectively to minimize power losses 23.
Authors in 24 presented a direct search algorithm to find location and size
of capacitors. This method suffers from high computational time since
iteratively searches for all possible locations based on loss reduction. In
25, authors solved capacitor problem in two stages with an objective of
minimization of total loss and maximization of savings. In the first stage loss
sensitivity factors used to identify locations and in second stage
Gravitational Search Algorithm (GSA) applied to find optimal size of capacitors.
Teaching Learning Based Optimization (TLBO) approach applied in 26 to address
capacitor placement problem to minimize power loss and energy cost. In 27,
cuckoo search optimization used to determine rating of capacitors to minimize
operating cost. Initially, potential locations obtained for capacitor
installation using power loss index. Differential evolution and pattern search
(DE-PS) and power loss indices (PLI) / loss sensitivity factors (LSF) used to
solve optimal capacitor placement problem 28 with an objective of minimizing
annual operational cost. Novel method based on analytical expression developed
in 29 to determine capacitor sizes and optimal locations obtained using loss
sensitivity factors. In 30, capacitor placement problem solved to maximize
annual savings and problem solved using mixed-integer linear programming.
Genetic algorithm used in 45, to address optimal placement of switched and
fixed types of capacitors in distribution system to minimise total power
losses.

Simulated Annealing (SA) and
loss sensitivity factors used to obtain optimal rating and location DG
respectively 31 for loss minimization and voltage stability improvement.
Bacterial Foraging Optimization Algorithm (BFOA) and loss sensitivity factors
used 32 to determine size and location of DG to minimize loss and improvement
of voltage stability index value. In 33, improved analytical (IA), loss
sensitivity factor (LSF) and exhaustive load ?ow (ELF) methods presented to
solve optimal DG placement problem. In IA, method analytical expressions
developed for optimal size of DG. Wherein ELF and LSF approaches follow
iterative procedure, which is inefficient and suffers from large computational
time. GA and PSO integrated approach 34 for DG placement problem in which GA
gives optimal locations and PSO optimizes the size of DG to minimize network
power losses, voltage deviation and enhanced voltage stability index.
Differential Evolution (DE) used in 35, to solve optimal DG placement
problem. In 36, analytical expressions developed to determine optimal rating
of DG at each bus subjected to minimization of total power loss. This method not
suitable to solve multiple DG placement problem. PSO algorithm applied in 37
to place different type of DGs in distribution system to minimize total power
loss. In 38, Arti?cial Bee Colony (ABC) algorithm used to solve DG placement
problem. Bacterial Foraging Optimization Algorithm (BFOA) used in 39 to find
the optimal size of DGs and capacitors. Fuzzy Genetic Algorithm (FGA) based
approach presented in 40 for DG and capacitor placement. Optimal reconfiguration of distribution systems
obtained using fuzzy multi objective approach 41. In their problem, a
multi-objective function formulated as loss minimization, voltage deviation and
enhancement of voltage stability. Authors in 42, addressed optimal capacitor
placement in the reconfigured network for loss minimization using Krill Herd
(KH) algorithm. Capacitor with reconfiguration problem solved in two phases
manner in 46 using ACO & HAS for loss minimisation. ACO used to determine
location and rating of capacitors, whereas HAS used to obtain optimal
reconfiguration.  

In view of the above research work published, it is observed
that many authors have focused on solving DG and capacitor placement problems
independently. Few authors in 8-11,13,14,16-20 have addressed simultaneous
placement of DG and capacitors placement. Electrical load in a power system mainly is
categorised as: residential, commercial and industrial. In order to study
the distribution system in real time, system operator has to consider these
load models together along with load curve variation. To study annual energy
loss savings in smart distribution network, time varying load models impact needs
to be addressed for capacitors and DGs placement. The main objective of the
paper is to determine optimal locations and sizes of capacitors and DGs in
distribution system with different load models considering time varying load
scenario. Power loss reduction, voltage profile improvement, and cost of energy
savings are calculated for the test system. Highlights of the paper are: (i) Combined
capacitor and DG allocation in distribution network using sensitivity approach
and hybrid optimization, (ii) impact of seasonal load variation on capacitor
and DG sizes for radial distribution network, (iii) impact of seasonal load
model on voltage stability index, (iv) cost of energy loss, cost of capacitor
and DG, and overall cost savings per annum. The results have been obtained for the
distribution network of UK Distribution Corporation consisting of 38 buses. The
results have also been obtained for radial and mesh distribution systems.

The rest of
the paper is organised as follows: section 2 provides the optimal location and
sizing of DG. Section 3 describes optimal capacitor placement. In section 4, voltage
stability index determination and cost analysis presented. Section 5 provides
results and discussions. Finally, the paper concluded in section 6.