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.

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.