Review of Application of Optimization Techniques in Smart Grids

Abstract

The smart grids can optimize the use of various generation sources and reduce cost. With new developments in renewable energy, distributed generation, the traditional power grids are being converted to smart grids. This paper provides a survey of the literature that focuses on the use of optimization techniques in SG, prominently in generation, transmission and distribution systems. It is anticipated that this paper will help in finding future research scopes and strategies efforts for improving the SG.

Full text

Review of Application of Optimization T
echniques
in Smart Grids
Md. Mainul Islam
School of Computing, Engineering and
Mathematics
Western Sydney University
Locked Bag 1797, NSW 2751,
Aust ralia
m.islam7@westernsydney.edu.au
Mahmood Nagrial
School of Computing, Engineering and
Mathematics
Western Sydney University
Locked Bag 1797, NSW 2751,
Aust ralia
m.nagrial@uws.edu.au
Ali Hellany
School of Computing, Engineering and
Mathematics
Western Sydney University
Locked Bag 1797, NSW 2751,
Aust ralia
a.hellany@uws.edu.au
Jamal Rizk
School of Computing, Engineering and
Mathematics
Western Sydney University
Locked Bag 1797, NSW 2751,
Aust ralia
j.rizk@uws.edu.au
Abstract—
The smart grids can optimize the use of various
generation sources and reduce cost. With new developments in
renewable energy, distributed generation, the traditional
power grids are being converted to smart grids. This paper
provides a survey of the literature that focuses on the use of
optimization techniques in SG, prominently in generation,
transmission and distribution systems. It is anticipated that
this paper will help in finding future research scopes and
strategies efforts for improving the SG.
Keywords— s
mart grid, optimization techniques, energy
management, renewable energy resources
I. INTRODUCTION
In general, the term grid is an interconnected system that
is utilized in the electricity system to deliver electricity from
generating stations to end-users. It incorporates electricity
generation, tran smission, distribution and control syst ems to
supply electricity to the consumers. The intelligence is being
implemented into electric grids that help to make the grid
smart. The smart grid (SG) tries to enhance operations and
maintenances by automating operations so that each
component can communicate with one another as and when
required [1]. An SG is a bi-directional grid that upgrades the
traditional power grid to one which operates more
economically and responsively. Table I shows the basic
difference between traditional grid and SG. The SG
incorporates modern information technologies,
computational intelligence along with advanced electronic
devices in an integrated manner throughout the generation,
transmission, sub-stations and consumption. It makes a
convenient electric grid system that is clean, secure, efficient,
reliable and sustainable.
By using the smart meter, phase management unit,
supervisory control, and data acquisition system, SG
performs real-time tracking and measurements of utilities.
An SG offers handy programs and features to the customers
to make their individual choices to utilize energy depending
on real-
time or time of use pricing information using the
home area networks. Moreover, a good business relationship
can be made between an individual customer and utility grid
by permitting two-way power flow and communications
between them [2]. Thus, the individual consumer can partake
in environmental improvement by providing their clean
energy to the SG which can be financially beneficial.
The energy crisis and environmental issues have reached
a high level due to use of fossil fuels [3, 4]. Therefore, the
researchers have been concentrating on the development and
utilization of renewable energy sources (RESs). The RESs
can increase the power system reliability, minimize the total
cost and peak demands of utility grid [5]. Furthermore, the
reduction of greenhouse gases can be done by utilizing
electric vehicles. Electric vehicles are considered as an
essential resource that can store energy from variable RESs
during excessive electricity generation and feed back to the
SG when required. The massive integration of electric
vehicles may have an unpleasant impact on the grid because
of taking excessive power when charging. This adverse
impact can be minimized by utilizing control strategies and
managing EV fleet. Fig. 1 shows the main electricity
producers and consumers in SG.
The consumers and suppliers are the two big performers
in SG where the consumers like to minimize the cost
whereas suppliers aim to make more profits and reduce the
peak demand. Because of the massive arena of the SG
research, many researchers disclose different perceptions in
regard to the SG. This study will investigate the applications
of optimization techniques in the SG system.
TABLE I. A BA SIC D IFFERE NCE B ETWEE N TRADITIONAL GRID AND
SMART GRID
Traditional Grid
Smart Grid
Electromechanical
Digital
Unidirectional communication
Bi-directional communication
Consol idate d gen eration
Dispersed generation
Manual monitoring and
restoration
Self-mo nitoring and self-
restoration
Limited control
Extensive control
Limited consumer choices
Various consumer choices
II. SMART GRID MODEL
An SG can be defined as the integration of various
technologies, components, networks and services under
different smart management and control systems. It provides
a significant number of advantages both for the consumers
and the utility companies.
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Fig. 1. Main el ectricity producers and consumers
Fig. 2 shows the SG conceptual model. It can be
classified into three major parts, smart infrastructure system,
management systems, and protection systems [6] as shown in
Fig. 3. Furthermore, the smart infrastructure consists of three
sub
-
systems, i.e. smart energy, smart information and smart
communication infrastructure sub-systems. Smart energy
sub-system deals with generation, transmission, distribution
and consumption. The information sub-system acts on smart
metering, monitoring and management system, and
communication sub-system ensures the connectivity and
information sharing among systems, appliances and
applications. The smart management system utilizes
numerous advanced techniques to pursue various
management schemes such as enhancement of energy
efficiency, supply and demand balance, reduction of
operation cost, and emission control. The smart protection
system deals with the enhancement of grid reliability, along
with failure protection, and security and privacy protection
services.
Distribution
Communicati on networks
Distribute d
generations
Conventional
Loads
Battery storages
Electri cal veh icles
Smart homes
Transm
i
ss
i
on Bulk generat
i
on
C
entral control
Smart load s
Power line
Communic ation line
Fig. 2. SG conceptual model
Smart Grid
Smart infrastructure
system
Smart Management
System
Smart Protect ion
system
Smart energy
sub
-system
Smart
information
sub-system
Smart
communication
sub-system
Management
objectives
Management
techniques
and tools
Stability
and fault
protec tion
Security
and privacy
Fig. 3. A brief cla ssification of SG
III. OPTIMIZATION TECHNIQUES
Optimization is a process or method used to provide the
optimal performance of a complex and complicated
application. The following sub-sections overview and
describe different optimization techniques.
A. Overview of Optimization Techniques
The optimization methods try to find the best or near best
parameter(s) from the initial parameter(s) that optimize the
given function. Numerous optimization techniques have been
applied to handle optimization problems and these
techniques can be divided into conventional mathematical
optimization and computer intelligent optimization
techniques. The conventional methods e.g. direct and
gradient-based methods use a deterministic approach to
determine the optimal solution. The limitation of the
conventional techniques is that it becomes complex to find
the best value when the search space size increases [
7].
The computational intelligence, gradient-free,
optimization systems have been largely utilized to solve the
complicated problem in many fields because of their
simplicity, large applicability, time-saving, and global
prospect. The intelligence algorithms can be classified into
heuristic and metaheuristic algorithms. The metaheuristics
can be further split into the swarm, evolutionary and physic-
based algorithms [8].
Swarm
-based algorithms mimic the social behaviour of
swarms, herds, animals and other individuals. The common
swarm algorithms are particle swarm optimization algorithm
(PSO), artificial bee colony and ant colony optimization.
Evolutionary algorithms work on the natural genetic
evolution such as recombination, crossover, mutation,
selection and adaption. The most common evolutionary
algorithms are the genetic algorithm (GA), genetic
programming, evolutionary programming and differential
evolution. Physic-
based algorithms are influenced by a
physical process such as music, metallurgy, science, complex
dynamic systems, and interaction between culture and
evolution. Simulated annealing is the most dominant
algorithm in this category. The other algorithms are lightning
search algorithm, harmony search algorithm, and teaching-
learning-based optimization.
B. Description of Optimization Techniques
1) Conventional mathematical optimization techniques:
The gradient-
based optimizati
on approaches use knowledge
of gradient/derivation information to find the optimal
solution. Gradient-based techniques often use two-step
iterative method to find the optimal solution. The first step
is to identify the search direction based on the gradient
information and the second step is to move in the identified
direction until new instruction made [9]. This two-step
method can be mathematically expressed as
xk+1=xk+αk.Dk (1)
where αk and Dk are the step size and direction vector
respectively.
Linear programming is an approach for the optimization
of a linear objective function, subject to the linear equality
and inequality constraints. Nonlinear programmin
g is
concerned with the nonlinear objective function subject to
inequality constraints. An integer programming is a


mathematical optimization program in which some or all of
the variables ar e defined to be integers. When some decision
variables are not discrete the problem is known as the mixed-
integer programming problem.
Dynamic programming is both a mathematical
optimization and a computer programming method. It has a
particular search strategy for multi-level decision process and
the strategy is splitting the problem into sub-problems.
Stochastic pr ogramming is a mathematical programming but
the objective function and constraints depend on
optimization variables and a random variable.
2) Computer intelligent optimization techniques:
a) Particle swarm optimization (PSO): A PSO is a
multi-agent optimization approach which was first
introduced by Kennedy and Eberhart [10]. The PSO method
imposes different particles for searching the global optimal
solution. The best knowledge for each particle is kept in the
particle memory (pbest) and the best global attained among
all particles is known as the global best particle (gbest). The
velocity (vj) and location (xj) of each particle are updated
after every iteration as follows:
)()(
2211
1
l
jj
l
jj
l
j
l
j
xgbestrmxpbestrmvv 

Z
(2)
11 

l
j
l
j
l
j
vx
x
(3)
where l is the iterations; ω is the inertia weight factor in
the range of [0.5, 1]; m1 and m2 are constants specified by the
users. r1 and r2 are the uniformly distributed random numbers
in the range of [0, 1]. Fig. 4 shows the basic flow chart of the
PSO. The limitations of PSO are premature convergence and
high possibility to get stuck in local optima
.
Start
Generation
of initial
population
Fitness
evalutatio n
Update
Pbest and
Gbest
Update
population
Termin ation
criteria
Satisfied?
stop
Yes
No
Fig. 4. Basic flowchart of PSO
b) Genetic algorithm (GA): The GA is a global search
technique, inspired by Darwin's theory of evolution [11].
This method follows the procedure of natural selection
where the best individuals are chosen for reproduction so as
to produce offspring for the next generation. They produce
offspring who obtain the features of the parents and will be
included in
the next generation. The offspring will be better
if parents are better and there is a higher possibility of
surviving. This process continues until it finds the best
fitness. Fig. 5 shows the basic flow chart of GA.
Start
Generation
of initial
population
Fitness
evalutatio n
Selec tion/
reproduction crossove r mutati on
Termin ation
criteria
Satisfied?
stop
Yes
No
Fig. 5. Basic flowchart of GA
c) Harmony search algorithm (HSA): HSA is a
population-based metaheuristic method influenced by the
musical process of looking for an ideal state of harmony
[12]. This algorithm considers following steps: initialization
of harmony memory, improvisation of a new harmony,
update the new memory. The harmony memory is a memory
where all the variables and fitness function values are
stored. Fig. 6 shows the basic flowchart of HSA.
Start
Initilization
of harmony
memory
Improvise a new
harmony
(memory consideration,
pitch adjustment,
randomization)
Termin ation
criteria
Satisfied?
stop
Yes
No
Add new
harmony to
harmony
memory?
Updat e the
harmony momery
Yes
No
Fig. 6. Basic flowchart of HSA
d) Tabu search algorithm (TSA): TSA is a
metaheuristic algorithm based on local search [13]. It
searches new solutions leading from a provided neighbour
structure and incumbent solution through moves. Tabu
algorithm iteratively replaces incumbent solution with a best
neighbouring solution until the termination criteria are met
and thus improve the solution in the neighbourhood. Fig . 7
shows the basic flowch art of TSA.
Start
Initialize
current
solutio n
Create a set of
neighbourhood
solutio ns
Evaluate the
neighbourhood
solutio ns
Find the best
admissible
solutio n
Termin ation
criteria
Satisfied?
stop
Yes
No
Update
tabu li st
Fig. 7.
Basic flowchart of TSA
IV. APPLICATION OF OPTIMIZATION TECHNIQUES IN SMART
GRID
Many researchers have been using different optimization
techniques in power system optimization over last decades.
These techniques have become an indispensable part of the
power grid optimization due to the massive integration of
RESs and distributed generations (DGs) in grid systems. The
optimization techniques can determine the optimal power
generation among different sources based on th e demand,
scheduling, dispatching, etc. in order to minimize the peak
demand and operational costs.
A. Smart Energy Infrastructure
The electricity generation and flow schemes in SG are
more flexible, e.g. the distribution grid can obtain electricity
from both the RESs and smart consumers. The energy sub-
system can be classified into smart electricity generation,
transmission and distribution grids.
1)
Smart generation
: Smart generation system
accumulates electricity from RESs, DGs along with
traditional power plants to improve system stability and
minimize the plant’s capacity and reduce the generation
capital costs. The study in [14] utilized PSO algorithm to
determine the optimal sizing of a hybrid energy system such
as photovoltaic, wind turbine, batteries and diesel generator
based on SG system considering load shifting-based load
priority. This method minimizes the total cost to supply the
required load demand while satisfying the system reliability.
Similarly, an optimization model for SG with diesel,
photovoltaic and wind generators, and battery storage
system was presented in [5]. The problem is formulated to
reduce the generation and start-up cost from the generators.
A hybrid differential evolution with HSA is employed to


solve the problem. In Ref. [15], the authors developed a
stochastic programming model that incorporates thermal
generation and wind penetrations with the demand response
(DR) program to reduce the gen eration plant investment
costs. The authors only utilize operational constraints to
optimize the system and the DR is accounted as a linear
price responsive function which is not realistic. The research
in [16] proposed an optimal hybrid power generation model
including RESs with different design schemes in the context
of costs, emission, and reliability. An improved PSO is
utilized to find the optimal power generation solution. The
study in [17] proposed a novel idea of a virtual power plant
that combines and manages a set of distributed energy
resources with the total capacity equivalent to a traditional
power plant. The virtual power plant has massive
advantages in system stability, flexibility and quick response
to fluctuations over traditional power plants. However,
intricate optimization and communication techniques are
essential for control and smooth operation of the virtual
plant.
2) Smart transmission: The technologies such as
advanced materials, power electronic devices, sensors,
control techniques, communications and policies are crucial
to upgrade the existing transmission infrastructure to smart
one. It will control, monitoring and automation and thus
increases the system performance e.g. stability, power
quality and security. The authors in [18] proposed a multi-
year transmission expansion model considering solar DG
and solved by using evolutionary PSO algorithm. The
results show that the existing transmission system needs to
be expanded or modified to meet the current demand even
though DG penetration increased up to 20%. Similarly, the
research in [19] suggested a grid optimization model in
terms of transmission expansion that incorporates network’s
renovation and design choices based on the consumers’
demand. This method is solved by CPLEX, a tool for
solving linear optimization problems, programming to
minimize the capital costs of infrastructure and operational
costs of electricity production. Their model does not
consider realistic data, and the uncertainty of generation and
consumption are also not incorporated in their study. For the
same purpose, the authors in [5, 20] consider the
transmission line as power flow constraints to determine the
optimal operation of SG in order to minimize the total
operational costs. The optimization problems are solved by
using HSA [5] and TSA [20] respectively. Nonetheless, the
research in [21] introduces an inspection approach by using
the GA to minimize the inspection and maintenance costs
and fault power loss to ensure safety and reliability. The
authors consider only climatic issues ignoring other
environmental factors to reduce power loss.
3) Smart distribution: Many researchers have been
concentrating on the distribution networks especially
distribution side energy management to ensure the basic
objectives such as system reliability, power quality and
electricity price. DR program is an important factor that
often persuades consumers to use standby generation or
reduce or shift their demands for limiting peak demands.
However, it may not only increase the demand during off-
peak hours but also increase the consumer's discomfort
level. Therefore, the energy management, optimization, and
control technologies are essential to reduce the limitations of
DR programs.
The authors in [2] proposed a method to reduce the
electricity cost of a single and multiple houses with integer
linear programming technique. Nonetheless, the suggested
method considers only a single time slot instead of multiple
time which may lead to sub-optimal results. The authors in
[22] developed a mathematical model for smart home
elements to minimize the cost and maintain the users'
comfort level with distributed energy r esources and DR. The
proposed multi-objective functions are solved by mixed
integer nonlinear programming algorithm considering only
one house. Moreover, mixed integer linear programming was
used in [23
] to determine the minimum cost control of a
micro grid by modelling supply and demand as decision
variables. However, the fixed electricity price is considered
throughout the study that may provide an unrealistic solution.
Two levels of optimization problems were proposed in [24]
to optimize the power consumption and minimize the cost.
This study considers few consumers to validate the proposed
models and the impact of storage devices in the utility grid is
ignored. The authors in [25] suggested a multi-objective
PSO algorithm to optimize the distributed energy sources for
reducing the operational costs and carbon footprint. A multi-
objective evolutionary algorithm was pr oposed in [2 6] to
minimize the electricity usage price and accomplishes the
load balancing technique in order to reduce the interruption
time for the operation of electrical devices. However, the
outcomes are obtained by using single algorithm without
further validation.
B. Smart Management System
An SG is not only the combination of energy,
communication information system but also the effective
utilization of smart energy management system. The
integration of energy sources and uncertainty of their
generation causes a significant difference in generation and
demand patterns. Therefore, smart energy management and
control is essential. The management objectives include
efficiency and demand profile enhancement, overall cost and
emission reduction while management methods incorporate
different optimization techniques such as dynamic
programming, convex programming and heuristics
algorithms to determine the optimal solution of the objective
functions.
The authors in [27] proposed a two-level stochastic
energy management technique considering the uncertainty of
RESs along with DGs and energy storage devices. This
technique is solved by CPLEX optimizer (commercial
softwar e) and OpenDSS (electrical distribution simulation
tool) to minimize the total operating expenses and power
losses. The author also studies the impact of electric vehicles
on energy management under different charging levels. The
research in [28] applied convex programming for automatic
load management in a smart home by dealing with schedule-
based appliances. However, this study is not extended to deal
with multiple household schemes. Ref. [29] formulated a
coordinated home energy management system problem in
which multiple residential distribu
ted units collaborate with
each other to obtain the actual power balancing with


minimum cost. The proposed model is solved by a certainty
equivalent control and decomposition method.
C. Smart Protection System
Smart protection system prevents SG from abrupt power
outages and cyber-attacks. It increases the system reliability,
information security and privacy, and also quick recovery
from failures. An optimization problem in terms of voltage
control and adaptive protection was developed in [30]
considering info
rmation and communication technology. GA
is applied in solving the problem. A grid protection appr oach
was suggested in [31] that aims to minimize the power losses
by choosing an optimal power factor. The commercial
LINDOGlobal software is employed to solve the problem
without any validation. On the other hand, the smart meter
data privacy approach was proposed in [32]. The online
control technique, Lyapunov optimization algorithm is
utilized to solve the problem. DR programs are totally
disregarded during optimizing the system.
The applications of different optimization techniques are
summarized in Table II.
TABLE II. APPLICATION OF DIFFERENT OP TIMIZA TION TECHNIQUES
Ref.
Target of the Objective
Solution Method
[4]
Minimize total generation and
operation cost
Harmony search algorithm
with differential evolution
[8]
Transmissi on inve stment cost
Evolutionary particle swarm
optimization algorithm
[13]
Minimize total generation
cost
Particle swarm optimization
algorithm
[14]
Generation plant invest ment
cost
Stochastic programming
[15]
Optimal power generation
Modified particle swarm
optimization algorithm
[18]
Capital costs of infrastructure
and operational costs of
electricit y production
Commercial CPLEX software
[20]
Minimize the inspection and
maintenance costs and fault
power loss
Genetic algorithm
[21]
Minimize the cost and
maintain the users' comfort
level
Mixed integ er nonlinear
programmi ng algorithm
[24]
Optimize distribu ted energy
sources, redu ce op erational
costs and carbon footpri nt
Particle swarm optimization
algorithm
[25]
Minimize the electricity
usage price and reduce the
interruption ti me for the
operation of electrical devices
Evolutionary algorithm
[26]
Minimize the total operating
expenses and power losses
Commercial CPLEX software
[27]
Optimal appliances
scheduling
Convex programming
[30]
Minimize the power losses
Commercial LINDOGlobal
softwa re
V. FUTURE DIRECTIONS
The SG can outperform the classical power grid in
reliability, efficiency, load scheduling and users’ satisfaction.
but, there are several challenges to make the SG system
practical.
x The users cann ot use the RESs and DGs as
independent electricity supplier because of their
uncertainty during electricity generation but can be
utilized as the virtual power plant to help the central
grid. Hence an effective communication and control
algorithm is essential to make SG reliable and
efficient.
x Different advanced techniques and metering need to
be promoted to take the advantages of DR programs.
x Effective optimization algorithm needs to be
developed to find the optimal energy management
solution that is reliable and less time-consuming.
VI. CONCLUSION
This review shows the perspective for the utilization of
optimization techniques in SG. Power grid advancement is
an ongoing process that may continue for decades and make
a robust platform for new applications and technologies. The
power industry has prospects and becoming progressively
imperative in the rising low carbon economy. It can be said
that the SG will lead to a sound power supply services and
eventually develop our daily lives how to make.
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