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A Hybrid Bacterial Foraging Tabu Search Heuristic Optimization for Demand Side Management in Smart Grid


Abstract and Figures

With the advent of Smart Grid (SG), it provides the consumers with the opportunity to schedule their power consumption load efficiently in such a way that it reduces their energy cost while also minimizing their Peak to Average Ratio (PAR) in the process. We in this paper target the appliances to schedule in such a way that it increases User Comfort (UC) and decreases electricity consumption load which benefits both consumer and utility. In this paper, we proposed hybrid of Bacterial Forging Algorithm (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI) to schedule appliances while balancing User Comfort which is the main objective of the Demand Side Management (DSM). This paper tries to reduce both waiting time and electricity cost simultaneously in the new hybrid Bacterial Foraging Tabu Search (BFTS) technique. Real time pricing (RTP) scheme was used to get the total cost of electricity consumed. We compared the results of proposed hybrid scheme with Bacterial Forging (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI). The result shows effectiveness of using hybrid Bacterial Foraging Tabu Search (BFTS) technique for Demand Side Management (DSM).
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A Hybrid Bacterial Foraging Tabu Search Heuristic
Optimization for Demand Side Management in
Smart Grid
Ahmad Jaffar Khan1, Nadeem Javaid1,, Zafar Iqbal2, Naveed Anwar3, Abdul Saboor1, Inzimam-ul-Haq4and
Umar Qasim5
1COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
2PMAS Arid Agriculture University, Rawalpindi 46000, Pakistan
3University of Wah, Wah Cantt 47040, Pakistan
4COMSATS Institute of Information Technology, Wah Cantt 47040, Pakistan
5Cameron Library University of Alberta Edmonton Canada
Corresponding author:,
Abstract—With the advent of Smart Grid (SG), it provides
the consumers with the opportunity to schedule their power
consumption load efficiently in such a way that it reduces their
energy cost while also minimizing their Peak to Average Ratio
(PAR) in the process. We in this paper target the appliances
to schedule in such a way that it increases User Comfort
(UC) and decreases electricity consumption load which benefits
both consumer and utility. In this paper, we proposed hybrid
of Bacterial Forging Algorithm (BFA) and Tabu Search (TS)
Algorithm using different Operational time Interval (OTI) to
schedule appliances while balancing User Comfort which is the
main objective of the Demand Side Management (DSM). This
paper tries to reduce both waiting time and electricity cost
simultaneously in the new hybrid Bacterial Foraging Tabu Search
(BFTS) technique. Real time pricing (RTP) scheme was used to
get the total cost of electricity consumed. We compared the results
of proposed hybrid scheme with Bacterial Forging (BFA) and
Tabu Search (TS) Algorithm using different Operational time
Interval (OTI). The result shows effectiveness of using hybrid
Bacterial Foraging Tabu Search (BFTS) technique for Demand
Side Management (DSM).
Index Terms—Smart grid, Demand side management, Real
time pricing, Heuristic techniques
In smart grid (SG) efficient electricity consumption using
modern technologies, is currently one of the significant target
area for research [1]. Advance metering groundwork and
demand management policies are important steps which help
us in development of distributed network for efficient energy
Demand Side Management (DSM) programs enable con-
sumer to efficiently control their electricity consumption loads
by shifting their load from on-peak hours to off-peak hours
to avail various economic incentives [2]. DSM programs
developed in the early days where based on unidirectional
communication from the utility to consumer like direct load
control (DLC) [3]. The DLC is most successful for consumer
that use high electric load appliances while it had almost no
effect for consumers using many low electric load appliances.
Demand response (DR) programs has two major categories; 1)
Incentives based (DLC based) 2) dynamic pricing (time-of-use
pricing (TOU) and real-time pricing (RTP) schemes). In the
above pricing schemes, consumers are encouraged to control
their electricity load based on economic incentives voluntarily
to avoid peak load conditions [4]. Considerable number of
techniques have been worked on solve different forms of DMS
as explained in [5] [6].
In literature the DSM is mainly discussed as a way of
reducing Peak to Average Ratio (PAR) and electricity cost.
Different techniques have been proposed such as mixed-integer
linear programming [5], game theory [7], backtracking algo-
rithm [8] and evolutionary algorithm (EA) [6] for scheduling
DSM schemes but EA gives way impressive results as its
adjustable nature allow us to increase UC by changing load
patterns based on consumer lifestyle. In the [9] EA gives
much better results in comparison to simulated annealing as
well as greedy search algorithm technique while EA such as
Bacterial Forging Algorithm (BFA) looks for optimal schedule
for consumer appliances by using steps like chemotaxis,
reproduction and elimination-dispersal. In BFA suboptimal
schedules are replaced by the more optimal during elimination-
dispersal step. The time required to find an optimal schedule
depends on number of population steps. In our proposed BFA-
based scheduling we have limit the population steps to thirty
as increasing steps doesnt increases the optimality of results.
In this article we will use hybrid Bacterial Foraging Tabu
Search (BFTS) technique made from Tabu Search-based (TS)
and BFA-based scheduling approach to schedule consumer ap-
pliances in SG. The both schedules consumer appliances based
on RTP information received from utility. The consumption of
electricity is reduced in a specific time slot by shifting loads as
compared to reducing the load. Energy management controller
unit (EMCU) can only schedule appliances that are Elastic in
nature in the home unit. We have considered only single home
use case for our simulation with different OTIs.
Remaining article is organized as follows. Related work
is presented in section II. Problem statement is described in
section III. System model in section IV. The simulation results
and their discussion in section V. The conclusion in section
Lots of work is being done by researchers nowadays in the
field of SG, Scheduling appliances on the basis of various
schemes.Some articles related to SG are discussed below:
K Muralitharan [10] put forward an optimization method
implemented using Multiobjective EA to optimize energy
consumption costs used by the consumer which helps in load
balancing so amount of time delay that occurs on execution
of an appliance is minimize. They used Time of Use (TOU)
pricing model in their paper. The results given in [10] reveals
that a consumer can use power under the threshold level to
bypass paying additional in term of utility bill thus allowing
consuming more power on lower costs. while [11] Awais pro-
posed how genetic algorithm can be used to reduce electricity
cost by shifting several electric appliances from on-peak hours
to off-peak which according to results helps in reducing cost
and PAR.
Study [12] proposed a Genetic DSM model to lower PAR
and waiting time of home appliances for home consumers. In
the paper they used genetic DSM with 20 different consumer
who were using different types of appliances with each of
them having different characteristics from each other. This
model used RTP scheme in its simulations. The results of
the simulation presented in G-DSM model showed lowering
of individual and 20 consumer costs by 39.39% ,45.85%
respectively and PAR by 17.17%, 45.24% for individual and
20 consumers respectively.
In paper [13] model is proposed for lowering the electricity
cost using dynamic pricing scheme to prevent peak hours. This
model comprises of smart meter, control node wireless sensor
networks and in-home display. Data flow between utility
and consumer smart meter is controlled by Advance meter
infrastructure and smart meter controls both master controller
and Advance meter infrastructure. The master controller coor-
dinates and controls both the controllable and uncontrollable
appliances schedules and the most efficient schedule is sent to
each control node through wireless sensor networks while in
home display starts the complete process.
Game theory based on dynamic pricing with respect to
residential and commercial sector of Singapore electric market
was studied [13]. Half hourly RTP, TOU and Day-Night (DN)
pricing schemes are assessed and compared with each other
to find which of the three pricing strategies help in reduction
of peak load reduction. Layered model [15] for DSM which
comprises of power generator, demand response aggregator
and consumers was proposed. Consumer discomfort level are
taking in to consideration in demand side management and this
model leads us to multiobjective problem thus allowing the
multiobjective algorithm to be used to find Pareto efficiency
helping us to find the fair solution.
Worked [16] on the economic effects of DMS in the smart
grid where electricity generation cost is one of the parameters.
They used IEEE 24 bus RTS with optimal power flow to get
the cost of power generation of 24 hours. They used day ahead
pricing scheme and implemented load data DSM to be able to
lower the power generation costs and in the end comparing
the cost of total power generation. They used differential
evolution to explain the optimal power flow (DC-OPF) in each
hour while finding a recent load curve. Another study [17] in
which authors proposed a model for distributed control system
based on Organization Multiagent System (MAS). The main
objective of this model was to address peak load problem
using Cyber Physical systems. An Artificial Immune Network
algorithm was implemented by the control decision process of
the system to regulate the energy consumption of all devices
connected to smart grid. In the first step ASPECS (Agent-
Oriented Software Process for Engineering Complex Systems)
methodology was used to develop the proposed models which
during simulation lowered energy consumption by 20%. They
used day ahead prices scheme in their simulations.
Proposed [18] a model on how to intelligently coordinate the
plug-in electric vehicles (PEV) demand of energy consumption
in a distribution system. They proposed a system based on
giving each PEVs a score using fuzzy expert system depending
upon PEV attributes like state of charge, battery capacity,
charger max rating and departure time of the vehicle and
the PEV efficiently charged to increase satisfaction of users
without breaking the grid constraints. The results of the
simulation indicate that PEVs which are more critical, having
shorter parking time and high charge time required benefit the
most among thus increase in user satisfaction.
Proposed [19] a hybrid approach of BFA and genetic
algorithm (GA) for cost minimization and load management
by shifting all the load form on-peak to off-peak hours.
They used RTP scheme in their hybrid model whose results
after simulations verify that the model reduces total cost
of electricity and peak average ratio. Another similar study
[20] proposed GA and Earthworm Optimization Algorithm
based schemes for minimizing PAR, and energy cost and
maximizing UC by scheduling appliances in home energy
management using Real time pricing scheme. Author [21]
gave comparative performances analysis of different heuristic
algorithms which include harmony search algorithm, enhanced
differential evolution and hybrid of both harmony search
differential evolution. They used multiple knapsack theory
in their problem that the highest capacity of the consumer
electric consumption must be in range compared to his electric
bill which he can bear. They used RTP, day ahead and TOU
pricing schemes in their simulations. The table.1 below gives
us a comparison between different techniques along with their
achievements and Drawbacks.
DSM programs are developed to solve energy shortage
problems, generated due to increase in consumption of energy.
Load management based on DR is most used DSM strategy,
which causes load to be shifted from on-peak towards off-peak
hours [2], thus reducing energy consumption cost in process.
But this causes peaks to generate at off-peak hours, while also
increasing appliances operational waiting time. Many systems
like [10]- [21] have been developed with aims to minimize
cost, PAR, and User comfort while using OTI of 1 hour. In
which trade off occurs between cost and PAR, and cost and
user comfort, which are not fully considered by most of the
researchers. As authors in [11], [12], [14], [16], [17], [19], [20]
and [21] never considered comfort. Few researcher ignored
PAR in [10], [11], [14], [16] and [18] due to which a burden
was created on utilities due generation of peaks on off peak
Cost minimization and shifting load to off peak hours does
not makes a system efficient, many other factors are involved.
User comfort in relation to waiting time of appliance is one
of the factors involved. Operation of the appliances need to
be flexible so that their operation can be rescheduled in such
a way that it neither generates peaks nor it effects cost and
PAR too much.
Fig. 1: Proposed HAN architecture
In a standard Home area network (HAN) as shown in Fig 1,
appliance load is generally divided in to two categories man-
ageable and non-manageable loads. Most of the past papers on
energy management work is being mainly done to minimize
manageable loads as it consumes the most energy and are
easier to predict in HAN. We can further sub categorized
manageable load in to three different categories.
A. Brustload shift-able
Appliances in these categories are those that can be delayed
and can be interrupted once they have started execution until
the scheduler allows them to run again. E.g., vacuum cleaner,
water heater, etc.
B. Brustload non-interruptible
Appliances in these categories are those which can be
delayed but once started the scheduler wont be able to stop
them in the middle of the execution until they complete their
required execution time. E.g., washing machine, dish washer,
oven, etc.
C. Base load
Appliances are considered as base loads those that cant be
delayed and interrupted in any case. E.g., AC, refrigerator, etc.
Non-manageable loads are of those that are interactive in
nature which allows for little scheduling window. Moreover,
energy consumption of these appliances is insignificant. E.g.
TV, lights, PC, phones, etc.
D. Detail of different home appliances
In our simulations we studied an average size modern home
having following major appliances: vacuum cleaner, water
heater, water pump, Refrigerator, air conditioner (AC), Dish
washer, washing machine, Cloth dryer, oven morning, over
usage in evening, electric vehicle. We understand that each
appliance has a defined power consumption and time cycle
required to complete its execution and we took these values
from [22] for our simulations shown in Table II. We have used
RTP to determine electricity cost in our simulations. Now we
will describe appliances in our home separately.
1) Vacuum cleaner:The vacuum cleaner is used for 360
minutes a day while its power load is 0.7kW. Vacuum cleaner
is categorized in the Brustload shift-able category.
2) Water heater:The water heater remains on for 720
minutes during a day and its power load is 5kW. It is
categorized in the Brustload shift-able category.
3) Water pump:The water heater remains on for 720
minutes during a day and its power load is 5kW. It is
categorized in the Brustload shift-able category.
4) Dishwasher:Dishwasher has an operation time of 105
minutes during a day and it has power load of 1.35kW.
Dishwasher is categorized in the Brustload non-interruptible
5) Washing Machine and Cloth Dryer:Washing machine
and Cloth dryer work in sync to each other. The washing
machine takes 45 minutes to complete its operation while 0.8
kW of power load. After 15 minutes execution of washing
machine dryer starts and takes 60 minutes to complete while
power load of 2.4 kW during process. Washing machine
and cloth dryer are both categorized in the Brustload non-
interruptible category.
6) Oven Usage in Morning:Oven is one of those appli-
ances in the house hold that is used more than once a day. As
it is used for cooking breakfast in the morning and making
lunch/dinner in the evening. So, we can treat oven used in
the morning and evening as separate appliances. Oven used in
the morning is only for 30 minutes having a power load of
1.2kW. Oven is categorized in the Brustload non-interruptible
7) Oven Usage in Evening:Oven used in the evening is
for longer time and more than one burner is used. The oven is
usage runs for 90 minutes in the evening while having power
load of 1.2kW.
8) Electrical Vehicle:Electrical vehicles nowadays are
gaining acceptance as alternative to petroleum based vehicles
among public. The batteries of Electric vehicles are mostly
charged using home electricity and it takes 150 minutes
to fully charge its batteries while having a power load of
3kW. Electrical vehicle is categorized in the Brustload non-
interruptible category.
TABLE I: Summary of Related Work
Technique(s) Targeted Area Objective(s) Limitation(s)
Multiobjective Optimization Technique
Residential area Cost minimization and UC
Compromising on PAR
An Efficient Genetic Algorithm [11] Residential commercial and
industrial area
Cost minimization Compromising on PAR and UC
A generic demand-side management
model [12]
Residential area PAR, Waiting time and Cost
Compromising on UC
Optimal Residential Appliance
Scheduling [13]
Home energy management Cost and UC minimization Initial Setup cost is ignored
Game-Theory based dynamic pricing
strategies [14]
Residential and commercial
Cost minimization Ignoring UC and PAR
A Layered Approach [15] DSM UC and energy efficiency Ignoring Cost
Optimal Day-Ahead Scheduling in SG
DSM Cost reduction Ignoring UC and PAR
Multiagent model for Distributed Peak
Shaving System [17]
DSM PAR reduction Ignoring UC and Cost
Online Intelligent Demand Manage-
ment of Plug-In Electric Vehicles in
Future Smart Parking Lots [18]
Distribution systems UC maximization Ignoring Cost and PAR
Demand Side Management using Hy-
brid Bacterial Foraging and Genetic Al-
gorithm Optimization Techniques [19]
DSM Cost and PAR minimization Ignoring UC
Genetic Algorithm and Earthworm Op-
timization Algorithm for Energy Man-
agement in SG [20]
DSM Cost and PAR minimization Compromising on UC
Towards optimization of metaheuristic
algorithms for IoT with HAS, EDE and
HSDE [21]
DSM Cost and PAR minimization Compromising on UC
TABLE II: Appliances data used in our Simulations
Group Appliance Power
Water Heater 5 12
0.7 6
Water Pump 1 8
Base Load Refrigerator 1.2 18
AC 2.5 15
Dish washer 1.35 1.45
Washing ma-
0.8 .45
Cloth Dryer 2.4 1
Oven Morn-
1.2 .30
Load Vacuum
1.2 1.45
Electrical car 3 2.30
9) Refrigerator:Refrigerator is one of those appliances
that work for all day long with only going to off state when
the inside temperature is lower than set one and turn on when
the inside temperature is higher than the set one. we have
used 18 hours as the estimated time of refrigerator runs, and
its power load is 1.2kW. Refrigerator is categorized in the
Baseload category.
10) Air Conditioner:Air conditioner is also an appliance
whose runtime depends on temperature of the rooms. If the
rooms temperature goes lower than the set one it turns off for
a while until temperature the temperature it not high again.
So, we estimated that it runs 15 hours and its power load is
2.5kW. Air conditioner is categorized in the baseload category.
E. Duration of operation
We are using in our simulation time slots of 96, 48, 24. In
the 96 timeslot each slot represents 15 mins and in 48 timeslot
each slot represents 30 mins of the day while in 24 timeslot
each time represents 1 hour of the day.
Animal that are successful in foraging strategies are favored
by nature as compared to those that are poor at it thus are
eliminated. The generations eliminated are replaced by healthy
ones. The main accomplishment of this algorithm is that it
allows the bacteria cell to swarm together towards an optimal
solution. The following three successive steps are performed to
accomplish this. a) Chemotaxis this step is used to know about
life time of bacteria and we find out fitness pi of bacteria by
finding closeness of it to other bacterias new position i after a
tumble and the step size Ci is between [-1,1] in the direction
of the tumble. b) Reproduction where reproduction of next
generation takes place by cells which have done good in their
lifetime. c) Elimination dispersal last step in which poor cells
are eliminated. Algorithm 1 gives us the idea on how the BFA
Basically, a neighborhood optimization algorithm [23]. TS
uses neighborhood searching technique to keep searching
repeatedly from a potential solution for an even better solution
in a neighborhood unless a criterion to stop the search is met.
It has following steps: a) Create a neighborhood solution from
potential solution. b) Create Tabu List (TS) which stores all the
forbidden moves. c) Add Aspiration criteria (AC). c) Perform
Algorithm 1 BFA
1: procedure
Initialize parameters;
Initialize the location of the population;
2: for j = 1:Ncdo
4: for j = 1:Npdo
5: calculating initial location
8: for e = 1:(a-1) do
9: Compute fitness: J=Fit
10: end for
11: s=0
13: while s < Nsdo
14: swim loop
15: if J(i) Jlast(i) then
16: Evaluating current fitness
17: J(i)=J(i)
18: calculate new position
20: for e = 1:(a-1) do
21: fitness function
22: end for
24: else
25: s=Ns
26: end while
27: end for
28: Appliances with best fitness are
29: end for
TS. d) After each loop updates the TL. e) If TL is not updated
then update AC. f) Checks ending criteria to end execution.
Algorithm 2 gives us the idea on how the TS works.
In the following section we will be discussing the results of
simulations carried out using MATLAB by applying BFA, TS
and our new hybrid BFTS technique. We will evaluate them
based on PAR, Energy consumption, UC, cost of electricity
and feasible region. We used in our simulations 11 appliances
in our home. Each appliance belongs to a specific group and
has specific power rating which is given in table 1 above. We
are using RTP scheme in our simulations to calculate cost of
energy consumption.
A. Energy consumption
Given above Fig. 2a, 2b, 2c shows us the power consump-
tion pattern when using BFA Scheduling, TS, Unscheduled
Algorithm 2 TS
Initialize all parameter
2: x’=best solution among trails
S(x) sample of neighborhood S(x)εN(x)
4: Current solution x0εX
Set tabu list TL=150
6: Set aspiration criteria AC=0
Set iteration counter K = 0
8: Randomly generate initial solution
Randomly generate trail solutions S(x)εN(x)and sort them
in ascending order to obtain SS(X).
10: Let x’ be the best trail solution in SS(X)
12: X00 =x
14: X”=x’
end while k¡= number of iterations
16: For i=1:TL
Perform tabu search
18: If X00 >X0
Update X” in tabu list
20: Else X” not present in tabu list
update X” in aspiration criteria
22: end end end
If stopping criteria is satisfied
24: perform termination
else k=k+1
26: end
and hybrid BFTS under different OTIs. The output shows us
that BFA, TS and hybrid BFTS scheduling is more efficient in
lowering load management as compared to unscheduled power
consumption. When using hybrid BFTS scheduling power
consumption has less peaks as all peaks are scheduled to off
peak hours from on peak and these peaks remain similar in
different OTIs showing the time interval has no effect on Load
peaks. So, we can conclude from the above figures that hybrid
BFTS load management technique is shifting appliances load
to off peak hours.
Energy consumption graph for multiple homes using OTI of
1 hour is shown in Fig. 3a, 3b, 3c for 50, 30, and 20 homes. All
multiple homes energy load graphs show shifting of load by
different scheduling algorithms. Increase in number of homes
only increases load but not scheduling algorithms behavior.
B. Cost of Electricity
Electricity cost which the consumer has to pay when using
BFA, TS, Unscheduled and hybrid BFTS scheduled under
different OTI is shown in Fig. 4. As shown in the Fig. 4
when using OTI of 1 hour electricity cost for BFA scheduled,
TS scheduled, BFTS scheduled and Unscheduled electricity
consumption is $1670.20, $1630.20, $1600.50 and $1920.56
respectively which shows that hybrid BFTS scheduling elec-
tricity costs considerably less as compared to other techniques.
When using OTI of 30 mins and 15 mins Fig. 3 clearly shows
that hybrid BFTS technique still has lower cost as compared
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0 5 10 15 20 25
Time (hours)
1 2 3 4 5 6 7 8 910 11121314151617181920 21222324252627282930 313233343536373839 40414243444546474849 50515253545556575859 60616263646566676869 70717273747576777879 80818283848586878889 90919293949697
Time (Minutes)
Fig. 2: Energy consumption with different techniques under
(a) 1-hour OTI; (b) 30-minutes OTI; (c) 15-minutes OTI;
to BFA and TS techniques thus proving that different OTI
changes have little to no effect on cost of electricity during our
simulations. So we can conclude our hybrid BFTS scheduled
reduces electricity cost more as compared to TS and BFA
BFTS scheduling algorithm’s cost of energy consumption
by multiple homes using OTI of 1 hour is still less as compared
to other scheduling algorithms as shown in Fig. 5.
0 5 10 15 20 25
Time (hours)
0 5 10 15 20 25
Time (hours)
0 5 10 15 20 25
Time (hours)
Fig. 3: Energy consumption by multiple homes with various
techniques using 1-hour OTI (a) 50 homes; (b) 30 homes; (c)
20 homes;
OTI 15 OTI 30 OTI 60
Total Cost (Cents)
Fig. 4: Total cost of electricity with different techniques
under various OTIs
20 Homes 30 Homes 50 Homes
Total Cost (Cents)
Fig. 5: Total cost of electricity consumed by multiple homes
with different techniques
OTI 15 mins OTI 30 mins OTI 60 mins
Appliance waiting time (minutes/hours)
Fig. 6: Waiting time with different techniques under various
20 Homes 30 Homes 50 Homes
Appliance waiting time (hours)
Fig. 7: Waiting time when energy consumed by multiple
homes with different techniques
We can relate UC to both the cost of electricity consumed
and amount of time waited before using it. But we are using
UC in relation to that amount of time a user must wait before
he can use an appliance. In the Fig. 6 waiting time of OTI 30,
15 is in minutes while hours in OTI 60. We can clearly see
that hybrid BFTS scheduled has less waiting time as compared
to BFA and TS scheduled when using different OTI so we can
conclude that hybrid BFTS scheduled gives maximum UC.
BFTS scheduling algorithm’s waiting time when scheduling
multiple homes using OTI of 1 hour is still less as compared to
other scheduling algorithms as shown in Fig. 7. Thus we can
conclude that increase in load won’t effect BFTS effectiveness.
OTI 15 OTI 30 OTI 60
Peak Average Ratio
Fig. 8: PAR with different techniques under various OTIs
20 Homes 30 Homes 50 Homes
Peak Average Ratio
Fig. 9: PAR when energy consumed by multiple homes with
different techniques
DSM in SG is beneficial to both the consumers and utility.
Lower Peak Average Ratio not only helps utility in retaining
stability in electric load but also sometimes helps consumers
in lowering costs in their electricity bill. The above Fig. 8
clearly gives us an idea of performance of BFA, TS and hybrid
BFTS technique in terms of PAR as compared to Unscheduled
electric consumption. From above figures it’s clear that hybrid
BFTS technique has higher PAR as compared to TS scheduled
thus we can conclude that it doesn’t minimizes PAR.
BFTS scheduling algorithm’s PAR when scheduling multi-
ple homes using OTI of 1 hour is still more as compared to
TS scheduling algorithms as shown in Fig. 9. Thus we can
conclude that increase in load won’t help in lowering BFTS
E. Feasible Region
Feasible region is the area that covers the set of some points
which contain all the solutions possible with respect to our
fitness function. Fig. 10a, 10b, 10c shows feasible region for
energy consumption and electricity cost for 1 hour, 30 mins
and 15 mins OTIs respectively. A set of points P1 (1.2000,
32.8200), P2 (1.2000, 9.7200), P3 (11.1000, 89.9100), P4
(11.1000, 303.5850) gives us the bounded region as shown
in Fig. 6a which gives us the cost region for OTI of 1 hour.
P1 (1.2000, 32.8200) represents when cost is maximum and
load is minimum and P2 (1.2000, 9.7200) represents when cost
and load both are minimum. While P3, P4 gives us the points
when load is maximum but prices are minimum and maximum
0 2 4 6 8 10 12
Power consumption (kWh)
Cost per hour ($)
P2(1.2000, 9.7200)
P1(1.2000, 32.8200) P5(6.56, 180)
P4(11.1000 , 303.5850)
P6(11.100, 180 )
P3(11.1000, 89.9100)
Power consumption (kW)
P2(0, 0)
P1(0, 0)
P5(3.29, 90.0015)
P4(6.8750 , 188.0313)
P6(6.8750, 90.0015 )
P3(6.8750, 55.6875)
0 0.5 1 1.5 2 2.5 3 3.5
Power consumption (kW)
P1(0, 0)
P2(0, 0)
P6(3.4375, 71.1100)
P4(3.4375 , 94.0156)
P5(2.60, 71.1100)
P3(3.4375, 27.8438)
Fig. 10: Feasible Region in (a) 1-hour OTI; (b) 30-minutes
OTI; (c) 15-minutes OTI;
respectively. We get feasible region made of points P1, P2, P3,
P4, P5, P6 after setting some constraints in which P6 (11.100,
180) shows us that schedule load must never increase more
then 11.100 kWh at any time slot. While P5(6.56, 180) shows
us that at any time slot price more then $180 must not be
Similarly, a set of points P1 (0, 0), P2 (0, 0), P3 (6.8750,
55.6875), P4 (6.8750, 188.0313) gives us the bounded region
as shown in Fig. 10b which gives us the cost region for
OTI of 30 mins. P1 (0, 0) P2 (0, 0) represents when cost is
maximum, load is minimum and cost, load both are minimum
respectively. While P3, P4 gives us the points when load is
maximum but prices are minimum and maximum respectively.
We get feasible region made of points P1, P2, P3, P4, P5, P6
after setting some constraints in which P6 (6.8750, 90.0015)
shows us that schedule load must never increase more then
6.8750 kW at any time slot. While P5(3.29, 90.0015) shows
us that at any time slot price more then $90.0015 must not be
scheduled. Feasible region of 15 mins OTI is shown in Fig.
10c which shows that an appliance must not be scheduled if
price is higher then $71.1100 at any given time slot or if the
load is higher then 3.4375 kW.
This paper presented an hybrid BFTS scheduled to man-
age consumers energy consumption. We reduce waiting time
and cost of electricity consumption using our hybrid BFTS
technique. We evaluated our proposed hybrid BFTS technique
by using BFA and TS technique. The simulation results of
hybrid BFTS-based scheduled clearly shows that the proposed
technique is better as compared to BFA, TS and Unscheduled
electricity consumption. The electricity cost and waiting time
is minimized thus increasing UC. We must understand that
we can’t reduce cost, waiting time and PAR simultaneously.
There needs to be a trade-off between either cost, PAR and
waiting time which can’t be lowered together. Hybrid gives
lower waiting time and cost with respect to other techniques.
We used RTP pricing scheme to calculate the cost of electricity
consumption after scheduling our appliances. In future work,
we would work on BFA and Flower Pollination Algorithm
hybrid scheduling algorithm.
[1] Davoli, F., Repetto, M., Tornelli, C., Proserpio, G. and Cucchietti,
F., 2012. Boosting energy efficiency through smart grids. International
Telecommunication Union (ITU).
[2] Gellings, C.W., 1985. The concept of demand-side management for
electric utilities. Proceedings of the IEEE, 73(10), pp.1468-1470.
[3] Costanzo, G.T., Zhu, G., Anjos, M.F. and Savard, G., 2012. A system
architecture for autonomous demand side load management in smart
buildings. IEEE Transactions on Smart Grid, 3(4), pp.2157-2165.
[4] Baharlouei, Z., Hashemi, M., Narimani, H. and Mohsenian-Rad, H.,
2013. Achieving optimality and fairness in autonomous demand response:
Benchmarks and billing mechanisms. IEEE Transactions on Smart Grid,
4(2), pp.968-975.
[5] Mohsenian-Rad, A.H., Wong, V.W., Jatskevich, J., Schober, R. and Leon-
Garcia, A., 2010. Autonomous demand-side management based on game-
theoretic energy consumption scheduling for the future smart grid. IEEE
transactions on Smart Grid, 1(3), pp.320-331.
[6] Miao, H., Huang, X. and Chen, G., 2012. A genetic evolutionary task
scheduling method for energy efficiency in smart homes. International
Review of Electrical Engineering (IREE), 7(5), pp.5897-5904.
[7] Samadi, P., Mohsenian-Rad, H., Schober, R. and Wong, V.W., 2012.
Advanced demand side management for the future smart grid using
mechanism design. IEEE Transactions on Smart Grid, 3(3), pp.1170-
[8] Mohsenian-Rad, A.H., Wong, V.W., Jatskevich, J. and Schober, R., 2010,
January. Optimal and autonomous incentive-based energy consumption
scheduling algorithm for smart grid. In Innovative Smart Grid Technolo-
gies (ISGT), 2010 (pp. 1-6). IEEE.
[9] Khan, M.A., Javaid, N., Mahmood, A., Khan, Z.A. and Alrajeh, N., 2015.
A generic demandside management model for smart grid. International
Journal of Energy Research, 39(7), pp.954-964.
[10] Muralitharan, K., Rathinasamy Sakthivel, and Yan Shi. ”Multiobjective
optimization technique for demand side management with load balancing
approach in smart grid.” Neurocomputing 177 (2016): 110-119.
[11] Awais, M., Javaid, N., Shaheen, N., Iqbal, Z., Rehman, G., Muhammad,
K. and Ahmad, I., 2015, September. An efficient genetic algorithm based
demand side management scheme for smart grid. In Network-Based
Information Systems (NBiS), 2015 18th International Conference on (pp.
351-356). IEEE.
[12] Khan, M.A., Javaid, N., Mahmood, A., Khan, Z.A. and Alrajeh, N.,
2015. A generic demandside management model for smart grid. Interna-
tional Journal of Energy Research, 39(7), pp.954-964.
[13] Shirazi, E. and Jadid, S., 2015. Optimal residential appliance scheduling
under dynamic pricing scheme via HEMDAS. Energy and Buildings, 93,
[14] Srinivasan, D., Rajgarhia, S., Radhakrishnan, B.M., Sharma, A. and
Khincha, H.P., 2017. Game-Theory based dynamic pricing strategies for
demand side management in smart grids. Energy, 126, pp.132-143.
[15] Li, D., Sun, H. and Chiu, W.Y., 2016, October. A layered approach for
enabling demand side management in smart grid. In Control, Automation
and Information Sciences (ICCAIS), 2016 International Conference on
(pp. 54-59). IEEE.
[16] Verma, P.P., Soumya, P. and Swamp, K.S., 2016, March. Optimal day-
ahead scheduling in smart grid with Demand Side Management. In Power
Systems (ICPS), 2016 IEEE 6th International Conference on (pp. 1-6).
[17] Lizondo, D., Araujo, P., Will, A. and Rodriguez, S., 2017, April. Mul-
tiagent Model for Distributed Peak Shaving System with Demand-Side
Management Approach. In Robotic Computing (IRC), IEEE International
Conference on (pp. 352-357). IEEE.
[18] Akhavan-Rezai, E., Shaaban, M.F., El-Saadany, E.F. and Karray, F.,
2016. Online intelligent demand management of plug-in electric vehicles
in future smart parking lots. IEEE Systems Journal, 10(2), pp.483-494.
[19] Khalid, A., Javaid, N., Mateen, A., Khalid, B., Khan, Z.A. and Qasim,
U., 2016, July. Demand Side Management using Hybrid Bacterial Forag-
ing and Genetic Algorithm Optimization Techniques. In Complex, Intel-
ligent, and Software Intensive Systems (CISIS), 2016 10th International
Conference on (pp. 494-502). IEEE.
[20] Khan, A., Mushtaq, N., Faraz, S.H., Khan, O.A., Sarwar, M.A. and
Javaid, N., 2017, November. Genetic Algorithm and Earthworm Opti-
mization Algorithm for Energy Management in Smart Grid. In Interna-
tional Conference on P2P, Parallel, Grid, Cloud and Internet Computing
(pp. 447-459). Springer, Cham.
[21] Kazmi, S., Javaid, N., Mughal, M.J., Akbar, M., Ahmed, S.H. and
Alrajeh, N., 2017. Towards optimization of metaheuristic algorithms for
IoT enabled smart homes targeting balanced demand and supply of
energy. IEEE Access.
[22] Qayyum, F.A., Naeem, M., Khwaja, A.S., Anpalagan, A., Guan, L. and
Venkatesh, B., 2015. Appliance scheduling optimization in smart home
networks. IEEE Access, 3, pp.2176-2190.
[23] Shafiq, S., Fatima, I., Abid, S., Asif, S., Ansar, S., Abideen, Z.U. and
Javaid, N., 2017, November. Optimization of Home Energy Management
System Through Application of Tabu Search. In International Conference
on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 37-49).
Springer, Cham.
... Authors of [18] suggested particle swarm optimization (PSO) based on home energy consumption-based technologies and the engineering optimization issue of particle swarm optimization secondary binary particles (QBPSO). To begin with, the author introduces a user-friendly family taxation model for the CPA to adopt. ...
... [17] 2018 Focused on load differ from real-time. [18] 2018 Energy optimized to balance home appliances. [19] 2018 Energy harvesting is minimized. ...
... Our systematic mapping study clearly shows the need for the efficient hybridization of two or more algorithms by taking the advantages of several strategies during a cycle, or during each cycle in the same optimization [3,35,90,133]. For complex problems that are often NP-hard (e.g., including energy storage systems with intermittent renewable energy sources), a simple new generation algorithm may fail to obtain a practical and good solution. ...
... We recommend a combination of an evolution-based metaheuristic with a swarmbased one [52]. They are both population-based algorithms that are adapted to our problem because (1) with renewables integration, the problem space becomes very big, and needs an efficient global search; (2) the fitness function is relatively easy to compute (energy cost), which disqualifies the motivation for single-point search methods (as used in [90]). ...
Full-text available
Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO2 emissions or communications load.
... In the second row of Table 1, Ahmad and others [21] proposed the bacterial foraging tabu search (BFTS) technique, a hybridization of BFOA and tabu search (TS) using different operational time interval (OTI) to schedule appliances while balancing user comfort (UC). His goal was to reduce both the waiting time and electricity cost simultaneously. ...
Full-text available
An Isolated Microgrid (IMG) is an electrical distribution network combined with modern information technologies aiming at reducing costs and pollution to the environment. In this article, we implement the Bacterial Foraging Optimization Algorithm (BFOA) to optimize an IMG model, which includes renewable energy sources, such as wind and solar, as well as a conventional generation unit based on diesel fuel. Two novel versions of the BFOA were implemented and tested: Two-Swim Modified BFOA (TS-MBFOA), and Normalized TS-MBFOA (NTS-MBFOA). In a first experiment, the TS-MBFOA parameters were calibrated through a set of 87 independent runs. In a second experiment, 30 independent runs of both TS-MBFOA and NTS-MBFOA were conducted to compare their performance on minimizing the IMG using the best parameter tuning. Results showed that TS-MBFOA obtained better numerical solutions compared to NTS-MBFOA and LSHADE-CV, an Evolutionary Algorithm, found in the literature. However, the best solution found by NTS-MBFOA is better from a mechatronic point of view because it favors the lifetime of the IMG, resulting in economic savings in the long term.
... They share the power among neighbors with no profit and loss. They considered on-peak, 39 off-peak and mid-peak prices. They considered three scenarios for their model i.e., without BESS, with BESS and with sharing the power of BESS. ...
Full-text available
The smart grid plays a vital role in decreasing electricity cost via Demand Side Management (DSM). Smart homes, being a part of the smart grid, contribute greatly for minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the Peak to Average Ratio (PAR) and electricity cost with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time where user waiting time is considered to be minimum for residential consumers with multiple homes. Hence, in contribution 1, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart electricity storage system is also taken into account for more efficient operation of the HEM System. Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is instigated in a smart building which is comprised of thirty smart homes (apartments). Critical Peak Pricing (CPP) and Real-Time Pricing (RTP) signals are examined in terms of electricity cost assessment for both a single smart home and a smart building. In addition, feasible regions are presented for multiple and single smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results prove the effectiveness of our proposed scheme for multiple and single smart homes concerning electricity cost and PAR minimization. Moreover, there subsists a tradeoff between electricity cost and user waiting. With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, DSM is modeled as an optimization problem and the solution is obtained by applying metaheuristic techniques with different pricing schemes. In contribution 2, an optimization technique, the Hybrid Gray Wolf Differential Evolution (HGWDE) is proposed by merging the Enhanced Differential Evolution (EDE) and Gray Wolf Optimization (GWO) schemes using the same RTP and CPP tariffs. Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between User Comfort (UC) and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the PAR is reduced up to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced up to 12.81%, 12.012% and 12.95%, respectively, for 15-min, 30-min and 60-min operational time intervals (OTI). On the other hand, the PAR and electricity bill are reduced up to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff. Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources. Microgrid generates power for electricity consumers and operates in both islanded and grid-connected modes more efficiently and economically. In contribution 3, we propose optimization schemes for reducing electricity cost and minimizing PAR with maximum UC in a smart home. We consider a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through Multiple Knapsack (MKP) then it is solved by existing heuristic techniques: GWO, binary particle swarm optimization (BPSO), GA and Wind Driven Optimization (WDO). Furthermore, we also propose three hybrid schemes for electricity cost and PAR reduction: (1) hybrid of GA and WDO named as WDGA; (2) hybrid of WDO and GWO named as WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system has also integrated to make our proposed schemes more cost-efficient and reliable to ensure stable grid operations. Finally, simulations have been performed to verify our proposed schemes. Results show that our proposed schemes efficiently minimize the electricity cost and PAR. Moreover, our proposed techniques: WDGA, WDGWO and WBPSO outperform the existing heuristic techniques. The advancements in smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In contribution 4, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a Home Energy Management Controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the electricity proving utility with the load profile of the home. The smart meter is connected to power grid having an advanced metering infrastructure which is responsible for two way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising UC. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and RTP information. Simulation results show that proposed algorithms reduce the PAR by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.
Full-text available
The use of smart grids has enabled a number of planning methods to be developed to optimize energy costs, Peak to Average Ratios (PARs), and consumer satisfaction for load management in industrial, commercial, and domestic sectors. From a technical point of view, achieving optimal outcomes requires Demand Side Management (DSM). In smart grids, utility companies and electric users communicate two-way using digital technology to make a sustainable and economic system. This paper proposes a novel framework within which an Energy Management Controller (EMC) keeps track of each appliance, its operational time, and the costs associated with them. Customers of smart grids are motivated to shift their Off-Peak Hours (OPH) from Peak Hours by presenting incentives in OPH. The metering devices would also save customers costs by preventing load shifting between high- and low-cost periods. In addition, the study proposes the bacterial foraging algorithm and grasshopper optimization algorithm for lessening power price and PAR without compromising user comfort (UC) through appliance planning. The simulation results on a practical test system advocate the high effectiveness and reliable performance of the proposed model.
Full-text available
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Full-text available
In this paper, we propose a solution to the problem of scheduling of a smart home appliance operation in a given time range. In addition to power-consuming appliances, we adopt a photovoltaic (PV) panel as a power-producing appliance that acts as a micro-grid. An appliance operation is modeled in terms of uninterruptible sequence phases, given in a load demand profile with a goal of minimizing electricity cost fulfilling duration, energy requirement, and user preference constraints. An optimization algorithm, which can provide a schedule for smart home appliance usage, is proposed based on the mixed-integer programming technique. Simulation results demonstrate the utility of our proposed solution for appliance scheduling. We further show that adding a PV system in the home results in the reduction of electricity bills and the export of energy to the national grid in times when solar energy production is more than the demand of the home.
Conference Paper
This paper shows the economic effects of Demand Side Management (DSM) in a grid where generation cost is considered as a parameter to be analyzed. IEEE 24 bus RTS system is taken for case study and optimal power flow (DC-OPF) is used for each hour to calculate the generation cost over a period of 24 hours. Over the load data DSM is implemented with the objective to minimize the generation cost. The effect of DSM is analyzed by comparing the total generation cost in both the cases. Differential Evolution (DE) is implemented to solve the DC-OPF in each hour and also to find the new load curve from DSM implemented in the system.
Conference Paper
In smart grid several scheduling techniques have been proposed for load management in commercial, industrial and residential areas to minimize electricity cost, Peak to Average ratio (PAR) and provide user comfort maximization. Demand Side Management (DSM) is necessary for optimized results. Smart grid is a digital technology with two-way communication between the utility company and electricity consumers. Energy Management Controller (EMC) are used to maintain record of all appliances, operation time of appliances and cost which we have to pay for it. Smart grid motivates users to shift the load in Off Peak Hours (OPH) form Peak Hours (PH) through providing incentive in OPH. By this act consumers save money against load shifting from high price hours to low price hours. In this paper, Genetic Algorithm (GA) and Earthworm Optimization Algorithm (EWA) based schemes is proposed to minimize electricity cost and Peak to Average Ratio (PAR) while maximizing User Comfort (UC) via appliances scheduling.
Conference Paper
In the past few years, a number of optimization techniques have been designed for Home Energy Management System (HEMS). In this paper, we evaluated the performance of two heuristic algorithms, i.e., Harmony Search Algorithm (HSA) and Tabu Search (TS) for optimization in residential area. These algorithms are used for efficient scheduling of Smart Appliances (SA) in Smart Homes (SH). Evaluated results show that TS performed better than HSA in achieving our defined goals of cost reduction, improving User Comfort (UC) level and minimization of Peak to Average Ratio (PAR). However, there remains a trade-off between electricity cost and waiting time.
Conference Paper
Energy management is one of the most important problems in the world today, due among other reasons to the increase in clients electricity demand which generates the Peak Load problem. The rise of Smart Grids as a new paradigm, developed mainly to address those requirements, allows the design and implementation of more complex management and monitoring systems. In this paper, we propose a modelization based on Organizational Multiagent System (MAS) for a distributed control system. Its main aims consist into address the Peak Load problem with Cyber-Physical Systems using the Demand-Side Management approach. The control decision process of the system implements an Artificial Immune Network algorithm to determine the state of consumption of any controlled device connected to the Smart Grid. The first stage of the ASPECS methodology was adopted in order to develop the proposed models. The simulation results show a reduction of about 20% in energy consumption. We conclude that the MAS approach adopted fits the requirements to model the proposed AIN-Peak Shaving System.
With the increasing demand for electricity and the advent of smart grids, developed countries are establishing demand side management (DSM) techniques to influence consumption patterns. The use of dynamic pricing strategies has emerged as a powerful DSM tool to optimize the energy consumption pattern of consumers and simultaneously improve the overall efficacy of the energy market. The main objective of the dynamic pricing strategy is to encourage consumers to participate in peak load reduction and obtain respective incentives in return. In this work, a game theory based dynamic pricing strategy is evaluated for Singapore electricity market, with focus on the residential and commercial sector. The proposed pricing model is tested with five load and price datasets to spread across all possible scenarios including weekdays, weekends, public holidays and the highest/lowest demand in the year. Three pricing strategies are evaluated and compared, namely, the half-hourly Real-Time Pricing (RTP), Time-of-Use (TOU) Pricing and Day-Night (DN) Pricing. The results demonstrate that RTP maximizes peak load reduction for the residential sector and commercial sector by 10% and 5%, respectively. Moreover, the profits are increased by 15.5% and 18.7%, respectively, while total load reduction is minimized to ensure a realistic scenario.
Conference Paper
Today, energy is the most valuable resource, new methods and techniques are being discovered to fulfill the demand of energy. However, energy demand growth causes a serious energy crisis, especially when demand is comparatively high and creates the peak load. This problem can be handled by integrating Demand Side Management (DSM) with traditional Smart Grid (SG) through two way communication between utility and customers. The main objective of DSM is peak load reduction where SG targets cost minimization and user comfort maximization. In this study, our emphasis is on cost minimization and load management by shifting the load from peak hours toward the off peak hours. In this underlying study, we adapt hybridization of two optimization approaches, Bacterial Foraging (BFA) and Genetic Algorithm (GA). Simulation results verify that the adapted approach reduces the total cost and peak average ratio by shifting the load on off peak hours with very little difference between minimum and maximum 95% confidence interval.
Power companies are unable to withstand the consumer power requirement due to growing population, industries and buildings. The use of automated electrical appliances have increased exponentially in day to day activity. To maintain a possible balance between the supply and demand the power companies are introducing the demand side management approach. As a result, consumers are adopted for load shifting or scheduling their loads into off-peak hours to reduce the electricity bill. When all the consumers are trying to run the scheduled electrical appliances at the same time then the usage of energy in the off peak hour curve is marginally high. However, service providers are in need of a load balancing mechanism to avoid over or under utilization of the power grid. In the existing works, threshold limit is applied for a home to maintain the balanced load and if the consumer exceeds it then the additional charges are applied in the bill. To overcome the above mentioned drawbacks there is a need to increase the power usage with minimum cost and reducing the waiting time. For this purpose, in this paper we implement multi-objective evolutionary algorithm, which results in the cost reduction for energy usage and minimize the waiting time for appliance execution. The result reveals that if the consumer exceeds the threshold limit, the scheduled running electrical appliances temporarily stops to maintain the energy usage under threshold level for cost benefit and resumes the stopped appliances later. Further, the proposed technique minimizes the overall electricity bill and waiting time for the execution of electrical appliances.