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Meta Heuristic and Nature Inspired Hybrid Approach for Home Energy Management Using Flower Pollination Algorithm and Bacterial Foraging Optimization Technique

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Nowadays, different schemes and ways are proposed to meet the user's load requirement of energy towards the Demand Side (DS) in order to encapsulate the energy resources. However, this Load Demand (LD) increases day by day. This increase in LD is causing serious energy crises to the utility and DS. As the usage of energy increases with the increase in user's demand respectively, the peak is increased in these hours which affect the customer's in term of high-cost prices. This issue is tackled using some schemes and their proper integration. Two-way communication is done by the utility through Smart Grid (SG) between utility and customers. Customers that show some good behavior and helps the utility to control this LD, can perform a key role here. In this paper, our main focus is to control the Customer Side Management (CSM) by reducing the peak generation from on-peak hours. In our scenario, we focus on saving the cost expenditure of users by giving them comfort and shifting the load of appliances from high LD hours to low LD hours. In this study, we adopt the optimization algorithms, like Bacterial Foraging Optimization Algorithm (BFOA), Flower Pollination Algorithm (FPA) and proposed our Hybrid Bacterial Flower Pollination Algorithm (HBFPA) to optimize the solution of our problem using the famous electricity scheme named as Critical Peak Pricing(CPP) with three different Operational Time intervals (OTIs). Simulations and results show that our scheme reduces the cost and peak to the average ratio by proper shifting the appliances from highly load demanding hours to the low demanding hours with the negligibly small difference between the maximum and minimum 90% of confidence interval.
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Meta Heuristic and Nature Inspired Hybrid
Approach for Home Energy Management using
Flower Pollination Algorithm and Bacterial foraging
Optimization Technique
Muhammad Awais1, Nadeem Javaid1,, Abdul Mateen1,
Nasir Khan1, Ali Mohiuddin1, Malik Hassan Abdul Rehman1
1COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Corresponding author: www.njavaid.com, nadeemjavaidqau@gmail.com
Abstract—Nowadays, different schemes and ways are proposed
to meet the user’s load requirement of energy towards the
Demand Side (DS) in order to encapsulate the energy resources.
However, this Load Demand (LD) increases day by day. This
increase in LD is causing serious energy crises to the utility
and DS. As the usage of energy increases with the increase in
user’s demand respectively, the peak is increased in these hours
which affect the customer’s in term of high-cost prices. This
issue is tackled using some schemes and their proper integration.
Two-way communication is done by the utility through Smart
Grid (SG) between utility and customers. Customers that show
some good behavior and helps the utility to control this LD, can
perform a key role here. In this paper, our main focus is to
control the Customer Side Management (CSM) by reducing the
peak generation from on-peak hours. In our scenario, we focus
on saving the cost expenditure of users by giving them comfort
and shifting the load of appliances from high LD hours to low
LD hours. In this study, we adopt the optimization algorithms,
like Bacterial Foraging Optimization Algorithm (BFOA), Flower
Pollination Algorithm (FPA) and proposed our Hybrid Bacterial
Flower Pollination Algorithm (HBFPA) to optimize the solution
of our problem using the famous electricity scheme named as
Critical Peak Pricing(CPP) with three different Operational Time
intervals (OTIs). Simulations and results show that our scheme
reduces the cost and peak to the average ratio by proper shifting
the appliances from highly load demanding hours to the low
demanding hours with the negligibly small difference between
the maximum and minimum 90% of confidence interval.
Index Terms—Demand side management, Bacterial foraging
optimization algorithm, Flower pollination algorithm, Hybrid
bacterial flower pollination algorithm, Smart grid, Home energy
management
I. INTRODUCTION
Science and its miracles have made the people life easy in
all aspects. As blessings are for manhood, electricity is also
one of these blessings which is for human-being and can be
used for multipurpose. As the world is progressing, population
of this world is also growing. World is going towards the
automation, due to which demand of electricity has been in-
creased. To handle this critical situation, utility starts teaching
the user to avoid their maximum electricity consumption in
few selected hours. This consumption of load varies from city
to city and country to country. The energy which is needed
by the residential buildings are 30 to 45 percent of the total
electricity [1]. In International Energy Outlook (IEO), it is
clearly mentioned that the Energy Demand (ED) is increasing
continuously, which will keep on increasing till 2040 up to
56% of the present. By using some intelligent systems, we can
also control this high demand of load. This paper presents CPP
price tariff in Home Energy Management (HEM) to reduce the
Electricity Price (EP) by reducing the Peak to Average Ratio
(PAR). 18% of the electricity is consumed by domestic sectors,
viewed in a survey in 2011 [2], which has been increasing
continuously.
In this paper, we extended the FPA with BFOA to its hybrid,
to optimize our problem. We designed a model using 14
appliances, each of these appliance has been categories on the
basis of their behavior. Length of Operational Time (LOT)
is different for all appliances taken from our base paper [4].
Parameters taken in consideration are PAR, cost reduction,
energy consumption and waiting Time (WT). As our main
focus is to shift the load from on-peak to off-peak hours by
applying different schemes and strategies i.e. load clipping we
have to minimized energy consumption using DSM by proper
planning of utility electricity usage and their deployment
techniques, which directly impact the customers LD. We will
optimize the problem by using the hybrid approach of BFOA
and FPA both for single and multiple homes i.e. 10, 30 and
50 homes with different power ratings and power consumption
patterns using three different OTIs of 20, 30 and 60 minutes.
Rest of the paper is organized as: related work is discussed
in section II, section III covers the problem statement, and
the proposed solution is discussed in section IV with de-
tailed description of previously proposed techniques. Proposed
methodology is explained in section V. Results and discussions
are verified via simulations which are illustrated in section VI
and conclusion is summarized in section VII respectively.
882
2018 IEEE 32nd International Conference on Advanced Information Networking and Applications
1550-445X/18/$31.00 ©2018 IEEE
DOI 10.1109/AINA.2018.00130
II. RELATED WORK
In few years back, many researchers proposed many opti-
mization techniques to achieve common objective functions
like reduction in cost. Many efforts have been made for the
purpose of economical usage of electricity. In this section,
existing work done on these optimization techniques are
presented.
The Harmony Search Algorithm (HSA), FPA are the meta-
heuristic techniques that are used by the author to evaluate the
performance in HEM [3]. Author considered a single home
with multiple smart appliances working automatic and man-
ually. The CPP is used as price signal and purposed scheme
shifts the load from on-peak to off-peak hours very well by
keeping in view of cost minimization and User Comforts (UC).
However, author did not consider the appliances power rating
as it varies from home to home.
In [6], authors present an efficient DSM model for res-
idential area people. DSM uses Genetic Algorithm (GA),
Binary Particle Swarm Optimization Algorithm (BPSO) and
Ant Colony Algorithm (ACO) which are totally heuristic
algorithms, which minimizes the user’s cost by reduction in
PAR by maximizing the user comfort. The main objective of
this paper was deduced from three techniques: GA, BPSO and
ACO. TOU and Inclined Block Rate (IBR) pricing schemes
were used by him.
Designing a HEM controller using heuristic algorithms,
controllers, BFOA, GA, BPSO, Wind Driven Optimization
Algorithm (WDO) and Genetic Binary Particle Swarm Op-
timization (GBPSO) in [7] to deals with RTP. Author’s focus
is on reducing the price while retaining PAR. GA performs
well in PAR reduction while BPSO and HEM controls the cost
reduction as well. Basically author tried to show that there is
a trade-off between cost and delay. Results shows that given
techniques performed well according to the given conditions.
HEM system is proposed in paper [8], which helps in
Renewable Energy Resources (RES), energy supplements side
and works with DSM simultaneously. The proposed HEM
helps the user in minimizing the cost of the electricity bill
and schedule household appliances to a certain threshold. If
the limit crosses the certain threshold, appliances will get-off
by utility itself. Basically author used the DAP signals and
then applied the heuristic algorithms to get optimal solution.
The Power Scheduling Technique (PST) was proposed for
an optimization problem in which user can adjust the starting
and ending time of the appliances and reduce the power
consumption of the appliances. Electricity cost used by the
author was announced by electricity providers before time [9].
Simulations demonstrate that scheduling technique can get the
required results in terms of less cost efficiently.
With consideration of service providers, there is a need to
balance the load to avoid much electricity consumption. In
this regard, utility has to generate extra electricity to fulfill
the load requirement. To minimize this problem, there is an
essential need to increase power usage convention with less
pricing rate [10]. Simulations and results show that, there is a
certain threshold after which we have to schedule the running
appliances and to stop some appliances temporarily. This is
done to maintain the LD for less cost and to stop the appliances
for being used later when cost will be low. Author uses the
TOU scheme for on-peak and off-peak hours to balance the
load properly.
When the load shifts, we can define new limits for maximum
LD. It can be crucial for the checker since the investor’s
capacity of adding energy resources, which can be limited
[11]. Only with new selected load shifting impacts, DSM can
be achieved. Basically, peaks can be reduced and valleys can
be fulfilled. Due to which LD will increase and Operating
System (OS) will keep on going at higher demand.
In [12], with consideration of the SM facility this model
involves DS generation to optimize the market cost. The
market cost was estimated by sensitive marketing teams to
regulate the standardization variables for quality solution. This
paper stops a new space-based pricing model for optimal
operations of the SG, which identify variables of Master
Control Program (MCP) and uses PSO to reach an optimal
solution, Demand Response (DR) stability and loss limits.
The BFOA is used in paper [13], which is a hybrid with
GA. In this paper, author used RTP scheme to optimize the
user’s load and the cost by keeping in mind about user’s
comfort, cost minimization and reduction in PAR. However,
the author did not consider the variable Power Rating (PR) for
variable homes and multiple homes with different number of
appliances.
The author reviews that DR patterns, procedures and clas-
sifying schemes in [14], affording to their device mechanism
and to reduce power for DR variables. In this paper, author
reduces the overall power consumption by successfully imple-
menting DR procedure, relies on the participation of user and
contributor to reduce power intake in peak hours by consuming
Dynamic Pricing Patterns (DPP). According to the author, it
is required to generate the DPP for forecasting techniques that
consider the probabilistic behavior of the appliances.
In [15], with consideration of pricing parameters to bal-
ance asymptotic stability and social welfare optimality of
equilibrium point using DR program, the system shows that
the Input to the State Stable (ISS). In this paper author
balances the supply and demand with the proper stability of
the system without disturbance. The power system is dividing
into different OTIs.
DSM strategy is used to achieve the objective of the
load flowing through the detraction problem for residential,
the commercial and industrial zone too [16] using PSO. It
decreases the request of the load in peak eras by reducing
the bill. Results and simulation shows a clear decrease in cost
while applying PSO.
In [18], this paper an efficient HSA algorithm was imple-
mented for scheduling the users DSM. The pricing scheme
used by the author was TOU. Finally, the author did many
simulations and their results shows that HSA is good in
performance than GA.
Fuzzy inference structure and FPA with hybrid approach is
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used to adopt probability which uses different mechanism by
which they accept change in both global and local pollination
[19] and FPA performs in a much better way as it is hybrid
with Fuzzy structures. The author compare its results with
the mathematical models but this technique performs better.
However, the author ignores the time computation.
The modified FPA is presented in [20], in which Scaling
Factor (SF) is used to control local pollination and some
phases were added to get better solution. The effectiveness is
calculated from different simulations, mathematical formulas
and four different power systems. However, author did not
considered the continuous objective function and convex prob-
lems.
Below is the Table (I) of brief summarized related work.
III. PROBLEM STATEMENT
By using scheduler we can schedule the home appliances
with which energy consumption is reduced in DSM. Many of
the researchers try to minimize the price of electricity which is
our main problem by shifting the load towards off-peak hours
[28] using GA and [13] BFOA. PAR reduction, minimization
of EC, UC maximization and minimize the power consumption
are the most common objectives of electricity management in
SG. A large amount of electricity is used by residential area
and its consumption is growing rapidly. To overcome these
issues, we proposed an proficient home energy management
using a meta heuristic hybrid approach named as HBFPA.
As in next section our proposed solution will be discussed.
However, many of scientists and researchers ignore the change
in power generation in these resources, which may came due to
change in atmosphere and weather. In these scenarios model
helps us to provide justification in cost reduction and peak
load. The main purpose of our work is to scheduling of home
appliances, for minimum cost and PAR reduction, we have to
balance the load, maximize the users comfort with minimum
delay. Then we will discuss trade-off between the cost and
user comfort.
IV. PROPOSED SOLUTION
In order to tackle the above problem, our focus is to
minimize the total cost as explain in Eq. (1) on CPP tariff for
cents and power consumption of different appliances in Kilo
Watt hour (kWh). Here,‘ Pap
rate’ is the PR of an appliance ‘ap’
and per slot electric rate is denoted as ‘EPt
rate’ of ‘t’ slots.
we have done load shifting using fitness calculation Eq.
(2). ‘Load’ donates load of the appliance as general while
Lsch
oad’ shows scheduled load, ‘Lunsch
oad ’ shows unscheduled
load, ‘Erate’ is the domain of electric rate where as ‘EF
represents our fitness function. We applied sum functions in
this equation one is of taking ‘mean’ other was of taking
minimum represented as ‘min’ and the last one was taking
‘standard deviation’ denoted as ‘std’.
In the next level, We used Eq. (3) to calculate total load
consumption of a complete day. ‘Lt
oad’ represents load per
slot and Eq. (4) evaluates the load per slot where alpha is the
TABLE I: Summarized related work
Techniques Objectives Limitations
GA, BPSO, ACO [6] Cost and PAR reduc-
tion
Users cost not consid-
ered
BFA, BFOA, GA,
BPSO, WDO,
GBPSO [7]
IT reduces the elec-
tricity cost and limits
PAR
Not considered the
trade-off between cost
and PAR
GA, PSO, WDO,
BFO, HGPSO [8]
Minimize the electric-
ity bill by scheduling
home hold appliances
The overall is not
considered reduction
in cost and PAR
BFOA [9] To reduce the cost
and increase the
user’s comfort
The trade-off among
expenses and the user
awkwardness is not
considered
BPSO [10] To develop an effi-
cient system, reduce
the cost but you also
have to uplift the ap-
pliances; utility
Cost, minimization
and appliances;
utility uplifting, and
not considering the
privacy of user
MOEA [11] Cost minimization
and reducing the WT
Consumer exceeds
the threshold limit is
not focused
DR programs [13] Reduce the overall
power consumption
Implement the DR
program Successfully
in peak demand hours
BPSO [16] Reducing the excess
demand from peak
hours along with re-
duce in the bill
Reduction in peak de-
mand and saving util-
ity bills are not con-
sidered,
FPA [17] side lobe level
minimization and
null placement
ignore interferences
in undesired direction
HSA [18] Have to reduce oper-
ational cost
User comfort is not
considered
DSM model was pre-
sented using GA [19]
Have to reduce oper-
ational cost, PAR
The time complexity
is completely ignored
In-place algorithm
(PL) generalized
algorithm [20]
deals with cost and
user comfort and dis-
cuss their trade-off
The writer ignores the
system complexity
GA, Advanced in-
flight measurement
techniques,
current procedural
terminology [21]
Cost and Par reduc-
tion
System complexity
increases which is
ignored
GA [22] Cost and PAR is min-
imized by the pro-
posed scheme
RER installation cost
is completely ignored
GA [23] Cost is reduced by us-
ing GA
User comfort is ig-
nored by the author
and PAR is also ne-
glected
GA [24] User’s electricity cost
is minimized with re-
duction in PAR
User comfort is ne-
glected
Hybrid scheme using
FPA and TS [25]
hybrid version to op-
timize unconstrained
problems
Ignored the
optimization problem
with multiple
constrains
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‘ON/Off’ status of an appliance as shown in Eq. (5) where as
app shows appliance.
T otal cost =
24
t=1
(EPt
rate ×Pap
rate)(1)
Ef=min
liNPop
oad mean(lUnsch
oad ),
(EPt
rate mean(Erate)
liNPop
oad >(std(lUnsch
oad )
liNPop
oad < mean(lUnsch
oad )),
(EPt
rate > mean(Erate)
(2)
Lsch
oad =
24
t=1
(Lt
oad)(3)
Lt
oad =(Pap
rate ×app)(4)
alpha =1, if the appliance is ON
0, if the appliance is OF F (5)
Our main focus is on our objective functions. We not only
have to reduce electric cost as shown in Eq. (1) but also to
minimize PAR gained as in Eq. (7). One of our main objective
is load shifting as evaluated in Eq. (8). For the purpose of this
idea we divided our day of 24 hours into two different parts;
one is on-peak hours where electricity cost is high and the
second is off-peak hours where electricity cost is quite low
while considering the ‘mean’ of given price tariff. As we have
to meet our objective function we will shift load from on to
off-peak hours where price is comparatively low. This idea will
definitely reduces the PAR and cost. PAR can be calculated
from the formula in equation Eq. (9) where we take ratio of
maximum of ‘Lsch
oad’ and average of ‘Lsch
oad’.
Object1=min(cost)(6)
Object2=min(PAR)(7)
Object3=(Load)(8)
PAR =max(Lsch
oad)
Average(Lsch
oad)(9)
V. P ROPOSED METHODOLOGY
A. System Model
In this section, the architecture of our proposed system is
discussed in detail and the model is shown in Fig. (1). We
have proposed a HEM scheme to schedule smart appliances
in order to reduce the electricity cost and PAR in order to
gain maximum user comfort. As a smart home is full of smart
appliances, Electric Management Controller (EMC) and SMs.
The SM acts as a server between home and utility. Appliances
have to send their usage pattern to EMC which schedules them
according to price signals sent by utility. SM picks up the price
signals from utility and then forward it to EMC. Then it picks
up the consumption pattern from EMC and drop it at utility
side. They communicate through Z-waves. They exchange info
through home area network.
TABLE II: PR and length of operational time for 20
minutes OTIs
Group Appliances PR(kWh) OTIs
Non-schedulable appliances
Oven 1.30 10.0
Kettle 2.00 1.00
Coffee Maker 0.80 4.00
Rice Cooker 0.85 2.00
Blender 0.30 2.00
Frying Pan 1.10 3.00
Toaster 0.90 1.00
Fan 0.20 15.0
Uninterruptible appliances Washing-Machine 0.50 6.00
Cloth Dryer 1.20 6.00
Schedulable appliances
Dish Washer 0.70 8.00
Vacuum Cleaner 0.40 8.00
Hair Dryer 1.50 2.00
Iron 1.00 6.00
B. Load Categories
Interruptible appliances
Uninterruptible appliances
1) Interruptible Appliances: Deferrable appliances are
called ‘interruptible appliances’. These can be shifted and
scheduled. The appliances in a particular time slot can be
shown as ON or OFF using [0, 1] notation.
0 if the appliance is OFF
1 if the appliance is ON
2) Uninterruptible Appliances: Un-interruptible appliances
are schedulable, but they cannot be interruptible. The
appliances in a particular time slot can be shown as ON or
OFF using [0, 1] notation.
0 if the appliance is OFF
1 if the appliance is ON
All appliances with their power rating and length of opera-
tional time using CPP are listed in Table (II).
C. Price Model
Price is calculated according to utility definitions. For this,
different pricing schemes are used to reduce the cost and the
PAR which encourages the user to shift load from on-peak to
off-peak hours. Dynamic pricing scheme includes TOU, IBR,
CPP, DAP and RTP etc. Among all mentioned before we use
CPP.
D. Optimization Techniques
1) BFOA: Nature has a beautiful rule, it eliminates animals
with reduced foraging schemes. It favors those who have best
searching tricks and schemes. After couple of generations, the
weak one is replaced with the healthy one as per rule of life.
First BFOA algorithm is given by ‘Passsino’ and ‘Kevin’ [26]
in 2002. The strategy includes in BFOA is that firstly it permits
the cells to swarm randomly and jointly by going towards
optimum condition.
The terms used in algorithm are; ‘Ne’ shows number of
elimination step, ‘Np’ shows maximum population size, ‘Nr
shows number of reproduction steps, ‘Nc’ shows number of
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Fig. 1: Proposed System Model
chemotaxis steps, ‘Ns’ shows Swarming Length, ‘Ef’ shows
fitness function for BFOA and ‘Pe’ denotes elimination dis-
persion probability while ‘Xnew’ shows the newly generated
population and ‘X’ shows old population. Three consecutive
steps are performed to achieve this which are; ‘Chemotaxis’,
‘Reproduction’ and ‘Elimination and Dispersal’. Algorithm for
BFOA is explained in Algorithm (1).
2) FPA: It is expected that there are more than one million
kinds of plants in nature, maximum of them are from flowering
classification. It is the still a secret that how these plants came
to land and dominate the land. The basic purpose of flower is
finally to reproduce their offspring’s via pollination process,
which is basically linked with the movement of pollen grains
from one flower to another using different pollinators such
as birds. There is a co-evolved in some flowers and insects,
such as a specific type of species of birds or insects should be
used for pollination to be successful. Types of FPA pollination
includes; ‘Biotic pollination’ and ‘Abiotic pollination’ while
pollination can be achieved by two different processes; first
one is ‘self-pollination’ and second is ‘cross-pollination’.
a) FPA steps: The FPA was developed by ‘Xin-She’
Yang in 2012 ‘FPA for Global Optimization’ [27]. For eas-
iness, the following four steps are used.
Biotic cross-pollination can be measured as a process
of global-pollination, and pollen vectors are carrying
pollinators which move in a path that follow Levy flights
(Rule I).
For ‘local-pollination’, ‘abiotic-pollination’ and ‘self-
pollination’ is used (Rule II).
Pollinators such as birds can maintain flower consistency,
Algorithm 1 Algorithm 1 BFOA
Require: Define the optimization problem and optimization
parameters
for Elim and disp 1toNedo
for Reproduction 1toNrdo
for Chemotaxis 1toNcdo
for Population 1toNpdo
Bacteria tumble first randomly
Go to new position
Compute the Ef
for i1toNsdo
if EfXnew < EfXthen
Update solution
Using swimming
Compute the Ef
else
Bacteria Tumble
Move in that path
Compute the Ef
end if
end for
end for
end for
Calculate Ef
Select the best bacteria using Eq. (2)
end for
end for
886
which is equivalent to a reproduction probability, which
is directly proportional to the resemblance of two flowers
involved in pollination (Rule III).
The switching of local-pollination and global-pollination
can be measured by a switch probability ‘p’ belongs to
[0, 1], which is slightly partial towards local pollination
(Rule IV).
Here ‘ηa’ shows WT after scheduling and ‘γa’ shows WT be-
fore scheduling. Algorithm for FPA is discussed in Algorithm
(2).
Terms used in FPA are represented as; ‘t’ shows OTI, ‘T’
denotes total time in hours, ‘α’ denotes ‘upper bound’ while
‘lower bound’ is denoted by ‘β’, ‘D’ shows appliances, Np
shows maximum population size, ‘F’ shows fitness function
for FPA, ‘x’ shows old population, ‘Xnew’ shows new
population and probability is taken (0.5) in our scenario.
Fitness ‘F’ in FPA can be calculated using Eq. (10) and in
Eq. (11).
3) Hybridization: Our proposed ‘HBFPA’ steps are ex-
plained in Algorithm (3). Terms used in HBFPA are repre-
sented as; ‘t’ shows OTI, ‘T’ denotes total time in hours,
α’ denotes ‘upper bound’ while ‘lower bound’ is denoted by
β’, ‘D’ shows appliances, ‘Np’ shows maximum population
size, ‘Ef’ shows fitness function for HBFPA, ‘x’ shows old
population, ‘Xnew’ shows new population and probability
is taken (0.5) in our scenario. Fitness in ‘HBFPA’ can be
calculated using Eq. (2).
F=(1u(1))2+D(10)
Where ‘D’ is
D= 100 (u(2) u(1)2)2+ 100 (u(3) u(2)2)2(11)
Here uis the appliance’s cost.
VI. SIMULATION RESULTS AND DISCUSSION
A. For Single Home
In this portion, we discuss about the simulations and re-
sults with proper justification. So, as to judge the hybrid
scheme performance, the superiority interval, effectiveness and
productivity of our proposed technique, we have done some
simulations in order to describe the optimality for a single
home. However, this scheduling is performed over multiple
homes too. BFA and FPA are well executed by us and we make
a hybrid version of these two algorithms to do scheduling of
single home appliances. We have done different simulations
and get different results using three different scenarios of 20,
30 and for 60 minutes OTIs respectively for 24 hours, starting
from 1 am to 1 am. The load, total cost, PAR and waiting
time plots are given below:
1) Electricity Load Consumption: The load consumption
peak of BFOA and FPA is smaller but our proposed hybrid
technique have better results as compared to unscheduled
load. This technique is intended to avoid peak formation in
any explicit slot of daily working hours including both on-
peak hours and off-peak. Shifting the load effect consumer’s
Algorithm 2 Algorithm 2 FPA
Initialize the optimization problem and optimization pa-
rameters
Set the bounds (α,β)
For all appliances dD
ForallOTIstinT
for Population 1toNpdo
for j1toD-1do
Random flowers generation
end for
end for
Take a Levy flight
N=Max-Iteration
for j1toNdo
for Population 1toNpdo
if rand>probability then
Switch
Levy flight
Update population
Using local pollination
Calculate F
else
Check
Random Population
Check
Simple bounds
Update population
Using global pollination
Calculate F
end if
if Fitness of Xnew <Fitness of x then
Update solution
Using new ‘F’
end if
Update global population
end for
end for
Return best solution
comfort. However, they will get reward in terms of price
reduction. The additional a client shifts the load and tolerates
changes in energy consumption pattern, the additional profit
will be given to him in terms of price reduction. There are
creating different peaks in our figures, but it will not affects
user much because of peaks in off-peak hours where cost is
low. Our planned techniques performed well and load plots
are given in Fig. (2) below respectively, for all three different
OTIs.
2) Overall Electricity cost: We computed the electricity
price using the Eq. (1). The performance of home appliances
in terms of cost is evaluated with the assistance of the
corresponding hybrid heuristic optimization techniques using
BFOA and FPA. We can clearly judge from the figures that
BFOA and FPA also responded better in terms of total cost
but over proposed hybrid algorithm performed better. Here,
887
Algorithm 3 Proposed Algorithm 3 HBFPA
Initialize the optimization problem and optimization pa-
rameters
Set the bounds (α,β)
For all appliances d D
ForallOTIstinT
Np= Maximum population size
for Population1toNpdo
for j1toD-1do
Random flowers generation
end for
end for
Take a Levy flight
N=Max-Iteration
for j1toNdo
for Population1toNpdo
for i1toNsdo
Bacteria Tumble using Levy flight
Compute the Ef
Go to new position
Compute the Efagain
if Ef(Xnew)<E
f(X)then
Update solution
Using swimming
Compute the Ef
else
Bacteria Tumble
Using levy flight
Move in that path
Compute the Ef
end if
end for
Update global population
end for
end for
Return best solution
a trend is making which elaborates clearly that more a user
sacrifice his comfort and bear the load consumption patterns,
more reward will be given to him by utility in terms of low
cost price. Overall Electricity cost plots are given in Fig. (3)
respectively for all three different OTIs.
3) User comfort: By applying our proposed algorithm and
CPP price tariff, users have to wait for off-peak hours where
cost is low and as a reward users have to pay less cost from
utility. OTI size effects WT very much i.e. if the size of OTI
will be larger then there will be some appliances which may
get their work complete before their proper given running time,
then their remaining time will be wasted. WT against different
OTIs are plotted below. You can easily see that our proposed
hybrid algorithm has maximum user’s comfort with minimum
delay. Fig. (4) shows waiting time respectively for all three
different OTIs.
4) PAR: When same technique is implemented to calculate
PAR using CPP, our proposed technique performed better than
4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Time (Minutes)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Load (kWh)
Unschedule
BFA
FPA
HBFPA
(a) Load for OTI 20 minutes
4 8 12 16 20 24 28 32 36 40 44 48
Time (Minutes)
0
0.5
1
1.5
2
2.5
3
Load (kWh)
Unschedule
BFA
FPA
HBFPA
(b) Load for OTI 30 minutes
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Time (Minutes)
0
0.5
1
1.5
2
2.5
3
3.5
4
Load (kWh)
Unschedule
BFA
FPA
HBFPA
(c) Load for OTI 60 minutes
Fig. 2: Load for OTI 20, 30 and 60 using CPP
unscheduled PAR. However, rest of the techniques performed
well as compared to unscheduled case. DSM is not solely
used for customer’s, however, additionally for utility too. The
Reduction in PAR helps utility to retain its stability and
ultimately it ends up in the reduction in the price. PAR concept
can easily be understood from the PAR plots given as in Fig.
(5) respectively, for three different OTIs.
B. For Multiple Homes
The load consumption peak of BFOA and FPA is also
smaller than unscheduled load but our proposed hybrid tech-
nique performed well than both FPA and BFOA using OTI of
60 with CPP price tariff for 10, 30 and 60 homes. Our tech-
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OTI 20 OTI 30 OTI 60
0
200
400
600
800
1000
1200
Total Cost (Cents)
Unschedule
BFA
FPA
HBFPA
Fig. 3: Cost for OTI 20, 30 and 60
OTI 20 OTI 30 OTI 60
0
50
100
150
200
250
Waiting Time (Minutes)
BFA
FPA
HBFPA
Fig. 4: User’s comfort for OTI 20, 30 and 60
OTI 20 OTI 30 OTI 60
0
1
2
3
4
5
6
7
PAR
Unschedule
BFA
FPA
HBFPA
Fig. 5: PAR for OTI 20, 30 and 60
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Time (Minutes)
0
5
10
15
20
25
30
35
40
45
Load (kWh)
Unschedule
BFA
FPA
HBFPA
(a) Load for 10 homes
24681012141618202224
Time (Minutes)
0
20
40
60
80
100
120
140
Load (kWh)
Unschedule
BFA
FPA
HBFPA
(b) Load for 30 homes
24681012141618202224
Time (Minutes)
0
50
100
150
200
250
Load (kWh)
Unschedule
BFA
FPA
HBFPA
(c) Load for 50 homes
Fig. 6: Load for 10, 30 and 50 homes using OTI 60
nique is intended to avoid peak formation in any explicit slot
of daily working hours including both on-peak hours and off-
peak. Shifting the load effect consumer’s comfort. However,
they will get reward in terms of price reduction. Load plots
for multiple homes are given in Fig. (6) below respectively for
all three different scenarios with different power ratings and
power consumption patterns. Figures show that BFOA and
FPA also responded better in terms of total cost for multiple
homes but over proposed algorithm performed better. HBFPA
reduces the overall cost and PAR from unscheduled cost.
Daily cost is minimized i.e. in Fig. (7) clearly shown that
proposed hybrid technique optimize the solution and schedule
the appliances for multiple homes in worth of less cost by
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sacrificing user comfort i.e. in Fig. (8) and PAR i.e. in Fig. (9)
Here, also a trend is made which elaborates clearly that more
a user sacrifice his comfort and bear the load consumption
patterns more reward will be given to him by the utility in
terms of low cost price.
10 Homes 30 Homes 50 Homes
0
1
2
3
4
5
6
7
Total Cost (Cents)
×104
Unschedule
BFA
FPA
HBFPA
Fig. 7: Cost for 10, 20 and 50 homes using OTI 60
10 Homes 30 Homes 50 Homes
0
2000
4000
6000
8000
Waiting Time (Minutes)
BFA
FPA
HBFPA
Fig. 8: User’s comfort for 10, 20 and 50 homes using OTI 60
10 Homes 30 Homes 50 Homes
0
50
100
150
200
250
PAR
Unschedule
BFA
FPA
HBFPA
Fig. 9: PAR for 10, 20 and 50 homes using OTI 60
C. Feasible Region
In mathematical optimization, a feasible region is a search
space which contains the set of all possible points or values of
the choice variables for an optimization problem that fulfill the
problem’s constraints, inequalities and integer constraints. This
is the original set of candidate solutions to the problematic
approach, before the set of candidates has been tightened
down. Fig. (10) shows the feasible region of our purposed
hybrid scheme, respectively for all three different OTIs which
may change according to scenario varies. Four parameters
should be kept in mind while calculating feasible regions as
in Fig. (10)
Minimum cost, Minimum load consumption
Minimum cost, Maximum load consumption
Maximum cost, Minimum load consumption
Maximum cost, Maximum load consumption
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Power consumption (kWh)
0
50
100
150
200
250
Cost (Cents)
P5
P4
P1
P2P3
(a) Feasible region for OTI 20
0 0.2 0.4 0.6 0.8 1 1.2
Power consumption (kWh)
0
50
100
150
Cost (Cents)
P5
P2
P1
P3
P4
(b) Feasible region for OTI 30
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
Power consumption (kWh)
0
50
100
150
200
250
Cost (Cents)
P3
P1
P2
P5
P4
(c) Feasible region for OTI 60
Fig. 10: Feasible region per slot with OTI 20, 30 and 60 for
single home
D. Performance Trade-off
There may exit some trade-off between different parameters
in order to attain the objective function. Simulations and
results show that there exits a trade-off between cost and WT
in our proposed hybrid algorithm. The electricity cost reduces
as the user scarify his comfort by delaying his activity work as
per our proposed model. So to get one benefit he has to scarify
his second wish. Plots clearly shows difference between FPA,
BFOA and our newly proposed hybrid version by reducing
cost, PAR with maximum user’s comfort with minimum delay.
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VII. CONCLUSION
A traditional grid is modified into a SG to maintain its
stability. DSM provides help in minimizing of cost and helps to
maintain stability of grid station. In this paper, we visualize the
performance of HEM using CPP price tariff. On the basis of
BFOA and FPA techniques and show that our hybrid technique
performed better than both, for single and multiple homes
equally. In this paper, we visualize the performance of HEM
using CPP on the basis of BFOA and FPA and show that our
hybrid technique performed better both for single and multiple
homes. We took simulations for single home and multiple
homes with different power ratings and different power con-
sumption patterns. All simulations showed that our purposed
technique performed well in reducing overall cost and PAR
by giving maximum user’s comfort with minimum delay. Our
simulations also showed the exiting trade-off between cost and
user’s comfort.
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... This work is an extension of [4]. Motivated from meta-heuristic algorithms, this paper considers the Flower Pollination Algorithm (FPA), Bacterial Foraging Optimization Algorithm (BFOA) and their hybrid algorithm for optimizing the energy consumption in single as well as multiple homes. ...
... The approach in BFOA is that, initially, it allows the cells to group arbitrarily. Three successive phases are of the BFOA are explained below and algorithm for BFOA is discussed in [4] . The algorithm for BFOA is explained in [4]. ...
... Three successive phases are of the BFOA are explained below and algorithm for BFOA is discussed in [4] . The algorithm for BFOA is explained in [4]. ...
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