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1
Demand Side Management Using Hybrid Genetic
Algorithm and Pigeon Inspired Optimization
Techniques
Malik Hassan Abdul Rehman 1, Nadeem Javaid 1,∗, Muhammad Nadeem Iqbal 2, Zaheer Abbas 1,
Muhammad Awais 1, Ahmed Jaffar Khan 1and Umar Qasim 3
1COMSATS Institute of Information Technology, Park Road, Islamabad 44000, Pakistan;
2COMSATS Institute of Information Technology, Wah Cantt 47040, Pakistan;
3Cameron Library University of Alberta Edmonton Canada
E-Mail: ∗nadeemjavaidqau@gmail.com
Abstract—In this paper, our goal is to minimize the
electricity cost, electricity consumption at minimum user
discomfort while considering the peak electricity con-
sumption. Electricity consumption may not be the same
in residential, commercial and industrial areas. It may
vary from each and every area. It is a challenging task to
maintain the balance between the conflicting objectives:
electricity consumption and user comfort. To meet the
rising electricity demand in residential area, schedule-
able devices can be equally distributed to the available
time slots on the basis of average power consumption.
The main objective is to minimize the electricity usage
during the electricity peak hours by distributing the
electricity load during the off-peak hours. In this regard,
Genetic Algorithm (GA), Pigeon Inspired Optimization
(PIO) and our proposed hybridization of GA and PIO
(HGP) in Demand Side Management(DSM) are applied
for residential load management to optimize the fitness
function. GA, PIO and HGP are evaluated on the basis of
real time pricing scheme (RTP) for single home with three
different operational time interval (OTI) and for multiple
homes with a single OTI. Simulations results shows that
GA, PIO and HGP are able to minimize electricity bill
and electricity consumption while minimizing the user
discomfort. The performance of HGP is better than GA,
PIO with respect to PAR, electricity load and electricity
cost for both single home and multiple homes scenario.
The feasible region between electricity cost and electricity
consumption is also represented. Moreover, the desired
trade-off between electricity cost and user comfort is also
achieved in both techniques.
Index Terms—demand side management; pigeon in-
spired optimization; genetic algorithm; smart grid;
I. INTRODUCTION
In recent decades, it is being observed that energy
demand is increased immensely. In China and USA,
residential area is considered to be the highest sector
where energy is consumed, as a result huge amount of
greenhouse gas (GHG) is emitted [17]. The traditional
grid has the capability to deliver the electric power
from power generation utility to the consumer. This
one-way communication is not of handling several
parameters of electrical network. In China, residential
area is responsible for most of the power consumption
and greenhouse emissions. In Arabian countries, 40 %
of electricity is consumed in residential area [5]. Bulk
generation and transmission infrastructure is needed
to be installed, to fulfill the increase in demand in
residential area. In respond to the high power demand,
electricity price also been increased. To remove the
deficiencies of the traditional grid, smart grid (SG) is
emerged to fulfill the high energy demand efficiently.
Smart grid plays an important role to overcome the
challenges of traditional grid. Smart grid has following
three components: smart meter, smart appliances and
energy management controller. Energy management
is to handle and overcome capacity bounds, environ-
mental issues, maintenance, operations. There are two
categories of energy management: supply side man-
agement (SSM) and demand side management (DSM).
The SSM is responsible for generating, managing and
delivering the energy to the consumers. The DSM is
responsible for managing the energy on the consumer
end. Demand response programs are designed by DSM
that are used in load shifting mechanism in case of
variable prices on different prices on different OTI.
Rasheed et al. in [2] used demand and response (DR)
technique to minimize the cost and PAR while achiev-
ing maximum user comfort. Electricity load shifting
from on-peak to off-peak hours significantly reduces
the electricity cost but user comfort is compromised
2
because operation of appliances is delayed [6]. In [11],
electricity load shifting is done by using distributed
algorithm developed by the authors. Residential load
scheduling quandary is proposed utilizing game theory
approach. The convergence rate of Nash equilibrium is
also expedited by the authors by applying the newton
method. Simulation results show that PAR is minimized
while minimizing the user discomfort.
Several aspects such as electricity cost, user com-
fort, electricity load and operational issues has to be
considered in order to achieve coordination between
utility and consumer. User comfort is the important
component that must be considered while reducing the
electricity bill. Lots of efforts has been done in the
literature to handle and overcome these challenges.
In [8], authors used GA to minimize electricity load
in residential, commercial and industrial sector. The
authors done the comparison of the performance of
GA with other Evolutionary algorithms. The results
shows that 21.9 % of electricity load reduced during on-
peak hours. Sahar et al. in [14] presents a new hybrid
approach in which they done hybridization of GA,
BPSO and ant colony optimization (ACO) techniques
for cost minimization, PAR reduction while considering
user comfort on TOU pricing scheme. In [9], authors
proposed a model for handling residential power con-
sumption within user budget. The authors used genetic
algorithm to solve the optimization quandary where
the goal is to increase user comfort while minimizing
electricity cost. Mahmood et al. in [20], presents Real-
istic scheduling algorithm (RSM), that maximizes the
appliance usage at low cost. Simulation results shows
that it maximize appliance usage while minimizing the
electricity cost.
The main goal of this work is to an effective mech-
anism to handle the consumer power consumption and
power demand. We applied GA,PIO and HGP on 14
appliances in a single home and for three different
OTIs: 20 minutes, 30 minutes and 60 minutes. More-
over, we also applied GA, PIO and HGP on 10 homes,
30 homes, 50 homes with 30 minutes OTI and different
power rating. GA,PIO and HGP are evaluated on the
basis of real time pricing scheme (RTP). Simulations
results shows that GA,PIO and HGP are able to mini-
mize electricity bill and electricity consumption while
minimizing the user discomfort. The feasible region
between electricity cost and electricity consumption is
also represented. Moreover, the desired trade-off be-
tween electricity cost and user comfort is also achieve
in both techniques.
II. RE LATE D WO RK
Zhou et al. [1], discussed the contemporary tenden-
cies among HEMS. They present a general overview
on the challenges that occurs while applying HEMS
and the impact of appliance scheduling on the factors
such as, electricity load and user comfort. However, the
impact of integration of RESs and ESSs on electricity
cost is not discussed. Calvillo et al. [3], discussed the
importance of appliance scheduling in HEMS. They
present a general review on the challenges that occurs
during the implementation of HEMS and the impact of
appliance scheduling on PAR. However, user comfort
is not considered in appliance scheduling. Beaudin
and Zareipour [4], discussed the importance of load
scheduling in residential area. They discussed all the
major factors that may effect the electricity cost, PAR,
and electricity load. However they did not considered
user comfort and the impact of user comfort on other
mentioned factors.
In [5], a new hybrid scheme, GAPSO, that is based
on binary particle swarm algorithm(BPSO) and ge-
netic algorithm(GA) is used that is evaluated on day
ahead price for single and multiple days. The pro-
posed hybrid scheme minimizes the electricity bill and
user discomfort. In [6], a new hybrid scheme, TLGO,
that is based on teacher learning based optimization
and genetic algorithm(GA) is used that is evaluated
on day ahead price for single and multiple days.The
proposed hybrid scheme minimizes the electricity cost
at minimum user discomfort or waiting time. The
performance of heuristics techniques is compared with
linear programming (LP) in term of peak electricity
consumption, PAR, electricity bill and user discomfort.
Unlike TLBO and GA, TLGO minimizes both cost
and user discomfort without effecting peak electricity
consumption and PAR. In [7], OHEMS is proposed
that facilitate renewable energy resources and energy
storage system. The results of the proposed scheme and
the heuristic algorithms shows that combining RSS and
RES reduces PAR and electricity bill by 21.55% and
19.94%.
In [8], authors used GA to minimize electricity load
in residential, commercial and industrial sector. The
authors done the comparison of the performance of GA
with other Evolutionary algorithms. The results shows
that 21.9% of electricity load reduced during on-peak
hours. In [9], authors proposed a model for handling
residential power consumption within user budget. To
solve the optimization quandary, the authors used ge-
netic algorithm where the goal is to increase user sat-
isfaction while minimizing electricity bill. Rasheed et
3
TABLE I Strength and limitations of State of the art work
Technique(s) Objective(s) Findings Remarks
GA and BPSO [5] Minimize electricity bill and
user discomfort in single and
multiple homes
Performance of proposed algo-
rithm is compared with evolu-
tionary algorithms
PAR is ignored
TLGO [6] Minimize electricity bill and
user discomfort
Performance of proposed algo-
rithm is compared with LP
Electricity consumption is ig-
nored
OEHMS [7] Minimize electricity bill and
PAR
Performance of proposed algo-
rithm is compared with heuris-
tic techniques
User comfort is not considered
GA [8] Minimize electricity load in
residential, industrial and com-
mercial area
Algorithm is compared with
evolutionary algorithms on the
basis of performance
PAR is effected
GA, BPSO, ACO [14] Minimize electricity cost and
PAR
Algorithm is compared with
evolutionary algorithms on the
basis of performance
User comfort is effected
GA [9] Minimize electricity bill while
maximizing user satisfaction
Manage the load in user de-
fined budget
PAR is ignored
Fractional Programming [10] Minimize electricity bill A novel concept of cost effi-
ciency is proposed
PAR is effected
Distributed algorithm [11] Minimize PAR and user dis-
comfort
PAR is minimized by using dis-
tributed algorithm
Electricity cost is not consid-
ered
MILP [12] Minimize electricity bill and
carbon emmission
Electricity bill is minimized
through MILP
PAR is effected
ILP [13] Minimize electricity cost and
PAR reduction
Electricity cost and PAR are
reduced significantly
User comfort and ESS not con-
sidered
al. in [2] used demand and response (DR) technique to
minimize the cost and PAR while achieving maximum
user comfort. Electricity load shifting from on-peak to
off-peak hours significantly reduces the electricity cost
but user comfort is compromised because operation of
appliances is delayed [6].
Chen et al. in [10], presented the residential load
scheduling model with RTP. To achieve an optimal
solution for electricity load scheduling, the authors
presented the concept of electricity cost efficiency. The
authors used fractional programming for the optimal
solution. Simulation results shows that consumers elec-
tricity cost is reduced. In [11], electricity load shifting
is done by using distributed algorithm developed by
the authors. Residential load scheduling quandary is
proposed utilizing game theory approach. The conver-
gence rate of Nash equilibrium is also expedited by
the authors by applying the newton method. Simulation
results show that PAR is minimized while minimizing
the user discomfort. Mahmood et al. in [20], presents
Realistic scheduling algorithm (RSM), that maximizes
the appliance usage at low cost. Simulation results
shows that it maximize appliance usage while mini-
mizing the electricity cost.
In [12], authors considered 30 homes, where each
home has 12 appliances. The multi-objective optimiza-
tion problem is solved using MILP. Carbon emissions
reduction and consumers electricity bill are achieved.
Shiftable appliances are not considered which plays
a major role in cost minimization. The authors in
[13], proposed an integer linear programming (ILP)
algorithm predicated HEMS with builtin RES to shift
the shiftable electricity load from electricity rush hours
to off-peak hours. User comfort and ESS integration
is not considered by the authors.The authors in [16],
used a decentralized framework to minimize the cost
in residential areas in smart grid by shifting the load
from on-peak to off-peak hours. In [8], authors used GA
to minimize electricity load in residential, commercial
and industrial sector. The authors done the comparison
of the performance of GA with other Evolutionary
algorithms. The results shows that 21.9 % of electricity
4
load reduced during on-peak hours.
III. PROB LE M STATE ME NT
Optimization of energy is the one of the difficult
challenge in smart grid because consumer energy de-
mand and electricity prices are not fixed. In [12],
authors considered 30 homes, where each home has 12
appliances. The multi-objective optimization problem
is solved using MILP. Carbon emissions reduction
and Consumers’ electricity bill are achieved. Shiftable
appliances are not considered which plays a major role
in cost minimization.
In this paper, single home, 10 homes, 30 homes and
50 homes with 14 different appliances are considered.
Three types of appliances are considered: schedule-
able, non-schedule-able and uninterruptable. Our goal
is to minimize the electricity cost, electricity load while
minimizing the user discomfort. We applied GA, PIO,
HGP to achieve our goals. The problem can be stated
as: Given are (a) appliances start and end time (b)
length of operational time (c) RTP signal (d) Time
interval (e) Total power demand of each appliance. To
be determined are (a) power consumption pattern To
find the optimal solution with minimum electricity cost,
electricity load and user discomfort, RTP is applied. We
evaluated our model on three different time interval
values: 20 minutes, 30 minutes and 60 minutes. The
four parameter on the basis of which our model is
evaluated are: electricity cost, peak-to-average ration
(PAR), waiting time (user discomfort) and electricity
consumption.
IV. SYSTEM MO DE L
In this paper, a single home with 14 different appli-
ances is considered. Appliances are categorized into
three categories: scheduleable, non-scheduleable and
uninterruptable. Moreover, 20 minutes OTI, 30 minutes
OTI and 60 minutes OTI time slots are considered in
the proposed model. All the appliances have different
length of operation time and energy consumption. All
appliances completes their allocated length of operation
time. Nonscheduleable and uninterruptable appliances
could not be shifted once they start operation. The
proposed system model is shown in Fig. 1.
V. PROPOSED METHODOLOGY
Problem that is stated in section III is solved using
GA,PIO and HGP. In the literature, several mathe-
matical techniques such as LP, MILP and ILP are
used to handle electricity consumption problem. The
computational complexity of mathematical techniques
is very high. We applied population based techniques
to address the electricity consumption problem. We
applied GA, PIO and our proposed HGP techniques
and compared them with previous researchers results.
A. GA
GA is inspired by the genetic process of living organ-
isms. GA has the ability to search for best solution in
minimum time. GA is able to handle complex problems
with minimum computational effort [15]. The initial
process of GA is to generate random population that
updates on every iteration. The status of appliances is
represented by chromosomes and number of hours for
scheduling are represented by length of chromosomes.
Fitness of each chromosome is evaluated based on the
fitness function. The elitism process is performed so
that chromosomes with high fitness value can be used
in next iterations. Two parent chromosomes are selected
after the elitism process is completed. Crossover is
applied to the selected two parent chromosomes and
a child/offspring, that contains the properties of both
the parents, is added to the existing population. In
mutation process the bits of the selected chromosomes
are inverted by mutation operator to reduce the pos-
sibility of repetition of selected chromosomes in the
population. The crossover rate is usually higher than
the mutation rate to get the best possible solution. The
mutation rate is used to maintain randomness to avoid
repetition of same chromosomes. When the crossover
and mutation process is done then fitness of current
population is compared with the previous one until the
termination criteria is achieved. The chromosome with
highest fitness value is selected when the whole process
is terminated.
B. PIO
PIO is derived from homing pigeons and it is pro-
posed by Duan and Qiao [18]. It have two majors
operators: map and compass operator and landmark
operator. Initially the population is randomly generated.
To find the best optimal solution, fitness function is
used. All the population is sorted according to the
fitness and half of the population is discarded using
the landmark operator.
C. HGP
Hybridization means to combine two or more tech-
niques [21]. GA,PIO and HGP are combined to form a
hybrid approach, HGP algorithm. All the steps of GA
performed in a same way as discussed earlier but the
5
Fig. 1. System Model
TABLE II Classification of Appliances
Groups Appliances Power rating (kWh) LOT
Non Schedule-able appliances Oven 1.3 3 hour
Fan 0.20 15 hour
Kettle 2.0 3 hour
Toaster 0.9 1 hour
Rice Cooker 0.85 2 hour
Blender 0.3 2 hour
Frying Pan 1.1 3 hour
Coffee Maker 0.8 4 hour
Non interrupt-able appliances Washing Machine 0.5 3 hour
Cloth Dryer 1.2 3 hour
Schedule-able appliances Dish Washer 0.7 4 hour
Iron 1.0 3 hour
Vacuum Cleaner 0.4 4 hour
Hair Dryer 1.5 2 hour
elimination and dispersal step of GA in which crossover
and mutation is performed is replaced by the map and
compass operator step of PIO.
VI. SIMULATION RESULTS AND DISCUSSION
In this section we evaluated the performance of GA,
PIO techniques and our proposed hybrid meta-heuristic
technique HGP. We evaluated the following Techniques
on the basis of four performance parameters: Electricity
cost, PAR, Energy consumption and user comfort. We
are considering a single home, consists of 14 appliances
using the RTP price scheme and three different OTI
of 20 minute, 30 minute and 60 minute. We also
considered 10 homes, 30 homes and 50 homes scenario
with 30 minute OTI and different power ratings. The
classification of appliances is shown in Table II.
A. Eelectricity Cost
Total cost for single home and multiple homes is
shown in Fig. 2. In single home scenario, GA reduces
4.54%, 2.94%, 20.54% of the total cost in case of
20 minutes OTI, 30 minutes OTI, 60 minutes OTI
repectively. PIO reduces 20.54%, 27.94%, 20.94% of
the total cost in case of 20 minutes OTI, 30 minutes
OTI, 60 minutes OTI repectively. HGP reduces 30.72%,
40.94%, 24.24% of the total cost in case of 20 minutes
OTI, 30 minutes OTI, 60 minutes OTI repectively.
In multiple homes scenario, the cost is less than the
unscheduled cost in all each case.
B. PAR
We applied GA, PIO and HGP on single home with
three different OTIs and on 10 homes, 30 homes and
50 homes with 30 minutes OTI. It is clear from Fig.
3 that our techniques are working fine with respect to
PAR ratio because in all cases the PAR after scheduling
is less than the PAR in unscheduled, it means that our
algorithms are scheduling the appliances and control-
ling the peak so that load is shifted evenly between the
time slots.
6
OTI 20 minutes OTI 30 minutes OTI 60 minutes
0
200
400
600
800
1000
1200
1400
TOTAL COST
Unscheduled
GA
PIO
HGP
(a) Total cost for a single home
10 Homes 30 Homes 50 Homes
0
2
4
6
8
10
Total Cost (Cents)
104
Unschedule
GA
PIO
HGP
(b) Total cost for multiple homes
Fig. 2. Overall cost for single and multiple homes
OTI 20 minutes OTI 30 minutes OTI 60 minutes
0
1
2
3
4
5
6
7
PAR
Unscheduled
GA
PIO
HGP
(a) PAR for a single home
10 Homes 30 Homes 50 Homes
0
1
2
3
4
5
Peak Average Ratio
Unschedule
GA
PIO
HGP
(b) PAR for multiple homes
Fig. 3. PAR for single and multiple homes
OTI 20 minutes OTI 30 minutes OTI 60 minutes
0
1
2
3
4
5
6
7
8
Waiting Time
GA
PIO
HGP
(a) Average waiting time for a single home
10 Homes 30 Homes 50 Homes
0
2
4
6
8
10
12
14
Waiting Time (hours)
GA
PIO
HGP
(b) Average waiting time for multiple homes
Fig. 4. Average waiting time for single and multiple homes
C. User Comfort
We applied GA, PIO and HGP on single home with
three different OTIs and on 10 homes, 30 homes and
50 homes with 30 minutes OTI. It is clear from Fig. 4
that algorithm is working fine with respect to waiting
time because in all cases the waiting time is just the
schedule-able appliances and the non-schedule-able and
uninterruptable appliances does not have any waiting
time it means that user comfort is increased. There is
a trade-off between user comfort and waiting time. In
case of schedule-able appliance the waiting time will
decrease the user comfort but in case of non-schedule-
able and uninterruptable as there is no waiting time
so user will not have to wait, as a result user comfort
increases.
D. Electricity Consumption
We applied GA, PIO and HGP on single home
having 14 appliances and multiple homes scenario. In
single home scenario, we applied these algorithms for
three different OTI to schedule the appliances from
electricity peak hours to off-peak hours. In multiple
homes scenario, we applied these algorithms for 30
minute OTI to schedule the appliances from electricity
peak hours to off-peak hours. It is clear from Fig. 5
that GA, PIO and HGP are able to reduce the peaks
7
0 10 20 30 40 50 60 70 80
Time (hours)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Load (kWh)
Unscheduled
GA
PIO
HGP
(a) Single home at 20 minutes OTI
0 5 10 15 20 25 30 35 40 45 50
Time (hours)
0
1
2
3
4
5
6
Load (kWh)
Unscheduled
GA
PIO
HGP
(b) Single home at 30 minutes OTI
0 5 10 15 20 25
Time (hours)
0
2
4
6
8
10
12
Load (kWh)
Unscheduled
GA
PIO
HGP
(c) Single home at 60 minutes OTI
0 5 10 15 20 25 30 35 40 45 50
Time (hours)
0
10
20
30
40
50
60
70
80
90
100
10 Homes Load(kWh)
Unscheduled
GA
PIO
HGP
(d) Power consumption of 10 homes
0 5 10 15 20 25 30 35 40 45 50
Time (hours)
0
50
100
150
200
250
300
30 Homes Load(kWh)
Unscheduled
GA
PIO
HGP
(e) Power consumption of 30 homes
0 5 10 15 20 25 30 35 40 45 50
Time (hours)
0
100
200
300
400
500
600
50 Homes Load(kWh)
Unscheduled
GA
PIO
HGP
(f) Power consumption of 50 homes
Fig. 5. Power consumption of single and multiple homes
8
Algorithm 1 Genetic Algorithm
1: Input: set of appliances Ai;
2: Initialization: P Hs,OP Hs,t=0,
avgga=0, H, PB = 0, 1;
3: for i=1 to T do
4: for j=1 to H do
5: Generate initial population
6: for j=1 to P do
7: Calculate fitness function
8: Select best solution in population P
9: Check status of Ai;
10:
11: if t == P Hs;then
12: wait until OP Hs;
13: Check the remaining t
of all Ai
14: end for
15: Generate new population
16: Crossover (Θi);
17: Mutation (Θi);
18: end for
19: end for
of load. Electricity load peaks have direct impact on
PAR. It is clear from Fig. 5 and Fig. 3, when the peak
electricity load is high then the PAR is high and vice
versa.
E. Performance Trade-off
Performance trade-off is achieved between the elec-
tricity cost and the user discomfort. As shown in Fig. 2
and Fig. 4 when the total cost is high then the waiting
time is low and when the total cost is low then the
waiting time is high. It is clear from Fig. 5 that GA,
PIO and HGP are able to reduce the peaks of load.
Electricity load peaks have direct impact on PAR. It is
clear from Fig. 5 and Fig. 3, when the peak electricity
load is high then the PAR is high and when the peak
electricity load is low then the PAR is low.
F. Feasible Region
Feasible region is a region that contains all the
possible solutions based on our fitness function [19].
Our primary goal is to reduce electricity cost and PAR.
Electricity cost depends on the electricity price and
electricity consumption.We can do load shifting by
shifting the load from on-peak hours where the elec-
tricity price is high to the off-peak hours in which the
electricity price is low.We have to focus on following
four parameters while reducing the electricity cost:
Algorithm 2 PIO Algorithm
1: Input: maximum iterations
2: Initialization: pigeonnum, D, map and compass
factor, T1, T2, Xg
3: Specify LOT of appliances and power ratings
4: Randomly initialized the population
5: set initial path Xiand velocity V for each appliance
6: set Xp=Xi
7: calculate the fitness of individual appliances
8: find the optimal solution
9: map and compass operator
10: for i=1:T1 do
11: for i=1:pigeonnum do
12:
13: while Xiis beyond the
search range do
14: calculate Xiand Vi
15: end
16: end
17: for i=1:D do
18:
19: while Xpis beyond the
search range do
20: sort all the appliances
according to their fitness values
21: pigeonnum=pigeonnum/2
22: keep half of the
appliances with better fitness
value and discard the other half
23: Xc= average of the
remaining appliances
24: calculate Xi
25: end
26: end
27: Output: Xgis output as the
global optima of fitness function
28: end
•Minimum Load,minimum price
•Minimum Load,maximum price
•Maximum Load,minimum price
•Maximum Load,minimum price
The blue shaded region in Fig. 6 is the feasible region
for 20 minutes, 30 minute and 60 minute OTI respec-
tively.
VII. CONCLUSION AND FUTURE WORK
In this paper, GA,PIO and our proposed HGP are
applied on single home having 14 appliances and
multiple homes scenario. In single home scenario, we
9
OTI Cases Load (kWh) Price (dollars) Cost (dollars)
Min.load, Min. price 0.462 8.1000 3.7422
Min.load, Max. price 0.462 27.3500 12.6357
20-minutes Max.load, Min. price 3.0167 8.1000 24.4353
Max.load,Max. price 3.0167 27.3500 82.5067
Min.load, Min. price 0.762 8.1000 6.1722
Min.load, Max. price 0.762 27.3500 20.8407
30-minutes Max.load, Min. price 5.925 8.1000 47.9925
Max.load,Max. price 5.925 27.3500 162.0488
Min.load, Min. price 0.462 8.1000 3.7422
Min.load, Max. price 0.462 27.3500 12.6357
60-minutes Max.load, Min. price 10.150 8.1000 82.2150
Max.load,Max. price 10.150 27.3500 277.6025
TABLE III Possible feasible regions for a single home
OTI Cases Load (kWh) Price (dollars) Cost (dollars)
Min.load, Min. price 40.7620 8.1000 330.1722
Min.load, Max. price 40.7620 27.3500 1115
10-homes Max.load, Min. price 500.956 8.1000 4058
Max.load,Max. price 500.956 27.3500 13701
Min.load, Min. price 40.762 8.1000 330.1722
Min.load, Max. price 40.762 27.3500 1115
30-homes Max.load, Min. price 300.0690 8.1000 2431
Max.load,Max. price 300.0690 27.3500 8207
Min.load, Min. price 15.762 8.1000 127.6722
Min.load, Max. price 15.762 27.3500 431.0907
50-homes Max.load, Min. price 101.2070 8.1000 820
Max.load,Max. price 101.2070 27.3500 2768
TABLE IV Possible feasible regions for a multiple homes
Algorithm 3 HGP Algorithm
1: Input: set of appliances Ai;
2: Initialization: P Hs,OP Hs,t=0,
avgga=0, H, PB = 0, 1;
3: for i=1 to T do
4: for j=1 to H do
5: Generate initial population
6: for j=1 to P do
7: Calculate fitness function
8: Select best solution in population P
9: Check status of Ai;
10:
11: if t == P Hs;then
12: wait until OP Hs;
13: Check the remaining t
of all Ai
14: end for
15: Generate new population
16: map and compass operator
17: end for
18: end for
applied these algorithms for three different OTI to
schedule the appliances from electricity peak hours to
off-peak hours. In multiple homes scenario, we applied
these algorithms for 30 minute OTI to schedule the ap-
pliances from electricity peak hours to off-peak hours.
GA,PIO and HGP are evaluated on the basis of real
time pricing scheme (RTP). Simulations results shows
that GA,PIO and HGP are able to minimize electricity
bill and electricity consumption while minimizing the
user discomfort. The feasible region between electricity
cost and electricity consumption is also represented.
Moreover, the desired trade-off between electricity cost
and user comfort is also achieved in existing and
proposed techniques. The performance of HGP is better
than GA, PIO with respect to PAR,electricity load
and electricity cost for both single home and multiple
homes scenario.
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