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Demand side management using meta-heuristic

techniques and ToU in smart grid

Sajeeha Ansar1, Wajeeha Ansar2, Kainat Ansar1, Mohammad Hashir Mehmood3,

Muhammad Zabih Ullah Raja1and Nadeem Javaid1,∗

Abstract In this paper, we perform performance evaluation of home energy man-

agement system (HEMS) for demand side management (DSM) in smart grid. In this

work, smart home is equipped with HEMS, smart meter, and smart appliances for

two-way communication between utility and consumer. HEMS performs scheduling

of smart appliances based on meta-heuristic techniques to balance load for whole

day to avoid peak creation in any hour. Smart meter performs electricity cost calcu-

lation for consumed energy based on time of use (ToU) pricing signal provided by

utility. Our focus is to efﬁciently handle user demand, reduction in peak-to-average

ratio (PAR) and electricity cost minimization. The implemented meta-heuristic tech-

niques in this work are: Enhanced differential evolution (EDE), harmony search al-

gorithm (HSA), bacterial foraging algorithm (BFA), and genetic algorithm (GA).

The simulation results show the performance of HEMS based on optimization tech-

niques using ToU.

1 Introduction

Smart grid is bi-directional communication between user and utility by installing

smart meter and HEMS for DSM. This bi-directional communication is useful for

energy optimization, load balancing, electricity cost reduction and minimizing PAR

[2], [3], [4]. Load balancing is basically efﬁcient management of energy consump-

tion by balancing load in on-peak hours and off-peak hours [13], [14]. User tries to

minimize electricity cost by shifting load from on-peak hour to off-peak hours. In

addition, this load shifting creates peak in any other hour which ultimately affects

PAR. Thus, load balancing is an efﬁcient way to avoid PAR for complete operational

1COMSATS Institute of Information Technology, Islamabad 44000, Pakistan

2National University of Sciences and Technology, Islamabad 44000, Pakistan

3Roots Millennium School, Islamabad 44000, Pakistan

∗Correspondence: www.njavaid.com, nadeemjavaidqau@gmail.com

1

2 Sajeeha Ansar et al.

time of smart appliances. However, utility wants reduction in PAR for efﬁcient sup-

ply of energy to fulﬁl user demand in any hour. In terms of user comfort, there is

always tradeoff between electricity cost and waiting time [4]. While, performing

load balancing through HEMS by optimization techniques, this ultimately affects

user deﬁned scheduled operational time of smart appliances.

Smart meter takes utility pricing schemes and appliances schedule as input. Con-

sequently, smart meter performs electricity cost calculation based on energy con-

sumption by smart appliances schedule provided by HEMS. Smart meter helps in

reducing electricity cost by providing calculations to HEMS and utility [2], [3], [4].

HEMS is also known as scheduler in smart grid environment. HEMS is respon-

sible for two-way communication between smart meter and user demand. HEMS

helps in giving efﬁcient demand response (DR) which is also known as DSM [2].

HEMS performs scheduling of smart appliances to balance load for operational time

according to user demand. This scheduling helps in reducing PAR in any hour to

minimize electricity cost.

Demand response model consists of aggregator, which is responsible for com-

munication between house hold appliances for their scheduling and running time

for predeﬁned time duration [1]. DSM became successful for multiple homes with

contribution of generators, retailers, large users and aggregator. Demand from mul-

tiple user including smart home, smart business, and industrial area is controlled by

control center. Power is generated from wind turbine, conventional, hydroelectric,

nuclear, and solar panels [2].

In [3], the grid power system with renewable energy resources is modeled to

generate extra energy. The system consists of batteries, multiple users, smart appli-

ances, and energy providers work efﬁciently under power generation from renew-

able resources. In this paper [4], a system model is proposed to examine system

performance, energy and power consumption calculations, performance parameter

optimization and energy management. The system consists of smart meter, HEMS

controller, and appliances. Local area network (LAN) is used to share appliances

control. Appliances are categorized in schedulable (ﬂexible) and real time devices

(less ﬂexible).

DSM in smart grid based on regularization, bi-directional framework, and new-

ton method is applied in this paper [5]. The regularization helps to improve in-

terrupts in appliance scheduling and minimizing PAR by reducing duration. Bi-

directional framework helps in bidirectional communication among agents and

HEMS. The newton method helps in fast convergence and this increase user comfort

in terms of waiting time. The HEMS system model in smart grid is shown in ﬁg 1.

In this paper, section 2 reﬂects the related work in smart grid. Section 3 illustrates

system model of this research work. In addition, meta-heuristic techniques including

HSA, EDE, BFA, and GA are highlighted in section 4. Moreover, simulation results

and discussions are presented in section 5. Finally, complete work is concluded in

section 6.

Demand side management using meta-heuristic techniques and ToU in smart grid 3

Fig. 1 Home Energy Management System in Smart Grid

2 Related Work

In this section, the previous work done in smart grid is described. Energy optimiza-

tion is focus in smart grid to control energy consumption. Energy maximization,

load balancing, controlling power consumption, using renewable energy sources,

and minimizing PAR is achieved by different work in smart grid.

In [6], authors focused on load scheduling and energy consumption using renew-

able energy resources. Appliances are categorized as interruptible and ﬁxed appli-

ances. In this proposed system, each user can sell excessive power generated by

renewable energy sources. To model the connection between user and generated

by renewable energy resources, a game theoretical approach adopted by authors.

The proposed system has reduced energy consumption from power grid, and mini-

mized electricity cost. Load balancing and PAR problems are addressed by gener-

ating power from renewable energy resources. However, cost of renewable energy

resources is neglected in this proposed system.

In this paper [7], the authors proposed a decentralized system to establish a con-

nection between demand response and user. The proposed system is used to manage

load to avoid peak in on-peak hours. Balancing load in on-peak hour cause reduction

in electricity cost. However, the waiting time of user in increased. In this proposed

system, when the HLM does not ﬁnd acceptable load proﬁle then complete load

shifted to peak hours. Convergence is very slow and it required many iterations to

ﬁnally receive balanced load proﬁle.

4 Sajeeha Ansar et al.

Efﬁcient cost reduction for residential load scheduling in smart grid is proposed

in [8]. The proposed load scheduling algorithm works for cost reduction in DSM

system. Day ahead bidding and RTP mechanisms are used in this proposed sched-

uler. The distributed energy resources (DERs) are used in proposed load scheduling

algorithms; this cause high computational cost and increase user waiting time. The

service charges are also considered for better results in cost efﬁciency. The cost of

implementing DERs is neglected in this proposed system.

In [9], the proposed DSM technique deals with the load management in resi-

dential area for single and multiple homes. The authors focused on maximizing user

comfort, minimizing electricity cost and PAR. The proposed system is a hybrid tech-

nique of genetic algorithm and wind driven algorithms. In this proposed system, the

scheduler shift load from on-peak hours to off-peak hours for interruptible appli-

ances to reduce energy consumption in peak hour. User comfort in terms of waiting

time of appliances is neglected in proposed hybrid technique during load balancing.

Energy consumption scheduling mechanism by load balancing for residential

area in smart grid is proposed in [10]. In this proposed technique, author achieved

balanced load for each hour using proposed scheduling mechanism. The proposed

system schedule appliances to minimize energy consumption in on peak hour, max-

imizing user comfort by scheduling power and operational time. Load balancing

achieves minimum power consumption, electricity cost and PAR in peak hours.

In [11], the authors focused on reducing electricity cost and PAR by schedul-

ing power usage in smart homes. The authors proposed energy management system

(EMS) and scheduling method for proposed EMS. In this proposed system, authors

combined RTP and inclining block rate (IBR) pricing schemes. Hybrid pricing sig-

nals performed better to achieve reduction in electricity cost and PAR. The authors

focused on optimizing power consumption, however proposed system implemented

by strong assumptions of same power consumption in each hour.

In this paper [12], the authors proposed a system to schedule appliances in smart

homes. The focus of the work is to minimize electricity cost by load balancing in

peak hour for interruptible appliances. Ahead of time pricing signal is used in this

proposed model. User comfort is compromised in terms of waiting time to run re-

quired appliance. The proposed system is based on wireless connection between

smart meter, smart appliances, and system model. However, unavailability of inter-

net cause rise in PAR and load unbalancing cause maximizing electricity cost.

3 System Model

The proposed system model is composed of 12 appliances for single smart home

in smart grid. However, the authors in [9], have used same appliances classiﬁcation

and power ratings for multiple homes using RTP pricing signal. Appliances are cat-

egorized as: shift able and non-shift able appliances as shown in ﬁg.2. In our system

model, we have used ToU pricing signals for electricity cost calculation. The sched-

uler performs appliance scheduling for 24 hours according to TOU pricing signal as

Demand side management using meta-heuristic techniques and ToU in smart grid 5

Fig. 2 System Model

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Time (hours)

8

10

12

14

16

18

Price (cent)

Price Signal

Fig. 3 TOU Pricing signal

shown in ﬁg. 3. The scheduler performs load balancing in on-peak hours and off-

peak hours to minimize PAR in any hour. Smart meter is installed in smart home for

bi-directional communication between utility and HEMS. Appliances classiﬁcation,

life time and power ratings are shown in table 1. We have used equation 1 to calcu-

late electricity cost for 24 hours, equation 2 is used to calculate load and as shown

in equation 3 PAR is calculated using this equation. These equations are used by the

authors in [13] for electricity cost, load, and PAR calculations.

Electricity-Cost =

24

∑

hour=1

Ehour

Rate ×PApp

Rate (1)

Load =PApp

Rate ×App (2)

PAR =max(load2)

ˆa(load2)(3)

4 Meta-heuristic Techniques

In this section, meta-heuristic techniques have been discussed to perform appliances

scheduling by HEMS for DSM. 12 appliances of single home are scheduled using

these techniques for HEMS performance evaluation.

6 Sajeeha Ansar et al.

Table 1 Appliances and Power Ratings

Appliance Categories Appliance Name Power Rating (kwh) Life Time (hours) Deferrable Load

Elastic and shift able appliances Space Heater 1 9 Yes

Space Heater 1 9

Heat Pump 0.11 4

Portable Heater 1.00 5

Water Heater 4.50 8

Clothes Washer 0.51 9

Clothes Dryer 5.00 5

Dishwasher 1.20 11

First-Refrigerator 0.50 24

Fixed load appliances Fan 0.5 11 No

Furnace Fan 0.38 8

Central AC 2.80 12

Room AC 0.90 5

4.1 EDE

It consists of multiple efﬁcient features over other optimization techniques. In EDE,

main steps are: mutation, crossover, and selection phase. Initially, random popu-

lation is generated using upper and lower bounds for random function. In mutation

phase, randomly three target vectors are selected from initially created memory. One

mutant vector is formed by taking difference to any two previously selected target

vector and adding the results in third target vector as shown in equation 4 given

below.

Vj,i,G+1=xbest,G+F(xr1,G) + F(xr2,G−xr3,G)(4)

In crossover phase, a random value is generated and compared with crossover rates.

However, these crossover rates are predeﬁned in EDE. If the random number is less

than crossover rate then information is taken from selected mutant vector. Mean-

while, if random value is greater than the crossover rate then target vector becomes

trial vector of this optimization technique. In EDE, it generates 5 trial vectors based

on 5 distinct crossover rates as shown in equation 5 to equation 9. After getting ﬁve

trial vectors, ﬁtness of these ﬁve trial vectors is calculated and the vector having

minimum value is selected for ﬁnal trial vector.

U1j,i,G+1=(Vj,i,G+1,if(randb(j)) ≤0.3=Irand

xj,i,G,if(randb(j)) >0.36=Irand (5)

U2j,i,G+1=(Vj,i,G+1,if(randb(j)) ≤0.6=Irand

xj,i,G,if(randb(j)) >0.66=Irand (6)

U3j,i,G+1=(Vj,i,G+1,if(randb(j)) ≤0.9=Irand

xj,i,G,if(randb(j)) >0.96=Irand (7)

Demand side management using meta-heuristic techniques and ToU in smart grid 7

U4j,i,G+1=(randb(j)) ×xj,i,G(8)

U5j,i,G+1=(randb(j)).vj,i,G+ (1−(randb(j)))×xj,i,G(9)

In selection phase, the selected trial vector is compared with target vector and the

vector having minimum ﬁtness value is selected for next generation. The authors in

[14] have used equation 5 to equation 9 for EDE algorithm 1. EDE Parameters and

Values are shown in table 3.

4.2 HSA

The steps involved in this evolutionary algorithm are: random initial population,

harmony improvising process, memory consideration, and pitch adjustment for new

generation. Initially, harmony memory is created randomly using random function

by specifying upper and lower number range. After completing ﬁrst step of ini-

tial random memory creation using equation 10, harmony improvising process gets

started. HSA Parameters and Values are shown in table 4.

xi,j=l j +rand() ×(Uj−lj)(10)

In harmony improvising step, generation of new harmony is based on harmony

memory consideration rate (HMCR) and pitch adjustment ratio. In this step, a ran-

dom number is generated and compared with HMCR. If the generated value is less

than HMCR then the existing harmony memory contributes in selecting new har-

mony. If the randomly generated number is greater than HMCR then a new random

value is generated to create new harmony using equation 11 as given below.

Vi,j=(xrandb(j),i f (rand()) <HMCR,

lj+(rand()) ×(Uj−lj),else (11)

The harmony selected in memory consideration process further go through the pro-

cess of pitch adjustment ratio. In this step, a random number is generated and if it

is less than pitch adjustment ratio then the existing harmony memory contributes

in selecting new harmony. If the randomly generated number is greater than pitch

adjustment ratio then a new random value is generated to create new harmony using

equation 12.

Vi,j=(Vi,j±(rand()) ×bwj,(rand()) <PAR,

Vi,j,else (12)

After getting a ﬁnal new vector, compare it with worst harmony value in existing

harmony memory. If new results are better than worst, replace it in previous worst

harmony value in existing harmony memory. HSA complete steps are shown in algo-

8 Sajeeha Ansar et al.

Table 2 EDE Parameters and Values

Parameter Value

Population Size 30

Number of appliances 12

Maximum pitch adjustment rate 0.9

Minimum pitch adjustment rate 0.4

Harmony memory consideration rate 0.9

Maximum bandwidth 1.0

Minimum bandwidth 0.0001

Maximum iteration 100

Lower limit 0.1

Upper limit 0.9

Stopping Criteria Max. iteration

Table 3 HSA Parameters and Values

Parameter Value

Population Size 30

Number of appliances 12

Number of target vectors 3

Number of mutant vector 1

Number of crossover rates equations 5

Number of trail vectors 5

Maximum iteration 100

Lower limit 0.1

Upper limit 0.9

Stopping Criteria Max. iteration

rithm 2. Detail description of all symbols, parameters used in equations, algorithms

are shown in table 5 and 6, respectively.

4.3 BFA

Among nature inspired optimization techniques, BFA is most commonly used opti-

mization technique. BFA technique is based on real bacteria foraging process. In this

optimization algorithm, stochastically and collectively it allows the cell to swarm for

optimal solution. The steps involve in BFA are: chemotaxis step, reproduction and

elimination-dispersal. In chemotaxis step, it is life duration of the bacteria based on

number of chemotactic steps. In reproduction cell, it is basically selection phase of

this algorithm. In this step, bacteria cells performed well over their life duration are

selected for next generation. Elimination dispersal step is based on ﬁtness function

in which previous expired cells are discarded and new population is inserted.

4.4 GA

To ﬁnd optimal solutions, GA is the most popular optimization technique. The con-

cept behind genetic algorithm is alike chromosomes. The main steps involve in GA

are: selection, crossover, and mutation. In selection phase, initially population is

generated randomly which is basically representation of chromosomes. Then for se-

lection process this generated population is broken down. Crossover phase is then

performed on selected chromosomes from selection phase. In mutation phase, bits

are changed randomly and ﬁnally a chromosome is selected based on ﬁtness func-

tion. GA is relatively better algorithm for optimal solution [4], [9]. While, prob-

abilistic nature of GA does not guarantee optimality. GA performs best for larger

Demand side management using meta-heuristic techniques and ToU in smart grid 9

population, while BFA performs best for small population [13]. Execution time of

GA is less as compare to other meta-heuristic techniques [14].

Algorithm 1 HSA

Input:(HMS,NVAR,HMCR,PAmin,PAmax,BW min,BWmax ,maxItr,X l ,

Xu)

1: for Hour =1→24 do

2: if hour <24 then

3: Select electricity cost of next hour

4: else

5: Select electricity cost of current hour

6: end if

7: for j=1→maxItr do

8: Pitch adjustment

9: for p=1→NVAR do

10: Bandwidth adjustment

11: end for

12: for I=1→NVAR do

13: if rand(1)<H MCR then

14: Select new harmony from existing

15: if rand(1)<PA then

16: V[´

i,´

j] + rand()

17: else

18: V[´

i,´

j]−rand()

19: end if

20: else

21: Randomly select new harmony

22: end if

23: end for

24: end for

25: if new <HM(worst)then

26: HM(worst) = new

27: else

28: HM(worst) = HM(worst)

29: end if

30: end for

5 Simulation Results and Discussions

In this section, the simulation results show performance comparison of implemented

meta-heuristic techniques. Moreover, meta-heuristic techniques do not guarantee

optimal solutions [9]. In addition, computational time and optimal solutions are im-

portant parameters in research. The implementation of meta-heuristic techniques is

based on random initial population generation process. Therefore, the conﬁdence

interval is calculated based 10 times average to evaluate performance of HEMS. Fig

3 elucidates the pricing rate for 24 hour. TOU is commonly used tariffs for electric-

10 Sajeeha Ansar et al.

Algorithm 2 EDE

Input:(po psize,NA,maxItr,Xl ,X u)

1: B1=rend perm(popsize)

2: Randomly select 3 target vectors T, T1, T2, T3

3: MutantvectorM =T1+0.5∗(T2−T3)

4: for m=1→maxItr do

5: for n=1→NA do

6: if rand(1)>=0.3then

7: t1 = T else

8: t1 = M

9: end if

10: end for

11: for n=1→NA do

12: if rand(1)>=0.6then

13: t2 = T else

14: t2 = M

15: end if

16: end for

17: for n=1→NA do

18: if rand(1)>=0.9then

19: t3 = T else

20: t3 = M

21: end if

22: end for

23: for n=1→NA do

24: if rand(1)∗t argetvector then

25: t4 = Telse

26: t4 = M

27: end if

28: end for

29: for n=1→NA do

30: if rand(1)∗Mutantvector +1rand (1)∗targetvector then

31: t5 = Telse

32: t5 = M

33: end if

34: end for

35: F1 = eletricitycost * t1

36: F2 = eletricitycost * t2

37: F3 = eletricitycost * t3

38: F4 = eletricitycost * t4

39: F5 = eletricitycost * t5

40: New trial vector = min [F1, F2, F3, F4, F5]

41: if Newtrialvector >T1then

42: New target vector = New trial vector

43: else

44: New target vector = T1

45: end if

46: end for

Demand side management using meta-heuristic techniques and ToU in smart grid 11

Table 4 Detail Description of Symbols

Symbols Description

Ehour

Rate Electric Rate per Hour

PApp

Rate Power Rate of an Appliance

App Appliance

Vj,i,G+1Mutant Vector

xbest,GBest target vector

xr1,GFirst target vector

xr2,GSecond target vector

xr3,GThird target vector

xj,i,GTarget vector

U1j,i,G+1First trial vector

U2j,i,G+1Second trial vector

U3j,i,G+1Third trial vector

U4j,i,G+1Fourth trial vector

U5j,i,G+1Fifth trial vector

xi,jInitial harmony memory

rand() Built in function for random value generation

ljLower limit of rand function

UjUpper limit of rand function

vi,jNew harmony memory

bwjBandwidth

Table 5 Detail Description of Parameters

Parameters Description

HMS Population size HSA

NVAR Number of appliances in EDE

HMCR Harmony memory consideration rate

PAmax Pitch adjustment maximum value

PAmin Pitch adjustment minimum value

BWmin Bandwidth minimum value

BWmax Bandwidth maximum value

maxItr Maximum iteration

Xu Random function upper limit

Xl Random function lower limit

Popsize Population size EDE

NA Number of appliance in EDE

F1 Fitness value 1st trail vector

F2 Fitness value 2nd trail vector

F3 Fitness value 3rd trail vector

F4 Fitness value 4th trail vector

F5 Fitness value 5th trail vector

t1 1st trial vector

t2 2nd trial vector

t3 3rd trial vector

t4 4th trial vector

t5 5th trial vector

HM(worst) worst value from harmony memory

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Time (hours)

0

20

40

60

80

100

120

140

160

180

Electricity Cost (cent)

Unscheduled

HSA

EDE

GA

BFA

Fig. 4 Electricity Cost

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Time (hours)

0

2

4

6

8

10

12

Load (kWh)

Unscheduled

HSA

EDE

GA

BFA

Fig. 5 Per hour Load Demand

ity pricing which varies in all countries. Fig 4 clearly demonstrates electricity cost

consumed for 12 appliances during 24 hours. The electricity cost is high in on-peak

hours from 11th hour to 17th hour for unscheduled load. Electricity cost for sched-

12 Sajeeha Ansar et al.

Unscheduled HSA EDE GA BFA

0

500

1000

1500

2000

2500

Total Cost (cent)

Fig. 6 Total Cost

HSA EDE GA BFA

0

1

2

3

4

5

Waiting Time (hours)

Fig. 7 Waiting Time

uled algorithms is less then unscheduled because load is balanced by scheduling

techniques.

The load is completely distributed in off-peak hours and on-peak hours; it cause

electricity cost within range of 40 - 60 cents for off-peak hours. HSA algorithms

performs selection of ﬁtness value less than worse; it enables the scheme to fully

distribute load in 24 hours. EDE algorithm compares electricity cost for two hours

including current and next hour; this helps to schedule high power consumption

appliances accordingly to reduce electricity cost. Load balancing cause balanced

energy consumption and reduction in electricity cost.

Fig 5 represents the load balancing through optimization techniques. Load is

balanced for 24 hours in on-peak hours to off-peak hours. To minimize the cost,

most of the schedulers shift load toward off-peak hours which cause maximization

in PAR. Load unbalancing through schedulers can lead the smart cities to starvation.

However, in our simulation results, it is clear that load is balanced in 24 hours.

Fig 6 shows total cost consumed during 24 hours. The total cost for unscheduled

is higher than scheduled algorithms. The behaviour of EDE is better among all opti-

mization techniques. Moreover, cost of BFA is highest among all implemented tech-

niques. Total cost for all implemented optimization techniques vary from 1500 cents

to 1700 cents. However, there is not a signiﬁcant difference in total cost consump-

tion. However, these scheduling techniques help in reduction of total cost consumed

in 24 hours as compare to unscheduled case.

Fig 7 illustrates the total waiting time in appliances operational time for unsched-

uled and scheduled algorithms. The EDE optimization algorithm performs best in

terms of less waiting time of appliances through scheduling criteria. However, wait-

ing time for BFA is highest among all scheduled algorithms. Thus, total waiting time

is reduced as compare to unscheduled scenario. Moreover, user comfort in terms

of waiting time is compromised in implemented algorithm. These implemented

Demand side management using meta-heuristic techniques and ToU in smart grid 13

Unscheduled HSA EDE GA BFA

0

0.5

1

1.5

2

2.5

3

3.5

PAR

Fig. 8 PAR

Unscheduled HSA EDE GA BFA

0

20

40

60

80

100

120

140

160

Total Load (kWh)

Fig. 9 Total Load

schemes perform efﬁcient where the user concern is to minimize electricity cost

and power consumption in on-peak hours.

Fig 8 clarify simulation result for PAR of implemented scheduled techniques

with comparison of unscheduled case. The result shows that the PAR is highest

for BFA. Moreover, HSA performs best among scheduling techniques. Appliance

having high power rate consumes high electricity cost in peak hours in unscheduled

scenario. In scheduled process, it balance load to avoid peak in any hours. Therefore,

this effect user comfort in terms of waiting time. However, total load for all opti-

mization techniques remains same as illuminated in ﬁg 9. Total load is operational

time of all smart appliances which is to be completed for whole day. However, these

scheduling techniques help in reduction of total cost in 24 hours. Therefore, total

load for all implemented scheduled algorithms and unscheduled are same. More-

over, the number of appliances for both are same and their power consumption re-

quired for complete day remains same for all techniques. Load is balanced in all

scheduled techniques.

6 Conclusion

Efﬁcient energy consumption, PAR reduction and load balancing for DSM is fo-

cus in smart grid to prevent starvation. In this paper, performance comparison of

meta-heuristic algorithms is evaluated in terms of cost minimization and PAR re-

duction. Efﬁcient energy consumption is achieved through scheduler, which helps

in scheduling smart appliances within smart home. Smart grid helps in reducing

electricity cost by load balancing. The objectives of study are achieved in terms of

minimizing power consumption in peak hours to reduce electricity bills. Secondly,

14 Sajeeha Ansar et al.

balancing load in on-peak hours and off-peak hours to minimize PAR. The utility

comfort is achieved in terms of controlling energy consumption in on-peak hours.

However, user comfort in term of waiting time of appliances. However, there is

trade-off between electricity cost reduction and increase in waiting time.

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