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An Eﬃcient Scheduling of User

Appliances Using Multi Objective

Optimization in Smart Grid

Haﬁz Muhammad Faisal1, Nadeem Javaid1(B

), Umar Qasim2, Shujaat Habib3,

Zeshan Iqbal4, and Hasnain Mubarak4

1Comsats University Islamabad, Islamabad 44000, Pakistan

nadeemjavaidqau@gmail.com

2Cameron Library, University of Alberta, Edmonton, AB T6G 2J8, Canada

3Air University, Multan, Pakistan

4NCBA&E, Multan, Pakistan

http://www.njavaid.com

Abstract. Electricity is the basic demand of consumers. With the pas-

sage of time this demand is increasing day by day. Smart grid (SG) trying

to fulﬁll the demand of customers. When demand increases then load is also

high. To maintain load from on peak hours to oﬀ peak hours, consumer

needs to manage their appliances by home energy management system

(HEMS). HEMS schedule the appliances according to customer’s needs. In

this paper, scheme is proposed which is used to minimize the electricity cost

and also maximize the user comfort. The proposed scheme is performed

better than existing meta heuristic techniques. The proposed scheme is

used real time price (RTP) price signal. Simulation results shows that the

algorithm has met the objective of DSM. Moreover, the proposed algo-

rithm outperforms earth worm algorithm (EWA) and single swam opti-

mization (SSO) in terms of electricity cost and user comfort.

Keywords: Smart grid ·Home energy management system ·

Real time price

1 Introduction

Energy is one of the most important resource, and energy demand is grow-

ing every day. Service providers are facing many problems to fulﬁll the energy

demand in residential building and industrial sectors. There are two ways to

solve this problem.

1. Produce additional energy and ﬁnd new resources to produce energy

2. Excellent usage of existing resources

The ﬁrst approach is costly and time consuming, as compared to the sec-

ond approach that is more eﬃcient and inexpensive. Information technology

evolved and many schemes are introduced for energy consumption optimization.

c

Springer Nature Switzerland AG 2019

L. Barolli et al. (Eds.): WAINA 2019, AISC 927, pp. 371–384, 2019.

https://doi.org/10.1007/978-3-030-15035-8_35

372 H. M. Faisal et al.

The bi-directional communication is not only concerned by the consumers for

electricity price and maintenance schedules of the distribution network, however,

also motivate the providers to monitor and analyze the real time power utiliza-

tion data. Smart meters (SM) are installed in the residential area. SM provides

the user’s complete information of demand, supply and price signal. The energy

consumption is increasing at a rapid rate in residential areas, hence the eﬃ-

cient use of energy is a big issue in the residential sectors. DSM has two main

functions; load management and demand response (DR). DR is one of the core

function of DSM. DR can be termed as the series of steps taken by customer

in reaction to the changing price rates announced by utility. Due to rapidly

changing grid conditions demand level can also be changed. This varying change

causes a mismatch between demand and supply. This mismatch is dangerous for

the integrity of grid that is spread over a large area. That is the reason DR is

used as it provides ﬂexibility at relatively low rates. DR always try to adjust

the power demand of consumers. The DR scheme helps the customers to save

electricity bills when the prices are high in peak hours. Customers can shift the

usage of their own appliances into oﬀ peak hours. Many researchers proposed

diﬀerent schemes in the literature. DR is divided into incentive based programs

and price based programs. The utility can control the appliances of the user

and provides the ﬁnancial incentives for demand reduction. However, privacy is

compromised by directly accessing the appliances of customers. In price based

programs, end users change the power consumption in their houses according to

the price schemes which is provided by the utility. The power generation and

power utilization is a one way process so, the power generation system is unable

to control and manage electricity consumption. SM provides two way communi-

cation between user and utility. Smart home (SH) and SM are very important

in residential building for reducing energy consumption due to information com-

munication technology advancement. In this paper, DSM technique implements

for scheduling the appliances in residential sectors. The main aim of proposed

scheme is to decrease the cost of electricity, minimization of peak to average

ration (PAR) and to obtain maximum user comfort. In this scheme, we consid-

ers a single home in which 15 appliances are present. Two Meta heuristic tech-

niques: earth worm algorithm (EWA) and single swarm optimization (SSO)are

proposed and implemented with diﬀerent price signals. Proposed scheme gives

better results as compared to EWA and SSO.

The rest of the paper is categorized as follows: Sect. 2deﬁnes the related

work. Section 3discuss the problem statement. In Sect. 4, explains the system

model. Section 5, we discusses the proposed scheme. Computational results are

shown in Sect. 6. Paper ﬁndings are presented in Sect. 7(Table 1).

An Eﬃcient Scheduling of User Appliances 373

Table 1. List of acronyms

DSM Demand side management

GWO Gray wolf optimization

BPSO Binary particle swarm optimization

EMC Energy management controller

DR Demand response

TOU Time of use

IBR Inclind block rate

SG Smart grid

MILP Mixed integer linear programming

GA Genetic algorithm

DP Dynamic programming

CSA Cuckoo Search algorithm

FP Fractional Programming

HSA Harmony search algorithm

EDE Enhance diﬀerential evaluation

LOT Length of operational time

PAR Peak to average ratio

RTP Real time pricing

CPP Critical peak pricing

RES Renewable energy sources

AMI Advance metering infrastructure

EDE Enhanced diﬀerential evaluation

HEMS Home energy management system

2 Related Work

Mixed integer linear programming (MILP), method is used in [1], to mini-

mize the total electricity bill paid by consumers is the main purpose of this

method. Authors worked on balance load management however, the user com-

fort is neglected in MILP. It shows an exchange between conventional systems

and today’s renewable energy sources (RES). A two-way communication between

utility and consumer through SM, a lot of energy wastage problem covered by

saving this 10–30%. In SG, the big challenge for researchers is cost minimization.

The genetic algorithm (GA) technique used in the paper [2], with the integration

of RES and stored energy a low level of cost minimization achieved. In particular

time changes when electricity price and user demand are higher than the stored

energy is helpful. Authors neglected the deployment and maintenance cost of

storage devices and RES in this technique. One of the big problem is to balance

the load in commercial and residential areas (Table 2).

374 H. M. Faisal et al.

Table 2. Related work

Technique Achievements Limitations

MILP [1]Reduction in PAR and

total electricity cost

Comfort preferences are

not considered

GA [2]Cost minimization Deployment and

maintenance cost of

storage devices and RES

are ignored

GA- DSM [3]Electricity consumption

reduced

Neglected the PAR value

and user comfort

feasibility

MINLP under time Of

use (ToU) [4]

Cost minimization Neglected the PAR

DP [5]Cost minimization Installation and

maintenance is ignored

Combination of GA and

binary partial swarm

optimization BPSO

Algorithms [6]

Cost and peak

curtailment

Ignored comfort

preferences and focus

only residential area

CSA [7]Shifting the load in

another time Interval

and peak load reduced

Neglect the electricity

cost

BBSA [8] Shifting the load and

electricity price reduced

Consider speciﬁc time

interval in a day and

hardware and software

installation expense

Two point estimation

method embedded with

PSO based method [9]

Compute load burden Neglected cost of

electricity and PAR

value

FP [10]Electricity cost reduction Neglect the PAR and

user comfort

HSA [11]HSA algorithm is

structure, and

applications

Real time

implementation is not

considered

Single knapsack [12] Energy consumption

optimization considering

six layer architecture

Harder architecture in

termsofmodelinginreal

time scenario

(EDE and HSA) [13]RESs startup and

generation cost

Computational time is

increased

GWO [16]Solving non-linear

economic load dispatch

problems

The user has to come up

with ways of handling

the constraints

Greedy algorithm [17]Minimized cost and user

frustration

PAR is ignored

An Eﬃcient Scheduling of User Appliances 375

Though, by using GA-DSM [3] algorithm in maximum hour, 21% of electricity

consumption is reduced in an industry which is very noteworthy. The PAR value

and user comfort feasibility ignored by authors. In [4] authors proposed scheme

of MINLP to solved the cost minimization under the price tariﬀ ToU. Even

though at the peak hour, cost minimization is achieved, however, authors don’t

considered PAR. Cost minimization and the scheduling of gadgets for various

duration achieved by using dynamic programming [5] technique. Authors in [5],

focused only on residential consumption. This achieved by the integration of

RES and ESS’s with SG. Residents are capable of producing the electricity from

RES. An additional electricity could be sold by consumers to their neighbors.

In RES, two important factors are: like installation and maintenance has been

ignored.

Combination of GA and binary partial swarm optimization (BPSO) algo-

rithms proposed in [6]. PAR minimization and electricity cost are the main goal

of this technique. User comfort ignored and it focused only on residential areas.

In DSM, the client can deal with their home appliances by moving the load to

some other time so the load request a key factor in such manner. By shifting the

load and using cuckoo search algorithm (CSA) [7] algorithm, approximately 22%

of peak load has been reduced. The curve of balanced load that is generated by

the CSA algorithm worked on the user partiality for appliance usage, this curve

used then to shift the load.

New binary backtracking search algorithm (BBSA) [8] was proposed for real-

time schedule controller. In comparison to PSO algorithm, home appliances

shifted from peak hour and electricity price reduced 21% per day by using the

load limit.

The two point estimation methods embedded with partial swarm optimiza-

tion (PSO) method is developed by Huang et al. [9] for dropping the computa-

tional complexity in a HEMS. In contrast, this scheme is intelligent enough than

GPSOLHA in the perspective of computational burden. However in HEMS the

cost of electricity and PAR value has not been considered by Author [9]. For

residential appliances, the authors proposed an improved model for HEMS in

[10]. The main goal is to minimize the cost by shifting the appliances. Using the

RTP tariﬀ and DER, fractional programming implemented for HEMS.

The authors discussed the harmony search algorithm (HSA) algorithm in [11].

Authors also deﬁned the searching criteria of diﬀerent techniques. The primary

steps of HSA is adaptation and used in diﬀerent ﬁelds.

Authors designed a model in [12] for microgrid systems. The microgrid sys-

tems are integrated with the RESs. The main goal of this model is to minimize

the cost of RESs startup and RESs generation cost. The desired objective is

achieved by the combination of EDE and HAS. PAR value is ignored in the

design model.

Authors proposed a model for HEMS with multiple appliances in [13]. Six

Layered are connected with each other to perform better results for the reduc-

tion of PAR and cost. The author in [4] proposed utilizing MINLP method cost

minimization to be accomplished under ToU price signal. The main goal of this

376 H. M. Faisal et al.

technique is cost minimization. Customers can save maximum energy cost using

MINLP algorithm. Scheduling the load is the core objective of load management

during high demand to low demand time. Evolutionary algorithms are used for

load shifting [15]. All service sides have data sets, where scheduling problem have

been managed to solve the eﬃciency problem, the industry faced more problem

because of big power consumption appliances. Due to high load users need to use

the energy more intelligently in both residential and commercial sector. Authors

in [15] proposed an algorithm for load management. The main goal of this paper

reduces the electricity cost. All service sides have data sets, where the scheduling

problem have been managed to solve the eﬃciency problem, the industry faced

more problem because of big power consumption appliances. Customers require

high load in eﬃcient way and more intelligently in the residential and commer-

cial area. Authors proposed a model for cost minimization in [17]. Using an

intelligent decision system (IDSS), minimum cost and minimum PAR problems

were solved. IDSS provides better result communication between the user and

utility. Authors discussed the EDE algorithm in [18]. An updated version of ED

was used. Authors used ﬁve trial vectors instead of one. Using three diﬀerent

random vectors, a new population was created. The mutant and trial vectors

were generated by the ﬁtness function. The authors proposed [19] evolutionary

accretive comfort algorithm (EASA) which is comprised of four postulations.

These postulations are deﬁned according to the time and device bases priori-

ties. Based on the input values EASA generates optimal energy solution pattern

which satisﬁes the user budget. The author deﬁnes three diﬀerent user budgets

to ﬁnd the absolute solutions. Ma et al. [20] deﬁnes discomfort function for two

diﬀerent type of gadgets. First category is ﬂexible starting time and the other

is ﬂexible power devices. Authors in [20] considered a multi-objective function

for user comfort and cost parameters. The proposed bat algorithm in [21]can

be applied to obtain the optimum result. By applying this algorithm energy

consumption can be reduced which is simply a non-linear optimization prob-

lem. The important goal of bat algorithm is to decrease the power usage and

increase consumer comfort standards in the residential area. In system models,

mathematical problems and scheduling, the bat algorithm used to solve these

problems.

3 Problem Statement

Total energy consumption, minimum electricity bill, minimum PAR and maxi-

mum user comfort are the most important problems in SG. DSM schemes are

used to overcome the aforementioned problems in the SG. In [1–4] purposed

meta heuristic techniques to solved the energy consumption problem according

to electricity bill and user comfort. Some authors used mathematical solutions

to solve these issues. Some authors [4–6] proposed RES with the integration of

ESS to tackle the energy demand problems. The main aim of all techniques is to

reduce the electricity bill with maximum user comfort. In some hours, when user

demand is higher than the total electricity production. It creates a peak load in

An Eﬃcient Scheduling of User Appliances 377

the smart home so HEMS is faced diﬃculty to maintain the balance. To avoid

such problems, proposed algorithm, which shifts the home appliances from on

peak hours to oﬀ peak hours, and thus achieve the minimum cost and maximum

user comfort.

4SystemModel

In SG, DSM controller is used for managing the user demand according to the

consumer demand. DSM provides more reliability and proﬁciency in user tasks.

Diﬀerent meta heuristic techniques are implemented in DSM to control the user

appliances. DSM techniques manages the load to consume the electricity at oﬀ-

peak hours instead on peak hours. Initially, considering single home with 15

appliances. These appliances are categorized into two main categories: schedu-

lable and non-schedulable appliances. First category is further categorized into

two sub types: interruptible and non-interruptible. Non-interruptible appliances

are those appliances that cannot be shifted when they are running and cannot be

switched on as per the user’s requirements. Where interruptible appliances are

the those appliances which can be allocated to various time intervals. Every single

home contains SM. SM decides the operation time of every appliance according

to the power rating. These 15 appliances are taken from [14] details of appli-

ances are shown in Table 3. SM provides the two-way communication between

consumers and service provider. Diﬀerent price signals (CPP, ToU, RTP) are

used to ﬁnd the electricity bills. The service provider provides the electricity

price signal. ToU used as pricing unit to calculate the electricity cost. The main

objective of our study is to minimize electricity consumption in order to reduce

the cost and PAR, however, the tradeoﬀ will occur between cost and user com-

fort. Equation 1is used to calculate the PAR. Cost value is calculated by the

Eq. 2. Total load formula given in Eq.3. Main architecture of system model is

shown in Fig. 1.

PAR =max(loads)

avg(loads)(1)

Cost =

24

t=1

(Ehour

Rate ∗PApp

Rate) (2)

Load =Papp

Rate ∗App (3)

378 H. M. Faisal et al.

Fig. 1. System model

Table 3. Appliances used in simulation

Appliances Power (kW) Category

Vacuum cleaner 1.2 Interruptible

Sensors 0.01 Interruptible

PHEV 3.5 Interruptible

Dish washer 1 Interruptible

Stove 3 Interruptible

Microwave 1.7 Interruptible

Other occasional loads 1 Interruptible

Clothes washer 1Non-interruptible

Spin dryer 2.5 Non-interruptible

Oven 5 Base

TV 0.6 Base

PC 0.3 Base

Laptop 0.1 Base

Radio/player 0.2 Base

Coﬀee maker 0.8 Base

5 Proposed Scheme

Mathematical optimization algorithms try to solve the energy consumption prob-

lems however, with large number of smart devices its harder to present the sat-

isfactory solutions. Behind this problem, use meta heuristic techniques to solve

An Eﬃcient Scheduling of User Appliances 379

the energy consumption problem and reduce the cost. The main objective of

EWO algorithm is to reduce the electricity bill and shift the appliances into oﬀ

peak hours. SALP algorithm provides the facility to manage the appliances using

diﬀerent price signals. Diﬀerent electricity price signals are discussed to deﬁne

the cost of electricity for a complete day. In our scheme, we consider RTP tariﬀ.

The RTP is updated for every one hour. Two-way communication requires to

interact with the user for RTP.

5.1 EWO

The reproduction of earthworms states multiple optimization issues, the repro-

duction steps of earthworms are following:

•Each earthworm have the capacity to reproduce oﬀ springs and every earth-

worm individual have two kinds of reproduction,

•Every child of earthworm contains all the genetic factor of parents,

•Singular earthworm is moved on next generation, and cannot be changed by

operators.

5.2 SALP

Salps belongs to the family of salpidae. Swarming behavior is one of the most

interesting behavior. In salps, population has two groups: 1. Leader 2. Followers.

The leader is at the front of the chain and rest of the salps are attached behind

it followers. Equation 4is used to update the position of the leader.

x1

j=Fj+c1((ubj−lbj)c2+lbj)c3≥0

Fj−c1((ubj−lbj)c2+lbj)c3<0(4)

5.3 Updated Population Scheme

Our proposed algorithm provides the facility for consumers to schedule the appli-

ances and reduce the cost while considering the user comfort. The population

size is 30. In our proposed algorithm, the step of reproduction contains two types

of reproductions: Reproduction 1 and Reproduction 2. In our research, we have

implemented a new updated population scheme for scheduling the home appli-

ances. The ﬁrst step is the initialization of all parameters with the maximum

generation and constant value. A ﬁtness function is deﬁned for choosing the

best solution. After applying the ﬁtness function, two types of reproductions are

applied. The main purpose of the updated population algorithm is to obtain the

maximum solution of appliances. For complexity, mutation and crossover steps

are taken in the proposed algorithm. The major contribution of the proposed

algorithm updates the population according to their ﬁtness function.

380 H. M. Faisal et al.

Algorithm 1. SALP Algorithm

1: Initialization the salp population xi(i=1,2,......n) considering ub and lb

2: while (end condition in not satisﬁed ) do

3: Calculate the ﬁtness of each agent (salp)

4: Set Fas the best search agent

5: Update civalue

6: for every salp (xi)) do

7: if (i==1) then

8: Update the position of the leading salp

9: else

10: Update the position of the follower salp

11: end if

12: end for

13: Update the salps

14: end while

15: Return

6 Simulation and Reasoning

In this section, simulation results are brieﬂy discussed. The implementation of

the proposed has been done in MATLAB. The main objective of DSM in the

smart home achieved by proposed scheme. The objective of DSM is maximize

user comfort, minimum electricity cost and minimum PAR. Proposed scheme

results are better as compared to EWO and SALP algorithm. For experimenta-

tion, we considered 15 appliances in the SH. By applying the ToU/CPP price

signal in the smart home, proposed scheme achieved the minimum cost of elec-

tricity and user comfort, however, PAR value is compromised. Figure 1deﬁnes

the electricity cost per hour in cents for the 24 h. It clearly deﬁnes the electricity

cost of unscheduled, EWA algorithm, SSO algorithm and updated population

proposed algorithm. Figure 2shows the total load of aforementioned algorithms

in (kwh), in a day. Figure 3shows the PAR values of unscheduled, EWA, SSO

and proposed scheme. It clearly shows that the EWA and SSO algorithm outper-

formed the proposed scheme in terms of PAR. EWA and SSO performed better

in terms of PAR as compared to own algorithm. However, there will be a tradeoﬀ

between PAR and user waiting time. The PAR is reduced 49.27%, 50.24% and

42.94% by EWA, SSO and proposed algorithm respectively with the unsched-

uled case. Figure 4represents the comparison of EWA, SSO algorithm and the

proposed scheme in the regard of electricity cost. In peak hours electricity cost

of appliances increases quickly. To overcome this, scheduling the appliances is

done on, on-peak hours to oﬀ-peak hours. The cost of appliances is reduced due

to scheduling. Figure 4shows the cost values of unscheduled, EWA, SSO and the

proposed algorithm. Proposed algorithm performs better as compared to EWA

and SSO. The user comfort is reduced by 1.75%, 10.23% and 11.76% by the

EWA, SSO and proposed scheme respectively. Considering the total electricity

cost in the form of cents. In Fig. 5, the user waiting time shown. The user wait-

ing time is calculated in terms of user comfort. User waiting time is inversely

An Eﬃcient Scheduling of User Appliances 381

proportional to user comfort. By applying the price signal user comfort value

of proposed scheme is low, as compared to EWA and SSO. The user comfort is

reduced by 76.36%, 75.36% and 86.16% by the EWA, SSO and proposed scheme

respectively (Fig. 6).

Fig. 2. Electricity cost

Fig. 3. Load

382 H. M. Faisal et al.

Fig. 4. PA R

Fig. 5. Total cost

Fig. 6. Waiting time

An Eﬃcient Scheduling of User Appliances 383

7 Conclusion

In this paper, proposed algorithm is used for shifting the appliances. The schedul-

ing is based on real-time data of price signal. Proposed scheme results are better

as compared to EWO and SALP algorithm. It is clear that the algorithm intro-

duced works eﬃciently as compared to EWO and SALP with the parameters of

cost, PAR and user comfort. With the proposed algorithm derived from EWO

and SALP proposed algorithm achieved minimum cost and maximum waiting

time of our proposed scheme. In the future, we will integrate renewable energy

system and ESS with more then one SH in order to minimize cost and maximize

user comfort.

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