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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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