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Efficient Scheduling of Smart Home Appliances for Energy Management by Cost and PAR Optimization Algorithm in Smart Grid

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As the energy demand for consumption is comparably higher than the generation of energy, which produce the shortage of energy. Many new schemes are being developed to fulfill the energy consumer demand. In this paper, we proposed our meta-heuristic algorithm Runner Updation Optimization Algorithm (RUOA) to schedule the consumption pattern of residential appliances. We compared the results of our scheme with other meta-heuristic algorithm Strawberry Algorithm (SBA) and Firefly Algorithm (FA). Critical Peak Price (CPP) and Real Time Price (RTP) are the two electricity pricing scheme that we used in this paper for calculation of electricity cost. The main objective of this paper is to minimize the electricity cost and Peak to Average Ratio (PAR). However, consumer comfort is not satisfied.
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Efficient Scheduling of Smart Home
Appliances for Energy Management
by Cost and PAR Optimization
Algorithm in Smart Grid
Sahibzada Muhammad Shuja1, Nadeem Javaid1(B
), Sajjad Khan1,
Hina Akmal2, Murtaza Hanif3, Qazi Fazalullah1, and Zain Ahmad Khan4
1COMSATS University Islamabad, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com
2University of Lahore (Islamabad Campus), Islamabad 44000, Pakistan
3Central South University, Changsha 410083, China
4COMSATS University Islamabad, Abbottabad 22010, Pakistan
http://www.njavaid.com
Abstract. As the energy demand for consumption is comparably higher
than the generation of energy, which produce the shortage of energy.
Many new schemes are being developed to fulfill the energy consumer
demand. In this paper, we proposed our meta-heuristic algorithm Runner
Updation Optimization Algorithm (RUOA) to schedule the consumption
pattern of residential appliances. We compared the results of our scheme
with other meta-heuristic algorithm Strawberry Algorithm (SBA) and
Firefly Algorithm (FA). Critical Peak Price (CPP) and Real Time Price
(RTP) are the two electricity pricing scheme that we used in this paper
for calculation of electricity cost. The main objective of this paper is to
minimize the electricity cost and Peak to Average Ratio (PAR). However,
consumer comfort is not satisfied.
Keywords: SA ·FA ·RUOA ·Meta-heuristic techniques ·
Home Energy Management System ·Smart Grid
1 Introduction
The traditional Grid (TG) has insufficient capabilities to solve electricity grid chal-
lenges: security of transmission line, bi-directional communication scalability and
robustness against any fault [1]. Therefore, advanced architecture of the TG is
strongly wished to overcome these challenges in an efficient way. Smart Grid (SG)
integrates various communications information to TG. SG allow consumer to con-
trol electricity consumption through bi-directional communication between con-
sumers and source via Smart Meter (SM) in Advanced Metering Infrastructure
(AMI) [2]. Demand Side Management (DSM) is the main section of SG to cre-
ate balance for both demand and supply sides [3]. DSM has two main features:
c
Springer Nature Switzerland AG 2019
L. Barolli et al. (Eds.): WAINA 2019, AISC 927, pp. 398–411, 2019.
https://doi.org/10.1007/978-3-030-15035-8_37
Efficient Scheduling of Smart Home Appliances 399
Demand Response (DR) action taken by consumer on dynamic pricing scheme and
load management to schedule the electricity consumption in an efficient way. Elec-
tricity consumption is optimize to reduce electricity cost by DR Program in [4] i.e.
different electricity consumption patter dynamic electricity price.
The main concern of SG is to reduce the PAR, electricity bills and maximize
the consumer comfort. Electricity bills and PAR are reduced at demand side by
efficient management of electricity consumption pattern. Utility provide differ-
ent pricing scheme: Real Time Price (RTP), Critical Peak Price (CPP), Inclined
Block Rate (IBR) etc. Electricity can be managed for the home appliances for
reduction of cost and Peak to Average Ratio (PAR) by pricing schemes. Cur-
rently many DSM mechanisms deployed to overcome previously mention chal-
lenges. In past research work, many of non-heuristic technique are presented to
optimize the home appliances [5], the researcher used Integer Linear Program-
ming (ILP) for reducing load at low price time slot to increase consumer comfort
(CC). However, this technique is not consider for complex energy consumption
pattern.
Scheduling of residential appliances in architecture of SG is provided by Home
Energy Management System (HEMS). HEMS is an intelligent system that opti-
mizes the load consumption pattern in peak hours. In previous work of HEMS,
authors categories the load management as real-time and predictable based. In
[6], predictable approach of load management is deployed. However, this app-
roach is costly and complex to produce uncertainty of solution. While real-time
optimization is employed to overcome the uncertainty issues that is deployable
for large scale of area [7].
In [8], real-time approach of load management is deployed for peak hours.
The exclusively management of energy utilization pattern in residential area is
feasible for optimization of different appliances at home. However, CC is not
satisfied. In this paper, we deployed the meta-heuristic algorithm for real-time
environment. In our work, Strawberry Algorithm (SA) and Firefly Algorithm
(FA) are simulated for their results to compare with our proposed scheme Runner
Updation Optimization Algorithm (RUOA). We developed a new scheme to get
optimal solution for reduction of total electricity cost and PAR as compared
with SA and FA scheme, while CC is sacrificed in our scheme. However, there
is trade-off exist for CC to cost and PAR. Moreover, many spaces exist to fill
the area of load management and more researches are going on to overcome the
energy crises.
The rest of the work is organized as follows: In Sect.2, we present the related
work for scheduling of appliances in according with different technique. Section 3
explains the problem statement to tackle the issues. In Sect.4, we discussed
our proposed scheme in detail along with pricing scheme that has been used.
Scheduling techniques are presented in Sect. 5to work out our proposed scheme.
Simulation results are discussed in Sect. 6. At the end paper conclusions are
drawn in Sect. 7.
400 S. M. Shuja et al.
2 Related Work
In HEMS lot of research has been presented by many authors for optimization
of residential appliances’ scheduling. Researchers trying to induce the efficient
scheduling of electricity consumption pattern by the appliances placed at any
home. Different authors have main objective to consumed electricity in reliable
and efficient way by reducing electricity cost and PAR or increasing CC. Further,
some related research works are discussed below and summarized in Table 1.
Authors highlighted the multi residential electricity load scheduling problem
in [9]. According to author, in previous research only single residential area is
considered, to keep this thing in mind they want to maximize the user comfort for
large residential area. They proposed electricity load scheduling algorithm named
as PL-Generalized Benders Algorithm. The aim of their proposed algorithm is
to schedule the load of appliances and maximize the user comfort. However, it
is not defined how will they schedule load when appliances are near optimal to
each other.
Discussion about the trend by authors in [10], to schedule the electrical load
from on peak hours to off peak hours. The consumer needs scheduling in an
online manner that they can easily find out the prices of electricity and schedule
the load of their appliances according to that manner. To make electricity users
aware about their load scheduling of appliances online Load Scheduling Learning
(LSL) algorithm is proposed in [10]. The aim of their algorithm is to reduce PAR.
However, cost of electricity and performance of proposed algorithm is not defined
by them.
Community based cooperative energy scheme is proposed in [11]. The aim of
proposed scheme is to discuss the electricity cost consumption of the user and
smart grid. In their work, they want to minimize the cost and PAR, to fulfill
their aim they considered community between SG to consumer and consumer to
SG. To evaluate the results of their proposed C2C (community to community)
scheme, MATLAB is used. The cost is minimized because of reducing PAR dur-
ing on peak hours. However, User comfort and security issues are compromised.
In [12], the HEMS system is proposed to schedule the operation of electric
appliances. Authors perceive about Quality of Experience in their work because
they want to check the effects of the proposed system. For this purpose they
consider the profiles of the users and also proposed allocation algorithm. By
applying the proposed system and algorithm electricity cost is minimized. On
the other hand, they have not consider PAR and CC in their proposed work.
The Binary Particle Swarm Optimization (BPSO), Genetic Algorithm (GA)
and cuckoo search meta-heuristic algorithm is used in [13]. In their paper, the aim
of authors is to schedule the load of homes appliances and convert them in smart
homes. They also want to reduce the peak load and electricity bill reduction.
To schedule the appliances load of the homes they also used renewable energy
resources in their work. The electricity bill is minimized. However, the electricity
bill can be reduced further.
The multiple problems such as reducing electricity bill cost and peak reduc-
tion is considered in [14]. They want to solve above mentioned problems, to
Efficient Scheduling of Smart Home Appliances 401
solve these problems meta-heuristic algorithm is proposed in their work. The
MATLAB is used to perform the simulations of their proposed meta-heuristic
algorithm. The simulations show the reduction of peak during peak hours and
it also affects the electricity bill. On the other hand, the CC is compromised.
The GA and Grey Wolf Optimization (GWO) algorithms are proposed in
[15]. The aim of these proposed algorithms in this work is to minimize the peak
load of SHs. They also want to reduce the electricity bills of the users without
compromising their comfort. Multiple SHs are considered at different time slot
to check the results of their proposed algorithms, which shows that reduction in
the load and electric bill is performed. However, the performance of optimization
is decreased.
Short term decision making model is proposed in [16]. In this paper, the renew-
able energy resources are used to manage the load of SHs and minimize the elec-
tricity cost. The mixed-ILP method is also used in this paper to make system more
efficient. The proposed model with this method also gives the offer for electricity
to use in fewer rates at specific hours. However, the implementation cost of this
proposed model on the basis of renewable energy resources is very high.
3 Problem Statement
The reduction of electricity cost and energy management in SG is our main
objective due to irregular behavior of energy consumption. Consumer comfort
is usually neglected while considering reduction of electricity price. A scheme is
presented in [17], to optimize the operation of residential appliances in such a
way that minimize the total electricity consumption cost and maximize the CC.
There is always trade off exist between cost and CC.
A model of Energy Management System (EMS) is presented in [18]. It pro-
poses a meta-heuristic technique with RTP and IBR pricing signal for efficient
optimization of home energy to manage the home appliances. Simulations depict
that the significant minimization of electricity cost and PAR. However, the CC
is not considered. In this paper, we performed simulation on SBA and FA algo-
rithms to compared the results with our new proposed RUOA algorithm for
scheduling of home appliances.
3.1 System Description
The proposed system model is shown in Fig. 1. Our system model is working for
scheduling SH appliances. Cost of electricity and PAR is increasing periodically
so there is need of well adaptable system, which can minimize average electricity
price and PAR. A HEMS is used for optimization of home appliances to regulate
the schedule of their operational time. HEMS ease the consumer with respect
of reduction in electricity dissipation and electricity cost. For optimization the
appliances are classified into three different categories; Shiftable appliances, Non-
Shiftable appliances and fixed appliances. Shiftable are those appliances whose
operational time can be shifted from peak hour time slots to non-peak hour time
402 S. M. Shuja et al.
slots, while Non-Shiftable operational time cannot be shifted or interrupted dur-
ing their operational time and fixed appliances operate on the basis of consumer
demand which cannot be fixed for specific time slots. Appliances of different
classification with their power rating are given in Table 2.
Fig. 1. Proposed system model
In SG system consumer can bi-directionally communicate with utility
through SM, this communication can give information to the consumer for
recently consumed electricity and estimated cost of electricity. In this paper
we used CPP and RTP pricing scheme through which load is shifted from peak
hours to non-peak hours, which enable us to know the energy consumed and cost
charged against per hour. We can reduce our electricity cost by knowing the con-
sumption of energy. In our work, we used our proposed algorithm RUOA in EMC
of SM, compared the result with SA and Firefly algorithm. The results shows
that proposed algorithm RUOA perform in a better way from other algorithm
for scheduling of residential appliances.
To estimate the cost of electricity CPP and RTP pricing scheme is used in
our work. The main objective of our work is to reduce the consumption of energy
to reduce PAR and electricity cost. In Eq. 1, total electricity cost is estimated
with PAR reduction.
Cost =
24
hour=1
(EHour
Rate ×PApp
Rate ×App(hour)),App(hour)=[1/0] (1)
In above equation EHour
Rate shows the electricity cost per hour, PApp
Rate shows the
power rating of each appliance and App(hour) values present the ON/OFF status
of appliances. In Eqs. 3and 4total load and PAR is estimated.
Load =
24
hour=1
(PApp
Rate ×App(hour)),App(hour)=[1/0] (2)
PAR =(Max(LoadApp)/Avg(LoadApp)) (3)
Efficient Scheduling of Smart Home Appliances 403
Table 1. Appliances classification
Appliances class Appliances Power rating
(kWh)
Operational
time (hours)
Shiftable Vacuum cleaner 1.2 6
Electric water heater 2.6 8
Water pump 1 8
Dish washer 2.5 4
Steam iron 1.2 3
Refrigerator 0.225 20
Air conditioner 1.5 14
Non-Shiftable Washing machine 3 5
Tumble d ryer 3.3 4
Fixed Oven 1.23 4
Blender 0.3 2
Ceiling fan 0.1 12
Desktop PC 0.3 10
TV 0.3 9
Laptop 0.1 8
4 Scheduling Techniques
Many of the mathematical techniques are presented for scheduling of residen-
tial appliances; MILP, DP, MINLP etc. However, due to slow computation for
large number of appliances consumer demand is not satisfied. Therefore, meta-
heuristic algorithms are used for efficient optimization of home appliances to
reduce the electricity price and manage the load between peak and non-peak
hour. In this paper, we choose some meta- heuristic algorithm SBA and FA to
propose our algorithm RUOA for optimization of energy consumption pattern
of appliances. Algorithms are described further in below subsection.
4.1 Strawberry Algorithm
SBA is a nature based meta-heuristic algorithm of strawberry plant presented in
[19]. These plants grow through runner because of their intelligent nature; they
get their food from nutrient, light and water. If a plant found a good location
for enough food, then it will never move from this location. In another case, if
plant is placed at a location where it found not enough food for its growth then
it try to finding some optimal location by sending long runner for its survival.
The plant propagates some long runner to calculate maximum optimal solution
for survival in Eq.(4). It is hard to provide enough sources to runner especially
when plant is located at place where no good resources for survival. Natural
resources decide the location for plant that its good or not.
r2=[r1+drunner (rand(m, N)0.5)r1+droot (rand(m, N )0.5)].(4)
404 S. M. Shuja et al.
r1=ul +(uh ul)(rand(m, N ).(5)
SA generates population in form of 1 and 0 for optimization of appliances
using Eq. (5). Every binary number represents a solution for respective time
slot and binary representation of number show the ON/OFF status of home
appliances. The SBA searches for local best solution of minimizing cost and
PAR through runner on the basis of fitness function. SBA performs global best
solutions for search in reproduction step from local solutions.
4.2 Firefly Algorithm
FA is developed on the basis of flashing feature of firefly in [20]. As the entire fire-
fly attracts toward the brighter light so brightness is the main objective function
of firefly. The quality of best solution is depend on the intensity of light emitted
by firefly. Every firefly has values for fitness of brightness as solution and attract
toward the brighter firefly. In simple understanding of FA, basic step of rules are
defined as:
Present brightness objective function.
Generate firefly population.
Calculate the light intensity of firefly.
Calculate attractiveness of firefly.
Movement toward the brighter firefly.
Update the light intensity rank and select the best solution.
4.3 Runner Updation Optimization Algorithm
RUOA is our proposed scheme for optimal scheduling of residential appliances. It
is derived from two other above mentioned nature based meta-heuristic scheme
SBA and FA. We built a feature of population updation of runner from SBA in
flashing scenario of FA. In SBA, the plant propagates few runners upon lower
and upper limit of resources to find optimal solution for survival. We get a
refined updated location of resources by comparing rand number of location.
When condition is satisfied then best solution is being updated for population
through runner [19].
While in FA ideal flashing algorithm is perform for the random population
to acquire the possible optimal solution. They get refined value when condition
for brighter firefly is true, which not better for optimal solution. The value of
updated population is unrefined output, because every time FA takes random
number for optimization so in comparison step population may not achieve the
best solution [20]. In RUOA, we refined the updated population with best fitness
feature of runner from SBA to deploy in FA.
5 Simulations Results and Reasoning
In this section, results of proposed algorithm and comparison with other algo-
rithm are evaluated. MATLAB based simulations are analyzed to verify the
Efficient Scheduling of Smart Home Appliances 405
Algorithm 1. Algorithm of Runner Updation Optimization Algorithm.
Require: Input: [Initialize random population, MaxIt, Electricity Price;]
1: Determine Length of operational time for appliances and their power rating;
2: for t=1:24do
3: Evaluate the value of xl and ul limits random population;
4: Evaluate LOT;
5: Find local best solution;
6: for i=1:10do
7: Determine the Population Size and no of appliances;
8: Identify initial random population between xl and ul;
9: for iter iterMaxIt do
10: Propagate Runner from SBA on search spaces;
11: Identify fitness criteria of notify location;
12: Evaluate the location and update it;
13: end for
14: end for
15: Rank the population;
16: Find the best population for identified location;
17: end for
18: end for
19: Update the LOT of appliances;
20: Decrement from currently selected appliances LOT;
21: end for
22: end for
results of proposed algorithm RUOA. In our work, we compared the result with
existing meta-heuristic SBA and FA algorithm. CPP and RTP pricing scheme
are used for our main objective of cost minimization, PAR reduction and load
management in simulations. Figures2and 3shows the hourly electricity cost
along with plots of pricing signal CPP and RTP.
The hourly load consumptions are shown in Fig. 4for CPP and Fig. 5for RTP
along with scheduled and unscheduled pattern of consumption. Our proposed
Fig. 2. Hourly cost with CPP signal
406 S. M. Shuja et al.
algorithm RUOA show that its peaks for hourly load graph are better than other
meta-heuristic algorithm SBA and FA for both CPP and RTP. We realize that pat-
tern of per hour load consumption of schedule is much better than unscheduled.
Our simulation result depict that proposed algorithm RUOA optimize the hourly
load consumption by shifting load from peak hours to non-peak hours.
Fig. 3. Hourly cost with RTP signall
Fig. 4. CPP hourly load
Fig. 5. RTP hourly load
Efficient Scheduling of Smart Home Appliances 407
The result of PAR value generated by SBA, FA and proposed scheme RUOA
for CPP and RTP are shown in Figs. 6and 7. Both CPP and RTP signals in
figure verify that proposed algorithm outperformed than other algorithm. PAR
is reduced by 70.76% in SBA, 45.21% in FA and 75.1% in proposed RUOA
for CPP, while 65.45% in SBA, 45.2% in FA and 75.02% in proposed RUOA
for RTP with refer to unscheduled PAR. Simulations show that our proposed
scheme RUOA performed in a better way than other algorithm.
The hourly cost of electricity shown in Figs.8and 9for CPP and RTP, which
simulated by SBA, FA and our proposed algorithm RUOA. Simulations depict
the result of our proposed scheme is good enough from other scheme, as schedule
peak of graph show the lower cost at higher price time slot as compared with
unscheduled peaks.
Total electricity cost value is estimated in Figs. 10 and 11 simulated by SBA,
FA and RUOA for CPP and RTP pricing scheme. Simulation shows that perfor-
mance of our proposed scheme is outclassing as compared with SBA and FA. In
Total Cost plots, the total electricity cost is reducing by 24.53% in SBA, 55.4%
in FA and 65.97% in proposed RUOA for CPP, while 19.78% in SBA, 15.21% in
FA and 35.19% in proposed RUOA with respect of unscheduled case. There is
trade-off between cost and waiting time, which mean that our proposed scheme
RUOA is reducing cost while sacrificing consumer waiting time.
Fig. 6. CPP PAR
Fig. 7. RTP PAR
408 S. M. Shuja et al.
Fig. 8. CPP hourly cost
Fig. 9. RTP hourly cost
Fig. 10. CPP total cost
Consumer waiting time for CPP and RTP pricing signal are presented in
Figs. 12 and 13. In our case, waiting time shows the time limit to turn ON the
home appliances. The consumer comfort level is calculated in term of consumer
waiting time, we have to minimize the waiting time for consumer to maximize the
comfort level and vice versa. The waiting time calculated for CPP is 3.8876 h by
SBA, 6.7775 h by FA and 4.9879 h by proposed RUOA, while for RTP is 3.8409 h
by SBA, 6.7775 h by FA and 4.5403 h by proposed RUOA. Simulation shows that
Efficient Scheduling of Smart Home Appliances 409
Fig. 11. RTP total cost
Fig. 12. CPP waiting time
Fig. 13. RTP waiting time
SBA well performed than other algorithm in consumer waiting time. However,
there is trade-off for our proposed RUOA to SBA and FA in waiting time and
total electricity price.
6 Conclusion
An electricity load management is concerned to evaluate at DSM. In this
paper, we develop the scheme for load management from existing meta-heuristic
410 S. M. Shuja et al.
technique SBA and FA with pricing scheme of CPP and RTP. We proposed an
RUOA algorithm to optimize the residential appliances based on their electric-
ity consumption pattern. We analyzed the performance of our simulation, which
shows that the performance of our proposed technique is better than other meta-
heuristic technique SBA and FA in term of total electricity price and PAR. On
the other hand SBA outperformed for consumer comfort as compared with our
proposed algorithm.
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The residential sector is a major contributor to the global energy balance. So far, the residential users demand has been largely uncontrollable and inelastic with respect to the power grid conditions. Demand Side Management (DSM) is an important function in smart grid that allows consumers to make informed decision regarding energy consumption, and helps energy providers to reshape the load profile and to reduce peak load demand. DSM can be mathematically formulated either to maximize the system total peak demand or to maximize overall system load factor and utility׳s revenue and to minimize customer electricity bill. This paper reviews the various optimization techniques applied to DSM as contrasting characteristics like individual users versus cooperative users, deterministic versus stochastic and day-ahead versus real time DSM. This paper reviews a survey on residential DSM, which can help general readers to have an outlook of the topic which includes the architecture, formulation of optimization problems and its various approaches. The issues, existing solutions and approaches are presented. In addition, the future research directions are also discussed to enhance the work in this domain.