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Energy Efficient Scheduling of Smart Home

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Recently a massive increase in the demand of energy has been reported in residential, industrial and commercial sectors. Traditional Grid (TG) with the aging infrastructure is unable to address the increasing demand problem. Smart Grid (SG) enhanced the TG by adopting information and communication based technological solutions to address the increasing electricity demand. Smart Home Energy Management System (SHEMS) plays an important role in the efficacy of SG. In this paper, an Improved Algorithm for Peak to average ratio Reduction (IAPR) in SHEMS is developed. To validate the effectiveness of the IAPR, comparison is made with the renowned meta-heuristic optimization approaches namely Strawberry Algorithm (SA) and Salp Swarm Algorithm (SSA) using two different pricing scheme. It is illustrated by simulations results that the IAPR reduced the PAR to a greater degree as compare to SA and SSA.
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Energy Efficient Scheduling
of Smart Home
Sajjad Khan1, Zahoor Ali Khan2, Nadeem Javaid1(B
),
Sahibzada Muhammad Shuja1, Muhammad Abdullah1, and Annas Chand3
1COMSATS University Islamabad, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com
2Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE
3COMSATS University Islamabad, Abbotabad 22010, Pakistan
http://www.njavaid.com
Abstract. Recently a massive increase in the demand of energy has
been reported in residential, industrial and commercial sectors. Tra-
ditional Grid (TG) with the aging infrastructure is unable to address
the increasing demand problem. Smart Grid (SG) enhanced the TG by
adopting information and communication based technological solutions
to address the increasing electricity demand. Smart Home Energy Man-
agement System (SHEMS) plays an important role in the efficacy of SG.
In this paper, an Improved Algorithm for Peak to average ratio Reduc-
tion (IAPR) in SHEMS is developed. To validate the effectiveness of the
IAPR, comparison is made with the renowned meta-heuristic optimiza-
tion approaches namely Strawberry Algorithm (SA) and Salp Swarm
Algorithm (SSA) using two different pricing scheme. It is illustrated by
simulations results that the IAPR reduced the PAR to a greater degree
as compare to SA and SSA.
Keywords: Smart Grid ·Optimization techniques ·
Salp Swarm Algorithm ·Strawberry Algorithm
1 Introduction
Over the last few decades, a substantial growth has been reported in the demand
of electricity. To cope with the increasing electricity demand problem, the power
generation companies focused on the installation of new energy generators. How-
ever, installing new generation sources is neither an economical nor environment
friendly option. It is due to the fact that these sources mostly operate by fos-
sil fuels like natural gas, oil and coal. It is believed that these sources emit
carbon dioxide into the atmosphere which causes global warming. An alter-
nate solution to the intensifying electricity demand is the efficient utilization
of existing resources. To efficaciously utilize the existing resources Traditional
Grid (TG) is experiencing numerous challenges such as centralized generation
system, high power losses, aged infrastructure and wired technology etc. One of
c
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L. Barolli et al. (Eds.): WAINA 2019, AISC 927, pp. 67–79, 2019.
https://doi.org/10.1007/978-3-030-15035-8_7
68 S. Khan et al.
the most important challenge is the reliability and sustainability of power grid.
The increasing energy demand in industrial, residential and commercial sectors
has a high influence on the grid reliability, as a result power failures or blackout
occurs frequently [1]. The major factors that affects the reliability of TG are the
lack of communication and inefficacious load management capability. Therefore,
to make the power system more reliable and sustainable, the infrastructure of
Smart Grid (SG) is introduced.
SG is an electric grid which is revolutionized by amalgamating Information
and Communication Technology (ICT) based solutions [2]. These ICT based
solutions enable SG to have a bidirectional communication between the con-
sumers and the utility. This is achieved by amalgamating the Advanced Meter-
ing Infrastructure (AMI) and smart appliances in the existing power system.
AMI enables the consumers to learn their real time energy usage and its respec-
tive cost. The advantages of SG over TG are: reliable grid operations, dis-
tributed energy production, Demand Response (DR) strategies, Smart Meter
(SM), Demand Side Management (DSM) and Smart Scheduler (SS) [3]. DSM
plays a key role in enhancing the efficiency of SG to improve the reliability of
power system. DSM refers to the strategies adopted by the utility companies
to motivate consumers to actively participate in efficacious energy utilization
[4]. For this purpose, incentive is provided to the consumers so that energy is
efficaciously utilized. In return, the consumers have to minimize or shift the
operational time of the electric appliances to other time slots (Off-peak hours).
DR aims to persuade the consumers to modify their energy utilization pattern
in distinct hours (On-peak hours). For this purpose two types of DR policies
are introduced. These policies are incentive based DR policy and price based
DR policy. In the incentive based DR policy, consumers agree to minimize their
energy utilization in order to get some incentives from the utility. Incentive to
the consumers is given in the form of minimized electricity bill. In price based
DR scheme numerous pricing schemes are used across the world to encourage
the consumers to alter their energy utilization pattern. The well known pricing
schemes are Critical Peak Pricing (CPP), Inclined Block Rate Pricing (IBRP),
day ahead Time of Use Pricing (ToUP) and Real Time Pricing (RTP) [5].
Some of the common objectives of SG are cost minimization, optimal load
management, the reliability and sustainability of power system and Peak to
Average Ratio (PAR) reduction. In order to achieve the conflicting objectives
researchers have implemented many meta-heuristic and Linear Programming
(LP) techniques [6]. In this paper, we present an Improved Algorithm for PAR
Reduction (IAPR) in the environment of a Smart Home (SH). The motivation for
PAR minimization in residential sector is due to two reasons: (i) industrial and
commercial users are reluctant to alter their energy utilization pattern: (ii) about
40% of the total energy is consumed by the residential sector. Furthermore, about
65% of electricity consumption can be minimized by SHs and smart buildings
[7]. The main contribution of this paper are following:
Energy Efficient Scheduling of Smart Home 69
An IAPR is developed to curtail the PAR.
A comparison analysis of renowned meta-heuristic techniques with the pro-
posed IAPR is performed.
The rest of this paper is organized as follows. Literature review of the existing
schemes is described in Sect. 2. Sections 3and 4discusses the problem statement
and proposed system model. Scheduling techniques are presented in Sect. 5.Dis-
cussion and results are presented in Sect. 6and finally Sect. 7concludes the
paper.
2 Related Work
For the last few years, many DSM strategies have been developed to optimize
the energy utilization in residential sector with varying objectives.
In [8], a SHEMS is developed to monitor and schedule household electrical
appliances in a conventional house to minimize electricity consumption cost. In
this scheme, if the load on the grid exceeds a predefined limit, the new appli-
ances are either shifted to other time periods or operated using battery power.
Batteries are either charged from the utility in the low price intervals or using
Photovoltaic (PV) cells in day time. Experimental results demonstrate that the
scheme reasonably minimizes electricity consumption cost. However, this work
did not consider the various pricing schemes. Yang et al. [9] present a scheme
using Improved Particle Swarm Optimization (IPSO) for scheduling home appli-
ances. The scheme mainly focuses on minimizing cost. It is evident that elec-
tricity tariff and objective curve have an inverse relationship with each other.
Experimental results show that the desired objective curve is achieved. More-
over, this work aims to achieve power system stability. For this purpose, the
high electricity consumption is reduced in high price intervals. As a result, UC
is compromised.
The authors in [10] propose a Mixed Integer Linear Programming (MILP)
based cost minimization and load equalizing scheme for domestic users. In this
scheme, electricity consumer is considered as a prosumer. Prosumer is the pro-
ducer as well as the consumer of electricity. The work considers that prosumers
have installed PV cells in their homes for electricity generations along with a
connection to a commercial grid in order to fulfill their energy requirements.
In the proposed work, prosumers store energy in batteries only when electricity
generation exceeds the user demand. However, this work ignores exporting sur-
plus energy to maximize their income. The authors in [11] develop a fuzzy based
energy management system. In this work, consumer is involved in changing the
status of appliances from ON-state to OFF-state. The work mainly focuses on
minimizing cost along with PAR. The UC in this scheme is compromised due to
increase in the appliances waiting time.
Israr et al. [12] present an optimization function to maximize the UC. The
proposed scheme aims to retain an intended environment inside a smart home.
Genetic Algorithm (GA) and PSO techniques are used to enhance the UC. For
70 S. Khan et al.
this purpose, the recorded data for a month in an indoor laboratory environment
is analyzed. Experimental results demonstrate that the proposed scheme achieves
desired results. However, this work did not consider electricity consumption cost
andPAR.In[13], a hybrid technique using Harmony Search Algorithm (HSA)
and Enhanced Differential Evaluation (EDE) algorithm is developed. The pro-
posed scheme formulate the demand and supply problem as Multiple Knapsack
Problem (MKP). The work incorporates ESS to maximize the UC and alleviate
PAR. Experimental results demonstrate that the hybrid scheme reasonably min-
imizes the consumer bill as compare to HSA and EDE. However, PAR reduction
using EDE is better as compared to their proposed technique. A chance con-
strained DR scheme is proposed in [14] to schedule electrical appliances in HEMS.
The scheme uses PSO and two-Point Estimation Method (2-PEM). Experimen-
tal results demonstrate that PSO along-with 2-PEM outperforms gradient based
PSO with Latin hyper cube sampling in terms of minimizing computational load.
However, this work, ignores PAR and consumption cost. PAR burdens the utility
that has a direct impact on consumer.
Wen et al. [15] propose HEMS using reinforcement learning technique to
meet the DR of residential buildings. The scheme adds intelligence to the HEMS
via a three step Observe, Learn and Adopt algorithm (OLA). In this technique,
an algorithm learns the electricity consumption pattern of the appliances based
on consumer preferences. Experimental results demonstrate that the proposed
scheme reasonably minimizes the user consumption cost and PAR. However, UC
is compromised due to shifting or delaying the operational time of the electricity
consuming appliances. The work in [16] minimizes PAR in SG via DR strategies.
This work considers the optimization problem as two stage problem. The con-
sumers try to boost their pay-offs and the utility tries to maximize its revenue.
At the consumer end, electricity consumption cost is calculated using an itera-
tive algorithm. Whereas, at the retailer end, the work used Simulated-Annealing
based Price Control Algorithm (SAPCA). SAPCA is used to maximize retailer’s
profit using RTP scheme. Experimental results demonstrate that the proposed
scheme is beneficial for the consumers as well as utility. Furthermore, the pro-
posed work reduces the PAR.
Farrokhifar et al. in [17] develop a scheme for scheduling home appliances to
reduce consumption cost within a building. The scheme considered optimization
problem as the ILP problem. Simulations results demonstrate that the proposed
scheme reduces consumption cost. However, this work ignores the PAR. Khan
et al. in [18] present a priority induced DSM scheme to efficaciously schedule
the home appliances in SHEMS. The main objective of this work is to mini-
mize cost and mitigate rebound peaks. In this work, the authors used renowned
meta-heuristic techniques namely EDE, BPSO, and GA. Furthermore, knapsack
capacity limit is used to enhance the grid reliability. Simulation results shows
that this scheme minimized the consumer bill by 60%. However, this work con-
sidered a limited number of home appliances.
Energy Efficient Scheduling of Smart Home 71
3 Motivation and Problem Statement
The common objectives of SG are cost minimization, optimal load management,
reliability and sustainability of power system and PAR reduction etc. PAR mini-
mization is one of the most challenging task in SG due to the irregular consump-
tion pattern of electricity in residential sector. The work in [15,16] minimized
PAR and energy consumption cost using the meta-heuristic techniques. How-
ever, these scheme compromised UC. In this paper, like the work presented in
[18] the main focus is to minimize the PAR and rebound in order to enhance the
reliability of power system. Although many techniques exists in the literature
for PAR minimization. In this paper, an enhanced meta-heuristic technique is
developed to tackle the PAR optimization problem in SH.
4SystemModel
This section discusses the proposed system model. In this paper, the aim is to
shift the execution time of home appliances such that PAR and cost is mini-
mized. A detailed description of our proposed system model is shown in Fig. 1.
To meet the energy demand, it is assumed that the SH is connected to util-
ity using AMI. AMI allows bi-directional communication between the consumer
and utility. Generally AMI comprises of a SM, energy management controller and
data management system. The SM acts as a communication gateway between
the consumer and the utility.
Smart meter
Utility
Fig. 1. System model
4.1 Appliances Categories
Considering Fig. 1, the proposed SHEMS consists of various appliances. Based
on their distinguishing role in PAR and cost minimization these appliances
are divided into three sub-categories namely shiftable and interruptible (ASI),
shiftable and non-interruptible (ASN) and regularly operated appliances (AR).
In our proposed SHEMS, the appliances considered in [19] are used. Table1pro-
vides an overview of the home appliances, their power ratings and Length of
Operational Time (LOT).
72 S. Khan et al.
Table 1. Appliances classification
Class Name Power (kWh) LOT (h)
ASI Vacuum cleaner 0.7 6
Electric car 2.5 14
Laptop 0.1 8
Dish washer 1.8 8
Desktop 0.3 6
ASN Washing machine 1 3
Cloth dryer 3 5
ARCooker top 2.15 2
Cooker oven 5 2
Microwave 1.7 2
Refrigerator 0.3 24
Interior lights 0.84 6
4.2 Problem Formulation
In this paper, the appliances scheduling problem is formalized as an optimization
problem where the objective is to minimize the PAR. The objective of our work
can be formulated as:
minimize PAR s.t. CS<C
U(1)
Here CSand CUrepresents the energy utilization cost in the scheduled and
unscheduled scenario. In order to compute the PAR, Eq. 2is used.
PAR =LPeak
LAverage
(2)
Here LPeak is the maximum load consumed by the home appliances in SH
for a single time slot and LAverage is the average load consumed in one day. To
compute the per hour and total load consumed by the SH we use Eqs. 3and 4.
LoadH=ρ(t)α(3)
LoadT=
24
t=1
LoadH(t) (4)
Here LoadHrepresents the load consumed in one hour, LoadTis the total
load consumed in one day, ρ(t) shows the power consumed by an appliance and
αis the ON and OFF status of an appliance.
5 Scheduling Techniques
This section discusses the existing and proposed scheduling schemes.
Energy Efficient Scheduling of Smart Home 73
5.1 Strawberry Algorithm (SA)
Plants are connected to the earth by means of their roots. It is evident that these
roots cannot move to different places like other animals and birds which migrate
to different places for their survival. However, there exist some grasses and plants
(i.e., Strawberry plant) which propagates by means of runners (stolon). Keeping
in view the vegetative propagation nature of strawberry plants Merrikh-Bayat
in [20] proposed SA. Strawberry plant propagates by means of runners as well
as roots hair. If the strawberry plant is in a location which is not favorable for
its survival, the plant performs exploration. Exploration in strawberry plants
is the process of sending long runners in different directions for its survival. A
runner is a creeping stalk which emerges from leaf axils of a parent or mother
plant. If a runner succeeds in locating a favorable condition for its survival, it
generates additional roots hairs and runners which directly effects the growth
rate of strawberry plant. SA for optimization problem can be modeled using the
following facts.
All the strawberry plants propagates by means of runners. These runners
arise randomly. Runners explore the global search space.
All the strawberry plants randomly generate roots and root hairs. It allows
the plant to exploit the local search space.
If a strawberry plant have easy access to nourishing resources, the plant will
grow faster. Furthermore, that plant will generate more runners and roots.
Whereas, if a plant do not have access to nourishing resources, they have a
high death rate.
5.2 Salp Swarm Algorithm (SSA)
Inspired by the swarming behavior of Salp swarms, Mirjalilia et al. in [21]pro-
posed SSA. Salps belongs to the family of Salpidae. They possess a transparent
barrel-shaped body. Their tissue structure resembles the tissue structure of jelly
fishes. Moreover, their movement behavior also resembles jelly fish i.e., to move
forward, they pumped water through their body as repulsion. The biological
research about Salp swarms is at its early stages. Because Salps are mostly
found in deep oceans. Furthermore, it very difficult to create a favorable living
environment for salps in a laboratory. In deep oceans, Salps form a Salp chain.
The main reason for forming a Salp chain is not yet clear. However, scientists
believe that foraging is one of the many reasons. The chain is divided into two
groups i.e., leader and followers. The leader of the salp chain is at the front
whereas the followers follow their leader directly or indirectly. For modeling the
Salp swarming behavior to optimization problems, it is assumed that there exist
a food source in the search space. The leading Salp targets the food source by
changing its position. Whereas, the followers gradually follow the leading Salp.
5.3 IAPR
It is evident that plant survives by their vegetative propagation nature. For
their purpose, plants send runners and root hair randomly. If the runners or the
74 S. Khan et al.
roots of a plant succeed in locating a nourishing resource, the plant survives,
otherwise the plant dies. Motivated by the vegetative propagative nature of
plants, we proposed a survival based meta-heuristic scheme. In this scheme,
first a pre-defined number of explorers are initialized. With a rich nourishing
resource available in the search space, these explorers move towards that source
independently. It is possible that some of the explorers may not be able to reach
the food source. This might endanger the plant survival. Therefore, to tackle
this problem, we categorize the explorers as best and worst explorers. A best
explorer is the one who succeed in locating the nourishing resource whereas, the
worst explorer is the one who failed to do so. In this scheme to prevent the worst
explorers from endangering the plant survival, the worst explorers are mutated
with best explorers. If the new explorers produced by mutation move towards
the food source, the explorers are updated by replacing the old explorers with
the new ones else if the new explorers do not move in the direction of nourishing
resources, they are discarded. In modeling the proposed IAPR to an optimization
problem, in this paper the food source is termed as the global optimum solution.
However, the global optimal solution to any optimization problem is not known
in advance, therefore, in this scheme, it is assumed that a nourishing source
located first will remain a global solution unless a better nourishing resource is
located. The pseudo code of our propose scheme is given below.
Algorithm 1. Algorithm for IPAR
1: Randomly generate a nourishing resource
2: while stopping criteria not met do
3: Compute c using the current and total iterations
4: for i=1tosize(explorers)do
5: Movement Probability= rand()
6: if P<MovementP robability then
7: U pdate explor er position by adding c ;
8: else
9: U pdate explor er position by subtracting c ;
10: end if
11: end for
12: for all explorers do
13: if An explorer lies outside the legal region then
14: P lace it on the boundry ;
15: end if
16: end for
17: Evaluate the f itness of the explorers
18: Cl assify the expl orers as best and worst ;
19: if New explorer moves towards the f ood source then
20: Replace new explorer with old worst explorer ;
21: else
22: Discard new explorer and keep old explorer ;
23: end if
24: end while
25: return operational pattern of home appliances
Energy Efficient Scheduling of Smart Home 75
6 Simulation Results and Discussions
In this section, results of our all the optimization schemes are discussed. The per-
formance of the IAPR is compared with SSA and SA in PAR reduction. Results
are obtained for two types of pricing tariffs namely CPP and RTP. For simula-
tion MATLAB R2017a installed on Intel(R) Core(TM) i5-3380M with 2.90 GHZ
processor is used. Due to the random nature of meta-heuristic optimization tech-
niques, we plot the mean results of ten runs.
Unscheduled SA SSA IAPR
0
2
4
6
PAR
Fig. 2. PAR reduction in CPP scheme
Unscheduled SA SSA IAPR
0
2
4
6
PAR
Fig. 3. PAR reduction in RTP scheme
Figures 2and 3represents that IAPR outperforms the well known meta-
heuristic techniques SSA and SA. The proposed IAPR minimized PAR using
CPP by 72%. Whereas, SA and SSA minimized PAR by 51% and 9% respectively.
In RTP, PAR alleviation is 17%, 48% and 68% in SSA, SA and IAPR respectively.
Figures 4and 5shows the per hour load consumption pattern of SH. These
figures gives a complete picture of the performance of IAPR using CPP and
RTP. The IAPR balanced the per hour load consumption, as a result PAR in
minimized. The maximum load recorded in a single time slot using CPP and RTP
schemes is 10.54, 7.80, 9.60 and 7.80 kWh for unscheduled, SA, SSA and IAPR
respectively. Here it is worth mentioning that PAR is minimized by balancing
load consumption throughout the day and total load consumed by all algorithms
in both of the pricing scheme was same.
76 S. Khan et al.
Time (hours)
Per hour load (kWh)
Unscheduled SA SSA IAPR
14 8 12 16 20 24
8
6
4
2
0
10
12
Fig. 4. Per hour load using CPP scheme
Time (hours)
Per hour load (kWh)
Unscheduled SA SSA IAPR
14 8 12 16 20 24
8
6
4
2
0
10
12
Fig. 5. Per hour load using RTP scheme
Figures 8and 9shows the performance in overall cost reduction. It can be
seen from these figures that all the algorithms minimized cost. In CPP tariff,
cost reduction as compare to unscheduled scenario is 34%, 76% and 32% using
SA, SSA and IAPR respectively. Whereas, in RTP scheme cost is minimized by
15%, 40% and 17% using SA, SSA, and IAPR. The proposed IAPR balanced the
hourly load consumption which has a direct impact on the total cost. Figures 6
and 7shows the per hour cost consumption is SH. These figures reveal that
IAPR successfully minimized cost by load shifting. Furthermore, cost reduction
in IAPR with CPP scheme is higher as compare RTP scheme.
Time (hours)
Per hour cost (cents)
Unscheduled
SA
SSA
IAPR
14 8 12 16 20 24
0
200
400
600
800
1000
1200
Fig. 6. Per hour cost using CPP scheme
Energy Efficient Scheduling of Smart Home 77
Time (hours)
Per hour cost (cents)
Unscheduled
SA
SSA
IAPR
14 8 12 16 20 24
200
150
100
50
0
Fig. 7. Per hour cost using RTP scheme
Unscheduled SA SSA IAPR
0
1000
2000
3000
4000
5000
Totalcost(cents)
Fig. 8. Total cost using CPP scheme
Unscheduled SA SSA IAPR
0
500
1000
1500
Total cost (cents)
Fig. 9. Total cost using RTP scheme
The waiting time of all the algorithms using CPP and RTP scheme is shown
in Figs. 10 and 11. The proposed IAPR has a higher waiting time as compare to
the other optimization algorithms. It can be seen from Figs. 2,3,4,5,6,7,8,9,
10 and 11 that all the algorithms with both the pricing strategies are confronted
with trade-offs. SSA minimized the electricity consumption cost. However, the
performance in PAR minimization is not good and multiple peaks are created
in Off-peak hours. SA minimized the PAR as compare to SSA. However its
performance in waiting time reduction is higher than SSA. Similarity IAPR
minimized PAR, however, it increased the appliances waiting time as compare
to SA and SSA.
78 S. Khan et al.
Unscheduled SA SSA IAPR
0
2
4
6
8
Waiting time (hours)
Fig. 10. Waiting time (hours) with CPP scheme
Unscheduled SA SSA IAPR
0
1
2
3
4
5
6
7
Waiting time (hours)
Fig. 11. Waiting time (hours) with RTP scheme
7 Conclusion and Future Work
In this paper, an IAPR to minimize the PAR is developed. Performance of the
IAPR is evaluated in the scenario of a SH. This scheme gives the residential
consumers an opportunity to eagerly participate in enhancing the reliability of
power system. To validate the effectiveness of IAPR, the performance in PAR
reduction is compared with the renowned meta-heuristic techniques namely SA
and SSA using the ToUP and CPP scheme. Simulation results confirm that the
IAPR in PAR minimization with both the pricing schemes exceeds SA and SSA.
PAR minimization with the CPP in the IAPR, SA and SSA is 72%, 51% and 9%.
Whereas, in the ToUP scheme PAR is minimized by 17%, 48% and 68% using
SSA, SA and IAPR. In future, we plan to extend this work by incorporating MG
and ESS at consumers’ end to enhance the stability of power grid.
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Adv. Eng. Softw. 114, 163–191 (2017)
... These techniques are important in order to establish a balance between the time-varying needs of customers [3], improve the performance of electrical devices, and enhance energy efficiency. ...
... On one hand, electricity companies apply a new way from network technologies using Demand Response Programs (DRP) such as Real Time Price (RTP), Critical Pick Price (CPP), Incremental Block Rate (IBR) price, 1-dayahead price [2] and motivate customers, to actively the optimal use of energy based on using smart meter. This strategies plays a vital role in enhancing the efficiency of electrical to improve energy efficiency, achieve the balance between the time varying demands of consumer [3]. On the other hand, the customer can limit the consumption by using software programs or applications such as schedule the household load through Home Energy Management Control System (HEMCS) [4], or by using peak load shifting through power scheduling [5], or use a home energy management system to schedule household appliances through heuristic techniques such as the Flower Pollination Algorithm (FPA) and Jaya Optimization Algorithm (JOA), which reduce average peak ratio (PAR), wait time and reduce the cost of electricity consumption [6]. ...
Conference Paper
The increasing number of home appliances and the variation of technologies between them (modern or smart and legacy or old) is the reason for homes energy problem. In addition, increasing the peak load of grids facilities often causes electricity cuts of homes' sectors to reduce the overloads due to peak load. All of these reasons lead to use the energy management systems to reduce energy consumption and automatic control of home appliances to reduce billing for consumers, as well as utilities and decrease peak and peak-topeak demand. In this paper, an Energy Management System to automatic control energy of home appliances will be presented, by using MATLAB software simulation and by hardware using a Microcontroller through prototype to analyze the Demand Response (DR). The proposed algorithm will handle consumption to achieve minimum power cost, decrease peak load according to the consumer priority, and guarantees total home power consumption without a certain limit. The accuracy of the algorithm illustrated through cases of different home scenarios showed that this algorithm is able to reduce the peak load by up to 39.5% compared to that without using the proposed algorithm.
... emissions, and executions time for the devised energy management control in a residential µG. The literature review of these algorithms are as: Khan et al. developed an energy scheduling technique to optimize the cost and peak to average ratio (PAR) of smart home by using Salp Swarm Algorithm (SSA) [11]. Barik and Das made attempts to achieve the active power management for renewable resources present in isolated µG in MATLAB using SSA [12]. ...
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Transformation of conventional energy systems into smart grids enables the integration of residential buildings with distributed generations, electro-thermal storages and demand response policies. Further, it improves the household comfort level and helps to preserve the ecological system. Keeping in view the techno-financial impact of residential energy management, optimal handling of residential customers may prove meaningful for peak load reduction, valley filling, and energy conservation. In this regard, this paper devises a residential energy management system (EMS) to optimally schedule appliances, energy sources and electro-thermal storage for reduction of consumption cost and greenhouse (GHG) emissions. Building integrates national grid, natural gas network and solar energy as input carriers, whereas, electricity, heat and cooling as output carriers. To resolve the risk of loss of load, conditional value at risk (CVaR) has been incorporated in the objective function. Comparison demonstrates that under risk-averse approach, energy retaining capability of electric vehicle and thermal energy storage increases by 28.56% and 53.34%, respectively. This stored energy acts as a reserve during absence of solar irradiance and outages on electric and natural gas networks. Moreover, to make EMS more efficient in tracking optimum solutions with faster convergence speed, a hybrid algorithm has been devised by concatenating the modified flower pollination algorithm with mixed-integer linear programming. The proposed algorithm has been validated by comparing its results with the Salp Swarm Algorithm, Grasshopper Optimization Algorithm, Polar Bear Algorithm, Coyote Optimization and Two Cored Flower Pollination Algorithm (FPA). Results manifest that the cost, GHG emissions and execution time drop by 8.98%, 10.81% and 35.064%, respectively.
... Various scheduling strategies have been deployed in smart home context to improve energy efficiency [7], [8], reduce power consumption [9] and improve response time [10]. However, the multi-channel/port concurrency issue has not yet been considered in the smart home network. ...
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Smart home gateway has to process different types of network traffic generated from several devices in an optimal way to meet their QoS requirements. However, the fluctuation of network traffic distributions results in packets concurrency. Current QoS-aware scheduling methods in the smart home networks do not consider concurrent traffic in their scheduling solutions. This paper presents an analytic model for a QoS-aware scheduling optimization of concurrent smart home network traffic with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in smart home network, propose an innovative queuing design based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports, and design an applicable model to implement auction game on both sides; traffic sources and the home gateway, without changing the structure of the IEEE 802.11 standard. Our experiments show the proposed solution achieves an improvement of 14% of packets that meet their required delay and 57% of delay for different number of concurrent flows in the system.
... Various scheduling strategies have been deployed in smart home context to improve energy efficiency [6], [7], reduce power consumption [8] and improve response time [9]. However, the multi-channel/port concurrency issue has not yet been considered in the smart home network. ...
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With arrival of advanced technologies, automated appliances in residential sector are still in unlimited growth. Therefore, the design of new management schemes becomes necessary to be achieved for the electricity demand in an effort to ensure safety of domestic installations. To this end, the Demand Side Management (DSM) is one of suggested solution which played a significant role in micro-grid and Smart Grid (SG) systems. DSM program allows end-users to communicate with the grid operator so they can contribute in making decisions and assist the utilities to reduce the peak power demand through peak periods. This can be done by managing loads in a smart way, while keeping up customer loyalty. Nowadays, several DSM programs are proposed in the literature, almost all of them are focused on the domestic sector energy management system (EMS). In this original work, four heuristics optimization algorithms are proposed for energy scheduling in smart home, which are: bat algorithm (BA), grey wolf optimizer (GWO), moth flam optimization (MFO), algorithm, and Harris hawks optimization (HHO) algorithm. The proposed model used in this experiment is based on two different electricity pricing schemes: Critical-Peak-Price (CPP) and Real-Time-Price (RTP). In addition, two operational time intervals (OTI) (60 min and 12 min) were considered to evaluate the consumer's demand and behavior of the suggested scheme. Simulation results show that the suggested model schedules the appliances in an optimal way, resulting in electricity-cost and peaks reductions without compromising users' comfort. Hence, results confirm the superiority of HHO algorithm in comparison with other optimization techniques.
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In recent past, to meet the growing energy demand of electricity, integration of renewable energy resources (RESs) in an electrical network is a center of attention. Furthermore, optimal integration of these RESs make this task more challenging because of their intermittent nature. Therefore, in the present study power flow problem is treated as a multi-constraint, multi-objective optimal power flow (MOOPF) problem along with optimal integration of RESs. Whereas, the objectives of MOOPF are threefold: overall generation cost, real power loss of system and carbon emission reduction of thermal sources. In this work, a computationally efficient technique is presented to find the most feasible values of different control variables of the power system having distributed RESs. Whereas, the constraint satisfaction is achieved by using penalty function approach (PFA) and to further develop true Pareto front (PF), Pareto dominance method is used to categorize Pareto dominate solution. Moreover, to deal with intermittent nature of RES, probability density function (PDF) and stochastic power models of RES are used to calculate available power from RESs. Since, objectives of the MOOPF problem are conflicting in nature, after having the set of non-dominating solutions fuzzy membership function (FMF) approach has been used to extract the best compromise solution (BCS). To test the validity of developed technique, the IEEE-30 bus system has been modified with integration of RESs and final optimization problem is solved by using particle swarm optimization (PSO) algorithm. Simulation results show the achievement of proposed technique managing fuel cost value long with the optimal values of other objectives.
Chapter
Swarm Intelligence (SI) is referred to the social conduct emerging within decentralized and self-organization of swarms. These swarms are summarized as the well-known examples such as bird groups, fish schools, and the most social in insects species for instance bees, termites, and ants. Among those, Salp Swarm Algorithm (SSA), that has been successfully utilized and held in different fields of optimization, engineering practice, and real-world problems, so far. This review carries out a extensive study for the present status of publications, advances, applications, variants with SSA including its modifications, population topology, hybridization, extensions, theoretical analysis, and parallel implementation in order to show its potential to show its potential to overcome many practical optimization issues. Further, this review will be greatly useful for the researchers and algorithm developers analyzing at Swarm Intelligence, especially SSA to use this simple and yet very efficient approach for several tough optimization issues.
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The emergence of the Demand Response (DR) program optimizes the energy consumption pattern of customers and improves the efficacy of energy supply. The pricing infra-structure of the DR program is dynamic (time-based). It has rather complex features including marginal costs, demand and seasonal parameters. There is variation in DR price rate. Sometime prices go high (peak load) if the demand of electricity is more than the generation capacity. The main objective of DR is to encourage the consumer to shift the peak load and gets incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this work, Game Theory (GT)-based Time-of-Use (ToU) pricing model is presented to define the rates for on-peak and shoulder-peak hours. The price is defined for each user according to the utilize load. At first, the proposed model is examined using the ToU pricing scheme. Afterward, it is evaluated using existing day-ahead real-time pricing scheme. Moreover, shifting load from on-peak hours to off-peak hours may cause rebound peak in off-peak hours. To avert this issue, we analysis the impact of Salp Swam Algorithm (SSA) and Rainfall Algorithm (RFA) on user electricity bill and PAR after scheduling. The experimental results show the effectiveness of the proposed GT-based ToU pricing scheme. Furthermore, the RFA outperformed SSA.
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A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.
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