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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
2University of Lahore (Islamabad Campus), Islamabad 44000, Pakistan
3Central South University, Changsha 410083, China
4COMSATS University Islamabad, Abbottabad 22010, Pakistan
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:
Springer Nature Switzerland AG 2019
L. Barolli et al. (Eds.): WAINA 2019, AISC 927, pp. 398–411, 2019.
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
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 =
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 =
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
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 non-interruptible appliances are clothes washer and spin dryer while the base appliances include oven, TV, PC, laptop, radio, and coffee maker. Shuja et al. [62] classified fifteen appliances into seven shiftable, two non-shiftable, and six fixed appliances. Shiftable appliances include water pump, water heater, vacuum cleaner, dishwasher, steam iron, air conditioner, and refrigerator. ...
... In [62], a new meta-heuristic algorithm is proposed for scheduling the consumption patterns of home appliances. This study has considered fifteen home appliances for scheduling. ...
Full-text available
Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer's flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.
... This is the most important issue in the SG that needs special attention. In a smart home, the energy management system controls energy consumption by scheduling residential appliances [21]. In [22], a scheme has been proposed that reduces the cost of electricity consumption and, on the other hand, tries to increase consumer comfort. ...
Full-text available
In recent years, energy demand has grown significantly relative to its production. The power companies have also offered a variety of schemes such as energy consumption management to meet this growing consumer demand. Energy consumption management is a set of strategies used to optimize energy consumption which includes a set of interconnected activities between the utility and customers to transfer the load from peak hours to off-peak hours. This reduces the electricity bill. This paper presents an optimal schedule for the consumption of residential appliances based on improved multi-objective antlion optimization algorithm to minimize the electrical cost and the user comfort. To prevent peaks, the peak-to-average ratio is considered as a constraint for the energy cost function. Also, two different tariff signals have been used to measure energy costs. The real-time pricing and critical peak pricing are considered as energy tariffs. The simulations results are compared with other meta-heuristic algorithms, including multi-objective particle swarm optimization, the second version of the non-dominated sorting genetic algorithm, and the basic antlion optimizer algorithm to show the superiority of the proposed algorithm. Final results show that using the proposed scheme reaches electricity bills less than 80%.
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The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints.
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With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.
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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.
Conference Paper
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Smart grid provides an opportunity for customers as well as for utility companies to reduce electricity costs and regulate generation capacity. The success of scheduling algorithms mainly depends upon accurate information exchange between main grids and smart meters. On the other hand, customers are required to schedule loads, respond to energy demand signals, participate in energy bidding and actively monitor energy prices generated by the utility company. Strengthening communication infrastructure between the utility company and consumers can serve the purpose of consumer satisfaction. We propose a heuristic demand side management model for scheduling smart home appliances in an automated manner, to maximise the satisfaction of the consumers associated with it. Simulation results confirm that the proposed hybrid approach has the ability to reduce peak-to-average ratio of the total energy demand and reduce the total cost of the energy without compromising user comfort.
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Due to technological improvement and the changing environment, energy grids face various challenges, which, for example, deal with integrating new appliances and generation such as electric vehicles and photovoltaic. Thus, managing such grids has become increasingly important for research and practice, as for example grid reliability and cost benefits are endangered. Demand Response (DR) is one possibility to contribute to this crucial task by shifting and managing energy loads in particular. Realizing DR thereby can address multiple objectives (such as cost savings, peak load reduction and flattening the load profile) to obtain various goals. However, current research lacks on algorithms that address multiple DR objectives sufficiently. Accordingly, this paper aims to design a multi-objective Demand Response optimization algorithm and purposes a solution strategy. We therefore first investigate the research field and existing solutions, and then design an algorithm suitable for taking multiple objectives into account. The algorithm has a predictable runtime and guarantees termination.
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The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.
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Demand Side Management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via Home Energy Management (HEM) systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, Evolutionary Algorithms (EAs) (Binary Particle Swarm Optimization (BPSO), Genetic Algorithm (GA) and Cuckoo search) based DSM model for scheduling the appliances of residential users is presented. The model is simulated in Time of Use (ToU) pricing environment for three cases: (i) traditional homes, (ii) smart homes, and (iii) smart homes with Renewable Energy Sources (RES). Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.
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In this paper, a community-based cooperative energy consumption (C2C) scheme in smart grid, which alleviates energy consumption cost to customers, is proposed. The concept of community among customers in the smart grid is discussed. To form different communities among customers, a community-based game among customers is orchestrated, while considering the dynamic nature of the composition of the community. A practical scenario involving multiple customers forming a group and cooperating with one another is considered. The proposed dynamic community formation scheme always achieves an equilibrium state. Furthermore, the proposed scheme also helps to reduce peak-to-average ratio of the energy demands from the customers in different time periods. Simulation results show that the proposed cooperation-based scheme outperforms the existing schemes. It is also shown that customers can minimize their energy consumption cost by approximately 16% using the proposed scheme, compared to non-cooperative approaches.
This paper presents a short-term decision-making model for an electricity retailer with self-production of renewable energy. In the proposed model, a new trading mechanism for short-term demand response (DR) between retail customers equipped with smart grid technology and an electricity retailer is presented. Through the proposed trading mechanism, retail customers submit short-term DR offer curves to the retailer in order to increase or decrease their energy consumption for a given time period at favorable prices. On the other hand, the retailer decides its involvement in the short-term DR market for every time period to avoid unfavorable prices in the real-time market and, thus, maximize its profit. The stochastic nature of day-ahead and real-time prices, renewable energy production, electricity demand, and DR participation of retail customers is taken into account in the formulation of this work. The resulting model is a mixed-integer linear programming (MILP) which can be solved by existing commercial solvers. Case studies using real-world data are performed to demonstrate the effectiveness of the proposed model.
Demand response program with real-time pricing can encourage electricity users towards scheduling their energy usage to off-peak hours. A user needs to schedule the energy usage of his appliances in an online manner since he may not know the energy prices and the demand of his appliances ahead of time. In this paper, we study the users' long-term load scheduling problem and model the changes of the price information and load demand as a Markov decision process, which enables us to capture the interactions among users as a partially observable stochastic game. To make the problem tractable, we approximate the users' optimal scheduling policy by the Markov perfect equilibrium (MPE) of a fully observable stochastic game with incomplete information. We develop an online load scheduling learning (LSL) algorithm based on the actor-critic method to determine the users' MPE policy. When compared with the benchmark of not performing demand response, simulation results show that the LSL algorithm can reduce the expected cost of users and the peak-to-average ratio (PAR) in the aggregate load by 28% and 13%, respectively. When compared with the short-term scheduling policies, the users with the long-term policies can reduce their expected cost by 17%.
Real-time energy management within the concepts of home Microgrids (H-MG) systems is crucial for H-MG operational reliability and safe functionality, regardless of simultaneously emanated variations in generation and load demand transients. In this paper, an experimental design and validation of a real-time mutli-period artificial bee colony (MABC) topology type central energy management system (CEMS) for H-MGs in islanding mode is proposed to maximize operational efficiency and minimize operational cost of the H-MG with full degree of freedom in automatically adapt the management problem under variations in the generation and storage resources in real-time as well, suitable for different size and types of generation resources and storage devices with plug-and-play structure, is presented. A self-adapting CEMS offers a control box capability of adapting and optimally operating with any H-MGs structure and integrated types of generation and storage technologies, using a two-way communication between each asset, being a unique inherent feature. This CEMS framework utilizes feature like day-ahead scheduling (DAS) integrated with real-time scheduling (RTS) units, and local energy market (LEM) structure based on Single Side Auction (SSA) to regulate the price of energy in real-time. The proposed system operates based on the data parameterization such as: the available power from renewable energy resources, the amount of non-responsive load demand, and the wholesale offers from generation units and time-wise scheduling for a range of integrated generation and demand units. Experimental validation shows the effectiveness of our proposed EMS with minimum cost margins and plug-and-play capabilities for a H-MG in real-time islanding mode that can be envisioned for hybrid multi-functional smart grid supply chain energy systems with a revolutionary architectures. The better performance of the proposed algorithm is shown in comparison with the mixed integer non-linear programming (MINLP) algorithm, and its effectiveness is experimentally validated over a microgrid test bed. The obtained results show convergence speed increase and the remarkable improvement of efficiency and accuracy under different condition.
In this paper, we study a multi-residential electricity load scheduling problem with multi-class appliances in smart grid. Compared with the previous works in which only limited types of appliances are considered or only single residence grids are considered, we model the grid system more practically with jointly considering multi-residence and multi-class appliance. We formulate an optimization problem to maximize the sum of the overall satisfaction levels of residences which is defined as the sum of utilities of the residential customers minus the total cost for energy consumption. Then, we provide an electricity load scheduling algorithm by using a PL-Generalized Benders Algorithm which operates in a distributed manner while protecting the private information of the residences. By applying the algorithm, we can obtain the near-optimal load scheduling for each residence, which is shown to be very close to the optimal scheduling, and also obtain the lower and upper bounds on the optimal sum of the overall satisfaction levels of all residences, which are shown to be very tight.
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.