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Towards Eﬃcient Energy Management

in a Smart Home Using

Updated Population

Haﬁz Muhammad Faisal1, Nadeem Javaid1(B

), Zahoor Ali Khan2,

Fahad Mussadaq3, Muhammad Akhtar3, and Raza Abid Abbasi3

1Comsats University Islamabad, Islamabad 44000, Pakistan

nadeemjavaidqau@gmail.com

2Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE

3NCBA&E, Multan, Pakistan

http://www.njavaid.com

Abstract. Energy management using demand side management (DSM)

techniques plays a key role in smart grid (SG) domain. Smart meters and

energy management controllers are the important components of the SG.

A lot of research has been done on energy management system (EMS)

for scheduling the appliances. The aim of current research is to organize

the power of the residential units in an optimized way. Intelligent energy

optimization techniques play a vital role in reduction of the electric-

ity bill via scheduling home appliances. Through appliance’s scheduling,

consumer gets feasible cost in response to the consumed electricity. The

utility provides the facility for consumers to schedule their appliances for

the reduction of electricity bill and peak demand reduction. The utility

company is allowed to remotely shut down their appliances in emer-

gency conditions through direct load control programs. A lot of research

has been done on energy management system (EMS) for scheduling the

appliances. In this work, an eﬃcient EMS is proposed for controlling the

load in residential units. Meta-heuristic algorithms have been used for

the optimization of the user energy consumption schedules in an eﬃcient

way. Our proposed scheme is used to minimize the user waiting time.

User waiting time is inversely proportional to the total cost and peak to

average ratio (PAR). Simulation result shows the minimum user waiting

time, however, the total cost is compromised due to the high demand

of the load. In the end, our proposed scheme will be validated through

simulations.

Keywords: Smart grid ·Home energy management system ·

Real time price

1 Introduction

Conventional energy production systems cannot fulﬁll the user’s need. It gives

rise to a number of diﬀerent challenges that are faced by the electric power

c

Springer Nature Switzerland AG 2020

L. Barolli et al. (Eds.): AINA 2019, AISC 926, pp. 39–52, 2020.

https://doi.org/10.1007/978-3-030-15032-7_4

40 H. M. Faisal et al.

industry. Approximately, 10–30% energy is wasted in the methods used to sup-

ply the electric power from the source to end consumers. As we know the power

generation and power utilization is a one-way process so, the power generation

system is unable to control and manage the electricity consumption. Tradition-

ally, power generation systems and its demand side management (DSM) methods

are centrally distributed and only focused on industrial customers. Smart grid

(SG) solve maximum problems that are present in conventional energy produc-

tion systems. The bi-directional ﬂow of power between the electricity generation

system and the end consumer allow many factors to be controlled to play a vital

role in energy wastage. The bi-directional communication is not only concerned

by the consumers for electricity price and maintenance schedules of the distri-

bution network, however, also motivate the providers to monitor and analyze

the real time power utilization data. Distributed energy system and information

technology are involved in the SG. Using SG, communication can be handled

and monitored with the proper mechanism. Recent developments of the SG allow

load management methodologies to be implemented more eﬃciently by permit-

ting them to utilize new technologies. DSM and demand response (DR) are

important components of SG. Between the power grid and consumers, DR play

a vital role. DR schemes are used to reduce the electricity consumption without

aﬀecting the user’s comfort. Two types of DR programs are considered:

•Incentive base DR

•Time based DR

Using the incentive-based DR program, utility can turn ON and OFF the con-

sumers appliances with the term notice. Whereas Time based DR requires the

consumers participation for scheduling their according to the change in price sig-

nals. DR is very helpful in reduction of the consumers electricity bills based their

participation. The core objective of using multiple DR and DSM methodologies

is to alleviate the burden of the electricity bill and energy usage. The objec-

tive of sellers is to increase their proﬁts whereas consumers want their personal

incentives. Smart meters (SM) are installed in the residential area. The rela-

tionship between SM and DSM consist of many diﬀerent and connected parts

with many features and perspectives to consider. Without regard to exceptions,

the target of SM usage is to minimize energy consumption as well as peak for-

mation without losing the consumers comfort. SM provides the user’s complete

information of demand, supply and price signal. There are some methodologies

including mixed integer linear programming (MILP), linear programming, and

mixed integer non-linear programming (MINLP), etc., which can be applied for

minimization of the energy consumption expanses while managing the eﬃcient

schedules of electricity. DSM itself is not a technique; it is more generally a col-

lection of strategies used to change the user’s energy patterns for obtaining their

suitable power distribution. The collection of strategies (i.e. DSM) encourage

the consumers to monitor and control multiple factors (e.g. power load, appli-

ance management) of power consumption which results in lower power wastage.

This can be achieved by applying various artiﬁcial intelligence algorithms. In

Towards Eﬃcient Energy Management 41

DSM strategy consumers perform load management and shifting the load into

diﬀerent time duration.

2 Related Work

Using MILP in [1] as a design technique, the main objective of this method

is to minimize the peak to average ratio (PAR) value and the total electricity

bill paid by consumers. The authors also focuses on balance load management

but the user comfort is not considered in MILP. There is a trade-oﬀ between

conventional systems and today’s renewable energy sources (RES). SG is a two-

way communication of utility and consumers and by saving this 10–30%, we cover

a lot of energy wastage problem. Cost minimization is a big challenge for today’s

researchers in SG. Using a genetic algorithm (GA) technique in the research

article [2], cost minimization is achieved at a low level with the integration of RES

and stored energy. When price and demand are higher than the stored energy

is helpful in speciﬁc time changes. Deployment and maintenance cost of storage

devices and RES has been ignored in this technique. Majority of researchers

have focused on residential areas only. Balancing the load in commercial and

residential areas is a big problem. However, using GA-DSM [3] algorithm in peak

hour, electricity consumption is reduced by 21% in an industry which is very

remarkable. The authors neglected the PAR value and user comfort feasibility.

The authors in [4] proposed scheme of MINLP is used to solve the cost min-

imization under the price tariﬀ ToU. Although the cost minimization at the

peak hour is achieved, however, the author disregarded the PAR value which is

another important factor in SG. Dynamic programming [5] technique was used

for cost minimization and by scheduling the gadgets for various duration. This

will be done by the integration of RES and energy storage systems (ESS) with

SG. Residents have the capability to produce the electricity from RES. A con-

sumer can sell the additional electricity to the neighbours. The important factor

of installation and maintenance has been ignored in RES. The novel schedul-

ing model [6] of the combination of GA and binary partial swarm optimization

(BPSO) algorithms. The goal of this technique is electricity bill and PAR min-

imization. In DSM, the user can manage their home appliances by shifting the

load to another time so the load demand is a key factor in this regard. By shifting

the load and by using cuckoo search algorithm (CSA) [7] algorithm peak load

has been reduced by 22%. Balanced load curve generated by the CSA algorithm

shows the user preference for appliance usage, load shifting can then be done

by using this curve. For real-time schedule controller, new binary backtracking

search algorithm (BBSA) [8] was proposed. By using the load limit home appli-

ances are shifted from peak hour and electricity price reduced 21% per day in

comparison to PSO algorithm. Huang et al. [9] developed the two point esti-

mation method embedded with PSO method for reducing the computational

complexity in a home energy management systems (HEMS). This scheme is

intelligent enough in comparison to GPSO-LHA in the context of computational

burden. Author [9] did not consider the cost of electricity and PAR value which

42 H. M. Faisal et al.

are the important factor in HEMS. The author in [4] proposed using MINLP

technique cost minimization is achieved under the price tariﬀ of ToU however,

PAR is not considered in this technique. Utilizing DSM, 30% power utiliza-

tion can be minimized without knowing the usage on the user side. The overall

objective of load management is to schedule a load during high demand to low

demand intervals. This can be done by the combination of the GA algorithm and

bacterial foraging (BF) in [10]. Hybrid technique [10] minimizes the electricity

bill and PAR using the load management shifting. Hybrid scheme uses the RTP

signal for reducing the electricity bills and PAR.

In HEMS, SG has a signiﬁcant functionality to minimize the users’ cost using

DSM. Meta heuristic scheme is designed in [11] for the reduction of cost and PAR

value. Applying the combination of GA and CSA, we achieved the minimized

cost and PAR value as well, under the RTP price tariﬀ, as compared to other

techniques with the desirable user waiting time. In DSM user performed pri-

orities that are set to schedule the appliances in HEMS. The authors proposed

[12] evolutionary accretive comfort algorithm (EASA) which is comprised of four

postulations. These postulations are deﬁned according to the time and device

bases priorities. Based on the input values EASA generates optimal energy solu-

tion pattern which satisﬁes the user budget. The author deﬁnes three diﬀerent

user budgets to ﬁnd the absolute solutions. Ma, Yao, et al. [13] deﬁnes discom-

fort function for two diﬀerent type of gadgets. First category is ﬂexible starting

time and the other is ﬂexible power devices. Authors in [13] considered a multi-

objective function for user comfort and cost parameters.

The proposed bat algorithm in [14] can be applied to obtain the optimum

result. By applying this algorithm energy consumption can be reduced which

is simply a non-linear optimization problem. The main goal of current work is

to decrease the power usage and increase consumer comfort standards in the

residential area. In Al Hasib [15], the author considered bidirectional energy

exchange between SG and small residential building. The main goal of this paper

is to maintain the balance between electricity cost and user comfort. Here the

appliances load was categorized into three categories. Based on a declining block

rates (DBR), the author proposed a comfort demand function. The authors in

[16] recommended a min max load scheduling (MMLS) algorithm used to reduce

the PAR while optimizing the operational comfort level (OCL) of user’s. It is

important to note the diﬃculty faced by user’s, under the control of HEMS when

reducing power consumption. For residential demand response, the author has

proposed an intelligent algorithm which analyze the eﬀect of HEMS operations

on the use’r comfort [18].

The authors in [19], smart homes are integrated with SG to purchase/sell

electricity in peak load demand. The proposed scheme objective is to minimize

the cost and PAR along with the increase in earning proﬁts. In this model two

optimization techniques, CSA and strawberry algorithm (SA) are used with RES:

wind turbine (WT), photovoltaic panels (PV) and ESS. The simulations results

show that the proposed scheme eﬃciently reduce cost and PAR with maximiz-

ing earning’s. CSA optimization technique outperforms than SA to minimizing

Towards Eﬃcient Energy Management 43

cost and PAR during peak load demand. Many techniques and models in [20–25]

were addressed using SG and micro SG with standalone and connected-grid with

HEMS are the emerging research areas in the last few years. In [26,27], many

authors have proposed scholastic programming models however, dynamic pro-

gramming schemes in [28,29] were proposed. These static and dynamic models

need precise tuning in their algorithm to manage the parameters and to control

them. The authors in [30] proposed DSM model, where RESs are connected.

The proposed model consist of three layers: the utility, the customer and the

DR aggregator. The role of the DR aggregator has been deﬁned as a mediator

that communicate with both customers and the utility. The experimental out-

comes demonstrate that consumers can get proﬁt from the proposed design: the

DR aggregator can make the proﬁt by providing DR services; the utility can

reduce the generation cost; customers can save money on their monthly elec-

tricity bill. Evolutionary algorithms are used for load shifting in order to reduce

the cost of the customers [32]. All service sides have data sets, where schedul-

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

faced more problem because of big power consumption appliances. Due to high

load user’s need to use the energy more intelligently in both residential and

commercial sector.

3 Problem Formulation

DSM techniques are proposed to handle the irregular consumption of electric-

ity which is the complex task to tackle. Consumers require more electricity at

certain time intervals, so, there is a possibility for peak formation and electric-

ity blackouts. In this situation, intelligent algorithms are required for EMS to

help user’s for scheduling the power from high demand intervals to low demand

intervals in an eﬀective manner. Most of the techniques have been designed to

reduce the peak formation, electricity cost and user’s discomfort [1,33,35]. How-

ever, there is always a trade-oﬀ between PAR, electricity bill and user comfort

standards. RES integration is lacking in [1] for enhancing the comfort standards

of the residents. In [33], appliance priorities are not considered in an automatic

fashion. The study in [35] prioritizes the appliances manually; however, they need

automatic priority speciﬁcations for controlling the whole system eﬃciently. So,

there is need to design an EMS which can optimize the energy consumption of

the residential sector consumers eﬃciently. Meta-heuristic algorithms are used

for the optimization of the energy consumption schedules deﬁned by user’s.

4SystemModel

We have developed a home energy management (HEM) method for controlling

the energy consumption load and price of the smart homes. Initially, this algo-

rithm starts with a single smart home and 15 appliances in it. These appliances

are categorized into two main categories: schedulable and non-schedulable appli-

ances. Smart meter decides the operation time of the appliances according to

44 H. M. Faisal et al.

their power rating and deﬁned pricing tariﬀ from the utilities. The power rating

varies for each appliance and scheduling of these appliances is done in such a

way to achieve the optimum solution from the designed objective function. In

this work, two pricing tariﬀs are used: RTP and CPP for checking their impact

on the customers electricity bills. DR and DSM used in SG provide more sta-

bility and reliability in grid operation. The aim of this work is to reduce the

PAR, energy consumption and cost, and to enhance the consumers preferences

according the consumers standards. Main architecture of this system is visual-

ized in Fig. 1. The electricity bill is conveyed to consumers through the smart

meter. HEM controller decides which appliances should be turned on using the

deﬁned pricing signals during the peak and oﬀ-peak hours. The core objective

of this study is to reduce the power utilization, PAR and electricity cost while

maximizing the user comfort. However, there is always a trade-oﬀ which occurs

between electricity bill and consumers preferences. Total energy utilization is

formulated using Eq. 1. Equations 2and 3is used to calculate the PAR and cost

respectively.

Load =

24

t=1

(PR∗S(t)),S(t)=[1/0] (1)

PAR =(Max(Load)/Av g (Load)) (2)

Cost =

24

t=1

(PP ∗PR∗S(t)),S(t)=[1/0] (3)

Fig. 1. System model

Towards Eﬃcient Energy Management 45

5 Optimization Algorithms

Traditional optimization algorithms, which belongs to mathematical techniques,

are not working satisfactorily if a large number of devices exists. Computa-

tionally power is also slow and time consuming. Behind this reason, we apply

heuristic schemes grey wolf optimization (GWO) and Jaya algorithm to obtain

our objectives. We proposed a JGO algorithm, which is discussed in details in

the subsection below.

5.1 GWO

GWO is a novel meta-heuristic algorithm. It consists of four types of wolves:

alpha, beta, delta and omega. There are three main phases of hunting in GWO.

Alpha is used as the most ﬁttest solution between beta, delta and omega. List

of main steps of GWO is given.

1. Encircling the prey

2. Hunting

3. Exploitation: It is also called attacking the grey

4. Exploration: It is also known as search for prey.

5.2 Jaya Algorithm

Suppose f(x) is the target function to be minimized or maximized. Assume that

there are “m” number of design variable and “n” number of candidate solutions,

at any iteration i, where k = 1, 2, 3, 4.....n. Let the best value of f(x) (i.e.

f(x)best) is obtained by the best candidate names as “best” and vice versa for

the worst candidate (i.e. f(x)worst). If the value for jth variable during the ith

iteration for kth candidate is Xj,k,i then this value is modiﬁed as per the following

equation given below:

´

Xj,k,i =Xj,k,i +r1,j,i (Xj, best, i..........) (4)

Where the value of j variable, for the “best” candidate is Xj, best, i and vice

versa for the worst candidate, where the updated value of Xj,k,i and r1,j,i and

r2,j,i which are the two random number for the jth variable is X’j,k,i in the range

of [0,1].

5.3 Updated Population

In this section, we described our proposed scheme. The population update is

performed in GWO, the updating is totally dependent on the placement of the

primary three accurate candidates. So in GWO, we tend to note initial 3 good

solutions, oblige the other wolves or search agents to change and update their

locations on the idea of the placement of the most eﬀective search agent. Here,

Eq. 5is used to ﬁnd the eﬀective search agent location.

X(t+1)= X1+X2+X3

3(5)

46 H. M. Faisal et al.

Hence, we are able to say that Jaya population update strategy is good as

compared to the GWO as a result of here, our aim is to search out the more

eﬀective result as possible and also the a lot of optimized result may be possible

by having a large random and various population. Where initialization of GWO

is better than Jaya initialization. So, we selected Jaya based population update

strategy and GWO based initialization strategy, so that, we proposed a new

proposed algorithm.

6 Pseudo Code of the Proposed Scheme

1. Generate initial search agents

Gi(i=1,2, ...., n) (6)

2. Initialize the vector’s

3. Calculate the ﬁtness value

4. Iter = 1

5. repeat

6. for i = 1:

7. end for

8. Calculate the ﬁtness value of all solutions

9. Update the value

10. Update the vectors

11. Iter = Iter+1

12. until Iter ≥maximum number of iterations

13. output best solution.

7 Simulation and Reasoning

In this section, we demonstrate simulation results and evaluate the performance

of the proposed algorithm. The load, cost and user’s waiting time for each appli-

ance are represented in terms of hours, cents and kWh. By applying RTP signal

in a smart home, we achieve maximum user comfort time, however, at some level

cost and PAR values are maximized. There is always a trade-oﬀ between PAR,

cost and user comfort. In RTP price signal tariﬀ, electricity price varies during

diﬀerent time slots of a single day. Figure 2shows the complete details of price

per hour in 24 h. In afternoon, the price rate is two times higher. Per hour cost is

increased due to unbalancing of the price at peak time. This is shown in Fig. 3.

Figure 4shows the hourly based energy consumption pattern in both scheduled

and unscheduled scenarios.

Figure 5represents the PAR value of GWO, Jaya and proposed algorithm. It

demonstrates that proposed technique result is better than the GWO algorithm,

however, Jaya algorithm is better than our proposed algorithm. PAR value of

proposed algorithm is 50% less than the GWO so as compared to GWO we

achieves PAR goal in a smart home, however; Jaya technique is suitable for the

Towards Eﬃcient Energy Management 47

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

8

10

12

14

16

18

20

22

24

26

28

Time (hour)

Price (cent/kWh)

RTP

Fig. 2. Total cost

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

50

100

150

200

250

Time (hour)

Per hour cost (cent)

Unscheduled

GWO

JGO

Jaya

Fig. 3. Per hour cost

reduction of PAR in RTP signal. Figure 6shows the total cost of GWO, Jaya and

proposed algorithm. It is shown that the overall cost of the algorithm is higher

as compared to GWO and Jaya. This eﬀects the overall cost per day in the RTP

signal. Reduction of electricity cost is the core objective of DSM in SG. With

the comparison of Jaya and GWO, cost of our proposed technique is higher due

to higher user demand. Figure 6shows the total cost of three diﬀerent schemes

in terms of cents. Complete load details of unscheduled load, Jaya algorithm,

GWO algorithm and proposed algorithm are shown in Fig. 7. It can be observed

that all values are approximately equal. Graphical representation of the load is

useful for calculating the overall cost in the RTP signal.

The user waiting time (hour) is shown in Fig.8. The consumer satisfaction

level is measured in terms of waiting time. In our proposed scheme, user’s satis-

48 H. M. Faisal et al.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

2

4

6

8

10

12

Time (hour)

Per hour load (kWh)

Unscheduled

GWO

JGO

Jaya

Fig. 4. Per hour load

Unscheduled GWO JGO Jaya

0

1

2

3

4

5

6

7

8

PAR

Fig. 5. PAR

Unscheduled GWO JGO Jaya

0

200

400

600

800

1000

1200

1400

1600

Total cost (cent)

Fig. 6. Total cost

Towards Eﬃcient Energy Management 49

Unscheduled GWO JGO Jaya

0

20

40

60

80

100

120

Total load (kWh)

Fig. 7. Total load

GWO JGO Jaya

0

1

2

3

4

5

6

Waiting time (hour)

Fig. 8. User waiting time

faction is the time limit a user waits for a particular appliance to turn on. So user

waiting time is inversely propositional to cost and PAR. We achieved minimal

user waiting time for high load and cost in DSM. Our proposed scheme achieved

minimum user waiting time however, cost and PAR values are compromised.

Proposed scheme of user waiting time is low as compared to GWO and Jaya.

While, increasing the cost and PAR, the waiting time increases, which is shown

in Fig. 8. Our proposed scheme tries to achieve the maximum trade oﬀ between

user waiting time and cost.

8 Conclusion

In this paper, we evaluate a load management problem in a smart home for

diﬀerent electrical appliances. These appliances are scheduled using the meta

heuristics techniques according to their consumption pattern. We evaluate two

50 H. M. Faisal et al.

meta heuristic algorithms performance on the parameters of cost, PAR and user

comfort. Simulation result shows the tradeoﬀ of cost and user waiting time.

Results show proposed updated population scheme is eﬀective as compare to

GWO and Jaya in term of waiting time. In future, we will combine RES into SG

for PAR reduction.

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