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Bio-Inspired Optimization Techniques for Home

Energy Management in Smart Grid

Abdul Mateen1, Nadeem Javaid1,∗, Muhammad Awais1,

Nasir Khan1, Urva Latif1, Ihtisham Ullah1

1COMSATS Institute of Information Technology, Islamabad 44000, Pakistan

∗Corresponding author: www.njavaid.com, nadeemjavaidqau@gmail.com

Abstract—Smart Grid (SG) plays a noteworthy role in min-

imizing the Electricity Cost (EC) through Demand Side Man-

agement (DSM). Smart homes are the part of SG, pays a lot

in minimizing EC via scheduling the appliances. Home Energy

Management (HEM) have been extensively used for energy

management in smart homes. In this paper, for the effective

utilization of energy in a smart home, we propose a solution

that consists of bio-inspired techniques: Genetic Algorithm (GA),

Flower Pollination Algorithm (FPA) and hybrid of these two,

Genetic Flower Pollination Algorithm (GFPA). All of these

techniques applied to the appliances that are essential in a home.

Our proposed solution leads to ﬁnd an optimal scheduling pattern

that reduces EC, Peak to Average Ratio (PAR) and maximize

User Comfort (UC). In our work, we have considered one home.

We divide appliances into three categories, non-interruptible,

interruptible and ﬁxed appliances. Simulation results show that

our proposed schemes performed better in terms of EC, UC and

PAR. We have done this work for three different Operational

Time Intervals (OTIs) 15, 30 and 60 minutes for each appliance.

Index terms— Demand response, Genetic algorithm,

Flower pollination, Smart grid, Critical peak pricing, Home

energy management

I. INTRODUCTION

SG uses new and forward-looking technologies comprising

independent controllers, intelligent hardwares, softwares and

other means for two way communication, between utilities and

clients for the organization of data to deliver electricity in an

effectual way. The major aims of the SGs are to increase the

efﬁciency, trustworthiness and safety of the system. A brief

overview about the smart HEMS is presented by the [8]. They

discuss the functional modules and architectures of HEMS in

their paper.

DSM also called energy demand management, which al-

lows clients to alteration their demand for electricity, so that

electricity practice is minimized during on-peak slots. Demand

Response (DR) plans are in the form of economic inducements

or other time based charges which offer sufﬁcient opportunity

for clients in shifting or dropping appliances load to off-peak

slots. Thus, DR has reﬂected the most reliable solution to

lessening the peak demand and ﬂat the request curve. DR

offers motivation in the form of price signals including CPP,

Time-Of-Use (TOU), Real-Time Pricing (RTP), Peak Load

Pricing (PLP), Day-Ahead Pricing (DAP) or Day-Ahead RTP

(DA-RTP), Inclined Block Rate (IBR).

EC minimization by appliances scheduling is one of the

challenging task in SG. The EC problem of appliances is

considered as an optimization task and explained using nu-

merous techniques in the literature. An important problem in

SG is UC which is mostly abandoned in EC minimization

problems. Studies expose that consumers wish to lessen their

EC. However, they do not want to negotiation on their comfort.

The simulations are accompanied using three scenarios by

taking different OTIs. GA and FPA also reduces and controls

the PAR of the SG. Moreover, the monthly and yearly EC is

conﬁrmed.

Most Common objectives of electricity management in SG

are PAR reduction, minimization of EC, UC maximization

and minimize the power consumption. A vast amount of

electricity is spent by residential area and its consumption

is growing speedily. Automated Demand Response (ADR)

based optimization is proposed in [6]. Scheduling of different

appliances at domestic level was under consideration. Their

aim was to maximize UC and minimize the EC. According to

[23], through energy production, dispersion and dissemination,

the electricity that is produced, more than 65% is wasted. With

the fruition of smart grid, we have good opportunity to save

maximum energy.

Rest of the paper is organized as follows: section II high-

lights the related work and describes the problem statement,

system model is at section III. GA and FPA is discussed

in Simulation and Discussion‘s section. Section VI covers

Conclusion.

II. RE LATE D WORK

A. Related Work

Many techniques have been proposed which includes mod-

els and implementation in the ﬁeld of SG using different

algorithms. In these techniques, writers facilitate the customers

and the utility through minimization in cost and load optimiza-

tion. Writers consider different scheduling parameters in these

techniques: such as appliances, price scheme, user demand

and many others. The HEM can provide an efﬁcient way of

optimizing energy usage in smart homes.

A GA base energy management system in SG is proposed in

[1]. In this proposed system, the authors objective is to lower

the EC and other problems that are created by high PAR. The

high PAR creates when the CPP scheme is used because there

is a possibility that, most appliances run during the time when

CPP provide the lowest electricity price. The proposed system

effectively minimized EC and PAR. The authors ignored UC

and cannot explain how much EC minimized. In [2], authors

presents another effective GA-based DSM model in the smart

grid. The objective of the proposed model is to minimize the

power utilization during a day by effectively distributing user

demand during max-peak and min-peak hours. The authors

are adopted evolutionary algorithm GA for minimization the

power utilization.

In [3], proposed model is categorized into three types:

an interruptible, non-interruptible and ﬁxed load. Simulation

performed in MATLAB by giving inputs of many types of

the load. In [4], the authors present Predictive Demand Side

Management (PDSM) system. In [5], a Non-dominated Sorting

Genetic Algorithm (NSGA) technique is used for solving the

DSM multi-objective optimization problem.

The authors proposed DR application in HEM system to

optimize the operational time of appliances [6]. The authors

addressed DR optimization problem in proposed application.

The objectives of proposed application is to minimize the com-

putational cost and operational time of appliances according

to given usage pattern. In [7], the main objective is to reduce

the cost, pollution emission and solve the uncertainty problem

of energy sources. In DSM, the end users alleviate the EC

and minimize PAR by scheduling home appliances from high

peak hours to low peak hours or by integrating Renewable

Energy Sources (RES). Main functionalities of DSM that are

discussed in [8], are management of load and DR.

The authors in [9], proposed a technique for balancing load

in industry, residential and commercial areas. Comparison of

electricity consumption through DSM with GA (GA-DSM)

and without GA is done by the authors. The comprehensive

simulation results shown that the recommended scheme GA-

DSM attained the objectives. During peak hours, reduction in

the electricity consumption is 21.91%. However, the authors

did not discuss about UC and PAR. In [10], smart charging

and appliance scheduling are features while targeting the

residential area. Main objectives are to reduce Cost and PAR,

but ignores UC, the initial and maintenance cost. In [11], the

authors used efﬁcient GA based DSM scheme for SG. What

the things he gains, are cost minimization and PAR, however,

inconsideration of UC and larger appliance delay. Queuing-

based Energy Consumption (QEC) monitoring for different

smart homes in SG is under consideration in [12].

In [13], networks employ low-cost, small devices that in-

tellect biological body structures (e.g. heat, heart beat) and

move forward it to places. The main reason is that the

desirability of these sensor nodes is its energy consumption

and data rate. In [14], the authors presented state of art EMC

model for HEM in order to avoid peak formations while

focusing on utilities electricity cost reduce, by consider UC

level within acceptable limits. The LP and GA algorithms are

used. Authors consider cost minimization, UC maximization.

However, they ignored PAR. In [15], the author deliberated

two different systems to explore the incremental price for

short term arranging of a distribution system with Demand

Grid (DG) units and presented three different scenarios with

various approaches via optimizing beneﬁt functions in a multi-

objective optimization framework. However, the author work

for short term scheduling.

Manageable load modeling is proposed in [16], appropriate

for equally local and direct control from a regulation system

for Distributed Energy Resources (DER) optimization based

on multi objectives. In [17], the author proposed load control

algorithm for DSM. In which author deliberated the problem

of power and load scheduling to lessen the energy expense

of users. In [18], the centralized optimization problem P1

is proposed to minimize PAR and P2 to reduce total energy

consumption to reduce cost.

The author used FPA in [20], for the tuning of Mass Damper

(MD) also known as harmonic absorber, usually used to reduce

the vibration in structures. FPA works when mathematical

equations cannot be applied, as it is meta heuristic just like

others algorithms e.g. GA and Harmony Search Algorithm

(HSA).The author used FPA in [20], for the tuning of Mass

Damper (MD) also known as harmonic absorber, usually used

to reduce the vibration in structures. FPA works when mathe-

matical equations cannot be applied, as it is meta heuristic just

like others algorithms e.g. GA and Harmony Search Algorithm

(HSA). Authors in [25], used GA for scheduling multiple

homes. However, they use to hybrid of GA with Cuckoo

Search Algorithm (CSA). Where, CPP and RTP schemes are

used as an input signal.

B. Problem Statement and Contribution

Most Common objectives of electricity management in SG

are PAR reduction, minimization of EC, UC maximization and

minimize the power consumption. A vast amount of electricity

is spent by residential area and its consumption is growing

speedily. To overcome these issues, we proposed an efﬁcient

HEM. We have used GA and FPA for optimization in our

proposed solution. Results demonstrate that in terms of EC,

GA and FPA techniques performed well with respect to un-

scheduling.The part of a system that connects the consumers of

low voltage from high voltage transmission system is called

distribution system. During primary and secondary distribu-

tion, loss incidence ratio is up to 70%. While remaining 30%

lost in transmission lines [24].

In our work, GA and FPA are used to compare the perfor-

mance with un-scheduling EC. We considered only one home

from residential area, this home contains different appliances.

Each appliances has its own TOU and OTI. Appliances are

categorized into three different categories: interruptible appli-

ances, ﬁxed appliances and non-interruptible appliances, more

details are in TABLE II, III and IV. This category of appliances

has taken from [5] . The main objectives of our work are

•EC minimization

TABLE I: Table for Related Work

Techniques Pricing

Schemes

Objectives Achievements Limitations

GA [1] RTP Minimization in cost and

PAR

Minimization of PAR UC ignore and cost cannot

reduce

GA [2] TOU Reduce the power utiliza-

tion

Minimize cost and PAR UC ignored

NSGA and FDM [3] RTP High energy residential ap-

plication and integration of

RES

Efﬁcient integration of

RES and increase UC

EC increase and the instal-

lation and maintenance cost

of RES ignored

Small HEM [4] TOU Reduction in EC Minimize EC UC ignored and not explain

how much EC is minimized

MOPSO and DR program

[7]

DA-RTP

and TOU

Operational costs and pollu-

tion emissions

Minimize cost and emis-

sions with RES

UC, RES installation and

maintenance cost ignored

Smart charging and appli-

ance scheduling [9]

CPP Residential Area Cost Minimization, PAR

reduction

Lack of Initial Installation

and maintenance Cost of

batteries and UC

Efﬁcient GA Based DSM

[10]

DA-RTP

and TOU

Covered commercial, resi-

dential and industrial areas

PAR reduce and cost min-

imize

Larger appliance delay and

UC ignored

Queuing-based Energy

Consumption (QEC)

monitoring for different

smart homes [11]

- Residential SG Networks Delay reduction and cost

minimization

Do not consider PAR and

RES

Delay and energy consump-

tion analysis [12]

RTP Low duty cycle data rate,

and energy consumption

Minimum energy

consumption

Cannot consider PAR and

delay

Try to meet the power grid

challenges [13]

RTP Stat of the art work EMC

for the residential energy

management system for

avoiding peak formations

PAR reduced and less cost UC ignored

LP, GA, and TLBO [14] - Cost minimization and UC

maximization

Minimize electricity con-

sumption cost

Do not bother PAR

Deliberate two different sys-

tems to explore the incre-

mental price [15]

- Multi-objective

optimization agenda

Multi-objective optimiza-

tion framework

Work for short term

scheduling

Manageable load modelling

[16]

RTP GSO for multiple object and

constrains

New problem of Shift able

load proposed

PAR and UC ignored

Algorithm for load control

[17]

- Less load and power

scheduling

Schedule different type of

appliances

UC and PAR not considered

Game theoretical approach

[18]

DA-RTP Less PAR and EC Lessen PAR and EC High communication over-

head and fail to protect cus-

tomer privacy

•UC maximization

•PAR reduction

•Find out trade-off between UC and EC

III. SYS TE M MOD EL

Heuristic techniques are problem dependent and these can

be used to boost/speed up the process of ﬁnding a satisfactory

solution from all possible solutions. This solution may be close

to the best one. However, these techniques never guarantees

you for the best solution.

A. Categorization of Appliances

Appliances can be divided into different categories, in our

case, we divide the appliances into three categories.

•Fixed appliances are those appliances that can not be

shifted from their position, in terms of ON/OFF status.

•Non-interruptible appliances can be shifted from one

position to another. However, once this type of appliance

is ON, it can not be interrupted until it done its work.

•Interruptible appliances can be shifted or interrupted at

any time.

In our case, the ﬁxed appliances category contains (refriger-

ator, Television (TV) and telephone), non-interruptible appli-

ances category contains (Air Conditioner (AC) and lighting),

interruptible appliances contains (printer, hair straightener,

desktop computer, oven, cooker hood, microwave, iron, toaster,

kettle, other ﬁxed, washing machine, dishwasher and hair

dryer).

We tackle the problem of cost by considering one home.

However, it can be applied over several homes. We considered

15, 30 and 60 minute OTI for each of the appliance. OTI varies

from problem to problem. Appliances can be more or less than

that of our case scenario. Length of Operation Time (LOT)

can also vary. The power rating of appliances represented as

follows

Residential Area

HEM

SMART METER

UTILITY

CPP

Electric vehicle Electric vehicle

Electric vehicle

Fig. 1: Proposed System Model

TABLE II: Fixed Appliances

Fixed Appliances Power Rating (kWh)

Refrigerator 1.666

TV 0.083

Telephone 0.005

TABLE III: Non-interruptible Appliances

Non-interruptible appliances Power Rating (kWh)

Air Conditioner 1.14

Lighting 0.1

TABLE IV: Interruptible Appliances

Interruptible appliances Power Rating (kWh)

Printer 0.011

Hair Straightener 0.055

Desktop Computer 0.15

Oven 1.14

Cooker Hood 0.225

Iron 2.4

Microwave 1.2

Toaster 0.8

Kettle 2

Other Fixed 0.05

Washing Machine 1.4

Dishwasher 1.32

Hair Dryer 1.8

IV. PROPOSED METHODOLOGY

GA is nature inspired algorithm which is based on the

theory of Darwin. It was invented by John Holland in the

middle 70s. We can use it for stochastic nature of problems or

when changes are adopted. GA works on the basis of genetic

population like chromosomes [21]. GA is a searched-based

optimization technique. It leads the problem towards its opti-

mal solution. The word optimization means moving towards

best solution. Optimization deﬁnition varies from problem to

problem. In our scenario, we use this optimization for cost and

PAR reduction in the HEM. GA applies on a set of population,

it ﬁnds the best/ﬁttest solution for this population and all other

population moving towards that solution (Algorithm 1).The

same method was expanded by Agnetis et al., in [22], by using

heuristic approach for household energy consumption, climate

comfort level and timeliness.

A. GA

Selection of the parent (chromosomes) may follow one of

the following procedure

•Roulette wheel

•Tournament

•Truncation selection

•Fittest

•By itself. Pick the best (in our scenario)

Algorithm 1 GA

1: initialization

2: set lower and upper limits

3: for appliances a belong to A do

4: for generate population i=1 to maxpopsize do

5: for generate appliances j=1 to maxappsize do

6: generate initial population

7: evaluate each individual‘s ﬁtness

8: while generation <max generation do

9: select two parents

10: for randomness in population do

11: crossover

12: mutation

13: end for

14: evaluate ending criteria

15: elitism (process of following the

16: best ones)

17: end while

18: end for

19: end for

20: end for

21: return best possible solution

22: end

1) Crossover: Types of crossover

•One-point crossover

•Two-point crossover (in our scenario)

•Uniform crossover

2) Mutation: Types of mutation

•Substitution

•Insertion

•Deletion

•Inversion

B. FPA

FPA is also nature-inspired algorithm and it is developed

by Xin-She Yang in 2012 [19]. It is one of the most recent

developed algorithm. The idea of this technique is taken from

the pollination process of the ﬂowers. The main purpose of a

ﬂower is ultimately reproduction via pollination (Algorithm 2).

The author claims in its paper that FPA performs better than

GA and Partial Swarm Optimization (PSO). Its main objectives

are

•Survival of the ﬁttest

•Optimal reproduction of the plant

There are two types of pollination that are usually done,

a-biotic and biotic. 90% of the plants fall in the category

of biotic while other 10% plants fall in a-biotic. In global

pollination (biotic), pollinators such that animals, bats and

birds are required to transfer the pollens. In local pollination

(a-biotic), wind and diffusion in water caused pollination.

Complete details of FPA are given in Algorithm 1.

1) Basic Steps for FPA:

•Global pollination by using Levy distribution formula

Algorithm 2 FPA

1: initialization

2: for all appliances a belong to A

3: for generate population k=1 to maxpopsize do

4: generate random ﬂowers (population)

5: for generate appliances m=1 to maxappsize do

6: if rand() >probability switch then

7: use levy-ﬂight formula for updation

8: else

9: select random population

10: check limits

11: end if

12: generate random population

13: calculate ﬁtness for each individual

14: end for

15: ﬁnd local best

16: if new solution is better than previous then

17: update solution

18: end if

19: update the global best solution

20: end for

21: return best solution

22: end

•Local or self pollination

•Reproduction uses ﬂower consistency by considering the

similarity of two ﬂower that are are used in pollination.

•Control switching probability for selecting local and

global pollination.

C. GFPA

GFPA is a hybrid of GA and FPA. We simply add the

crossover and mutation steps from the GA to the FPA (Al-

gorithm 3). More details are discussed in the section of

simulation and discussion.

V. SIMULATION AND DISCUSSION

In this section, we will discuss simulation results in detail.

For showing productiveness of our proposed work. We have

conducted simulations, in order to show optimal scheduling

for single smart home. We have done our scheduling task on

a single home. However, this task (scheduling) can be done

over multiple smart homes. We have successfully implemented

GA and FPA for applying scheduling on a single smart

home. The time schedule is composed of 96, 48 and 24-time

slots respectively in a day, starting from 12 am to 12 am.

Simulations result of our load are shown in Fig. 2

We have applied GA, FPA and GFPA on different OTIs, i.e.

15, 30 and 60. Every OTI generates different results. PAR is

directly proportional to the price (greater the PAR greater the

cost) and vice versa. It can be easily judged that GA, FPA

and GFPA in every OTI has less PAR than unscheduled PAR

(Fig. 3). So, GA, FPA and GFPA performs better for PAR.

Our proposed schemes shufﬂed appliances and turned them

Algorithm 3 GFPA

1: initialization

2: for all appliances a belong to A

3: for generate population k=1 to maxpopsize do

4: generate random ﬂowers (population)

5: for generate appliances m=1 to maxappsize do

6: if rand() >probability switch then

7: use levy-ﬂight formula for updation

8: else

9: select random population

10: check limits

11: end if

12: generate random population

13: calculate ﬁtness for each individual

14: end for

15: ﬁnd local best

16: if new solution is better than previous then

17: update solution

18: end if

19: update the global best solution

20: for randomness in population do

21: crossover

22: mutation

23: end for

24: end for

25: return best solution

26: end

on during a beneﬁcial time and also keep avoid to generating

peaks. PAR can be easily understand by the Fig. 3

There is a trade-off (compromise) between UC and cost. If a

person wants more comfort, he has to pay more cost and if he

took less comfort, he has to pay less cost. Unscheduled waiting

time never exists in our scenario, since consumer can turn on

appliances at any time. However, by applying GA, FPA and

GFPA‘s proposed solution, the user has to wait for a proper

time and as a reward he has to pay less bill. There are three

scenarios that we have discussed in our scheduling. If we take

bigger OTI, it increases the waiting time. Similarly, if some

appliance stop before its total running time, then remaining

time is wasted. To avoid the wastage of this time, we work for

different OTIs. Consumers can choose any OTI for scheduling

according to their comfort. Delay against different OTIs shown

in Fig. 4

GA, FPA and GFPA are also performed better while we

discuss the total cost, as these schemes decrease the total cost

as compared with the unscheduled total cost. Our proposed

schemes reduce cost slot by slot (15, 30 and 60) and as a

result of this work, hourly cost is minimized and hourly cost

reduction means minimization in daily cost and so on. The

Fig. 3 shown comparison between cost of GA, FPA, GFPA

and unscheduled cost. It is worth mentioning here that the

cost pattern of our suggested scheduling techniques are quite

optimal as compared to the unscheduled load. By shifting

2 4 6 8 10 12 14 16 18 20 22 24

Time (slots)

0

2

4

6

8

10

12

14

Load (kWh)

Unsheduled

GA

FPA

GFSA

(a) OTI 60 minutes

5 10 15 20 25 30 35 40 45

Time (slots)

0

2

4

6

8

10

12

14

Load (kWh)

Unsheduled

GA

FPA

GFSA

(b) OTI 30 minutes

10 20 30 40 50 60 70 80 90

Time (slots)

0

2

4

6

8

10

12

14

Load (kWh)

Unsheduled

GA

FPA

GFSA

(c) OTI 15 minutes

Fig. 2: Load against different OTIs

the load consumer comfort effects. However, it beneﬁts the

consumer in terms of cost reduction. The more a consumer

shifts the load and tolerates changes in the energy consumption

pattern, the more beneﬁt will be provided by the utility in form

of cost reduction.

A. Performance Trade-off

There is a trade-off or compromise between the cost and

waiting time, if consumer wants less bill, he has to sacriﬁce

on his comfort. If consumer can not wait for proper time

(suggested by algorithm) and turned on appliances at any time,

he has to pay more bill. As, there is indirect relation between

cost and delaying time. So, consumer can choose only one

of them, either less cost or less comfort. We compared EC

and UC by applying GA, FPA and GFPA with unscheduled

OTI 60 minutes OTI 30 minutes OTI 15 minutes

1

2

3

4

5

6

7

8

9

PAR

Unscheduled

GA

FPA

GFPA

Fig. 3: PAR against different OTIs

OTI 60 minutes OTI 30 minutes OTI 15 minutes

0

0.5

1

1.5

2

2.5

3

3.5

Cost (cents)

×103

Unscheduled

GA

FPA

GFPA

Fig. 4: Cost against different OTIs

OTI 60 minutes OTI 30 minutes OTI 15 minutes

2

4

6

8

10

12

14

16

18

20

Waiting time (hours)

GA

FPA

GFPA

Fig. 5: Delay against different OTIs

load. When these techniques are compared with each other

by considering the simulation results, GFPA perform better as

its cost is minimum in all cases. On the other hand, it takes

average waiting time and PAR, as compared to FPA and GA.

It generates some peak by shifting the load from off-peak to

on-peak hours and price is still minimum.

B. Feasible Region

A region that covers all possible solutions according to our

ﬁtness function. In our scenario we target the EC and PAR,

by means of minimization. The EC is based upon two factors:

electricity price and electricity consumption. The notable thing

is that, we only have authority while we discuss electricity

price. However, we can handle electricity consumption by

shifting the load from on-peak slots to off-peak slots. During

calculation of EC, there are four parameters that we have to

be follow. Feasible regions are shown in Fig. 5

•Minimum price, minimum energy consumption

•Minimum price, maximum energy consumption

•Maximum price, minimum energy consumption

•Maximum price, maximum energy consumption

The Pointers (P1, P2, P3, P4, P5) have shown the possible

feasible region against different OTIs. The area of feasible

region is shaded with cyan color. For more depth details,

follow the Fig. 6.

0 5 10 15

Power consumption (kWh)

0

500

1000

1500

2000

Cost per hour (cents)

P1

P2

P3

P4

P5

P6

(a) OTI 60 minutes

0 2 4 6 8 10 12 14

Power consumption (kWh)

0

500

1000

1500

Cost per hour (cents)

P1

P3

P6

P5

P4

P2

(b) OTI 30 minutes

0 2 4 6 8 10

Power consumption (kWh)

0

200

400

600

800

1000

1200

1400

Cost per hour (cents)

P1

P2

P3

P4

P5

P6

(c) OTI 15 minutes

Fig. 6: Feasible regions against different OTIs

VI. CONCLUSION

In this paper, we proposed DSM scheme using HEM

constructed on GA, FPA and hybrid of these two schemes

to minimize users EC with a reasonable delay. To show the

effectiveness of our proposed scheme we take simulations for

single home by using different OTIs and this home consists of

18 appliances. Each appliance has its own power rating. The

optimization procedure is assessed on the base of EC, PAR

and UC. The UC is digniﬁed in terms of waiting time. The

simulation results show the efﬁciency of our proposed GFPA

performs even better than GA and FPA in terms of EC, UC

and PAR reduction.

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