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A Smart Home Energy Management Strategy

based on Demand Side Management

Zafar Iqbal1, Nadeem Javaid2,∗, Mobushir Riaz Khan1,

Farman Ali Khan3, Zahoor Ali Khan4,5, Umar Qasim6

1Pir Mehr Ali Shah Arid Agriculture University, Rawalpind 46000, Pakistan

2COMSATS Institute of Information Technology, Islamabad 44000, Pakistan

3COMSATS Institute of Information Technology, Attock 43600, Pakistan

4Faculty of Engineering, Dalhousie University, Halifax, NS, B3J 4R2 Canada

5Higher Colleges of Technology, Fujairah 4114, United Arab Emirates

6University of Alberta Edmonton, Alberta T6G 2J8, Canada

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

Abstract—In this paper we propose an ECG optimization

model for a smart home based on DSM.The proposed model

is an efﬁcient SHEM strategy. The model is proposed keeping

in view the minimization of energy consumption,energy con-

sumption cost and energy generation cost.The model is based

on efﬁcient scheduling of appliances and an ECG optimization

algorithm is proposed.We are using and optimizing energy

from two energy sources namely lceg and lcd which are also

known as macrogrid and microgrid respectively.The problem is

solved as cost optimization problem using genetic algorithm and

mathematically formulated using binary MNKP. The simulation

results show that our ECG model efﬁciently reduces the cost of

energy consumption, energy generation and energy consumption

utilization.

Keywords:-Demand Side M anagement,

Microgrid,Distributed Energy Resources,

Smartgrid,Appliance scheduling.

I. INTRODUCTION

Demand side management and demand response program

are the area of smart grid research which encourage the utility

consumer to shift their high peak load to to low peak hours

to reduce their electricity bill,avail user comfort and ensure

balanced use of energy.In recent days the smart grid and

microgrid is replacing the traditional electricity grid due to the

increasing demand of the energy for industrial and residential

area,this increasing demand of energy leads to more genera-

tion of energy and exploring new ways to fulﬁll increasing

energy demand which leads to more air pollution and carbon

emission.Smart grid means traditional electricity grid plus IT

which can also be named as macrogrid while microgrid is

a local energy generation set up for homes,residential area

and buildings on a small scale which is related to distributed

energy resources(DER) e.g. local diesel or gas generator,solar

power,wind power,vehicle-to-grid,waste-to-energy etc.

In this work we proposed an optimization model known as

ECG.The model aim to minimize energy consumption,balance

use of energy,energy consumption cost and energy generation

cost.We have proposed a set of appliances S(sh)in a smart

home which in our model is using energy from two main

sources that is lceg and lcd which is further divided into

load categories as discussed,and each one is mathematically

formulated for one time slot and 24 hour time horizon.The

subcategories of load is not given here in this work due to

space limitation.The energy generation model is using local

generator and solar energy to generate power for running

regular load in a smart home.

A similar work is done by [1].In which the authors proposes

a distributed optimization framework for demand response

based on cost optimization and each user in this model

is receiving information in response to varying electricity

prices.For scheduling of appliances they use approximate

greedy iterative algorithm to minimize the cost.In [2]the

authors proposes a new demand side management technique

and a new energy efﬁcient scheduling algorithm to operate the

appliances in such a way to reduce monetary expense of the

consumers based on varying price model taking into account

the uncertainties in appliances operation time and renewable

energy generation. [3] Proposes a communication based load

scheduling protocol for the smart home appliances.They use

two types of appliances one which have delay ﬂexibility

and second which consume power as they desire.They use

energy management controller to decide variation in electricity

prices and formulate the optimization problem as cost saving

problem.

The rest of the paper is organized as follows.In section two

we discuss the related work,and section three present a com-

prehensive system model consisting of energy consumption

model,energy consumption cost model and energy generation

model,while section four discusses the mathematical model of

the problem including problem discussion,objective function

and proposed algorithm.Section ﬁve are conclusion and future

work and ﬁnally references in section sex.

II. RELATED WORK

In this section we discuss the similar work done by re-

search community on appliances scheduling and optimization

of cost and energy consumption.In [4] the author consider

the minimum electricity cost scheduling and optimization

problem for smart home appliances and solve the problem

using mixed integer linear programing.They consider several

realistic scenarios based on real time price and optimal power

proﬁle signal to minimize cost.

In [5] the authors discusses consumer demand response

signal so that consumers can control their electricity price

and carbon footprints.The consumers use this information as

an incentive to minimize peak load demand by balancing

consumption of load.In work [6] a new user aware demand

side management technique is proposed for handling resi-

dential load while keeping in view the appliance waiting

time.Knapsack technique is used to keep trade off between

energy consumption and appliance waiting time while for

electricity cost and peak load demand reduction K-Wind

Driven Optimization technique is used.

In [7] the authors propose a user comfort aware efﬁ-

cient residential management strategy and considering the

minimization of user frustration model,peak-to-average ratio

and electricity cost and solve the optimization problem by

proposing EREM algorithm.

In [8]the authors proposes a residential demand response

algorithm for the scheduled plan of home appliances.They

used mixed integer linear programming to minimize electricity

cost and peak load demand based on time-varying electricity

price.On the other hand they using solar system and storage

to make the schedule plan more effective.In [9] the authors

presents an optimal residential load commitment framework

to achieve cost saving and PAR reduction.

III. SYSTEM MODEL

Distributed energy resources-DER are power generation

systems or power storage technologies on a local and small

scale consisting of energy generation and energy storage like

co-generation, solar energy generation, wind power genera-

tion,local power generation via diesel or gas generator, hydro

power generation, waste-to-energy generation, vehicle-to-grid

generation, PV storage, ﬂywheels, etc, which is also called

microgrid. The microgrid is a combination of local sources

of energy generation. The microgrids are modern, localized,

decentralized and small scale power generation units (having

capacity of 10 MW or less) contrary to large scale, centralized

and traditional electric grid called macrogrid. Microgrids are

typically low voltage source and generate AC power, and work

independently from electric grid and are usually installed by

the community or homes which serve them locally.In our

system model we take load from two main sources i.e. from

electric grid and microgrid which are known as lceg and lcd

load and divided these two main types of load into further

categories as mentioned below.Our microgrid include local

gas generator and solar energy,the system model consists of

three sub model 1. Energy consumption model 2. Energy con-

sumption cost model 3. Energy generation model to minimize

electricity cost,energy consumption and minimization of DER

cost.

A. Energy Consumption Model.

In this section the energy consumption is discussed.The

energy consumption from energy sources are categorized into

two main types i.e. lceg and lcd which are further divided

into sub categories, and the appliances load is also cate-

gorized according to these types of loads.The total load of

appliances is running on lceg source except regular or base

load which is using lcd for energy consumption.This type of

load includes home lighting,television,laptop,mobile charging

etc.These types of load usually consume very less wattage and

are must run load and users can not compromise on comfort

here,such load are not scheduled and can not be included

in demand side management otherwise user comfort will be

severely compromise and not enough saving in cost.Energy

consumption of such type of load from DER can be calculated

as in equation(1).

E(totalec,rl,α)=

24

X

t=1

nrl

X

α=1

e(α,rl,t)−P(pvg)(t)=0 (1)

where

E(totalec,rl,α)is total energy consumption by appliances αfor

base load from microgrid,αis appliance,totalec is total energy

consumed from microgrid,rl is base load,nrl is total no of

appliances consuming base load,P(pvg)(t)is power generated

by local generator and PV.We are assuming a smart home

with a set of αappliances denoted by S(sh).The mathematical

formulation of energy consumption of each type of load

(appliances) is given in following equations.In our scenario

t is a time slot which is one hour and belong to T which

is a total time horizon or total schedulable time which is 24

hours i.e. T=24 ,each appliance is denoted by αwe assume

that appliance αconsume an energy Eα(t) in time slot t and

t∈T.The energy consume by each appliance αand total

energy consume by all appliances i.e. of a set of all appliances

Ssh, means the overall energy consumption of smart home

is formulated in equation (2) and equation (3)respectively as

below.

Etotalec(S,α)=

24

X

t=1

n

X

α=1

eα(t)(2)

E(totalec,t,S(sh))=

n

X

α=1

nleg

X

lega=1

mder

X

ldera=1

e(legα,lderα,Ssh,t)(3)

∀t∈T, α ∈Ssh,lega ∈Segα,lder ∈Ssderα ∀Segα ∈

Ssh, lder ∈Sder,α,i.e.Ssh = (Segα )S(Sder,α)

Where

Etotalec(S,α)is total energy consumption by appliance α

belong to set S, eα(t)is energy of appliance αin time slot t,

totalec is total energy consumption, Ssh is set of appliances

in a smart home, nleg is no of appliances consuming lceg

load, mder is no of appliances consuming lcd load, legα

is an appliance consuming lceg load, lderα is an appliance

consuming Microgrid load,As we already assumed that S is a

set of appliances αin a smart home and αi∈S i.e.

S={α1, α2, α3, . . . , αn}

The set S comprises of all the appliances belong to different

categories of appliances which are categorized based on

load types.The energy consumption of each appliance α

for one time slot and for 24 hour time horizon as in

equation(2) and equation(3) respectively. The load types

deﬁned are of two categories,load type which are connected

to electric grid and load type which are connected to DER’s-

Distributed Energy Resources,in our case the DER include

PV(Photovoltaic) and local diesel generator,which is also

called microgrid,thus second category of load is connected to

microgrid source.Hence we have two main sources of load

for our smart home appliances of set Ssh i.e. lceg and lcd.The

ﬁrst category of load lceg-Load connected to electric grid is

further divided into the following load types.1. Deferrable

load-dl:Pool pump,dryer,water heater,washing machine,Air

conditioner,kettle,hob,washer,dishwasher,heating,EV battery

charging,batch printing,photocopying 2. Non deferrable

load-ndl:lights 3. Non deferrable and non interruptible

load-ndnil:refrigerator,lightening and servers 4. Deferrable

and interruptible load-dil:washer,dryer,dishwasher,batch

printing,photocopying 5. Deferrable and non interruptible

load-dnil: 6. Non deferrable and interruptible load-ndil:The

second category of load lcd-load connected to DER is

further divided into the following two types i.e.7. Regular

load-rl:lightening,heating etc. 8. Elastic load-el:washing

machine,dishwasher,

Load of both categories is scheduled except regular load or

base load. Each type of load energy consumption and energy

consumption cost is calculated, energy consumption of the

load connected to electric grid in one hour or one time slot t

and in total time horizon i.e. 24 hours is given in equation(4)

and equation(5) respectively.

EL(lceg ,t,ec)=

24

X

t=1

nlceg

X

α=1

EG(t,lceg )(4)

EL(lceg ,ec,t)≤EL(lceg,ec,t)(4a)

EL(lceg ,ec,t)< EL(lceg,ec,t)< Q(t)(4b)

where

lceg means load connected to electric grid, nlceg is no of

appliances consuming lceg load,t is time slot such that t ∈

T, EG is electric grid,lceg includes {dl,ndl,ndnil, dil,dnil,ndil}

types of load, ec means energy consumption,Q(t)is the total

quantity of energy in time slot t.The energy consumption of

second category of load lcd for one time slot and for 24 hour

time horizon is given in equation (5) and equation (6) as

follow.

EL(lcd,t,ec)=

n

X

α=1

eα(lcd,α,t)(5)

EL(lcd,t,ec)=

24

X

t=1

nlcd

X

lcdα=1

eα(lcd,α,t)(6)

∀t∈T, α ∈Sα

where

nlcd is no of appliances consuming lcd load, lcdα is an

appliance consuming lcd load,lcd means load connected to

microgrid. In the above section the total energy consumption

load is calculated,energy consumption of grid-connected and

DER-connected and for each appliance for one time slot and

for 24 hour time slot is separately calculated and mathemati-

cally formulated.

B. Energy Consumption Cost Model

In this section we discuss the energy consumption cost cal-

culation and formulation.The energy consumption cost model

of the set Sαof appliances αis as follow.Now we have to

calculate energy consumption cost for lceg load, lcd load

and its sub types deferrable load,non deferrable load,non de-

ferrable and non interruptible load,deferrable and interruptible

load,deferrable and non interruptible load,deferrable and non

interruptible load,non deferrable and interruptible load,regular

load,elastic load.The energy consumption cost of each type

of load is calculated by using the following equations.First

we calculate the total energy consumption cost of overall

load including grid-connected and DER-connected is given in

equation (7) and equation (8) below.

E(totalecc,Ssh,t,α)=

24

X

t=1

mlceg

X

lcegα=1

olcder

X

lcderα=1

e(totalecc,Ssh,lceg α,lcderα,t)EP (t)(7)

∀t∈T, α ∈SA

Total daily energy consumption cost is calculated by consid-

ering the energy generation cost as well.So,daily energy con-

sumption cost is calculated by using the following equations.

E(totalecc,S(sh),t)=

24

X

t=1

E(lceg,ecc,t)

+E(lcd,ecc,t)+P(pvg)(t)(8)

∀t∈T, α ∈SA

where, E(totalecc,S(sh),t)is the total daily energy consumption

cost of the user including energy generation cost and energy

consumption cost,E(lceg,ecc,t)is the energy consumption cost

of lceg power source, E(lcd,ecc,t)is the energy consumption

cost of lcd power source, P(pvg)(t)is the energy consumption

cost of power generated by local generator, totalecc is total

energy consumption cost, lceg is load connected to electric

grid, lcd is load connected to der, pvg is der sources i.e. solar

and generator energy, mlceg is no of appliances consuming

lceg load, olcder is no of appliances connected to microgrid,

lcegα is an appliance consuming lceg load, lcderαis an

appliance consuming lcd load.

The above energy consumption cost model include the total

consumption cost of a set S of appliances αin a smart home

also the set can be denoted by Ssh i.e. a set of all appliances

in a smart home.As the appliances load is divided into two

categories of which cost of each one is separately calculated

and of each load types inside these two categories.

Now the energy consumption cost of load connected to electric

grid for each appliance αfor one time slot and for 24 hour

time slot is given in equation (9) and equation (10) below.

E(lceg,ecc,t,α)=

nlceg

X

α=1

e(α,lceg,t)E P (t)(9)

E(lceg,ecc,t,α)=

24

X

t=1

nlceg

X

α=1

e(α,lceg,ecc,t)E P (t)(10)

∀t∈T, α ∈S

where

lceg is load connected to electric grid, nlceg is no of ap-

pliances consuming lceg load, ecc is energy consumption

cost,where lceg includes {dl,ndl,ndnil,dil,dnil,ndil} types of

loads.The energy consumption cost of lcd-connected load for

one time slot and for 24 hour time slot is given in equation

(11) and equation(12) as below.

E(lcd,ecc,t,α)=

n

X

α=1

e(α,lcd,ecc,t)EP (t)(11)

E(lcd,ecc,t,α)=

24

X

t=1

nlcd

X

lcdα=1

e(α,lcd,ecc,t)EP (t)(12)

C. Energy Generation Model.

As our second category of appliances are running on der

or microgrid which include in our case, photovoltaic(PV)

generation and local diesel generator generation and the der-

connected load is using it.Therefore the energy generation

model is formulated as total expense minimization cost of der

as given in equation (13) below, the total expense include gen-

eration fuel cost GFEp(g),cost due to losses C(lo)negligible in

this case ,ﬁxed cost F(ct)i.e. startup cost ST(ct),depreciation

cost DP(ct),maintenance cost M(ct).

Minimize

n

X

g=1

λ(g)P2g

+GFEp(g)Pg+F(ct)+M(ct)(13)

subject to

P(g,mn)≤Pg≤P(g,mx)(13a)

24

X

t=1

n

X

g=1

Pg(t) = E(t,der)(t)(13b)

n

X

g=1

Pg(t) = E(t,der)(t)(13c)

where t is number of time slots, g number of generators, λ(g)is

cost due to losses, negligible in this case, GFEp(g)is total fuel

expense of g units, F(ct)is ﬁxed cost Fct =ST(ct)+DP(ct),

ST(ct)is start up cost, DP(ct)is depreciation cost, M(ct)

is maintenance cost, P(g)is power produced by generator

g, P(g,mx)is maximum power generation limit, P(g,mn)is

minimum power generation limit, E(t,der)is total energy

consumed by der-connected load.

IV. MATHEMATICAL MODEL OF THE PROB LEM

A. Problem Discussion.

In this section we discuss and formulate the mathemat-

ical model of our problem.Our problem is to reduce total

energy consumption,minimize total energy cost and also to

reduce the expenses incur by der energy source.To solve

this optimization problem we propose an optimization model

discussed in system model.Our optimization model includes

energy consumption minimization,energy cost minimization

and energy generation.We have a set of appliances in a smart

home which will consume energy from two main sources i.e.

lceg and lcd which are discussed and formulated in previous

section.These two types of energy source is divided into

further load types which are discussed in start.The regular load

is running on lcd load which is also known as microgrid or

der.After modeling the parameters of our optimization problem

and and its constraints,the model is formulated using binary

MNK P (multiple knapsack problem).The problem is solved

using Genetic Algorithm.Fitness function of genetic algorithm

can be written as below in equation(14).

F F =

24

X

t=1

n

X

α=1

BV(αt)EL(lceg,t,ec)EP (t)

+

24

X

t=1

n

X

α=1

BV(αt)EL(lcd,t,ec)EP (t)(14)

where FF is ﬁtness function.

B. Objective Function.

The objective function can be written as follow.

Minimize

24

X

t=1

n

X

α=1

BV(αt)EL(lceg,t,ec)

+

24

X

t=1

n

X

α=1

BV(αt)EL(lcd,t,ec)(15)

subject to the following constraints

24

X

t=1

n

X

α=1

BV(αt)=LOTα(15a)

24

X

t=1

EαBV(αt)≤ψ(t)(15b)

24

X

t=1

EαBV(αt)≤ψ(t)(15c)

δα≤T−LOTα∀lceg (15d)

tst

α≤δα≤tst

α+LOTα∀lcd (15e)

δα=tst

α∀lcd, tst

α∈Tst

lcd (15f)

BV(αt)∈ {0,1}(15g)

where

Tst

lcd time in which the appliance αis ON,ψ(t)is energy

consumption limit or total quantity of energy, δαis scheduled

time of appliance α,tst

αis starting time of appliance

α,BV(αt)is a binary variable which shows that appliance

is on or off,LOTαis appliance αtotal time in which it operate.

ECH Algorithm

1: Required pattern of appliances before scheduling,tbs

α,ps,

mxgeneration,αn.

2: Start random population representing the pattern of

appliances.

3: for t=1:24 do

4: d=0

5: for α=1:ps do

6: Execute ﬁtness function equation (14)

7: FF=ﬁtness

8: if (F(α)< F (α−1)&&(ET(t)) < CP (t)) then

9: F(α) = F(α)

10: if lcegα(t−1) == 1 then

11: tst

α=t

12: dα=tbs

α-tst

α

13: if (dα≥3)&&(δα≤LOTα)

14: lceg(t)=1 end if end if

15: if lcdα(t)==1 then

16: tst

(α)=t

17: dα=tbs

α-tst

α

18: if (dα≥3)&&(δα≤LOTα)

19: lcd(t)=1 end if end if else

20: F(α) = F(α−1) end if end for

21: ap(1,:)=pn(1,:)

22: if apα==1 then

23: LOTα=LOTα−1end if

24: d=lcd(t)

25: solve optimization problem equation(13).

Generation of new population.Selection of crossover pair x,y

26: if Pc> rand then

27: crossover (x,y) end if

28: if Pm> rand then

29: crossover (x,y) end if

30: pn(ps,αn) end for

where αnis no of appliances,ap is appliances pattern,CP(t)

is total energy capacity or limit in time slot t,ps is popsize and

pn is popnew.

V. CONCLUSION

In this research work we proposed an efﬁcient ECG op-

timization model including energy consumption minimiza-

tion,energy consumption cost minimization and energy gen-

eration cost optimization for smart home energy management

based on DSM. An ECG algorithm is proposed for balancing

total energy consumption load, cost of energy consumption

and energy generation cost for efﬁcient scheduling of appli-

ances.We are using two main types of energy sources i.e. lceg

and lcd can also be named as macrogrid and microgrid for our

smart home appliances.The lcd energy source act as microgrid

or DER are used to reduce burden of load consumption and

run regular load on it.The simulation results show that our

proposed model and ECG algorithm balencing the energy

consumption and reduces both types of cost as compared to

the results without ECG model.In future we intend to further

elaborate this work by including PAR model,user discomfort

model and implementation of appliances speciﬁc constraints.

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