Content uploaded by Nadeem Javaid
Author content
All content in this area was uploaded by Nadeem Javaid on Aug 23, 2017
Content may be subject to copyright.
Heuristically Optimized Home Energy Management in Smart gird
By
Hafiz Majid Hussain
UET-15S-MSEE-CASE-10
Supervisor
Dr. Abdul Khaliq
Co-supervisor
Dr. Nadeem Javaid
ELECTRICAL & COMPUTER ENGINEERING DEPARTMENT
CENTER FOR ADVANCED STUDIES IN ENGINEERING
UNIVERSITY OF ENGINEERING AND TECHNOLOGY TAXILA
Spring 2017
TABLE OF CONTENTS
Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
Final Approval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Chapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 2: Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 3: System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 HEMS architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Energy consumption model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Load categorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.1 Regularly operated appliances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.2 Shift-able appliances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.3 Elastic appliances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Energy cost and unit price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 4: Problem formulation and Proposed solution . . . . . . . . . . .
2
5
6
15
16
16
16
17
17
18
18
19
22
4.1 PAR . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 User comfort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Objective function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Optimization techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.1 GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.2 WDO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.3 HSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .
4.4.4 GHSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .
4.5 Feasible region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .
4.5.1 Feasible region for SH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.2 Feasible region for MHs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 5: Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . . .
5.1 Simulation and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.1 Load profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.2 Cost per hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.3 Cost per day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.4 PAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.15 User comfort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 6: Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 7: References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23
24
24
26
27
28
29
31
33
33
37
41
42
43
47
49
50
53
54
55
56
iii
DECLARATION
.
I certify that research work titled “Heuristically Optimized Home Energy Management in
Smart grid” is my own work. The work has not been presented elsewhere for assessment.
Where material has been used from other sources it has been properly acknowledged/referred.
Signature:
Hafiz Majid Hussain
Sp/2015/MSEE/015
iv
ABSTRACT
The smart grid appears an advanced and upgraded form of the power grid. As an essential
component of the smart grid, demand side management (DSM) enhances the energy efficiency of
electricity infrastructure. In this paper, we propose home energy management controller (HEMC)
based on heuristic algorithms to reduce electricity expense, peak to average ratio (PAR), and
maximize user comfort. We consider proposed HEMC for a single home and multiple homes. In
particular, for multiple homes we classify modes of operation for the appliances according to their
energy consumption with varying operation time slots. This strategy influences the consumers to
reshape energy consumption profile in response to electricity cost. In order to achieve an optimal
scheduling of energy consumption profile of the household appliances, we explore heuristic
algorithms, such as wind-driven optimization (WDO), harmony search algorithm (HSA), and
genetic algorithm (GA). We also propose a hybrid optimization algorithm genetic harmony search
algorithm (GHSA) that can schedule energy consumption profile in an appropriate way. The
existing and proposed optimization algorithms are investigated by considering single home and
multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs.
Finally, simulation results are conducted which shows proposed algorithm GHSA performs
efficiently to reduce electricity cost, PAR, and maximize user comfort.
v
DEDICATION
Dedicated to my mother Shahida Nasreen
and my sister Engr. Wajeeha samer without whom none of my success would be
possible.
vi
ACKNOWLEDGEMENT
In the name of Allah, We praise Him, seek His help and ask for His forgiveness.
Whoever Allah guides, none can misguide, and whoever He allows to fall astray, none
can guide them aright. First, it is a great privilege for me in expressing most sincere gratitude to
my supervisor, HoD/Chairperson Prof. Dr. Abdul Khaliq chairman department of electrical
engineering CASE, Islamabad, and my co-supervisor Associate. Prof. Dr. Nadeem Javaid for
their support, guidance, encouragement in fulfilling my ambitions. Their kindness, friendly
accessibility, and considerations have been a great encouragement to me. My sincere thanks to
my supervisor and co-supervisor for their valuable support.
I would like to acknowledge my family, my friends, and the cooperative COMSENCE lab
attendants. They all kept me motivated and energetic, and this work has not been possible
without them.
Finally, I offer my regard and blessing to everyone who supported me in any regard during the
completion of my thesis.
vii
LIST OF ACRONYMS
HEMS Home energy management system
HSA Harmony search algorithm
PAR Peak to average ratio
RES Renewable energy source
RTEP Real-time electricity pricing
TOU Time of use
CPP Critical peak pricing
EDE Enhanced differential
WDO Wind-driven optimization
GA Genetic algorithm
IBR Inclined block rate
BPSO Binary particle swarm optimization
EMC Energy management controller
ILP Integer linear programming
GHSA Genetic harmony search algorithm
GWDO Genetic wind driven optimization
AMI Advanced metering infrastructure
HMCR Harmony memory consideration rate
viii
MKP Multiple knapsack problem
DSM Demand side management
HEMC Home energy management controller
Pm Probability of mutation
Pc Probability of crossover
Vnew Veloctiy in the current iteration
HSA Harmony search algorithm
ix
LIST OF FIGURES
1.1 Abstract picture of smart grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.1 HEMS architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.1a Electricity cost and Consumption of SH using RTEP . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1b Electricity cost and Consumption of SH using CPP . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2a Electricity cost and Consumption of MHs using RTEP . . . . . . . . . . . . . . . . . . . . . . . 36
4.2b Electricity cost and Consumption of MHs using CPP . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3a Electricity cost and Waiting time of SH using RTEP . . . . . . . . . . . . . . . . . . . . . . . . 37
4.3b Electricity cost and Waiting time of SH using CPP . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.4a Electricity cost and Waiting time of MHs using RTEP . . . . . . . . . . . . . . . . . . . . . . . 38
4.4b Electricity cost and Waiting time of MHs using CPP . . . . . . . . . . . . . . . . . . . . . . . . 39
5.1a RTEP tariff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.1b CPP tariff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.2a Load profile for SH using RTEP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.2b Load profile for SH using CPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2c Load profile for MHs using RTEP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2d Load profile for MHs using CPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.3a Cost per hour for SH using RTEP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.3b Cost per hour for SH using CPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.3c Cost per hour for MHs using RTEP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.3d Cost per hour for MHs using CPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.4 Cost per day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.5 PAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.6 Average waiting time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
x
LIST OF TABLES
2.1 Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Heuristic techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.1 Load description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1 GA parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 WDO parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.3 HSA parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.4 Computational time of heuristic techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.1 Load profile comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.2 Total cost comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.3 PAR comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
xi
Final Approval
This thesis titled
Heuristically Optimized Home Energy Management in Smart grid
By
Hafiz Majid Hussain
Has been approved for
Center for Advanced Studies in Engineering, Islamabad
Supervisor: ___________________________________________________________
Dr. Abdul Khaliq
Associate Professor, Department of Electrical Engineering
Center for Advanced Studies in Engineering, Islamabad
Co-Supervisor: ________________________________________________________
Dr. Nadeem Javaid
Associate Professor, Department of Computer Science
COMSATS Institute of Information Technology, Islamabad
External Examiner: ____________________________________________________
Muhammad Naeem
Assistant Professor, Department of Electrical Engineering
Capital University of Science and Technology, Islamabad
Chapter 1
Introduction
1
1.1 Introduction
The traditional grid is facing numerous challenges, including old infrastructure, lack
of communication, increasing demand for energy, and security issues. To address
these issues, the concept of smart grid has emerged which comprises of information
and communication technologies that allow bidirectional communication between the
utility and consumer. Broadly speaking, smart grid appears as a next generation grid
and incorporates advanced technologies in communication, distributed generation,
cyber security, and advanced metering infrastructure [1]. These features of the smart
grid ultimately enhance the efficiency, reliability, and flexibility of the power grid.
The key objective of the smart grid is the transformation of the traditional grid to a
cost effective and energy efficient power grid.
DSM is an essential component in energy management of the smart grid. Generally,
DSM refers to manage the consumer’s energy usage in such a way to yield desired
changes in load profile and facilitates the consumers by providing them incentives
[2]. For this purpose, various DSM techniques have been proposed in literature,
including peak clipping, valley filling, load shifting, strategic conservation, strategic
load growth, and flexible load shape [3]. Furthermore, DSM is capable of handling
the communication infrastructure between end user and utility and also enables the
integration of distributed energy resources to optimize energy consumption profile.
Recently, one of the crucial DSM activities is demand response (DR), it is presumed
that DR is the subset of DSM in a broader aspect. DR is defined as the tariffs or
programs established to influence the end users to reshape their energy consumption
profile in response to electricity price [4]. DR program is further categorized into two
types; incentive based program and price-based program. Incentive based program
provides monetary incentive to the end user on the base of load curtailment. Various
incentive programs are discussed in the literature, including direct load control (DLC),
curtailable load, demand bidding and buy back, emergency and demand. On the
other hand, price based program provides the price of electricity during different time
intervals. The purpose of the price-based program is to reduce electricity usage when
2
the electricity price is high and thus, reduce demand during peak periods. Price-based
programs are; time of use (ToU), RTEP, inclined block rate, CPP, and day ahead
pricing. DR is considered as a key feature in smart grid to improve the sustainability
and reliability of power grid. However, it is examined in the literature that researchers
considered the DSM and DR are interchangeable [5], [6].
Home energy management system (HEMS) is considered as an integral part for the
successful DSM of the smart grid [7]. HEMS provides an opportunity for the residen-
tial sector consumers to communicate with the household appliances and the utility
to improve the energy efficiency regarding electricity tariff and consumer’s comfort. A
wide range of research has been made to study scheduling problems in HEMS. A hy-
brid genetic particle swarm optimization (HGPO) is proposed to schedule the energy
consumption of appliances in HEMS with the integration of renewable energy sources
(RESs) [8]. A heuristic optimization algorithm, such as GA is used to schedule appli-
ances for the residential, commercial, and industrial sectors [9]. Similarly, in [10], [11],
binary particle swarm optimization (BPSO) and mixed integer linear programming
(MILP) are used to schedule the appliances and mitigate electricity cost and PAR. In
[12], authors propose a general architecture of HEMS based on GA in the presence of
RTEP and inclined block rate to reduce electricity cost and PAR.
In this paper, we propose a HEMC based on an optimization algorithm GHSA for the
optimal scheduling of energy consumption. There are two contributions in the paper.
First, we propose an optimization algorithm GHSA to minimize electricity cost, PAR,
and average waiting time of the appliances. In order to validate the effectiveness
of the proposed algorithm, it is compared with the existing algorithms, including
WDO, HSA, and GA. Second, the paper gives insight the impact of optimization
algorithms on a single home (SH) and multiple homes (MHs) with RTEP and CPP
tariffs. Specifically, for MHs we consider fifty homes with different operational modes
of the appliances regarding energy consumption and time interval. Finally, results
of the proposed and existing algorithms are compared which show that our proposed
algorithm performs significantly to achieve better results in terms of electricity cost,
PAR, and average waiting time of the appliances.
3
The rest of the paper is structured as follows. Section II presents related research
work. Section III presents a comprehensive study of the system model. Section IV
describes the simulation results along with the discussion. At the end, we present the
conclusion of the paper in section V.
The rest of the thesis is structured as follows. Chapter 2 presents related research
work. Chapter 3 presents a comprehensive study of the system model. Chapter 4
describes the problem formulation and proposed solution. Chapter 5 deals simulation
results along with the discussion. At the end, we present the conclusion of the thesis
in chapter 6.
Power
flow
Figure 1.1: Abstract picture of smart grid
4
Table 1.1: Nomenclature
Symbols Description Symbols Description
Ec,T L Total energy consumption
in a day
Pcur Pressure of air parcel in a
current location
ςRa,TLEnergy consumption of reg-
ularly operated appliances
xnew Position of air parcel in the
new location
ςSa,TLEnergy consumption of
shift-able operated appli-
ances
xcur Position of air parcel in the
current location
ςEa,TLEnergy consumption of elas-
tic operated appliances
xold Position of air parcel in the
previous location
%TL
RaCost per day of regularly
operated appliances
Xnew Updated value of harmony
%TL
SaCost per day of shift-able
appliances
ταEnding time of Operation
after scheduling
%TL
EaCost per day of elastic ap-
pliances
Tmax Maximum time of the appli-
ance operation
εElectricity pricing signal Tmini Minimum time of the appli-
ance operation
ζON-OFF states of appli-
ances
ΥT L Total electricity cost for
fifty homes
aαStarting time of appliance Ω Rotation of the earth
bβEnding time of appliance ∇Pressure gradient
WWaiting time of appliance ρAir parcel density
OtOperation time interval µvelocity of the wind
Vcur Velocity of air parcel in cur-
rent iteration
δV Infinite mass and volume
Vnew Velocity of air parcel in new
iteration
gEarth’s gravity
ςaiEnergy consumed by appli-
ance i
5
Chapter 2
Related work
6
2.1 Related work
With the emergence of the smart grid, the consumer and the utility can exchange
real-time information based on electricity pricing tariffs and energy demand of the
consumer. The two way communication benefits not only the consumers, but also
improve stability of the power grid. With this motivation, various models are designed
to schedule energy consumption usage. Authors in [13], comparatively evaluate the
performance of HEMC which is designed to schedule energy consumption on the
basis of heuristic algorithms: GA, BPSO, and ant colony optimization (ACO). For
energy pricing, combined model of ToU and inclined block tariff (IBR) are employed.
Simulation results show that designed model for energy management acts significantly
to reduce the electricity cost, PAR, and satisfy user comfort level. However, the
computational complexity and time are increased. In [14], authors aim to reduce
electricity cost, PAR , and considering user comfort. The appliances are classified
into five groups by taking consideration of user comfort constraints in response to
RTEP signal. Multiple knapsack problem (MKP) is used for the problem formulation
and an optimization algorithm GA is employed to schedule the load profile. The
proposed model shows efficient results to reduce the electricity cost and improve user
comforts. However, if the number of users increases the proposed algorithm provides
infeasible solution.
Jon et al. [15], propose HEMS model for energy optimization and categorize house-
hold appliances as thermostatically controlled appliances and interruptible appliances.
The major objective of the proposed model is to tackle the uncertainties with a differ-
ent kind of load and reduce the electricity cost while taking into account of the user
comfort. BPSO is amalgamated with integer linear programming (ILP) to solve the
proposed problem. The proposed algorithms are tested with the scrutiny of required
home appliances and day-ahead pricing scheme. However, the scheme will be more
robust by considering RTEP tariff and predictive model. Authors in [16], present an
improved model for the energy consumption of residential appliances. The desired
objective is to minimize the cost by managing energy consumption of the appliances.
Fractional programming (FP) is used for scheduling energy consumption by consid-
7
ering RTEP tariff and distributed energy resources. Despite the cost minimization,
authors do not address the user comfort.
The work in [17], mitigates the energy consumption behavior and the electricity cost.
The DSM techniques take into account in the presence of distributed generation, time-
differentiated prices, and preference of loads. The minimization problem is solved
using a constructive algorithm with GA while considering the user comfort and en-
ergy cost. The proposed model is tested using radial residential electrical network
to verify the results. However, authors do not mention any strategy to control PAR.
Authors in [18], provide a detailed study of HSA algorithm, the primary steps, its
adaptation, and its specialty in different fields. Also, authors comparatively discuss
the searching criteria of different optimization techniques and HSA. Improved ver-
sions of the HSA are proposed, such as improved harmony search (IHS), global best
harmony search algorithm (GBHSA), and chaotic harmony search algorithm (CHSA)
in order to achieve efficient results. Additionally, the contribution of HSA in the
various disciplines are elaborated, including electrical engineering, civil engineering,
computer science, biomedical, economics, and ecology. At the end, authors inferred
that HSA is a better choice by attaining efficient results in many complex scenarios.
Authors in [19], design day ahead scheduling model for microgrids system with the
integration RESs in order to minimize the start up cost and generation cost of the
RESs. To address the problem, the mathematical formulation is carried out as an
optimization problem. Authors, propose an algorithm which is a hybrid of enhance
differential evolution (EDE) algorithm and HSA. The improvement in the tuning pa-
rameters of EDE and HSA are also carried out which enhances the search diversity.
Moreover, the results of the proposed algorithm are compared with other scheduling
algorithms and the proposed model verification is done using IEEE standard bus sys-
tem. As the number of the buses increases, the proposed algorithm shows effective
results, however, convergence rate is decreased and real-time based constraints are
not considered. The work in [20], demonstrates the day ahead load shifting technique
and problem is formulated as a minimization problem. A heuristic-based evolutionary
algorithm is used for solving minimization problem. Three different kinds of the area
are considered: residential, commercial, and industrial areas. The primary objective
8
is to reduce the utility bills for the consumers in the mentioned areas. Simulation
results show the substantial saving in electricity cost while reducing peak load de-
mand. The proposed algorithm achieves major results, however, the complexity of
system increases due to the consideration of a large number of appliances and also
user comfort is ignored. Danish et al. in [21], present HEMS model based on heuristic
algorithm BSPO. The aim of the authors is to minimize the electricity cost while con-
sidering user comfort. In this regard, the total time slots i.e., 24 hours is divided into
4 sub time slots to effectively manage the electricity consumption and cost. Simula-
tion results show that proposed HEMS model act significantly to achieve the desired
objective. However, the computational time of the system is increased.
Authors in [22], present a generic model of DSM in order to optimize energy consump-
tion in the residential sector. In a home environment, energy management controller
(EMC) is used to control energy consumption of the appliances during peak hours.
In this regard, GA based EMC is used for scheduling purpose and RTEP pricing
signal is used for billing mechanism. The performance parameters electricity cost,
PAR and waiting time of the appliances are compared with EMC and without EMC
unit. Simulation results show that GA-based EMC reduces electricity cost, and PAR.
However, GA-based EMC does not provide an effective approach to improve the user
preference. In [23], authors propose a comprehensive model for energy management in
homes with multiple appliances. The proposed model consists of six layered architec-
ture and each layer is connected with other in order to achieve better results in terms
of cost reduction and PAR. The objective of the study is to provide electricity in a
sophisticated manner to the consumer by taking into account theft and fault detec-
tion, greenhouse gases, ToU pricing signal, and single knapsack scheduling algorithm.
Results show that reduction in electricity cost and PAR, however, the architecture of
the proposed model is complicated and sensitive i.e., any inappropriate information
can devastate overall architecture.
The work in [24], provides a comprehensive study of WDO technique, the basic con-
cepts, structure its variants, and its application in electromagnetics. A numerical
study is presented using unimodal and multimodal test functions and results of WDO
and other optimization techniques like; PSO, GA, and differential algorithm (DE)
9
are compared. Moreover, WDO along with PSO, GA, and DE are applied to three
electromagnetics optimization problems and the results demonstrate the WDO out-
performs PSO, GA, and DE. A recent work in [25], proposes a novel approach of DSM
with the integration of RESs. The energy provider inspects the load profile and the
price of the electricity. Authors aim to reduce the deviation of average load energy
demand by scheduling the energy consumption and storage devices. To solve schedul-
ing problem, authors model the energy consumption and storage as a non-cooperative
game. The simulation results demonstrate the minimization of cost reduction in peak
load of the system.
In [26], authors demonstrate the electricity load scheduling problem for multi-resident
and multi-class appliances using problem ladson generalized bender algorithm while
considering energy consumption constraint. The main objective of proposed algo-
rithm is to protect the private information of the residences and maximize the users
satisfaction. The proposed scheme is efficient by tackling private information of resi-
dence and obtain the optimal load scheduling for each residence in a multi-residential
system. However, due to the slow convergence rate of proposed algorithm overall
computational time is increased. Authors in [27], give insight of scheduling the en-
ergy management in the residential sector and propose two horizon algorithms. The
proposed algorithms are efficient to reduce electricity cost with less computational
time. Moreover, authors also discuss the implementation of proposed algorithms and
challenges related to its implementation. Results ensure the validity of the proposed
model in terms of cost reduction and fast convergence.
Mohsenian et al. in [28], propose an autonomous DSM model in which energy is
provided to multiple homes and each home is equipped with energy management
scheduling (EMS) device. EMS interacts with the utility and consumer to schedule
the energy consumption using distributive algorithms. Authors formulated the prob-
lem as an optimization problem which is solved using game theory approach with
an aim at reducing electricity cost and PAR. Simulation results show that signifi-
cant amount of reduction in electricity cost and PAR. However, user preference is not
addressed. The work in [29], provides an improved HEMS architecture considering
various appliances in the home. Multi- time scale optimization is formulated in order
10
to schedule energy consumption of appliances. A predictive model based-heuristic
solution is proposed and its performance is compared with benchmark algorithms.
Results show the effectiveness of proposed model in terms of electricity cost reduction
and computational complexities. However, PAR is ignored which is a key parame-
ter for load curtailment. In [30], authors present power scheduling based protocol to
keep the electricity cost below a pre-defined budget. Appliances are classified into two
groups with an aim at reducing peak demand and electricity cost. However, optimiza-
tion of real time appliances is not considered, which exceeds the predefined budget.
Table 2.1 lists the summary of the research work based on heuristic techniques.
11
Table 2.1: Heuristic techniques
Techniques Aims Distinctive attributes Limitations
Hybrid technique
(GA and PSO) [8]
Minimization
of cost and
PAR
HEMS model is considered
with distributed energy
resources and energy
storage system
User comfort is
ignored
EA [9] Cost reduction
Energy optimization in
residential, commercial, and
industrial area
System
complexity is
enhanced
BPSO [10] Cost and PAR
minimization
Working of BPSO in
complex, nonlinear, and
continuous problems
Practical
implementation
is not addressed
MILP [11] Cost
minimization
Scheduling energy
consumption of appliances,
its simplicity
PAR is not
addressed and
user comfort is
ignored
GA [12]
Cost reduction
and user
comfort
Cost reduction by
optimizing energy
consumption and time slots
are divided
System deals
with large
number of
appliances in
multiple sector
which increases
system
complexity
12
GA, BPSO,
ACO [13]
Cost and PAR
reduction by
satisfying user
comfort
HEMC schedules the
appliances by considering
user satisfaction and RESs
integration
System
complexity and
computational
time are
increased
GA [14]
Cost reduction
and user
comfor
Optimizes energy
consumption behavior with
RESs incorporation
Challenges
related to RESs
are not
addressed and
PAR is ignored
Hybrid
technique
(LP and
BPSO) [15]
Cost reduction
and user
comfort
maximization
Thermostatically and
interruptible appliances are
considered with day ahead
pricing model
PAR is not
considered
FP [16] Electricity cost
reduction
Cost efficient model with
distributed energy resources
and practical
implementation of the
model proposed
PAR and user
comfort are not
taken into
account
GA [17] Cost and PAR
reduction
Proposed model is tested
using radial residential
electrical network
System
complexity and
computational
time enhances
HSA [18]
Basic concepts
of HSA, its
structure, and
applications
Improved and hybrid HSA
with application
Real time
implementation
is not considered
13
Hybrid
technique
(EDE and
HSA) [19]
Start up and
generation cost
of RESs
Verification is done using
IEEE standard bus system
Computational
time is increased
BPSO [21]
Electricity cost
minimization
considering
user preference
Simplicity and robustness
of BPSO
System
complexities are
increased as
time slot is
divided into sub
time slots
GA [22]
Minimization
of electricity
cost, PAR, and
waiting time
Generic model of DSM with
EMC using RTEP
User comfort is
not addressed
efficiently
Single
knapsack [23]
Energy
consumption
optimization
considering six
layer
architecture
Comprehensive model for
energy management
addressing six layer
architecture
Complicated
architecture in
terms of
modeling in
practical
scenario
14
Chapter 3
System model
15
3.1 System model
As mentioned before, for the effective deployment of smart grid, HEMS is crucial.
HEMS can manage, control, and optimize energy consumption in home environment.
As an essential element of HEMS, HEMC is used to schedule energy consumption
based on heuristic techniques to effectively reduce electricity cost and monitor user
preference.
The system model comprises of HEMS architecture, energy consumption model, load
categorization, energy cost and unit price, and problem formulation.
3.2 HEMS architecture
HEMS effectively visualizes the load consumption information in home and con-
tributes towards the energy balancing between the supply and demand side. HEMS
comprises of HEMC, smart meter (SM), advance metering infrastructure (AMI), in-
home display device (IHD), and smart appliances. These advanced tools provide
bidirectional flow of information and power between the utility and consumer to effi-
ciently reduce electricity cost. The pictorial view of HEMS is shown in Figure. 3.1.
Moreover, HEMC consists of embedded system which schedules the energy consump-
tion of the appliances using heuristic optimization algorithms.
3.3 Energy consumption model
We consider a home with a set of appliances A,{a1, a2, a3, . . . , aN}, such that
a1, a2, a3, . . . , aNrepresents each appliance over the time horizon t T,{1,2,3, . . . , T }.
Each time slot represents one hour and the total time interval is 24 hours (T= 24),
in accordance with the single day. The total energy consumption of the appliances in
a day can be mathematically represented as:
Ec,TL=
T
X
t=1 N
X
j=1
E(aj,t)!∀t T, a A (3.1)
16
In smart grid, each home is equipped with HEMC to optimize energy consumption
and reduce electricity cost. Therefore, we categorize the appliances into three groups
which are presented in the following subsection.
Internet
Utility
Company
Smart Meter
HEMC
Elastic Appliances
Regularly operated Appliances
Shift-able Appliances
Power flow
Information flow
Figure 3.1: HEMS architecture
3.4 Load categorization
We have categorized each appliance on the bases of energy consumption, operating
time, and user preference. Suppose An={RaSSaSEa}represents a set of ap-
pliances, where Rais regularly operated appliances, Sashiftable appliances, and Ea
elastic appliances. Table 3.1 shows power ratings and time of operation of the appli-
ances.
1) Regularly operated appliances
These are also called fixed appliances because their energy consumption profile can not
be modified by HEMC. They are vacuum pump, water pump, dishwasher, and oven.
Regularly operated appliances are represented as (Ra) and their energy consumptions
are represented by ςRa. While power rating of Rais expressed as ξRaand the total
17
energy consumption in a day is given as:
ςRa,TL=
T
X
t=1 X
RaAn
ξt
Ra×ζ(t)!(3.2)
Similarly, the total cost per day of the Rais given as:
%TL
Ra=
T
X
t=1 X
RaAn
ξt
Ra×ε(t)×ζ(t)!(3.3)
Where ζ(t)[0,1] is the operation state of appliances in time interval t Tand ε
represents the pricing signal.
2) Shift-able appliances
These are controllable appliances and their operation time can be shifted to any time
slot without performance degradation, however, once they turn ON their length of
operation must be completed. They are also named as burst load for example, washing
machine and cloth dryer. Shift-able appliances are denoted by (Sa) and power rating
of Sais νSa. The total energy is computed as:
ςSa,TL=
T
X
t=1 X
SaAn
νt
Sa×ζ(t)!(3.4)
The total cost calculated of Sain a day is calculated as:
%TL
Sa=
T
X
t=1 X
SaAn
νt
Sa×ε(t)×ζ(t)!(3.5)
3) Elastic appliances
These are considered as a flexible appliances i.e., their time period and energy con-
sumption profile are flexibly adjusted. They are also named thermostatically-controlled
appliances, such as water heater, air condition, water dispenser, and refrigerator. Let
us consider µEais the power rating of elastic appliances (Ea) and the total energy of
Eais computed as:
ςEa,TL=
T
X
t=1 X
EaAn
µt
Sa×ζ(t)!(3.6)
18
The total cost per day of Eais given as:
%TL
Ea=
T
X
t=1 X
EaAn
µt
Ea×ε(t)×ζ(t)!(3.7)
Let us assume that the total energy consumed (ςT) by appliances in total time interval
24 hours is given:
ςTL=ςRa,TL+ςSa,TL+ςEa,TL(3.8)
Similarly, total cost per day of Ra,Sa, and Eaappliances is calculated:
%TL=%TL
Ra+%TL
Sa+%TL
Ea(3.9)
The energy consumption of each appliance in given time period can be mathematically
shown in matrix form as:
Ec=
ςt1
Ra. . . ς t1
Sa. . . ς t1
Ea
ςt2
Ra. . . ς t2
Sa. . . ς t2
Ea
.
.
..
.
..
.
.
ςT
Ra. . . ς T
Sa. . . ς T
Ea
(3.10)
3.5 Energy cost and unit price
Various electricity tariffs are proposed to define electricity cost for a day or for a short
time period. In our model, we consider RTEP and CPP tariffs.
The RTEP tariff is typically updated for each hour during a day and is capable of
contributing better approximation of real time power generation cost. RTEP imple-
mentation requires two way of communication in order to interact with the consumer
in a real time. Therefore, the aim of RTEP is to reduce demand of consumer during
peak demand times. RTEP is also referred as dynamic pricing.
The CPP tariff has resemblance with ToU pricing regarding fix prices in different
time intervals. The implementation of CPP during critical event imparts profitable
response to the utility [5]. However, due to stress on the power grid, the prices are
19
replaced by the predefined higher rate in order to reduce energy demand. Thus, the
aim of the CPP tariff is to assure the reliability and sustainability of the power grid.
In our research work, we consider RTEP and CPP tariffs because in normal operation
of power grid, the RTEP behaves more flexibly as compared to other pricing signals.
During critical conditions (high electricity demand and low generation) of the power
grid consumers have to pay high electricity prices in the respective days or hours.
Thus, both pricing signals are considered and electricity cost is reduced by scheduling
energy consumption in off-peak hours.
20
Table 3.1: Load description
Appliances
group
Appliances Power ratings
(kW)
Time of oper-
ation (hours)
Vacuum
pump
0.6 6
Regularly op-
erated appli-
ances
Water pump 1.18 8
Dish washer 0.78 10
Oven 1.44 18
Shift-able ap-
pliances
Washing ma-
chine
[3.60 0.5 0.38 ] [5 4 3]
Cloth dryer [4.4 2 0.8] [4 3 2]
Refrigerator [1 0.75 0.5] [18 16 15]
Elastic appli-
ances
AC [1.5 1.44 1] [15 13 14]
Water heater [4.45 1.2 1] [7 5 4]
Water Dis-
penser
[1.5 1 0.5] [11 10 9]
21
Chapter 4
Problem formulation and Proposed solution
22
In this research work, we considered SH and MHs with household appliances and our
desired objectives are: to reduce electricity cost by scheduling energy consumption
in low price hours (off-peak hours), to maintain grid stability by minimizing PAR,
and to maximize user comfort level. We formulate our objective function using MKP
approach which is based on the following assumptions:
•Assuming Anas number of items (N).
•Each of the items comprises of two attributes i.e., weight and the value. The
weight of the items expresses the energy usage of the appliances in time interval
(t). And the value of the items denotes the energy cost of the appliances.
However, the weight of the appliances is independent of the time interval.
•We consider Nnumber of knapsacks in order to limit power consumption of
each category of the appliances and also to limit the total power capacity (Cg).
By considering aforementioned assumptions, the utility and consumers can actively
cooperate in energy demand management in order to reduce electricity cost and PAR.
To achieve the grid sustainability, total energy consumption of the appliances in each
time interval t Tshould not exceed Cg. For this reason, we limit the total energy
consumption as:
0≤ςTL≤Cg(4.1)
If the constraint in Eq. 4.1 is satisfied the inadequacy of power and stresses on the
grid can be eliminated.
4.1 PAR
PAR is the ratio of the maximum aggregated load consumed in a certain time frame
and the average of the aggregated load. PAR informs about the energy consumption
behavior of the consumers and the operation of the power grid. The high PAR
jeopardizes the grid stability and increases the electricity cost. While reduction in
PAR simultaneously enhances the stability and reliability of the power grids and
23
reduces the electricity bill of the consumers. Mathematically, it is expressed as:
Lpeak =max
tTςT(t) (4.2)
Lavg =PT
t=1 ςT(t)
T(4.3)
Lpeak and Lavg show the maximum aggregated load and average load in a time frame
(t). ς(t) represents the total energy consumption of the appliances in an hour.
P AR =Lpeak
Lavg
=
T max
tTςT(t)
PT
t=1 ςT(t)(4.4)
4.2 User comfort
In energy optimization, the load is shifted from peak hours to off-peak hours in order
to reduce electricity cost. In this context, Raconsumption patterns are not changed
and they must run with first preference, whereas Saand Eaoperation time interval
(Ot) are flexibly shifted. Saand Eacan be delayed to operate during peak hours to
reduce electricity cost, however, it incurs discomfort to the consumer. To evaluate
waiting time of appliances, we assume starting and ending time instant of appliances
aαTand bβT, such that (aα< bβ) and ταis the time period of appliances to finish
their operation and Wis expressed as waiting time of the appliances.
W=|τα−aα|
|bβ−Ot−aα|(4.5)
Wavg =PAn
a|τα−aα|
PAn
a|bβ−Ot−aα|(4.6)
Where, Eq. 4.6 shows the average waiting time of the appliances.
4.3 Objective function
Generally, objective function in optimization problem is defined as follows [17]:
minimize
F(c) = f1(c), f2(c), f3(c),...fn(c)
24
subject to:
gj(c)≤0, j = 1,2,3,...m
hk(c) = 0, k = 1,2,3,...n
cl
p≤cp≤cu
p, p = 1,2,3,...q
Where F(c) represents objective functions, gj(c) is inequality constraint, while hk(c)
shows equality constraint and cl
p,cU
pshows lower and upper bound of decision vari-
ables. In this context, we introduce objective function given as:
minT
X
t=1
ε(t)
An
X
ai
ςai(t)×ζ(t),
T
X
t=1
Wavg (t)(4.7)
subjected to:
ςTL≤Cg(4.8a)
P AR =
T max
tTςT(t)
PT
t=1 ςT(t)≤Tmax (4.8b)
Tmin ≤t≤ T max (4.8c)
bβ
X
t=aα
νt
Sa=ςSa,TL, νt
Sa= 0,∀t T \ Ta(4.8d)
bβ
X
t=aα
µt
Ea≤ςEa,TL, µt
Ea= 0,∀t T \ Ta(4.8e)
0≤νt
Sa≤ςt
Ea,TL,∀t T(4.8f)
ςmini
Ea,TL≤µt
Ea≤ςmax
Ea,TL,∀t T(4.8g)
Wavg ≤5 (4.8h)
T
X
t=1
ςTL(t)unsche =
T
X
t=1
ςTL(t)sche,∀t T(4.8i)
Eq. 4.7 depicts objective function i.e., minimization of cost per day and waiting time
of the appliances. Eq. 4.8a-Eq. 4.8i are constraints of the objective function. Eq.
4.8a shows the total power consumption of appliances should not overreach the power
25
grid capacity. Eq. 4.8b limits the PAR less than Tmax the ideal value of Tmax is
equal to 1 which shows perfect flat load profile and can not obtain by any scheduling
algorithms. Eq. 4.8c guaranteed that time scheduled by appliances should not violet
the restriction. Eq. 4.8d and Eq. 4.8e are constraints represent the energy load
balance equation of Saand Eain any time slot (t). While Eq. 4.8f and Eq. 4.8g
indicate the maximum and minimum energy consumption of Saand Eaappliances in
each hour of the operation. Eq. 4.8h shows the maximum time that user postpone
the operation of Saand Ea. At last, Eq. 4.8i clearly demonstrates the total energy
consumption of the appliances in scheduled case and unscheduled case are always
equal i.e., the HEMC schedules the appliances by taking into account that appliances
must complete their length of operation.
4.4 Optimization techniques
Generally, mathematical techniques provide an accurate solutions to the problem
which is either feasible or infeasible. However, they are incapable of addressing the
complex problems due to the curse of dimensionality, slow convergence rate, and
complex calculations. While heuristic optimization algorithms are not guaranteed
the exact solution and provide approximate solutions, however, they are capable of
handling complex calculations. In spite of approximate solutions, optimization algo-
rithms are fair enough to converge faster, reach the desired solution, and applicable
in all fields of engineering and computer science [31]. For this purpose, we computed
our problem as an optimization problem and four heuristic optimization techniques:
WDO, HSA, GA, and GHSA are employed. The technique and algorithm are inter-
changeable used for optimization purpose in this paper. Each heuristic optimization
technique is explained as follows:
26
4.4.1 GA
GA is an adaptive algorithm based on the biological process [9]. Initially, a set of
random solutions is generated called chromosomes and the set of the chromosome is
considered as a population. Each chromosome comprises of genes and the value of
gene is either binary or numerical value. We consider the value of gene as 1 or 0 which
actually shows ON and OFF states of the appliances. The fitness of each chromosome
is evaluated using Eq. 17 and the stochastic operators, crossover and mutation are
used to generate new populations. Two point crossover with crossover rate Pc= 0.9
is used and the obtained chromosome is further mutated through mutation process
which diversifies the search space of the algorithm the mutation rate is considered
Pm= 0.1. The process of crossover and mutation enables to reach at global optimal
results. At the end, binary array [0 0 1 0 0 0 0 0 1 1] is obtained which shows the
appliance is ON at 3, 9 and 10 location of the array. We then find out electricity
cost and energy consumption to achieve our desired objective. The optimal results
are obtained by considering the parameters in the table 4.1.
Table 4.1: GA parameters
Parameters Values
Maximum itera-
tion
200
Population size 30
Pc0.9
Pm0.1
The process is continued until the best optimal vector is achieved. Additionally, in
comparison to other existing optimization techniques, GA is more robust and solves
the complex non-linear problem with high convergence rate. GA also exhibits the
property of independence of problem domain and imparts divergent solution in a
single iteration.
27
4.4.2 WDO
WDO is the meta-heuristic algorithm which is inspired by the atmospheric motion of
wind. In WDO, infinitely small air parcels move in a search space and wind blows
to equalize pressure on air parcels using four different forces. These forces are cariols
forces, pressure gradient forces, gravitational forces, and the frictional forces. Coriolis
force tends to move the wind horizontally i.e., rotate the wind around the earth, while
pressure gradient force is defined as; a change in wind pressure over distance covered
by the wind. When both coriolis force and pressure gradient force are equal they
balance the wind pressure horizontally. Furthermore, the gravitational force pulls
the wind towards its center and it is in the vertical direction, while the friction force
lowers the speed of the wind which in turn slow down the speed of coriolis force. All
of these forces are expressed mathematically as [21]:
CF=−2Ω ×µ, (4.9)
PGF =−∇P δV , (4.10)
Fg=ρδV g, (4.11)
Ff=ραµ, (4.12)
At first, the random solution (vi) is generated using Eq. 4.13.
vi=Vmax ×2×(rand(populationsize, n)−0.5).(4.13)
Each of the random solution is evaluated using fitness function and relatively good
solutions are reproduced, while bad solutions are neglected. In each step, position and
the velocity of the air parcel is evaluated and the new value of velocity is assigned to
each air parcel. The Eq. 4.14 shows the updated velocity (Vnew ) of air parcels
Vnew = (1 −α)Vcur −Vcur ×g(R×T|1
j−1|
(xnew −xold)) + cVcur
Pcur
,
(4.14)
Vnew =Vmax if Vnew > Vmax (4.15)
Vnew =Vmin if Vnew < Vmax (4.16)
28
After updating the velocity of the particle. New generation is obtained using Eq. 4.17
and the process will continue until stopping criteria is reached i.e., optimal scheduling
of energy consumption and minimization of electricity cost.
xnew =xcur + ( Vnew × 4t),(4.17)
The optimal results are obtained by considering the parameters in the table 4.2.
Table 4.2: WDO parameters
Parameters Values
Maximum itera-
tion
200
Population size 30
Vmin 0.9
Vmax 0.1
RT 3
α0.4
DimMax 5
DimMin -5
g 0.2
4.4.3 HSA
HSA is the music inspired technique proposed by Zong Woo [18]. In HSA each musi-
cian plays note repeatedly to improve its harmony and generates new random variable
according to Eq. 4.18. Tuning parameters are also adjusted to achieve best harmony.
The initial population is generated randomly as:
Xij =Li+rand(Ui−Li) (4.18)
29
Where Ui, Lishows upper and lower bound. Eq. 4.18 shows randomly generated
variables in matrix form:
HM =
X1
1X2
1X3
1. . . Xm
n
X1
2X2
2X3
2. . . Xm
n
.
.
..
.
..
.
..
.
.
Xn
1Xn
2Xn
3. . . X m
n
(4.19)
The initial population generated (HM) by Eq. 28 is compared with the harmony
memory consideration rate (HMCR). HMCR specifies the probability of employing
the value of randomly generated matrix. However, suitable HMCR rate is considered
as 70% to 90% of the value from the entire pool of harmony memory (HM). The
condition for HMCR is:
Xnew =
X{x1i, x2i, x3i. . . xHM }, W ith P (H M CR)
X{x1, x2, x3. . . xN}, W ith P (1 −HM C R).
(4.20)
The values are selected from HM and their pitch are adjusted using pitch adjust-
ment ratio (Par). Par diversify the search space and increases the optimality of the
algorithm. Par can be adjusted as:
Xnew =
Y ES, W ith P (P ar)
N O, W ith P (1 −P ar ).
(4.21)
After achieving new harmony vector fitness function is evaluated using Eq. 4.7. If
new harmony vector is better than worst harmony replace the worst harmony in the
HM. The new harmony is binary coded string and shows the appliances ON/OFF
states. The optimal results are obtained by considering the parameters in the table
4.3.
30
Table 4.3: HSA parameters
Parameters Values
Maximum itera-
tion
100
Population size 30
HMCR 0.9
P Amin 0.4
P Amax 0.9
Bwmin 0.0001
Bwmax 0.1
4.4.4 GHSA
We propose heuristic optimization algorithm by combining the attributes of GA and
HSA in order to achieve better results as compared to existing algorithms. It is noticed
in [18], that HSA has quality to perform searching with high speed i.e., converges at
faster rate, while GA has capability to search for global optimal solution. For this
reason, we combine the attributes of GA and HSA to achieve global optimal solutions
with faster convergence rate. Moreover, we have seen that GA reduces electricity
cost, however, incapable of handling the user comfort. On the other hand, HSA
efficiently addresses user comfort of the end user. Thus, we incorporate complete
steps of HSA then we adopt crossover and mutation steps of GA. Hence, results show
that hybrid algorithm performs better in terms of cost minimization and user comfort
maximization compared with other existing optimization techniques. Table 4.4 shows
the computational time (sec) for existing and proposed algorithms.
31
Algorithm 1: GHSA
Initialization of parameters;
Randomly generate population using Eq. 4.18;
for i=1:HMS do
for j=1:Ncdo
Randomly generated Xi
jin HM ;
end for
end for
End of initialization step;
while Maximum number of iteration reached do
Construction and assessment of new candidate;
if (rand(0,1) ≤HMCR) then
Choose randomly from existing harmony
if (rand(0,1) ≤par) then
Adjust the tone randomly using par
end if
else
Select pair x, y randomly from existing harmony
if rand(0,1) ≤Pcthen
crossover (x,y)
end if
if rand ≤Pmthen
mutate (x,y)
end if
end if
Evaluate fitness function a: F(a) using Eq. 4.7 ;
End of the construction and assessment step;
Construction and assessment of new candidate: a ;
if F(a) has best value than the worst member of HM then
Replace the worst HM member with new candidate: a
else
Discard a
end if
End of HM update;
Until a preset criterion is met
end while
32
Table 4.4: Computational time of heuristic techniques
Schemes Techniques Computational
time (sec)
WDO 2.61
HSA 2.01
SH GA 1.5
GHSA 1.43
WDO 100.21
HSA 96.1
MHs GA 70.46
GHSA 60.33
4.5 Feasible region
Feasible region is defined as the set of optimal points which satisfies all the constraints
given in a scenario, including inequalities, equalities, and integer constraints. We
consider feasible region of electricity cost versus energy consumption and electricity
cost versus average waiting time for a SH and MHs using RTEP and CPP tariffs.
4.5.1 Feasible region for SH
In this segment, we figure out the feasible region for electricity cost and energy con-
sumption of SH by considering RTEP and CPP tariffs. Firstly, we consider a SH with
RTEP tariff and the electricity cost per hour is given:
ξT L(t) = An
X
ai
ς(ai,t)×ε(t)×ζ(t)!. t T(4.22)
33
Similarly, the total electricity cost is calculated as:
ξT L =
T
X
t=1 An
X
ai
ς(ai,t)×ε(t)×ζ(t)!. t T(4.23)
To minimize the Eq. 4.23 we introduce constraints regarding RTEP tariff and energy
consumption of the appliances are:
R1: 1.215 ≤ξT L(t)≤3.7
R2:ξT L ≤21.6
R3: 1.5≤ς(t)≤13.84
In Figure. 4.1a points P1, P2, P3, P5, and P6show the feasible region of electricity
cost and energy consumption. The electricity cost is calculated using RTEP tariff and
the unscheduled cost per hour is 3.71 cents. The total cost per day is 21.6 cents and
based on the parameters electricity cost and energy consumption the constraints are
defined. The constraint R1shows that the electricity cost in hour should not exceed
3.71 cents including peak hours and off-peak hours. Constraints R2shows total cost
per day should not increase than the 21.5 cents as total cost shown here is in the
unscheduled case, therefore, the scheduling algorithms must schedule the cost in such
way that it does not violate the limit as in R2. While the constraint R3shows the
total energy consumption of the appliances must be with in limits i.e., between 1.5
and 13.84 kWh to reduce electricity cost. Similarly, Figure. 4.1b shows feasible region
for SH considering CPP tariff and the constraints are given as:
C1: 1.215 ≤ξT L(t)≤6.37
C2:ξT L ≤37.5
C3: 1.5≤ς(t)≤13.84
Constraints associated with CPP show that the electricity cost of an hour should not
exceed 6.37 cents. Constraint C2shows the cost for a single day should be less than
37.5 cents. While the constraint C3shows for the optimal scheduling of the energy
34
Energy consumption (kWh)
0 2 4 6 8 10 12 14
Cost (cents)
0
5
10
15
20
25
30
35
40
P1(1.5,4.09)
P2(1.5,1.21)
P4(13.84,37.78)
P6(7.9,21.5)
P3(13.84,11.08)
P5(13.84, 21.5)
Figure 4.1a: Feasible region of energy consumption for SH using
RTEP
Energy consumption (kWh)
0 2 4 6 8 10 12 14
Cost (cents)
0
10
20
30
40
50
60
70
80
P1(1.5,8.20)
P2(1.5,1.21)
P5(13.84,38.2)
P4(13.84,75.7)
P3(13.84,11.22)
P6(7,38.2)
Figure 4.1b: Feasible region of energy consumption for SH using
CPP
consumption, energy consumed in an hour should be with in limits of 1.5 kWh and
13.84 kWh.
We also computed feasible region for waiting time of the appliances in order to de-
termine the user comfort. User comfort is inversely related with the waiting time of
35
Average waiting time (hours)
012345
Cost (cents)
5
10
15
20
25
P2(5,18.6)
P3(5,10)
P4(1.36,18.6)
P1(0,21..5)
Figure 4.2a: Feasible region of average waiting time for SH using
RTEP
Average waiting time (hours)
012345
Cost (cents)
10
15
20
25
30
35
40
P3(5,15)
P2(5,32.4)
P1(0,37.7)
P4(1.48,37.7)
Figure 4.2b: Feasible region of average waiting time for SH using
CPP
the appliances and electricity cost. We consider maximum 5 hours average waiting
time for the appliances i.e., allowable delay in day for the SH scheme. In Figure. 4.2a
points P1, P2, and P3show the feasible region for electricity cost and average waiting
time using RTEP tariff. The point P1shows that the average waiting time of the Sa
and Eaare restricted to zero then the electricity cost is reached to a maximum value
36
i.e., 21.5 cents whereas, point P2shows the average waiting time of Sais 5 hours
then the cost is reduced to 18.6 cents. While point P3shows that the average waiting
time of all the appliances, including Saand Rais 5 hours then the cost is reduced to
10 cents, however, in this case consumers have to pay less cost, but compromise its
comfort level. Similarly, in Figure. 4.2b point P1shows that the consumers have to
pay maximum cost 37.7 cents when the average waiting time is zero. Whereas, point
P2shows that the cost is reduced to 18.6 cents when the average waiting time of the
Sais 5 hours. While point P3presents that the cost is decreased to 15 cents as the
average waiting time of the appliances is increased to 5 hours. The feasible region
of Figure. 4.2a and Figure. 4.2b depicts the trade-off between the electricity cost
and average waiting of the appliances. In order to minimizes the trade-off optimal
scheduling of the energy consumption profile is essential in peak hours and off-peak
hours.
Energy consumption (kWh)
0 50 100 150 200 250 300 350
Cost (cents)
0
100
200
300
400
500
600
700
800
900
1000
P4(351,947.7)
P3(351,284.3)
P6(240,648.4)
P5(351,648.4)
P2(75,60.8)
P1(75,202.5)
Figure 4.3a: Feasible region of energy consumption for MHs
using RTEP
4.5.2 Feasible region for MHs
The feasible region of electricity cost and energy consumption for MHs is determined
using RTEP and CPP tariffs. Similar to previous subsection, we consider MHs using
37
Energy consumption (kWh)
0 50 100 150 200 250 300 350
Cost (cents)
0
200
400
600
800
1000
1200
1400
1600
1800
P3(351,284.3)
P4(351,947.7)
P5(351,1087)
P1(75,284.3) P2(75,60.7)
P6(243,1087)
Figure 4.3b: Feasible region of energy consumption for MHs
using CPP
Average waiting time (hours)
0 50 100 150 200 250
Cost (cents)
100
200
300
400
500
600
700
P4(38.24,580)
P1(0,648.8) P2(250,580)
P3(250,150)
Figure 4.4a: Feasible region of average waiting time for MHs
using RTEP
RTEP and CPP tariffs and the total cost is calculated as:
ΥT L =
50
X
N=1
T
X
t=1 An
X
ai
ς(ai,t)×ε(t)×ζ(t)!. t T(4.24)
38
Average waiting time (hours)
0 50 100 150 200 250
Cost (cents)
100
200
300
400
500
600
700
800
900
1000
1100
1200
P2(250,600)
P1(0,1087)
P4(75,600)
P3(250,150)
Figure 4.4b: Feasible region of average waiting time for MHs
using CPP
Constraints associated with electricity cost and energy consumption for MHs using
RTEP are given as:
S1: 50.6≤ξT L(t)≤94.4
S2: ΥT L ≤648.43
S3: 75 ≤ς(t)≤351
In Figure. 4.3a the shaded region shows the feasible region of electricity cost and
energy consumption of MHs using RTEP tariff. Constraint S1indicates the cost per
hour must be restricted with in limits of 50.6 and 94.4 cents per hour. While the cost
per day is restricted to 648.43 cents by the constraint S2. Constraint S3presents that
the energy consumption should not exceed upper and lower bound which is 75 and
351 kWh. Figure. 4.3b shows the feasible region of the electricity cost and energy
consumption for MHs using CPP tariff. The constraints related to electricity cost and
energy consumption for MHs using CPP are:
39
W1: 58.7≤ξT L(t)≤156.8
W2: ΥT L ≤1087
W3: 75 ≤ς(t)≤351
Similar to prior Constraints, constraint W1indicates that electricity cost in an hour
should be restricted by upper and lower bound i.e., 58.7 and 156.8 cents. Constraint
W2shows that heuristic algorithm should schedule the cost such that it should not
increase 1087 cents. While constraint W3simply means that for minimization of
electricity cost, energy consumption of the appliance should not exceed the given
values as in W3.
In Figure. 4.4a points P1, P2, and P3illustrate the feasible region of electricity
cost and average waiting time of MHs using RTEP tariff. In the case of MHs, we
consider maximum 250 hours average waiting time for the appliances in day. The
point P1shows that the electricity cost is maximum when the average waiting time is
zero, while point P3represents that total cost is reduced to 150 cents as the average
waiting time of the appliances reaches up to 250 hours for MHs. However, at this
stage user comfort is decreased. P2shows the cost is reduced to 580 cents when the
operation time of Sais delayed to 250 hours in MHs. Similarly, Figure. 4.4b shows the
feasible region bounded by the points P1, P2, and P3with CPP tariff. The maximum
cost in case of CPP is raised to 1087 cents at zero average waiting time. Whereas,
P2shows the cost when operation time of Sais delayed. While P3shows that the
average waiting time is maximum for all the appliances the cost is minimum i.e., 150
cents. Moreover, to address the trad-off in a better way, optimal scheduling of the
appliances and user preference should monitor equally and appropriately.
40
Chapter 5
Simulations and Discussion
41
5.1 Simulations and Discussion
In this section, we inspect the numerical simulation of four heuristic algorithms and
their performances are evaluated in terms of electricity cost, PAR, and user comfort.
A hybrid algorithm is proposed and simulation results are compared with existing
algorithms using MATLAB 2014b with Intel(R) Core(TM) i5-2450M CPU @ 2.50
GHz and 6 GB of RAM on Windows operation system .
We assume SH and MHs with household appliances which are categorized into three
groups: Ra,Sa, and Ea. While arbitrary operational time and power ratings are
assigned to Saand Raappliances in the case of MHs. Our objectives are to minimize
electricity cost, PAR, and waiting time of the appliances. In this regard, heuristic
algorithms are incorporated like; WDO, HSA, GA, and proposed algorithm GHSA.
Comparative analysis is made among heuristic algorithms and unscheduled case by
adopting RTEP and CPP tariffs (shown in Figure. 5.1a and Figure. 5.1b) and results
are demonstrated the proposed algorithm efficiently addressed the objectives. The
performance parameters comprise of load profile, cost per hour, electricity cost per
day, PAR, and user comfort. The detail of each parameter is provided as follows:
Time (hours)
1 2 3 4 5 6 7 8 9 1011 12 1314 15 16 17 1819 20 21 22 23 24
Price (cents/kWh)
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
RTEP signal
Figure 5.1a: RTEP tariff
42
Time (hours)
1 2 3 4 5 6 7 8 9 1011 12 1314 15 16 17 1819 20 21 22 23 24
Price (cents/kWh)
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
CPP signal
Figure 5.1b: CPP tariff
Time (hours)
1 2 3 4 5 6 7 8 9 1011 12 1314 15 16 17 1819 20 21 22 23 24
Load (kWh)
0
2
4
6
8
10
12
14 Unscheduled
WDO
HSA
GA
GHSA
Figure 5.2a: Load profile of SH with RTEP
5.1.1 Load profile
The energy consumption of appliances for SH and MHs using RTEP and CPP tariffs
is shown in Figure. 5.2a-Figure. 5.2d. It can be seen that during peak hours i.e., 7 to
15 hours each heuristic algorithm performs better than unscheduled case. In Figure.
5.2a maximum unscheduled load during peak hours is 13.84 kWh, and among other
heuristic algorithms GA schedules the peak load competently to 5.01 kWh. While
43
Time (hours)
1 2 3 4 5 6 7 8 9 1011 12 1314 15 16 17 1819 20 21 22 23 24
Load (kWh)
0
2
4
6
8
10
12
14 Unscheduled
WDO
HSA
GA
GHSA
Figure 5.2b: Load profile of SH with CPP
Time (hours)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Load (kWh)
0
50
100
150
200
250
300
350
400
Unscheduled
WDO
HSA
GA
GHSA
Figure 5.2c: Load profile of MHs with RTEP
in Figure. 5.2b GHSA schedules the peak load to 3.73 kWh which is comparatively
less than existing heuristic algorithms and unscheduled load (13.84 kWh). Similarly,
Figure. 5.2c and 5.2d illustrate the load profile for MHs using RTEP and CPP tariffs
and the energy consumed by unscheduled load during peak hours is 351 kWh, however,
compared with the existing algorithms GHSA is scheduled the load to 60.29 kWh and
136.09 kWh for MHs using RTEP and CPP tariffs, respectively. The overall results
show that proposed algorithm performs better to schedule the load profile for SH and
44
Time (hours)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Load (kWh)
50
100
150
200
250
300
350
400
Unscheduled
WDO
HSA
GA
GHSA
Figure 5.2d: Load profile of MHs with CPP
Time (hours)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Cost per hour (cents/hour)
0
0.5
1
1.5
2
2.5
3
3.5
4
Unscheduled
WDO
HSA
GA
GHSA
Figure 5.3a: Cost per hour of SH with RTEP
MHs compared to existing algorithms and unscheduled case. Table 5.1 presents load
profile in peak hours of the appliances in unscheduled case, scheduled case, percentage
decrement, and improvement of heuristic algorithms.
45
Table 5.1: Load profile comparison
Schemes Techniques
Unscheduled WDO HSA GA GHSA
SH with RTEP
Load profile
(kWh)
13.84 8.66 6.01 5.01 5.80
Percentage
decrement
— 37.42% 56% 63.80% 63.29%
Improvement — 5.18 7.83 8.83 8.76
SH with CPP
Load profile
(kWh)
13.84 5.12 6.37 5.10 4.95
Percentage
decrement
— 63.21% 53.97% 63.15% 65.12%
Improvement — 9.27 7.47 8.74 10.12
MHs with RTEP
Load profile
(kWh)
351 128.88 125.28 241.46 124.29
Percentage
decrement
— 63.28% 65.92% 31.20% 65.72%
Improvement — 222.12 290.72 109.54 290.71
MHs with CPP
Load profile
(kWh)
351 114.49 140.38 134.68 136.01
Percentage
decrement
— 67.38% 60% 61.6% 61.25%
Improvement — 236.51 210.62 216.32 214.99
46
Time (hours)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Cost per hour (cents/hour)
0
1
2
3
4
5
6
7
Unscheduled
WDO
HSA
GA
GHSA
Figure 5.3b: Cost per hour of SH with CPP
Time (hours)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Cost per hour (cents/hour)
0
10
20
30
40
50
60
70
80
Unscheduled
WDO
HSA
GA
GHSA
Figure 5.3c: Cost per hour of MHs with RTEP
5.1.2 Cost per hour
The electricity cost per hour is calculated using Eq. 4.22. The results in Figure.
5.3a-Figure. 5.3d depict electricity cost per hour with the comparison of heuristic
algorithms. It is observed that each algorithm tends to schedule the cost in low
pricing hours i.e., off-peak hours. In Fig. 5.3a results show that the cost is reduced
to 2.61, 1.72, 1.12, and 1.34 cents/hour by WDO, HSA, GA, and GHSA, respectively.
47
Time (hours)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Cost per hour (cents/hour)
0
20
40
60
80
100
120
Unscheduled
WDO
HSA
GA
GHSA
Figure 5.3d: Cost per hour of MHs with CPP
SH with RTEP SH with CPP MHs with RTEP MHs with CPP
Total cost (cents)
0
200
400
600
800
1000
1200
Unscheduled
WDO
HSA
GA
GHSA
Figure 5.4: Electricity cost per day
Similarly, Figure. 5.3b shows maximum unscheduled cost is 6.83 cents/hour and
it is reduced to 2.25, 2.04, 2.02, and 1.60 cents/hour using WDO, HSA, GA, and
GHSA, respectively. Figure. 5.3c and 5.3d represent results for MHs using RTEP
and CPP tariffs. All heuristic algorithms are compared with each other and also with
unscheduled case. The unscheduled cost per hour during peak hours for MHs using
RTEP and CPP is 94.38 and 156.78 cents/hour while proposed algorithm is reduced
it to 20.28 and 58.46 cent/hour, respectively, which represents maximum reduction
48
SH with RTEP SH with CPP MHs with RTEP MHs with CPP
PAR
0
5
10
15
20
25
Unscheduled
WDO
HSA
GA
GHSA
Figure 5.5: PAR of SH and MHs with RTEP and CPP
SH with RTEP SH with CPP MHs with RTEP MHs with CPP
Average waiting time (hours)
0
50
100
150
200
250
Allowed delay
WDO
HSA
GA
GHSA
Figure 5.6: Average waiting time of SH and MHs using RTEP and CPP
compared to other existing algorithms.
5.1.3 Electricity cost per day
The primary objective of our work is the minimization of electricity cost. The elec-
tricity cost is reduced using heuristic algorithms to schedule the energy consumption
profile and also with the constraints mention in section 3. Figure. 5.4 illustrates the
49
electricity cost per day (total cost) for SH and MHs using RTEP and CPP tariffs.
In detail, total cost for SH using RTEP tariff in unscheduled case is 21.53 cents and
using heuristic algorithms: WDO, HSA, GA, and GHSA the total cost is reduced to
18.65, 17.04, 16.01, and 15.10 cents, respectively. Likewise, SH with CPP tariff the
total cost for unscheduled case is 38.23 cents and the heuristic algorithms: WDO,
HSA, GA, and GHSA are reduced the total cost to 25.68, 23.98, 22.63, and 20.21
cents, respectively. In the case of MHs, using RTEP tariff the total cost is reduced
from unscheduled case i.e., 648.83 cents to 320.74, 480.36, 445.36, and 284.39 cents
by WDO, HSA, GA, and GHSA, respectively as shown in Fig. 9. Accordingly, for
MHs the total cost associated with CPP tariff is 631.02, 643.18, 608.18, 500.01 cents
using WDO, HSA, GA, and GHSA, respectively. The overall effects of the total cost
associated with RTEP and CPP rates in the case of SH and MHs are analyzed which
show that the proposed algorithm GHSA outperforms the other existing algorithms.
Table 5.2 shows the total cost, percentage decrement, and improvement of heuristic
algorithms.
5.1.4 PAR
PAR describes the behaviour of the consumer’s load profile and directly affects the
operation of the power grids. Figure. 5.5 illustrates PAR of scheduled and unsched-
uled case for SH and MHs with RTEP and CPP tariffs and results show that each
heuristic algorithm is competent to reduce PAR compared to unscheduled case. PAR
comparison of unscheduled and scheduled case for MHs using RTEP and CPP tar-
iffs shows that the proposed algorithm GHSA is reduced the PAR, even the number
of the homes is increased. Table 5.3 represents PAR, percentage reduction of PAR,
and improvement of heuristic algorithms. It is evident from Figure. 5.5 that GHSA
exhibits maximum reduction in PAR comparative to the existing algorithms. More-
over, In order to reduce PAR, load profile of the consumer should schedule effectively
which ensures the reliability and stability of the power grids. In our work, we employ
heuristic algorithms to reduce the PAR by scheduling the load in off-peak hours.
50
Table 5.2: Total cost comparison
Schemes Techniques
Unscheduled WDO HSA GA GHSA
SH with RTEP
Total cost
(cents)
21.53 18.65 17.04 16.01 15.01
Percentage
decrement
— 13.37% 20.58% 25.63% 29.86%
Improvement — 2.88 4.49 5.52 6.43
SH with CPP
Total cost
(cents)
37.5 25.68 23.98 22.63 20.21
Percentage
decrement
— 31.52% 36.05% 39.65% 46.19%
Improvement — 11.82 13.52 14.87 17.21
MHs with RTEP
Total cost
(cents)
648.43 320.74 480.36 445.36 284.39
Percentage
decrement
— 50.54% 25.91% 31.31% 56.06%
Improvement — 327.69 168.07 303.7 364.04
MHs with CPP
Total cost
(cents)
1087 631.02 643.18 608.18 500.21
Percentage
decrement
— 41.94% 40.82% 44.04% 54.04%
Improvement — 445.98 443.82 478.82 587
51
Table 5.3: PAR comparison
Schemes Techniques
Unscheduled WDO HSA GA GHSA
SH with RTEP
PAR 5.01 4.31 3.24 3.73 3.09
Percentage
decrement
— 13.97% 35.32% 25.54% 38.32%
Improvement — 0.7 1.77 1.28 1.92
SH with CPP
PAR 5.01 4.68 3.48 3.64 3.13
Percentage
decrement
— 6.58% 30.53% 27.34% 37.52%
Improvement — 0.33 1.53 1.37 1.88
MHs with RTEP
PAR 22.46 12.78 14.70 12.70 11.73
Percentage
decrement
— 43.09% 34.55% 43.45% 47.77%
Improvement — 11.78 9.84 11.48 12.78
MHs with CPP
PAR 24.54 13.92 12.98 14.01 12.01
Percentage
decrement
— 43.27% 47.01% 42.66% 50.08%
Improvement — 10.62 11.56 10.53 12.53
52
5.1.5 User comfort
The average waiting time of the appliances is calculated which refers to user comfort.
User comfort disturbs when the user faces minimum amount of delay in order to oper-
ate the appliance. However, if the waiting time of the appliances increases eventually
cost of electricity reduces. In this vein, there is sort of trade-off between waiting time
of appliances and electricity cost. Figure. 5.6 shows average waiting time for SH and
MHs using RTEP and CPP tariffs with heuristic algorithms: WDO, HSA, GA, and
GHSA. The maximum allowable delay i.e., average waiting time of the appliance in
case of SH using RTEP and CPP tariffs is 5 hours, however, the proposed algorithm
GHSA is achieved minimum average waiting time of appliances compared with the
existing algorithms i.e., 4.3 hours and 2.4 hours, respectively. Similarly, for MHs max-
imum allowed delay is 250 hours using both pricing tariffs and the proposed algorithm
GHSA is achieved minimum delay compared with other heuristic algorithms i.e., 170.9
hours and 175 hours, respectively. Among other existing techniques GHSA is capable
of minimizing the average waiting which in turn increases user comfort. Although,
it is stated that there is trade-off between electricity cost and waiting time, however,
proposed algorithm GHSA performs efficiently to minimize the trade-off compared to
other existing optimization algorithms.
53
Chapter 6
Conclusion
54
6.1 Conclusion
HEMS can effectively and intelligently control home appliances by providing real-time
load management. In this paper, we proposed HEMC based on heuristic algorithm
GHSA for SH and MHs. Simulation results are conducted on the basis of SH and
MHs with household appliances by adopting RTEP and CPP tariffs. In addition, for
MHs arbitrary power ratings and time of operations are assigned to the appliances.
HEMC performance is evaluated on the bases of existing optimization algorithms:
WDO, HSA, GA, and proposed algorithm GHSA. Feasible regions are computed which
ensures the validity of the proposed algorithm GHSA in the case of SH and MHs.
Moreover, Comparative analysis of heuristic algorithms is performed which shows
the better performance of GHSA in terms of cost reduction, PAR reduction, and
user comfort maximization. Finally, it is assured that the proposed HEMC based on
GHSA is the reliable and efficient solution for the future smart grid.
55
Chapter 7
References
56
Bibliography
[1] Fadlullah, Z.M., Quan, D.M., Kato, N. and Stojmenovic, I., 2014. GTES: An
optimized game-theoretic demand-side management scheme for smart grid. IEEE
Systems journal, 8(2), pp.588-597.
[2] Gellings, C.W., 1985. The concept of demand-side management for electric utili-
ties. Proceedings of the IEEE, 73(10), pp.1468-1470.
[3] Bozchalui, M.C., Hashmi, S.A., Hassen, H., Caizares, C.A. and Bhattacharya, K.,
2012. Optimal operation of residential energy hubs in smart grids. IEEE Transac-
tions on Smart Grid, 3(4), pp.1755-1766.
[4] Christian Roselund, and John Bernhardt, Lessons learned along europes road to
renewables, IEEE Spectrum, May 4, 2015.
[5] Vardakas, J.S., Zorba, N. and Verikoukis, C.V., 2015. A survey on demand re-
sponse programs in smart grids: Pricing methods and optimization algorithms.
IEEE Communications Surveys Tutorials, 17(1), pp.152-178.
[6] Ma, R., Chen, H.H., Huang, Y.R. and Meng, W., 2013. Smart grid communication:
Its challenges and opportunities. IEEE transactions on Smart Grid, 4(1), pp.36-46.
[7] Rossell-Busquet, A. and Soler, J., 2011. Towards efficient energy management:
defining HEMS and smart grid objectives. International Journal on Advances in
Telecommunications, 4(3).
[8] Ahmad, A., Khan, A., Javaid, N., Hussain, H.M., Abdul, W., Almogren, A.,
Alamri, A. and Azim Niaz, I., 2017. An Optimized Home Energy Management
System with Integrated Renewable Energy and Storage Resources. Energies, 10(4),
p.549.
57
[9] Logenthiran, T., Srinivasan, D. and Shun, T.Z., 2012. Demand side management
in smart grid using heuristic optimization. IEEE transactions on smart grid, 3(3),
pp.1244-1252.
[10] Pedrasa, M.A.A., Spooner, T.D. and MacGill, I.F., 2009. Scheduling of demand
side resources using binary particle swarm optimization. IEEE Transactions on
Power Systems, 24(3), pp.1173-1181.
[11] Sousa, T., Morais, H., Vale, Z., Faria, P. and Soares, J., 2012. Intelligent en-
ergy resource management considering vehicle-to-grid: A simulated annealing ap-
proach. IEEE Transactions on Smart Grid, 3(1), pp.535-542.
[12] Zhao, Z., Lee, W.C., Shin, Y. and Song, K.B., 2013. An optimal power scheduling
method for demand response in home energy management system. IEEE Trans-
actions on Smart Grid, 4(3), pp.1391-1400.
[13] Rahim, S., Javaid, N., Ahmad, A., Khan, S.A., Khan, Z.A., Alrajeh, N. and
Qasim, U., 2016. Exploiting heuristic algorithms to efficiently utilize energy man-
agement controllers with renewable energy sources. Energy and Buildings, 129,
pp.452-470.
[14] Rasheed, M.B., Javaid, N., Awais, M., Khan, Z.A., Qasim, U., Alrajeh, N., Iqbal,
Z. and Javaid, Q., 2016. Real Time Information Based Energy Management Using
Customer Preferences and Dynamic Pricing in Smart Homes. Energies, 9(7), p.542.
[15] Ma, J., Chen, H.H., Song, L. and Li, Y., 2016. Residential load scheduling in
smart grid: A cost efficiency perspective. IEEE Transactions on Smart Grid, 7(2),
pp.771-784.
[16] Nguyen, H.K., Song, J.B. and Han, Z., 2015. Distributed demand side man-
agement with energy storage in smart grid. IEEE Transactions on Parallel and
Distributed Systems, 26(12), pp.3346-3357..
[17] Flores, J.T., Celeste, W.C., Coura, D.J.C., das Dores Rissino, S., Rocha, H.R.O.
and Moraes, R.E.N., 2016. Demand Planning in Smart Homes. IEEE Latin Amer-
ica Transactions, 14(7), pp.3247-3255.
58
[18] Gao, X.Z., Govindasamy, V., Xu, H., Wang, X. and Zenger, K., 2015. Harmony
search method: theory and applications. Computational intelligence and neuro-
science, 2015, p.39.
[19] Zhang, J., Wu, Y., Guo, Y., Wang, B., Wang, H. and Liu, H., 2016. A hybrid
harmony search algorithm with differential evolution for day-ahead scheduling
problem of a microgrid with consideration of power flow constraints. Applied En-
ergy, 183, pp.791-804
[20] Wang, J., Biviji, M.A. and Wang, W.M., 2011, February. Lessons learned from
smart grid enabled pricing programs. In Power and Energy Conference at Illinois
(PECI), 2011 IEEE (pp. 1-7). IEEE.
[21] Mahmood, D., Javaid, N., Alrajeh, N., Khan, Z.A., Qasim, U., Ahmed, I. and
Ilahi, M., 2016. Realistic scheduling mechanism for smart homes. Energies, 9(3),
p.202.
[22] Khan, M.A., Javaid, N., Mahmood, A., Khan, Z.A. and Alrajeh, N., 2015. A
generic demandside management model for smart grid. International Journal of
Energy Research, 39(7), pp.954-964.
[23] Mahmood, A., Baig, F., Alrajeh, N., Qasim, U., Khan, Z.A. and Javaid, N., 2016.
An Enhanced System Architecture for Optimized Demand Side Management in
Smart Grid. Applied Sciences, 6(5), p.122.
[24] Bayraktar, Z., Komurcu, M., Bossard, J.A. and Werner, D.H., 2013. The wind
driven optimization technique and its application in electromagnetics. IEEE trans-
actions on antennas and propagation, 61(5), pp.2745-2757.
[25] Wang, J., Li, Y. and Zhou, Y., 2016. Interval number optimization for household
load scheduling with uncertainty. Energy and Buildings, 130, pp.613-624.
[26] Moon, S. and Lee, J.W., 2016. Multi-Residential Demand Response Scheduling
with Multi-Class Appliances in Smart Grid. IEEE Transactions on Smart Grid.
[27] Beaudin, M., Zareipour, H., Bejestani, A.K. and Schellenberg, A., 2014. Resi-
dential energy management using a two-horizon algorithm. IEEE Transactions on
59
Smart Grid, 5(4), pp.1712-1723.
[28] Mohsenian-Rad, A.H., Wong, V.W., Jatskevich, J. and Schober, R., 2010, Jan-
uary. Optimal and autonomous incentive-based energy consumption scheduling
algorithm for smart grid. In Innovative Smart Grid Technologies (ISGT), 2010
(pp. 1-6). IEEE.
[29] Yu, Z., Jia, L., Murphy-Hoye, M.C., Pratt, A. and Tong, L., 2013. Modeling and
stochastic control for home energy management. IEEE Transactions on Smart
Grid, 4(4), pp.2244-2255.
[30] Xiong, G., Chen, C., Kishore, S. and Yener, A., 2011, January. Smart (in-home)
power scheduling for demand response on the smart grid. In Innovative smart grid
technologies (ISGT), 2011 IEEE PES (pp. 1-7). IEEE.
[31] Maringer, D.G., 2006. Portfolio management with heuristic optimization (Vol.
8). Springer Science Business Media.
60
