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COMSATS Institute of Information Technology,
Islamabad Campus
Synopsis For the degree of M.S/M.Phil. Ph.D.
PART-1
Name of Student
Sakeena Javaid
Department
Department of Computer Science
Registration No.
SP16-PCS-007
Date of Thesis
Registration
Spring 2017
Name of Research
(i) Supervisor
(ii) Co-Supervisor
(i) Dr. Nadeem Javaid
(ii) Dr. Mariam Akbar
Research Area
Energy Management in Smart Grid
Members of Supervisory Committee
1.
Dr. Nadeem Javaid
2.
Dr. Mariam Akbar
3.
Dr. Manzoor Illahi Tamimi
Title of Research Proposal
Towards Green World: Renewable Energy Source based Energy
Management in Residential Sector making Appliances, Homes
and Buildings Smart
Signature of Student:
Summary of the Research
This synopsis describes that residential energy management systems which are leveraged
by the penetration of the renewable energy sources (RESs) such as: photovoltaic (PV)
systems and wind turbines. These are the major sources used in residential energy and the
concurrent penetration of these resources will considerably change the residential energy
management systems’ (EMS) functionality. This research focuses on smart homes and
buildings which are equipped with automation technologies to enhance occupants’
comfort level and provides savings on the electricity bills. The occupants’ activities can
be controlled or monitored by the EMS in order to schedule the appliances daily energy
consumption patterns. Another significant aspect of this research is integration of the
smart homes into smart grid architecture. It can support the traditional power grid by
integrating smart grid’s energy management programs (i.e., demand side management
programs).
In addition, energy management in the residential sector is challenged by many factors.
The major challenge is that energy consumption is continuously growing despite of the
enforcement of the various energy efficiency policies. Some important factors of the
energy growth are: increasing the usage and number of appliances, improper schedules
and control of the appliances and more comfort demand. For improving these challenges,
EMSs are used to manage and reduce residential energy usage and cost by incorporating
different energy management programs, such as: energy conservation and energy
efficiency. EMSs utilize RESs for residential energy generation to fulfill users’
requirements and to support reliability and robustness of energy supply systems. For
effectively managing the residential users’ energy, home energy management systems
(HEMS) and buildings energy management systems (BEMS) are the focus of this
research and they are comprised of the following applications which are based on
controlling the daily usage of appliances and seasonally usage of appliances: 1)
monitoring and scheduling the flexible loads for accommodating daily activities, user
preferences and requirements of residential consumers, 2) reduction of peak load demand
through optimal control of flexible residential loads, storages and generation systems, 3)
cost effectiveness with market oriented strategy of implementing EMSs by effectively
managing energy use in response of fluctuating energy prices. Furthermore, we also
consider the prevention measures for rebound effects by informing consumers to utilize
more energy after the implementation of RESs. In addition, some major barriers to the
large scale (multiple homes and residential buildings) implementations are also
considered as: peak formation, cost maximization and user comfort sacrifices by
analyzing complexity of the proposed solutions.
For solving the aforementioned issues, this research considers the meta-heuristic
algorithms (genetic wind driven algorithm (GWD)) for the efficient HEMS in the
residential units (single and multiple homes) at first stage. Since meta-heuristic
algorithms are well suited for solving the stochastic nature of problems as randomness in
energy consumption patterns and users’ schedules. Using these algorithms; we are
simulating two scenarios for the above mentioned applications: 1) energy cost and user
comfort without RESs for single, multiple homes and buildings, 2) energy cost and user
comfort with RESs for single, multiple homes and buildings. We will also consider the
HEMS and BEMS using fuzzy logic in our subsequent work for the aforementioned
scenarios while considering the seasonally used appliances (i.e., by providing heating and
air conditioning system control). Furthermore, the comparative analysis of these
techniques will also be conducted.
1 Introduction
Smart grid (SG) is referred to as next generation power grid which is envisaged to be at a large
scale: distributed systems composed of renewable energy resources (RESs), storage units, elec-
tric vehicles and bidirectional communication infrastructure [1]. SG becomes more consumer-
oriented by considering demand response (DR) mechanisms [2]. Some examples of DR pro-
grams are described: firstly, it considers consumers’ behaviors when they sign up for some
demand side management (DSM) pricing schemes, which permit the utility company to manage
the electricity use in order to reduce peak hour (PH) grid load. Whereas, second example con-
siders the energy trading with the utility by using electric vehicles or battery storage devices.
Here, energy market enable consumers to sell electricity which they own in storage units and it
helps in economic and technical aspects of utility company. A power grid can be generally cate-
gorized into two major components: electric power transmission and electric power distribution
[3]. Electric power transmission component supplies the amount of generated power at power
plants through transmission lines to other stations while electric power distribution component
is used for delivering energy to the consumers’ premises. Storage units can be battery banks,
electric vehicles or other storage devices for energy trading among grid stations. Centralized ob-
jective function is used to solve the system level optimization problem in classical power grid.
Whereas, in SG, each device can be tackled with separate objective function.
According to the natural resources and Canada’s office of energy efficiency, Canadians have
spent $152 billion for energy in 2012 [4]. They have used this amount for different divisions,
such as heating, ventilation and air-conditioning (HVAC) system for residential, industrial and
institutional buildings along with the common operating appliances and other industrial pur-
poses. Figure 1 presents the distribution of power usage by five sectors of economy: residential,
commercial (institutional), industrial, transportation and agriculture. The focus of this research
is on the residential sector. Here, residential sector is the third sector which consumes most of
the energy to the total energy used in Canada. It is also worth mentioned that 21% of the total
energy consumption is utilized in residential buildings in US. In addition, the amount of energy
used in homes is dependent on various aspects which can be inhabitants’ habits, needs and pref-
erences. They can be also varied over different areas of a city, for instance, climate differences
and sizes of houses effect the aggregated energy required for heating and cooling in residential
area. Figure 2 shows the distribution of power used by end-users’ devices in 2012 and it depicts
that 64% of the residential energy is consumed in HVAC system.
Figure 1. Energy distribution by different sectors in Canada [4].
Figure 2. End-users’ energy consumption in Canada [4].
In order to make a robust and more reliable power grid, peak demand is taken into account
rather than the average demand. As a consequence, natural resources are wasted, and the gen-
eration and distribution systems are under-utilized. Fast responding generators (e.g., coal and
gas units) are used to meet the peak demand, however, they are expensive and also have a high
carbon emission rate. As a solution, different programs have been presented to shape the energy
consumption profiles of users. Such programs aim to efficiently utilize the available generation
so that new transmission and new generation infrastructures are minimally installed. These pro-
grams are known as DSM programs which aim either at scheduling consumption or reducing
consumption [5].
A DSM program provides support towards power grid functionalities in various areas, such
as electricity market control and management of decentralized energy resources [6]. In elec-
tricity markets, it informs the load controller about the latest load schedule and possible load
reduction capabilities for each time interval of the next day. Using this procedure, it schedules
the load according to the objectives of interest associated with the power distribution systems
[7] and [8]. The load shapes indicate the daily or seasonal electricity demands of industrial or
residential consumers between PHs and off-peak hours (OPHs). These shapes can be modified
by six techniques [9] and [10]: peak clipping, valley filling, load shifting, strategic conservation,
strategic load growth and flexible load shape.
Peak clipping and valley filling are direct load control techniques. Peak clipping deals with
the reduction of the peak loads, whereas, valley filling considers the construction of loads for
the off peak demands. Load shifting is the most effective and widely-used technique for load
management in current power supply networks. It is concerned with shifting of the load from
PHs to OPHs. Strategic conservation [9] applies demand reduction methods at the customer side
for achieving optimized load shapes. If there is a larger load demand, then the daily responses
are optimized by load growth techniques (distributed energy resources) [9]-[11].
The working of a generic DSM controller is shown in Figure 3. This figure shows that DSM
controller aims for: (i) electricity cost minimization, (ii) energy consumption minimization, (iii)
peak to average ratio (PAR) minimization and (iv) user comfort (UC) maximization. In the liter-
ature, many DSM techniques are proposed [12]-[14] to achieve the aforementioned objectives.
However, UC is not considered in most of these techniques, like [13], [15]-[20]. Authors aim to
reduce the electricity cost in [14], [21], [22], whereas, some techniques focus on minimizing the
aggregated power consumption using integer linear programming and mixed integer linear pro-
gramming in [23] and [24]. Similarly, electricity bills and aggregated power consumption are
reduced in [25] using mixed integer non-linear programming. However, these techniques do not
take into account the large number of different household appliances. Moreover, randomness in
users’ load profiles makes the scheduling task more challenging.
1.1 Objectives
In order to overcome the aforementioned challenges, there are following objectives of this re-
search work for home energy management (HEM) and building energy management (BEM) in
residential sector:
• HEM controller is required to effectively manage the electricity schedules during peak
demand hours in order to optimize the electricity bills, energy consumption and PAR
along with the UC.
• Similarly, a BEM controller is also required for scheduling loads in residential buildings
for achieving all the aforementioned challenges.
• Appliances are operated in efficient fashion regarding energy consumption (i.e., HVAC
systems) using DSM techniques (i.e., load shifting and load curtailment).
Distribution
management
system
Distributed
grid load
information
Consumer (customer) management system
Detailed consumer information
- Specific industries (hospitals, schools)
- Customer equipment state (such as holding
medical equipment)
- Past request results and effects on history.
Weather System
Weather
information
(such as solar
radiation and
wind speed
information)
AMI System
Consumers’
load
information
Quatity forecast of the
power supply and demand
Request value caculation
Estimation
Setting available amount
Supply (system) side
management
Customer (consumer side
management)
Regional Business (common API)
Authentication or access Cntrol
Data process control
(Supply side)
Common API
(Consumer side)
Common I/F
Equipment configuration
information Event/State management
Weather sytem
Customer system
Power distribution
system
Equipment
management system
AMI system
HEMS integrated
managemnet
BEMS integrated
management
EV integrated
mangement
Information/ Control Shared foundation part
Figure 3. DSM using AMI [5],[6].
1.2 Contributions
In this synopsis, following schemes are proposed for achieving the aforementioned objectives
in residential sector.
• At first stage, a meta-heuristic algorithm-based DSM controller is proposed and imple-
mented for a residential area for the efficient scheduling of the daily used appliances
using the real time pricing (RTP) scheme. During scheduling, we have applied load shift-
ing and load curtailment strategies. In the designed DSM controller, five meta-heuristic
algorithms are implemented: genetic algorithm (GA), binary particle swarm optimiza-
tion (BPSO), wind-driven optimization (WDO), bacterial foraging optimization algorithm
(BFOA) and our proposed hybrid genetic wind-driven (GWD) optimization algorithm.
These algorithms are chosen for implementation due to their flexibility for the specified
constraints and their low computational complexity [26]. More distinctively, prioritized
load shifting is carried out between PHs and OPHs using a large number of appliances
in the residential area. For effective scheduling and according to the usage of appliances,
the appliances are divided into two classes: (i) Class A (non-shiftable appliances) and (ii)
Class B (shiftable appliances). Simulations are conducted for all of the presented meta-
heuristic algorithms and they are compared in terms of selected parameters: electricity
cost, energy consumption, PAR and UC. This scheme is tested for the single and multiple
homes. Results show that our proposed hybrid GWD algorithm performs better than the
other compared techniques in terms of aforementioned performance metrics.
• Secondly, we have proposed a fuzzy logic energy management controller for controlling
the HVAC system (seasonally used appliances) using the load shifting and load curtail-
ment techniques in residential buildings. We will also evaluate the thermal comfort for
the HVAC system in this case. Thermal comfort comprises on three subcategories: 1)
environmental, 2) personal, and 3) contributing. We shall consider the environmental
comfort which will be evaluated on the bases of air temperature, air movement, humidity
and radiation level.
• At the third stage, we have proposed a residential building energy management controller
for controlling the energy used for enlightening buildings. We will also compute the
visual comfort for the occupants based on their activity level.
• We will also integrate the RESs for the single, multiple homes and buildings. All of the
proposed schemes will be evaluated in terms of the cost minimization and UC maximiza-
tion. This research will benefit the users, societies and the economy when the consumers
act as prosumers, they can sell the surplus energy back to the grid and it is also helpful in
reducing carbon emissions.
• After the aforementioned contributions, we have also proposed a scheme to compare the
meta-heuristic and fuzzy energy management controllers in terms of the cost and comfort
evaluation to measure their performance with and without integration of the RESs.
• Along with the above mentioned scenarios, we have proposed a scheme to compare the
UC regarding the appliance delay, cost saving, thermal comfort and visual comfort for
evaluating the better performance in each case discussed earlier.
It is worth mentioning that the nomenclature and list of abbreviations are given below, respec-
tively.
2 Related work
In this section, we describe the relevant literature of the SG’s demand side management (DSM)
strategies for effective load management based on the load shifting and curtailment strategies.
Nomenclature
tTime interval T Total time (24 hours)
ECi,tUnits of energy consumed by
an appliance
hrs hours
PRi,tElectricity price at time t AiSet of appliances
PPopulation NTotal number of appliances
Sch_Cost Cost obtaining after scheduling Xi,tAppliance ON and OFF Status
Max_Gen Maximum generation mt_rate Mutation rate (0.1)
p_size Population size cr_rate Crossover rate (0.9)
Initial_Sol Initial best solution wWeight of particles
Ecost Savings Electricity cost savings αCost function variable
βDelay variable delay Delay specified for appliances
Ea p pUt il Appliance utility RT RT coefficient
gGravitational constant cCoriolis force constant
Max_Vel Maximum velocity HTotal homes
h Home counter Ned Number of elimination-dispersal opera-
tions
Max_Cost Maximum cost Nre Number of reproduction operations
Nsb Number of chemotactic opera-
tions
new_Pop New population
best_Sol Final best solution rand_Pos Random position of particles
rand_vel Random velocity of particles App_PB Appliances’ priority bits
Press_par Pressure of particles θpPosition of bacterias
pos_par Global best position of particles pos_vel Global best velocities of particles
cr_op Crossover operation mt_op Mutation operation
sSeconds
List of abbreviations
ANOVA Analysis of variation AC Air conditioner
ACO Ant colony optimization ADA Activity-dependent appliances
AMI Advanced metering infrastruc-
ture
ANN Artificial neural network
BPSO Binary PSO BFOA Bacterial foraging optimization
algorithm
CAC Central AC CPP Critical peak pricing
CN Control node CW Clothes washer
DSM Demand side management DR Demand response
DW Dish washer EMC Energy management controller
HVAC Heating, ventilation and air-
conditioning
F Fan
FCFS First come first serve FF Furnace fan
GA Genetic algorithm HG Home gateway
HP Heat pump IHD In-home display
IBR Inclined block rate LOT Length of operation time
MC Master controller ODA Occupancy-dependent appli-
ances
OIA Occupancy independent appli-
ances
OPH Off peak hour
PSO Particle swarm optimization PAR Peak to average ratio
PH Peak hour PB Priority bit
RAC Room AC RF Refrigerator
RTP Real-time pricing RES Renewable energy resources
SG Smart grid SM Smart meter
SH Space heater TOU Time of use
UC User comfort WDO Wind-driven optimization
WH Water heater WSN Wireless sensor network
HEM Home energy management
Load shifting strategies are “those which involve shifting loads to off peak periods using energy
storage or smart controls”, whereas, load curtailment strategies are “those which reduce service
demands without affecting the economic benefit derived from the energy use.”
2.1 Demand side management using load shifting strategies
In [13], the authors propose a technique for controlling the residential energy loads while max-
imizing user comfort (UC) and minimizing the electricity bill. However, this scheme does not
consider the peak reduction at the significant level during the OPHs. A survey of HEM for
residential customers is presented in [27], where the authors focus on different techniques re-
lating to shiftable, non-shiftable load and peak shaving. They use various pricing schemes:
RTP, time of use (TOU), critical peak pricing (CPP), inclined block rate (IBR), etc. In [28], a
fully-automated EMS for residential and commercial buildings is presented and they solve the
problem using Q-learning algorithm for optimal DR mechanisms. Cristopher et al. [29] design
a new framework using the smart meter (SM) to decide the appliances’ schedules for their load
or power consumption patterns. After scheduling, all of the relevant data is transferred to the
aggregator module, where the power consumption of all appliances is determined. The concept
of load clustering is introduced in this approach which comprises of three clusters for schedul-
ing purposes, as the first cluster is from 1 a.m. to 7 a.m., the second is from 8 a.m. to 3 p.m.
and the third is from 3 p.m. to midnight. Two battery scheduling scenarios are used as: (i) the
FCFS (First come first serve) scheduling policy and (ii) appliance first scheduling policy. In
FCFS, requests to consume electricity from clients are assigned priorities based on their arrival,
whereas in the appliance first scenario, all electrical devices’ requests are given priority over
battery charging.
Another methodology is proposed for minimizing the energy price under the dynamic pric-
ing scheme to avoid PHs in [30]. Its architecture comprises of: SM, control node (CN), wireless
sensor nodes (WSN) and in-home displays (IHD). AMI controls bidirectional data flow between
the utility and SM. The SM operates between master controller (MC) and advance metering
infrastructure (AMI). The MC organizes and controls the schedules of both controllable and
uncontrollable electrical appliances such that the optimal schedule is transmitted to each CN
via the WSN. IHD invigilates the whole process. The authors present a novel approach known
as the realistic scheduling mechanisms in [31] for minimizing the customer inconvenience us-
ing the TOU pricing scheme. They organize three categories of appliances (activity dependent
appliances (ADA), occupancy dependent appliances (ODA), occupancy independent appliances
(OIA)) and the algorithms relevant to their working times. They also use the BPSO algorithm
for the scheduling of these appliances. In [12], the researchers elaborate an efficient energy
scheduling model and an algorithm based on artificial intelligence for residential area energy
management in order to minimize the electricity cost. BPSO and GA are used for scheduling
the optimal time of appliances and also for obtaining the best fitness values of the objective
function.
For solving the numerically-constrained optimization problems, a review of BFOA is pre-
sented in [32]. The authors discuss the taxonomy of constraint handling techniques, the main
steps and adaptations to different schemes including: search space, step size, tumble-swim op-
erator and the elimination-reproduction process. In [33], a case study describes the electric
demand model in rural households of Narino. Distributed privacy-friendly DSM is presented
in [34], which preserves users’ privacy by integrating data aggregation and perturbation. The
authors describe that the users schedule their requests of appliances according to the aggregated
energy consumption measurements as an additive white Gaussian process.
The authors in [35] focus on cost and emission minimization approaches in data centers and
corresponding cloud network infrastructures. They use renewable energy generation capabil-
ity to enhance the reliability and energy efficiency in SG. They also improve the latency using
the information and communication technologies (ICTs). The decentralized energy manage-
ment framework presents DR mechanisms for the residential users to minimize electricity bills,
maximize the UC and privacy in [36]. In this framework, customers’ SMs integrate home load
management modules for exchanging the load profiles’ information. Agents exchange informa-
tion until they find an accurate load profile where the system does not get more improvement in
the solution.
In [37], an energy consumption management approach considers household users in which
each house consists of two types of requests or demands: (i) essential and (ii) flexible, where
flexible demands are further delay sensitive and delay tolerant. To optimize energy for both
delay-sensitive and delay-tolerant demands, a new centralized algorithm is presented for schedul-
ing. This approach also aims to minimize the total cost and delay of the flexible demands for
obtaining optimal energy decisions. The authors design a cost-efficient demand side day-ahead
bidding process and RTP mechanisms by using fractional programming methods in [38].
In [39], the authors present a survey of DSM optimization methods for the residential cus-
tomers. They classify the DSM techniques into three dimensions as: (i) DSM for individ-
ual users and cooperative consumers, (ii) DSM as a deterministic model versus the stochastic
method, and (iii) day-ahead DSM versus real-time DSM. The dynamic load priority method
presents priorities to modify load priorities during the occurrence of demand response events in
[40]. A DR technique formulates the two-stage stochastic problem for energy resource schedul-
ing by inciting the challenges of the renewable sources, electric vehicle and market price un-
certainty. It reduces the overall operational cost of the energy aggregator by using stochastic
programming [41]. In [42], global load balancing schemes describe the data center power man-
agement for minimizing the total electricity cost. They explain different components of the data
centers: information technology equipment, the power delivery system and the cooling system
in relationship with the SG’s features (power delivery, sustainability, peak shaving, etc.). A
multi-objective optimization solution is designed using the market operator and the distributed
network operator for a microgrid in [43]. The generation of the price signal from the market
operator and the power distribution system is specified using the Pareto-optimal solution.
In [44], a novel pricing strategy is proposed to investigate the robustness against RES power
inputs. This scheme also focuses on the marginal benefits and marginal cost of the power market
using all existing information related to electricity demand, supply and energy imbalance.
2.2 Demand side management using load curtailment strategies
SG initiatives are designed and applied by utilities to encourage consumers to reduce their elec-
tricity usage during peak load demands or high electricity prices by shedding the loads during
PHs or shifting loads to OPHs. Seven reasons are described in [45] to demonstrate the impor-
tance and the role of residential thermostats in reducing peak load demand. For instance, they
concluded that by reducing the set point temperatures in winter during DR events or high elec-
tricity prices, these thermostats can participate in shaving peak load demand. Thus, the DSM
programs for residential devices such as HVAC systems can potentially benefit both consumers
and utilities. However, consumers’ knowledge regarding DR, DSM programs and even their im-
pacts on electricity bill and energy management is fairly limited [46]. Furthermore, the lack of
knowledge among the residential customers regarding how to respond to RTP and DR programs
and the lack of intelligence in residential EMSs (i.e., PTs and PCTs) are two major obstacles for
optimally utilizing the advantages of SG’s incentives.
The authors discuss buildings EMS using the FLC in [47]. This technique considers four
input parameters: P
rate ,O,Tem poutdoor and the ISPs. However, that system lacks the adaptive-
ness in thermostat. The system considered in [48] is the extension of [47] and authors extend
it to make adaptive using the same fuzzy logic approach. This approach introduces the training
of ISPs of thermostat by considering the three consecutive changes of the same setpoint in the
same day of the week; it automates this setpoint to the optimized setpoint. In [49], authors
present IOFClime scheme in order to control the temperature using internet of things along with
Table 1 Recent trends: state of the art work
Technique Targeted Area Objective Limitations
Optimal energy consump-
tion scheduling algorithm
[12]
HEMS Cost minimization Compromising the UC and RES
Smart charging and appli-
ance scheduling approaches
[16]
Appliance scheduling and
storage
Cost minimization, PAR reduction Lack of installation and mainte-
nance cost of batteries and UC
Optimal residential appli-
ance scheduling via HEM-
DAS [30]
HEM Cost minimization, PAR reduction Inconsideration of the UC maxi-
mization
Enabling privacy in a dis-
tributed game-theoretical
scheduling systems [34]
Game-theoretic DSM Focused on privacy, electricity bills
minimization and PAR reduction
Inconsideration of total bill reduc-
tion
Residential load manage-
ment in smart homes [13]
Residential area Cost minimization PAR is not reduced upto significant
level
BFOA in constrained nu-
merical optimization [14]
Residential area PAR reduction and cost minimiza-
tion
Inconsideration of larger population
size
Queuing-based energy con-
sumption management [37]
Residential SG networks Cost minimization and delay reduc-
tion
Inconsideration of PAR and RES
Home energy management
for residential customers
[27]
HEMS Concentrates on UC, Energy con-
servation and PAR
Effective maintenance of cost is ig-
nored
Realistic scheduling mecha-
nisms [21]
EMS Cost savings and UC maximization Inconsideration PAR reduction us-
ing BPSO
Optimal DR mechanisms
[28]
Commercial and residential
buildings
Cost reduction, Minimized the user
discomfort
Inconsideration of PAR
Electricity demand model-
ing [33]
Rural households Energy consumption minimization Inconsideration of control variables
for electric demand
Information and communi-
cation infrastructures [35]
ICTs Energy efficiency Inconsideration of UC
Optimal residential load
management [36]
Residential customers Energy efficiency Inconsideration of cost
Residential load scheduling
in SG [38]
DSM Cost and UC PAR is not considered, Limited up
to 50 customers
SG and smart home security
[33]
DR Energy efficiency Trade-off between demand limit
and supply
their platforms and fuzzy logic for saving energy and cost upto 40%. However, this approach
has several limitations: first is that they have used the fuzzy logic configuration based on their
experience, second is that they do not use the indoor historical temperatures which can help
in improving energy savings, etc.. Furthermore, authors in [50] describe that power systems’
resilience can be improved during critical situations using micro-grids and the critical state of
the system is predicted by the fuzzy logic battery storage controller using its subservient and re-
silient operation modes. This scheme reduces the load curtailment upto 92%, however, it suffers
from the higher operational cost.
In short, the existing optimization techniques in [13], [15]-[17] are not sufficient to han-
dle the complexity of cost minimization and UC maximization problems. Therefore, we use
meta-heuristic algorithms (GA, BPSO, WDO and BFOA) to solve these two problems. These
algorithms support the multi-objective optimization problems and have flexible constraints and
parameters, which are easy to handle. These algorithms are similar to population-based search
methods [51], which move from one population to another population in a number of iterations
with improvement using a combination of probabilistic rules. The comparison of some afore-
mentioned techniques along with their achievements and drawbacks is listed in detail in Table
1.
2.3 Analysis
In smart grid, effective management of the power during both the PHs and OPHs is very complex
and challenging issue. Major challenges of the energy management are: 1) cost minimization, 2)
energy consumption maximization, 3) PAR minimization and 4) UC maximization. For effec-
tively tackling with these challenges; a DSM controller is required to be designed. Load shapes
can be modified by six DSM strategies: peak clipping, valley filling, load shifting, strategic
conservation, strategic load growth and flexible load shape.
Many DSM techniques are proposed in the literature as [14] to tackle the aforestated chal-
lenges which occurred due to improper energy management. However,
• Many DSM techniques are proposed in the literature as [14] to tackle the existing chal-
lenges which occurred due to improper energy management. However, UC (in terms of
appliance delay) is not considered in most of these techniques, like [13], [14] and [15].
• Peak formation is increased during OPHs in [13].
• The studies in [14], [15], [24] aim to reduce the electricity cost by minimizing the ag-
gregated power consumption using integer linear and mixed integer linear programming,
however, they did not consider the PAR minimization and carbon emission reduction ra-
tio.
• Moreover, increase in energy prices and transition from flat-rate to dynamic pricing such
as TOU rates will impact the households’ energy bills, which are significantly relevant
to HVAC system [47] and [48]. The energy consumed by the HVAC system increase the
electricity bill if it is not properly controlled during the PHs.
• Techniques considered in [47] and [48], are used for load curtailment, however, UC is
compromised in them during the PHs. These techniques do not take into account the
large number of multiple households’ appliances or buildings’ load along with the users’
thermal and visual comfort. These techniques are also not sufficient to address the chal-
lenges of making the appliances smart in both the homes and buildings. There is a need to
integrate RES with these techniques in order to make the electric appliances more smarter
than earlier.
• Furthermore, randomness in users’ load profiles makes the scheduling task more chal-
lenging. Meta-heuristic algorithms are used for tackling the issues like, randomness in
user’s load profile and different users’ preferences level. Following algorithms are used
for this purpose: GA, BPSO, BFAO and WDO, which provide the optimal solution be-
cause these are designed to handle stochastic nature of problems. They efficiently guide
the search space and find the optimal solution (efficiently find the optimal slot for the
appliance scheduling). In addition, all of these algorithms use suitable exploration rates
to find the effective scheduling patterns. Furthermore, they are also used to solve the
multi-objective optimization problem with the conflicting objectives.
3 Problem statement
In literature [13]-[15], [24], [47], [48], author have discussed load shifting [13]-[15], [24], and
load curtailment [47], [48], strategies. Now we have presented the problem statement in detailed
form by giving the alternative solutions for overcoming these problems.
“In order to make DSM controllers intelligent in residential sector (i.e., scheduling appli-
ances in homes and buildings), the prioritized load shifting and load curtailment need to be
handled efficiently. In the existing literature [13]-[15], [24], [47], [48], to shift appliances at an
optimal operational time slot, static priorities are assigned because of which the dynamic adjust-
ment of appliances with respect to change in electricity prices from the utility and increase load
is difficult. Moreover, the load varies from situation to situation and consumer to consumer,
thus, when fixed parameters are used for load curtailment then it is very hard to cope with sud-
den changes in the load resulting in high user discomfort and more electricity bills. Thus, more
load creates peaks because of which consumer is unable to operate appliances at desired time
slot leading to high user discomfort [13]-[15], [24].
Similarly, load curtailment [47] and [48] is not handled efficiently due to which it disturbs
the user thermal comfort. Further, during peak hours, the setpoints of the heating, ventilation
and air-conditioning (HVAC) systems consume high power and utility becomes over-burdened
without load curtailment. Thus, utility suffers from the energy scarcity problem and consumers
have to pay high electricity bills during the peak demand period of the HVAC systems. For
instance, HVAC systems consume 64% of the total electricity in Canada [4] as shown in Figure
2. So, it needs efficient mechanisms to operate these devices.
The intelligent energy management controllers for the residential (homes and buildings)
consumers are required to be designed so that appliances can work efficiently. RESs are required
to be integrated for the consumers in order to define prioritized schedules of the appliances along
with the prevention of the load curtailment which minimize the energy scarcity problem, reduce
electricity bills, and enhance UC.”
4 Problem formulation
In this research, the major objectives are: (i) to reduce consumers’ electricity cost by optimizing
the energy consumption of end users, (ii) to maximize the UC of consumers. Here, the problem
is formulated as an optimization problem in consideration of fixed, shiftable and elastic loads.
4.1 Cost minimization
Cost minimization refers to the minimum charges for the consumed loads provided by the utili-
ties to consumers. The elastic and shiftable loads are considered as the deferrable loads for the
cost minimization problem which is formulated as follows:
Minimize
N
∑
i=1
T
∑
t=1
(Xi,t×PRi,t×ECi,t)(1)
such that:
Xi,t=0,i f t ∈H1
1,i f t ∈H2
(1a)
1≤t≤T(1b)
1≤i≤N(1c)
Where, Xi,trepresents the states of the appliances as ON or OFF (1 = ON and 0 = OFF) and
PRi,tshows the price of the electricity consumed during any time interval t, which is the index
for time upper bounded by T = 24 hours in a day. H={1,2, ..., T}, where H shows the time
for the 24 h of a day, including PHs and OPHs. Here, H1={7,8,9,10}indicates the PHs and
H2={H/H1}describes the OPHs. i denotes the appliances’ index number, which is taken as N
= 12.
4.2 User comfort maximization
UC is modeled in terms of the minimum delay of appliances and optimal amounts for the elec-
tricity bills. Therefore, consumers always expect utilities with minimum delay and cost. More-
over, it also helps in minimizing the customers’ frustrations when the energy consumption is
high during the OPHs. In this scenario, the appliances are assigned a specific priority, and
high priority appliances are scheduled at the first and available time intervals during the OPHs.
The operations of the low priority appliances can be canceled or delayed during the PHs. In
this way, appliances’ waiting time is minimized and UC is achieved maximally. This is the
multi-objective problem; several authors handle it using different approaches, as mentioned in
the literature [15, 16, 17, 18, 19, 20]. This study handles it using the heuristic algorithms for
scheduling the residential area loads in order to reduce the electricity cost and maximize the UC.
UC is influenced by minimum delay of appliances, load shifting towards OPHs, load curtail-
ment during PHs and OPHs and cost saving in our current scenario. However, in our subsequent
work, we will also consider the influences of integration of RESs, indoor and outdoor temper-
ature. Here, UC is evaluated only for the appliances’ delay and cost saving, however, in our
future work, we will consider the thermal comfort and visual comfort which will use the indoor
and outdoor temperature. There will be some challenges which exist while maximizing UC.
It can be effected by the installation and maintenance cost of RESs and complexity increases
during appliances’ switching time towards RESs. In addition, [41] discusses the UC in terms
of delay and cost saving of the appliances, however, they did not consider indoor and outdoor
temperature. Articles [47] and [48] discuss the UC in terms of indoor and outdoor tempera-
ture, however, they ignored the delay and cost. So, trade-off exists between cost and comfort,
cost and delay, and cost and indoor, outdoor temperature. Furthermore, we will also consider
the trade-off between the cost, delay and indoor, outdoor temperature. Here, it is calculated by
using the equations given below,
Maximize(E a p pU til +E costSavings/TotalCost)(2)
such that:
EappUtil = (α−(delay/24)) (2a)
EcostSavings =β×(cost/100)×(Sch_cost/Max.cost)(2b)
0.3≤α≤0.7 (2a.1)
1≤delay ≤4 (2a.2)
0.3≤β≤0.7 (2b.1)
α+β=1 (2b.2)
αand βare the delay variables. Moreover, del ay is the maximum allowed appliances’ delay
during the day, and it is restricted to four hrs in our scenario. It is worth mentioning that these
4 hrs are chosen from PHs for elucidating the maximum delay of the appliances. If the delay is
greater than 4 hrs then the utility pays a penalty by either paying back to customers or providing
them with reductions in the electricity bills. According to constraint 2b.2, the sum of αand β
is equal to one because UC ranges between zero and one. Cost is the variable and its values are
taken from 20% and 70%. Below 20%, its values are assumed to be negligible and above 70%
cost values are used for the micro-grids. Sch_cost is the cost of the non-deferrable appliances
during the full day and Max.cost is the cost of PHs of the day; Sch_cost is obtained from the
status bits of the appliance x power rating x price rate at any interval of the day. The values of
α,βand cost are taken from [41].
4.3 Multi-objective function
Multi-objective optimization deals with multiple objectives. Here, we are using weighted sum
method to evaluate the considered objectives in this problem (i.e., cost and UC). Weighted sum
method assigns equal preferences to all objectives in any problem and the sum of weights should
be equal to 1. Two weights: c1and c2are used for the both objective functions. From the
objective functions in Equations (1) and (2), it is clear that the optimization problem is multi-
objective. We formulate the combined objective function as follows:
Minimize(c1
N
∑
i=1
T
∑
t=1
(Xi,t×PRi,t×ECi,t
TotalCost ) + c2
1
EappUtil +EcostSavings )(3)
Where, c1=c2=0.5 and it is also worth mentioned that the combined objective function in
Equation (3) is subject to the respective constraints of objective functions described in Equations
(1) and (2).
UC
AMISM SM
SM
HG
FF,
F,
CAC
,
RAC
SH,
HP,
PH,
WH
CW,
DW,
CD,
RF
Class A Class B
HG
FF,
F,
CAC
,
RAC
SH,
HP,
PH,
WH
CW,
DW,
CD,
RF
Class A Class B
FF,
F,
CAC
,
RAC
SH,
HP,
PH,
WH
CW,
DW,
CD,
RF
HG
Class A Class B
Home 1 Home N
Home 2
...
Figure 4. Proposed system design.
5 Proposed system model
At this stage; we are considering the single and multiple homes without integrating the RESs.
We will consider the residential buildings and RESs in our subsequent contributions.
The proposed DSM techniques deal with the load management in a residential area for
single and multiple homes. It’s architecture consists of the number of homes, SMs, AMI and
the utility companies. Let multiple homes be connected with a utility and SMs be installed
in all of the homes as shown in Figure 4. The AMI is used for bidirectional communication
between SM and the utility. All homes have three types of appliances: (i) fixed, (ii) elastic,
and (iii) shiftable. These appliances are also categorized into Class A and Class B based on
their fixed or interruptible load profiles. Fixed load appliances are included in Class A, whereas
elastic and shiftable are included in Class B. In other words, Class B contains interruptible
appliances, which take part in the scheduling process. In this work, the following terms are used
interchangeably: interruptible, shiftable and deferrable.
The RTP tariff model is used for tracking the pattern of the total hourly costs of the con-
sumed energy. Figure 5 shows that the appliances are scheduled by the appliances’ handler
(EMC) during the specified time intervals using the given frame format. EMC schedules and
checks appliances’ PB using the frame format. Each frame format consists of an eight-bit pat-
tern, such that each appliance uses a specified bit pattern relating to its class ID, appliance ID,
scheduling bit, interruptible or non-interruptible bit and priority bit (PB). Based on the oper-
ational status of an appliance, system’s hourly cost schedule is tracked. In each class, every
attribute uses a single bit, except class ID and appliance schedule, which use three and two-bit
patterns respectively. This scenario is specific to these sets of the appliances using the given
frame format for the initial evaluation of the proposed system; however, it can be further ex-
tended to a larger set of appliances, and frame length can also be extended accordingly. Peak
formation is considered in worst case scenarios in the literature. However, we have prevented
the peak formation during the PHs using the load shifting and load curtailment strategies. The
Class ID Appliance
ID
Appliance
schedule
Interruptible
or not Priority bit
213 1 1
Number of bits assigned
for each attribute
00 0
1
000, …, 111
0
11 1000, …, 111 1 1
11 000 1 0 1
App1 Appliances’
handler
(EMC)
11 111 1 0 1
App Schedular
HG
FCS frame
Schedules
FCS frame
Schedules
Bit pattern Class A
Bit pattern Class B
Figure 5. RTP tracking system.
target of the energy management system is to utilize the energy in an efficient way and maintain
the appliances’ schedules as per user demands. Only base load appliances cannot be scheduled;
for example: air conditioner, microwave oven, lights, irons, etc. However, there is no such sce-
nario described in the literature where appliances cannot be scheduled. Evolutionary algorithms
are efficient in terms of computational complexity, however, at the cost of reduced accuracy. We
prefer frame tracking over other evolutionary algorithms because it provides simple and efficient
procedure in terms of relative accuracy and relative computational complexity. In the following
subsections, the algorithms of GA, BPSO, WDO, BFOA and our proposed GWD algorithm are
discussed in detail.
5.1 Genetic algorithm, binary particle swarm optimization algorithm, wind driven
optimization algorithm and bacterial foraging optimization algorithm
In this section, we have modified the existing versions of GA, WDO, BPSO and BFOA to opti-
mally schedule shiftable appliances. Firstly, the load is shifted during the OPHs for electricity
cost minimization. In order to reduce peaks during the OPHs, each appliance is assigned a spe-
cific PB. If an appliance is demanded to run in any requested time slot, its PB = 1; otherwise,
its PB = 0. This status bit information is communicated via an RTP frame format. The authors
in [16] have proposed a GA-based home energy management controller for a single home in a
residential area using RTP tariffs.
In this manuscript, a modified GA (an improved form of [16]) is presented, which is shown
in Algorithm 1. Objective functions (refer to Equations (1)–(3)) and their constraints are used
by all of the selected optimization algorithms to find feasible solutions. GA creates a random
population initially, which consists of a number of chromosomes represented by binary strings as
the ON/OFF status of each appliance. Each time objective function is evaluated using Equations
(1)–(3) using RTP pricing scheme.
In [18], another energy management model is presented in which BPSO is used to meet the
DSM challenges. The goal of this study is to minimize the electricity cost for residential area by
scheduling shiftable loads. The authors use the TOU pricing model to calculate electricity bills
of customers by investigating DR; however, they have ignored UC. Furthermore, in our proposed
work, the objective function is formulated for cost minimization and UC maximization. BPSO
is used to solve the designed optimization problem. Thus, this proposed work gives a more
significant solution for electricity bill minimization, PAR minimization and UC maximization.
All steps of the proposed work are shown in Algorithm 2. Compared to [18], BPSO is modified
according to the consumers’ requirements. Each particle in the generation is represented by a
binary string denoted as states of an appliance which is applicable for single and multiple homes
in residential areas.
A WDO-based scheduling technique is presented in [10] for comfort maximization of resi-
dential users. By considering appliance classes; user preferences and weather status, they model
the UC and electricity cost. The WDO algorithm is used for minimizing electricity cost and
maximizing UC. This work also analyses peak cost reduction in electricity bills by consider-
ing the TOU tariff. In this proposed work, household appliances are categorized on the basis
of length of operation time (LOT) and appliance power consumption. In order to make the
scheduling process more efficient, delay and PB criteria (which are not considered in [13]) are
incorporated here for reducing electricity bills. In this study, WDO is enhanced in which LOT
and the energy consumption of each appliance are calculated by evaluating the objective func-
tion (refer to Equations (1)–(3)) using constraints. All steps of the implemented WDO algorithm
are shown in Algorithm 3.
In [20], the authors propose a BFOA technique for grid resource scheduling. This tech-
nique is based on the hyper-heuristic resource scheduling algorithm, which has been designed
to effectively schedule jobs on available resources in a grid environment. The authors evaluate
the performance of the proposed BFOA algorithm by comparing it with the existing heuristic
scheduling algorithms (GA and simulated annealing) using the makespan and cost performance
metrics. Experimental results show that the proposed algorithm outperforms the existing algo-
rithms in terms of cost minimization. In comparison to [20], the proposed work introduces a
new methodology of appliance scheduling for minimizing electricity cost, energy consumption
and PAR, which benefits both customers and the utility. In this study, objective functions (refer
to Equations (1)–(3)) and their constraints are modified according to the designed scenario.
Algorithm 1: Genetic algorithm.
Input: set of appliances Ai;
Initialization: p_size;Max_gen; t; PHs; OPHs, mt_rate,cr_rate; PB;
Generate Population Randomly;
for t=1toTdo
for h=1toHdo
for For p=1 to p_size do
Evaluate fitness function using (3);
Initial_Sol=best_f it ness(value);
while Max_gen do
if t == PHs then
swap(PHs_load,OPHs_load);
end
if t==OPHs && EC==high then
check App_PB;
end
perform cr_op;
perform mt_o p;
end
Generate new_Pop;
identify best_Sol;
end
end
end
Algorithm 2: Binary particle swarm optimization algorithm.
Initialization: p_size, t, Max_gen,PHs, OPHs, pos, vel, PB;
Generate feasible Prandomly;
for t=1toTdo
for h=1toHdo
for p=1 top_size do
assign rand_Pos and rand_vel to air parcels;
Evaluate fitness function using (3);
Initial_Sol=best_fitness(value);
while Max_gen do
if t == PHs then
swap(PHs_load,OPHs_load);
end
if t==OPHs && EC==high then
check App_PB;
end
Update w of the particles using piecewise linear function [18] ;
Update vel using sigmoid function ;
Update position vector pos using piecewise linear function [18];
end
identify best_Sol;
end
end
end
Algorithm 3: Wind driven optimization algorithm.
Initialization: Press_par ; Max_vel; p_size; RT; c; g; Max_gen; t; PHs; OPHs, rand_pos;
rand_vel; PB;
Generate initial random population;
for t=1toTdo
for h=1toHdo
for p=1 to p_size do
Assign rand_pos and rand_vel to air particles;
Evaluate fitness function using (3);
Initial_Sol=best_fitness_value;
while Max_gen do
if t == PHs then
swap(PHs_load,OPHs_load);
end
if t==OPHs && EC==high then
check App_PB;
end
Update pos_par;
Update vel_par;
end
Identify best_Sol;
end
end
end
Algorithm 4: Bacterial foraging optimization algorithm.
Input: randomly initialize the swarm of bacteria θp(j,k,l);
Initialize: PHs, OPHs, p_size,Max_gen,t=0,H,PB=0,1;
Generate initial population randomly;
for t=1toTdo
for h=1toHdo
for p=1 to p_size do
Compute for f(θp(j,k,l));
Evaluate fitness function using (3);
Initial_Sol=best_fitness_value;
for l=1 to Ned do
for k=1 to Nre do
for j=1 to Nsb do
while Max_gen do
if t == PHs then
swap(PHs_load,OPHs_load);
end
if t==OPHs && EC==high then
check App_PB;
end
Evaluate objective function using Equation (3);
end
Calculate f(θp(j,k,l));
Perform chemotactic procedure;
Check tumble-swim operations;
Check reproduction process by swapping;
Perform the elimination-dispersal;
end
end
end
end
end
end
5.2 Developing a hybrid genetic wind driven optimization algorithm
In this algorithm, all of the stages of WDO are performed in a similar way as explained in
Section 4.1; however, the velocity updating steps for the global air pressure is replaced with
GA’s crossover and mutation operations. In some cases, pressure values are very large, such
that the updating velocities become too large, which degrade WDO’s performance. Thus, we
replace these with GA’s crossover and mutation values. The scheduling procedure is followed
as the same described in GA, BPSO, BFAO and WDO. It is evaluated with the help of the same
objective functions (refer to Equations (1)–(3)). Detailed steps of this algorithm are shown in
Algorithm 5.
Algorithm 5: Genetic wind driven optimization algorithm.
Initialization: p_size;Max_gen; Press_par, c, g, RT, t, PHs, OPHs, mt_rate,cr_rate,
PHs, OPHs, H, PB;
Generate initial random population;
for t=1toTdo
for h=1toHdo
for p = 1 to p_size do
assign rand_Pos and rand_vel to air parcels;
Evaluate fitness function using (3);
Initial_Sol=best_fitness_value;
while Max_gen do
if t == PHs then
swap(PHs_load,OPHs_load);
end
if t==OPHs && EC==high then
check App_PB;
end
perform cr_op and mt_op;
Update pos_par and vel_par;
end
identify best_Sol;
end
end
end
The meta-heuristic algorithms do not guarantee exact reachability of the global optimum
solution. The obtained solution is dependent on the set of random variables generated at the
start of the meta-heuristic optimization process. In our scenario, BPSO, BFOA and WDO suffer
from the global optima, and GA is a relatively better suited algorithm for the global optimal
solution. In order to filter out the effects of random initializations, simulation runs of these
algorithms are increased in number. However, this filtration is achieved at the cost of increased
computational time. The complexity of each algorithm is θ(n5+k)except BFOA; because
it takes θ(n7+k)due to its four major steps. Complexity can be increased if the multiple
residential areas are considered and time slots are taken in very small intervals (i.e., in minutes),
however, this controller will be scalable for the multiple residential areas. We have presented
the statistical analysis of all algorithms using the ANOVA in the results (Section 6) after taking
the average of 10 runs.
6 Simulation results and discussion
In order to evaluate the proposed work, simulations are conducted in MATLAB using the RTP
scheme. MATLAB is trusted by the millions of the engineers and scientists. It combines a desk-
top environment tuned for iterative analysis and design processes with a programming language
that expresses matrix and array mathematics directly. Its toolboxes are professionally developed,
rigorously tested, and fully documented. With interactive application facility; its applications
allow to see how different algorithms work with the data and iterate until users’ got the results
according to the claims. Its applications automatically generate a MATLAB program to repro-
duce or automate users’ work. MATLAB also has the ability to scale the required analyses to run
on clusters and clouds with only minor code changes. There is no need to rewrite code or learn
big data programming and out-of-memory techniques. In our case, MATLAB provides good
toolboxes and features for designing HEM and buildings’ energy management simulators, i.e.,
Table 2 Appliances used in simulations.
Class Name Appliance Name Power Rating Length
of op-
eration
time
Deferrable
Load
Class A Fan 0.5 11 0
Class A Furnace Fan 0.38 8 0
Class A Central AC 2.80 12 0
Class A First-Refrigerator 0.50 24 1
Class B Room AC 0.90 5 0
Class B Space Heater 1.0 9 1
Class B Heat Pump 0.11 4 1
Class B Portable Heater 1.00 5 1
Class B Water Heater 4.50 8 1
Class B Clothes Washer 0.51 9 1
Class B Clothes Dryer 5.00 5 1
Class B Dishwasher 1.20 11 1
simulink, fuzzy logic, and neuro-fuzzy toolboxes. The 24-hrs time period is divided into PHs
and OPHs for tracking the real-time behavior of the system. Four hours are taken as PHs (from
7 a.m.–10 a.m.), such that the PHs vary from season to season [52]. From December–February,
PHs are 5 p.m.–9 p.m.; from March–May, PHs are 6 p.m.–10 p.m.; from June–August, PHs
are 7 a.m.–10 a.m.; and from September-November, these vary accordingly. Four hours are
used in this case (from 7 a.m.–10 a.m.) of one season, and the remaining all are included in
OPHs. There are two simulation scenarios that are discussed here: (i) single home and (ii) fifty
homes. For validating the preliminary results, we are considering 12 appliances for each home
and appliances are categorized into two classes: (i) Class A with fixed load appliances and (ii)
Class B with shiftable and elastic load appliances, as shown in Table 2. In the subsequent work,
simulation results will be tested on larger dataset. Figure 4 shows the RTP rates during each
hour of the full day. The parameters of GA, BPSO, WDO, BFAO and GWD are given in Tables
3–7, respectively. To evaluate the performance of these algorithms, the following performance
metrics are used.
• Cost: Amount of electricity bills for the total number of units consumed per unit time in
cents.
• Energy Consumption: It is calculated as the total energy utilized per unit time in kilowatts
per hour.
• PAR: It is defined as the total peak load divided by average load during the whole day.
• UC: It is calculated in terms of minimum cost and minimum appliance delay.
6.1 Single home
The energy consumption of our proposed scheme hybrid GWD with respect to GA and WDO
in unscheduled and scheduled cases is shown in Figure 6. This figure shows that the maximum
energy consumption values are 16.2 kWh, 11.8 kWh, 8.2 kWh and 4.1 kWh for the unscheduled
case, scheduled GA, WDO and the hybrid GWD approach, respectively. The energy consump-
tion of all algorithms is below their unscheduled cases, which is 56.89%, 67.18% and 65.87%;
and it is obtained by dividing the scheduled and unscheduled cost with percentage. It is also im-
portant to note that the hybrid GWD algorithm is better than the simple WDO and GA in terms
of energy consumption. GWD uses crossover and mutation operations from the GA, which
helps in faster convergence of the solution for achieving the optimized results.
Table 3 Genetic algorithm parameters and values.
Parameter Value
Parameter Value
Population Size 200
Selection Tournament
Selection
Elite Count 2
Crossover 0.9
Mutation 0.1
Stopping Criteria Max. Genera-
tion
Max. Generation 1000
Table 4 Binary particle swarm optimization parameters and values.
Parameter Value
Swarm Size 20
Max. Velocity 4 ms
Min. Velocity 4 ms
Local Pull 2 N
Global Pull 2 N
Initial Momentum
Weight
1.0 Ns
Final Momentum
Weight
0.4 Ns
Stopping Criteria Max. iteration
Max. Iteration 600
Table 5 Wind driven optimization parameters and values.
Parameter Value
Swarm Size 10
Max. V 4 m/s
RT-Coefficient 3
g 0.2
c 0.4
Dimensions [−1, +1]
Stopping Criteria Max. Iteration
Max. Iterations 500
Table 6 Bacterial foraging optimization algorithm parameters and values.
Parameter Value
Population Size 10
Maximum Number
of Steps
30
Number of Chemo-
tactic Steps
5
Number of Elimi-
nation Steps
5
Number of Repro-
duction Steps
25
Probability 0.5
Step Size 0.1
Stopping Criteria Max. Genera-
tions
Max. Generations 100
Table 7 Genetic wind driven parameters and values.
Parameter Value
Particle Size 20
Number of Itera-
tions
500
Max. V 0.4
Dimensions [−1, +1]
RT-Coefficient 3.0
g 0.2
c 0.4
α0.4
Crossover Rate 0.9
Mutation Rate 0.1
The maximum amount of the electricity bill in the unscheduled case is 318.88 cents, as
shown in Figure 7. It is reduced to 78 cents in the case of GA, while it is reduced from 318
cents to 245 cents in WDO and up to 75 cents in GWD. The electricity cost in GA, WDO and
GWD is 60%, 62% and 30%, respectively. During PHs, sufficient electricity cost reduction is
achieved for all designed algorithms (GA, WDO and GWD). GWD performs better than the
other algorithms in terms of the electricity cost reduction due to the amalgamation of crossover
and mutation. The WDO’s cost is high due to its high pressure values. The PAR performance
of all algorithms (GA, WDO and GWD) is shown in Figure 8. This figure shows that PAR
is significantly reduced in hybrid GWD as compared to the GA, WDO and unscheduled case.
Results prove that our proposed algorithm effectively tackles the peak reduction problem. The
PAR graph for GA, WDO and hybrid GWD displays that the power consumption of appliances
is optimally distributed without creating peaks during the OPHs and PHs of the day. The PAR
in GA, WDO and GWD is reduced upto 60%, 75% and 40%. WDO has higher PAR than GA
because it has higher pressure values of the particles, and GA is more effective in PAR reduc-
tion due to its ability to generate new populations of more feasible solutions using crossover and
mutation. From these results, it is shown that the hybrid GWD approach outperforms all other
schemes because it uses the best features of both. Peak formation is a major limitation in the
traditional electric power system because it causes consumers to pay high electricity bills, and
the utility also suffers from high demand which leads to blackouts or load shedding. The per-
formance of these algorithms in this scenario is improved due to load shifting using appliances’
PBs which causes utilities to fulfill the demands of consumers and gives customers a chance to
reduce their electricity bills. In our proposed hybrid scheme, we have achieved the desired UC
as shown in Figure 9. It shows that UC is significantly reduced for GWD, GA and WDO as
compared to the unscheduled case. By applying priority scheduling on the objective functions
(refer to Equations (1)(3)), this work enhanced the performance in terms of UC. UC of the un-
scheduled case is better while in schedule WDO, GA and GWD, it is 60%. The maximum delay
considered here is 4 hrs; otherwise, the utility has to pay a penalty for the users. There is trade-
off between UC and cost in this scenario because we have adopted multi-objective optimization
(weighted-sum method). However, the performance of this work is much better by considering
the priority bits and minimum delay during scheduling. All above simulations are performed
for a single home; however, for testing the effects of the proposed scheme in multiple homes,
multiple homes are considered in the next section. All of the modified algorithms (GA, BPSO,
WDO and BFOA) are tested for 50 homes to investigate them in terms of energy consumption
minimization and electricity cost reduction. As these algorithms are designed to satisfy the con-
straints of the objective function in 24 hrs, so that residential users get facilitated by reducing
their electricity bills.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0
2
4
6
8
10
12
14
16
18
Energy Consumption (kWh)
Unscheduled Load
WDO Scheduled Load
GA Scheduled Load
GWD Scheduled Load
Figure 6. Energy consumption.
6.2 Fifty homes
The energy consumption of GA, BPSO, WDO and BFOA is 15.00 kWh, 7.90 kWh, 11 kWh and
14.5 kWh, respectively, which is less than the unscheduled case as 16.5 kWh, approximately;
as shown in Figures 10-13. The energy consumption in GA, BPSO, WDO and BFOA is 79%,
47%, 45% and 88%. GA is efficient among all of the others, although it considers a larger pop-
ulation size. It uses a natural selection operator, which reduces the convergence time towards
the efficient solution during scheduling. BFOA is faster than BPSO and consumes less energy
because BFOA is faster for a small population size. On the other hand, BPSO is suitable for a
larger population size and it also escapes from the local minima. WDO consumes more energy
as compared to BPSO, BFOA and GA, because it uses explicit pressure values of particles.
The electricity cost of the simulated algorithms is shown in Figures 14-17, which is achieved
during the scheduling process. In each case, the scheduled costs of all four algorithms, GA,
BPSO, WDO and BFOA are 125.20, 175, 215 and 160 cents, respectively which are lower than
the unscheduled cost of 350 cents. Furthermore, by using the PBs during appliance scheduling,
the overall cost is reduced as compared to the unscheduled cases. After scheduling, the obtained
electricity cost by using GA, BPSO, WDO and BFOA is 35%, 50%, 61% and 45%, respectively.
In this case, GA is the most effective algorithm even considering a larger population size than
the other algorithms. GA uses the crossover and mutation operation, which is efficient in con-
vergence and at finding the global optimal solution. BPSO uses linear and piecewise functions
instead of natural selection operators and it is mostly used for a large population size to avoid lo-
cal minima. BFOA is suitable for a small population size and it is more efficient than BPSO and
GA in terms of convergence and energy efficiency. WDO suffers from higher pressure values,
so it gives a higher cost than the others.
Overall, the scheduled peak formation rate is better than the unscheduled cases, and the
desired results of the load shifting are achieved by the scheduling. The PAR obtained in GA,
BPSO, WDO and BFOA and is 26%, 25%, 12% and 2% respectively. All of the high profile
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time (hours)
0
50
100
150
200
250
300
350
Cost (cents)
Unscheduled Cost
WDO Scheduled Cost
GA Scheduled Cost
GWD Scheduled Cost
Figure 7. Total cost.
appliances are scheduled to low price rate hours. If the consumed energy in OPHs is high
(creating peaks), then appliances are scheduled according to their PBs for reducing load and
avoiding peak formation even during the low pricing rate hours. PAR in WDO, BPSO and
BFOA is better than GA because GA is tested for a large set of populations, whereas, all of the
others are tested for a small population size, as shown in Figure 18. UC achieved by GA and
BFAO is significantly greater than BPSO, WDO and the unscheduled case as shown in Figure
19. The UC achieved in GA is nearly 0.9; BPSO is 0.5; WDO is 0.55; BFAO is 0.85; and it is
90%, 50%, 50% and 85%. Because during scheduling, all high power utilization appliances are
shifted to OPHs, which facilitates the customers to pay less on the bill, so UC is maximized in
BFOA and GA as compared to WDO and BPSO, which are the desired results obtained by the
designed objective functions, and it is also beneficial for both customers and utilities.
Un Scheduled WDO Scheduled GA Scheduled GWD Scheduled
0
2
4
6
8
10
12
14
PAR
Figure 8. Scheduled and unscheduled PAR.
Unscheduled WDO Scheduled GA Scheduled GWD Scheduled
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
UC
Figure 9. Scheduled and unscheduled UC.
2 4 6 8 10 12 14 1618 20 22 24 2628 30 32 3436 38 40 424446 48 50
0
2
4
6
8
10
12
14
16
18
Number of Homes
Energy Consumption (kWh)
GA Scheduled Load
Unscheduled Load
Figure 10. GA energy consumption.
2 4 6 8 10 12 14 1618 20 22 24 2628 30 32 343638 40 42 4446 48 50
Number of Homes
0
2
4
6
8
10
12
14
16
18
Energy Consumption (kWh)
BPSO Scheduled Load
Unscheduled Load
Figure 11. BPSO energy consumption.
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Number of Homes
0
2
4
6
8
10
12
14
16
18
Energy Consumption (kWh)
Unscheduled Load
WDO Scheduled Load
Figure 12. WDO energy consumption.
2 4 6 8 10 12 14 1618 20 22 24 2628 30 32 343638 40 42 4446 48 50
Number of Homes
0
2
4
6
8
10
12
14
16
18
Energy Consumption (kWh)
Unscheduled Load
BFOA Scheduled Load
Figure 13. BFOA energy consumption.
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
0
50
100
150
200
250
300
350
Number of Homes
Cost (cents)
GA Scheduled Cost
Unscheduled Cost
Figure 14. GA total cost.
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Number of Homes
0
50
100
150
200
250
300
350
Cost (cents)
Unscheduled Cost
BPSO Scheduled Cost
Figure 15. BPSO total cost.
2 4 6 8 10 12 14 1618 20 22 24 2628 30 32 343638 40 42 4446 48 50
Number of Homes
0
50
100
150
200
250
300
350
Cost (cents)
UnScheduled Cost
WDO Scheduled Cost
Figure 16. WDO Total Cost.
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Number of Homes
0
50
100
150
200
250
300
350
Cost (cents)
Unscheduled Cost
BFOA Scheduled Cost
Figure 17. BFOA total cost.
Unscheduled BPSO Scheduled GA BFOA Scheduled WDO Scheduled
0
5
10
15
20
25
30
PAR
Figure 18. PAR of GA, BPSO, WDO and BFOA.
Unscheduled BPSO Scheduled GA BFOA Scheduled WDO Scheduled
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
UC
Figure 19. UC of GA, BPSO, WDO and BFOA.
In order to quantify the computational burden of the algorithms, we have chosen algorithms’
execution time in s as a performance metric. Figure 20 shows the execution time of the five
simulated algorithms: GA, BPSO, WDO, BFOA and GWD. From the figure, it is evident that
BPSO has the maximum computational burden (execution time = 88 s), and BFOA has the
minimum computational burden (execution time = 8 s); a difference of 80 s. Similarly, GA,
WDO and GWD take 13 s, 43 s and 32 s (to execute), respectively. The previous figures in the
simulation results (Section 6) show that GWD is relatively better than the compared algorithms
in terms of the selected performance metrics, and Figure 20 shows the execution time of GWD
as relatively moderate (better than WDO and worse than GA). To sum up, the GWD pays the
cost of moderate execution time to achieve a considerable increase in UC and a decrease in both
PAR and price.
WDO GA BPSO BFOA GWD
Optimization Algorithms
0
10
20
30
40
50
60
70
80
90
Execution Time (sec)
Figure 20. Execution time of GA, BPSO, WDO, BFOA and GWD.
6.3 Performance trade-offs in the proposed technique
After conducting the simulations, we have found some trade-offs and achievements. This ap-
proach is evaluated with the help of the following parameters: cost minimization, energy con-
sumption minimization, UC maximization and PAR reduction. The achievements and trade-offs
are mentioned in Table 8.
6.4 Statistical validation of genetic wind driven and counter part algorithms using
analysis of variation
In order to prove the meta-heuristic algorithms’ stochastic nature, we have done the statistical
analysis for checking their correctness and efficiency. Two algorithms are taken for comparison
with our proposed algorithm in terms of the variance. The ANOVA is based on three assump-
tions [53]: (i) all samples of the populations are normally distributed; (ii) all samples of the
populations have equal variance; and (iii) all observations are mutually independent. In the ta-
ble below, the analysis is described in detail for each sample population generated by the each
individual algorithms. Where, df denotes the degrees of freedom; MS indicates the mean square
test; and F depicts the F test (i.e., dividing the sum of squares and MS); and these are calculated
using the equations from [53]. We have done the ANOVA of three algorithms including our
Table 8 Trade-offs in proposed algorithms.
Technique Tariff Model Achievement Trade-off
GA RTP Minimizes cost up to 56%
and reduces PAR to 26%
in individual testing and hy-
brid case cost is minimized
up to 30% and PAR is re-
duced up to 49%
UC is compromised in
scheduled case up to 60%
in hybrid case while it is
improved in individual
testing to 90%
WDO RTP Reduces cost up to 67.18%
and reduces the PAR to
26% in individual testing
and hybrid case cost is min-
imized up to 30% PAR is
70% reduced
UC is compromised in
scheduled case up to 60%
in hybrid case and in
individual testing to 50%
GWD RTP Reduces cost up to 17.87%
and reduces the PAR to
26% in individual testing
and hybrid case cost is min-
imized up to 30% PAR is
17% reduced
UC is compromised in
scheduled case up to 60%
BPSO RTP Reduces cost up to 70% and
reduces the PAR to 25%
UC is compromised up to
50%
Table 9 ANOVA results for the proposed algorithm with the existing algorithm.
Technique Source of Variation Sum of Squares df MS F Prob > F
WDO Between Groups 1.4383 11 0.13075 0.48 0.9134
Within Groups 29.5488 108 0.2736
Total 30.9871 119
GA Between Groups 3.058 11 0.27803 1.18 0.2956
Within Groups 562.86 2388 0.2357
Total 565.918 2399
GWD Between Groups 0.6647 11 0.06043 0.61 0.813
Within Groups 10.6203 108 0.09834
Total 11.285 119
proposed algorithm. In this way, we have finally estimated that our proposed algorithm varies
from them by a significant rate as shown in Table 9.
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7 Research Tasks and tentative Time Table
The list of the currently addressed research tasks and some proposed future tasks along with
their objectives and expected outcomes are listed below for the full PhD tenure.
Research Tasks
Number of Modules Proposed Tasks Objectives Expected Outcomes
Module 1 Home Energy Management Controller for
daily used Appliances
Effective Appliances’
Scheduling
Cost Minimization and User Comfort
(in terms of minimum appliances’ de-
lay, thermal comfort and visual comfort)
Analysis
Module 2 Residential Buildings’ Energy Manage-
ment Controller for Seasonally used Ap-
pliances
Effective Energy Manage-
ment
Cost Minimization and User Comfort
(in terms of minimum appliances’ de-
lay, thermal comfort and visual comfort)
Analysis
Module 3 Integration of Renewable Energy Sources
in Module 1 and Module 2
Energy Preservation and Pre-
vention of Load Curtailment
Cost Minimization and User Comfort
(in terms of minimum appliances’ de-
lay, thermal comfort and visual comfort)
Analysis
Module 4 Residential Buildings’ Energy Manage-
ment Controller for Controlling Lights
Automating Lightening Sys-
tem
Cost Minimization and User Comfort
(in terms of minimum appliances’ de-
lay, thermal comfort and visual comfort)
Analysis
Module 5 Comparative Analysis of Thermal Com-
fort and Visual Comfort using Buildings’
Energy Management Controller
Efficient Resource Schedul-
ing
Cost Minimization and User Comfort
(in terms of minimum appliances’ de-
lay, thermal comfort and visual comfort)
Analysis
Module 6 and Onwards Comparative Analysis of Fuzzy Logic
Scheme to the Heuristic Techniques for
HEM and BEM
Energy Conservation and Ef-
ficient Resource Scheduling
Cost Minimization and User Comfort
(in terms of minimum appliances’ de-
lay, thermal comfort and visual comfort)
Analysis
Tentative Time Table
Sr No. Activity Date
1 Background study and detailed literature review Septermber-November 2017
2 Formulation of problem and proposing solution December-Januaruy 2018
3 Analysis and dissemination of results Feburary-March 2018
4 Thesis Writing April-August 2018
PART II
Recommendation by the Research Supervisor
Name: Dr. Nadeem Javaid Signature_____________________ Date________
Recommendation by the Research Co-Supervisor
Name: Dr. Mariam Akbar Signature_____________________ Date________
Signed by Supervisory Committee
S.#
Name of Committee member
Designation
Signature & Date
1.
Dr. Nadeem Javaid
Associate Professor
2.
Dr. Mariam Akbar
Assistant Professor
3.
Dr. Manzoor Ilahi Tamimi
Associate Professor
Approved by Departmental Advisory Committee
Certified that the synopsis has been seen by members of DAC and considered it suitable
for putting up to BASAR.
Secretary
Departmental Advisory Committee
Name:__________________________________ Signature:_____________________
Date: _________________
Chairman/HoD ____________________________
Signature: _____________________________
Date: _____________________________
PART III
Dean, Faculty of Information Sciences & Technology
_________________Approved for placement before BASAR.
_________________Not Approved on the basis of following reasons
Signature ____________________________ Date__________________
Secretary BASAR
_________________Approved from BASAR.
_________________Not Approved on the basis of the following reasons
Signature ____________________________ Date__________________
Dean, Faculty of Information Sciences & Technology
________________________________________________________________________
________________________________________________________________________
Signature:_______________________________ Date__________________
Please provide the list of courses studied
1. Special Topics in Artificial Intelligence
2. Special Topics in Wireless Technologies
3. Performance Evaluation of Networks
4. Advanced Topics in Multimedia Technologies
5. Advanced Topics in Machine Learning
6. Special Topics in Computer Networks
