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Modeling and Optimization of Demand Side Management in Smart Grid
by
Eity Sarker B.Sc. (Hons) in Telecommunication and Electronic Engineering
A thesis submitted in fulfilment of the requirements for the degree of Master of Engineering
School of Software and Electrical Engineering
Faculty of Science, Engineering and Technology
Swinburne University of Technology
Hawthorn, VIC 3122, Australia
July 2020
i
Declaration
I am affirming that the works presented in this thesis is my own work and have not been used
by any person for any other degree. To the best of my knowledge, this thesis does not contain
any materials which have been previously published, unless where due acknowledgement and
reference are provided.
Eity Sarker
July 2020
ii
Acknowledgements
I would like to thank my principal supervisor, Senior Lecturer Mehdi Seyedmahmoudian,
who helped me throughout my candidature. His continuous supports and encouragements
help me to complete this thesis.
I also acknowledge the support and motivation of my associate supervisor Professor Alex
Stojcevski. He helped me a lot to overcome the difficulties when I changed my principal
supervisor.
I am also grateful to my husband, Pobitra Halder for his continuous support and inspiration. I
also acknowledge the continuous support from my family.
At the end, I would like to thank my senior and junior fellows at Swinburne and Melbourne
who made my journey enjoyable during and outside of my study. I would like to
acknowledge the Swinburne University of Technology for providing me Tuition Fee
Scholarship and logistic supports for conducting the research.
iii
Dedicated to my husband and my parents
iv
Abstract
Demand side management is considered as a key technique that could address the issues of
increasing energy demand and environmental pollution as well as help to achieve
socioeconomic sustainability. It can also facilitate residents’ transfer into smart homes and
sustainable cities. However, demand side management is required to overcome a number of
challenges in terms of energy transmission, distribution, and effective utilization of energy
resources. In order to overcome these challenges, researchers are developing new model and
upgrading the existing smart grid model. Therefore, algorithms are used to solve the
optimization problems of these models. The aim of this thesis work is to minimize electricity
consumption from the main grid and maximize the use of renewable energy resources to
reduce the total energy cost of the consumer.
In this study, the loads were scheduled based on flexible pricing and time of use pricing tariff
rate employing binary particle swarm optimization (BPSO) algorithm in MATLAB platform.
The microgrid was mathematically modelled and analyzed for different households in terms
of electricity cost reduction. The total renewable energy from solar PV and wind turbine was
estimated based on the Victorian solar data and wind data.
The findings of the case study suggested that the BPSO based load scheduling strategy
provided more stable cost curve compared to that of genetic algorithm based scheduling
strategy. The analyses showed the potential benefits of implementation of demand response
in terms of reduction of peak load and electricity cost for all the case studies. As a result of
both demand response program and renewable energy integration, the consumers required a
minimal amount of electricity from the grid.
v
List of Publications
1. Eity Sarker, Pobitra Halder, Mehdi Seyedmahmoudian, Elmira Jamei, Ben Horan, Saad
Mekhilef, Alex Stojcevski (2020). Progress on the demand side management in smart grid
and optimization approaches. International Journal of Energy Research. John Wiley &
Sons. IF 3.34 (Q1)- Accepted. DOI: 10.1002/er.5631
2. Eity Sarker, Mehdi Seyedmahmoudian, Elmira Jamei, Ben Horan, Alex Stojcevski
(2020). Optimal management of home loads with renewable energy integration and
demand response strategy. Energy. Elsevier. IF 5.54 (Q1)- Under revision.
3. Eity Sarker, Mehdi Seyedmahmoudian, Ben Horan, Alex Stojcevski (2020). Optimum
scheduling of residential, industrial and commercial loads using BPSO algorithm.
(Extension of conference paper). Draft ready.
4. Eity Sarker, Mehdi Seyedmahmoudian, Ben Horan, Alex Stojcevski (2019). Optimal
scheduling of appliances in smart grid environment using BPSO algorithm. 11th
International Conference on Applied Energy. Västerås, Sweden.
vi
Table of Contents Declaration ........................................................................................................................... i
Acknowledgements .............................................................................................................. ii Abstract .............................................................................................................................. iv
List of Publications .............................................................................................................. v
List of Figures................................................................................................................... viii
List of Tables ...................................................................................................................... ix
Abbreviations ...................................................................................................................... x
CHAPTER 1: INTRODUCTION ....................................................................................... 1
1.1 Energy and sustainability ...................................................................................................... 1
1.2 Smart grid .............................................................................................................................. 2
1.3 Demand side management ..................................................................................................... 2
1.4 Algorithms for solving optimization problems...................................................................... 3
1.5 Problem statement and research questions ........................................................................... 4
1.6 Aims and objectives ............................................................................................................... 4
1.7 Outline of the thesis ............................................................................................................... 5
CHAPTER 2: LITERATURE REVIEW ........................................................................... 6
2.1 Introduction ........................................................................................................................... 6
2.2 Techniques and approaches of DSM ..................................................................................... 7
2.3 Challenges of DSM implementation .................................................................................... 11
2.4 Progress of DSM models and applications of algorithms ................................................... 15
2.5 Integration of renewable energy sources and storage in SG .............................................. 37
CHAPTER 3: OPTIMAL SCHEDULING OF APPLIANCES IN SMART GRID ENVIRONMENT USING BPSO ALGORITHM ............................................................ 44
3.1 Introduction ......................................................................................................................... 44
3.2 Methodology......................................................................................................................... 46
3.3 Simulation Results and discussion ....................................................................................... 49
3.3.1 Data for simulation ........................................................................................................ 49
3.3.2 Analysis of residential appliances................................................................................... 51
3.3.3 Analysis of industrial appliances .................................................................................... 53
3.3.4 Analysis of commercial appliances ................................................................................. 55
3.3.5 Comparative analysis with GA-DSM .............................................................................. 58
3.4 Conclusions .......................................................................................................................... 59
CHAPTER 4: OPTIMAL MANAGEMENT OF HOME LOADS WITH RENEWABLE ENERGY INTEGRATION AND DEMAND RESPONSE STRATEGY ....................... 60
4.1 Introduction ......................................................................................................................... 61
vii
4.2 Load modeling and DSM implementation for multiobjective optimization ....................... 63
4.3 Microgrid Modeling ............................................................................................................. 66
4.3.1 PV system ....................................................................................................................... 67
4.3.2 Wind turbine .................................................................................................................. 67
4.3.3 Energy savings from renewables .................................................................................... 68
4.4 Results and discussion ................................................................................................. 69
4.4.1 Load profiles and scheduling of the loads ...................................................................... 70
4.4.2 Performance gain in terms of energy and cost ............................................................... 73
4.4.3 Renewable energy integration ........................................................................................ 76
4.4.4 Trade off from DSM integrated with microgrid .............................................................. 78
4.5 Conclusions .......................................................................................................................... 79
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS .................................... 81
References.......................................................................................................................... 83
Appendix ......................................................................................................................... 101
viii
List of Figures
Figure 1. 1 Smart grid conceptual model [12] ........................................................................ 2
Figure 2. 1 DSM techniques [41] ........................................................................................... 7
Figure 2. 2 Classification of DR programs [59] ................................................................... 10
Figure 3. 1 Flowchart of BPSO algorithm ........................................................................... 48
Figure 3. 2 Load curve for the residential area ..................................................................... 52
Figure 3. 3 Hourly cost curve for the residential area ........................................................... 52
Figure 3. 4 Load curve for the industrial area ...................................................................... 54
Figure 3. 5 Hourly cost curve for the industrial area ............................................................ 55
Figure 3. 6 Load curve for the commercial area ................................................................... 57
Figure 3. 7 Hourly cost curve for the commercial area ......................................................... 57
Figure 3. 8 Comparison between BPSO and GA based DSM............................................... 58
Figure 4. 1 Conceptual design of proposed microgrid model ............................................... 67
Figure 4. 2 Average hourly load profile of households during weekday ............................... 72
Figure 4. 3 Average hourly load profile of households during weekend ............................... 73
Figure 4. 4 Hourly cost curves for average load of households during weekday ................... 74
Figure 4. 5 Hourly cost curves for average load of households during weekend ................... 75
Figure 4. 6 Average power output from renewable energy sources (A) Weekday (B) Weekend
........................................................................................................................................... 77
Figure 4. 7 Hourly energy surplus and deficit for each of the households after load shifting
(A) flexible pricing weekday, (B) TOU weekday, (C) flexible pricing weekend and (D) TOU
weekend .............................................................................................................................. 78
Figure 4. 8 Monthly cost analysis under different scenarios (A) flexible pricing tariff (B)
TOU pricing tariff ............................................................................................................... 79
ix
List of Tables
Table 2. 1 Challenges of DSM implementation in the SG network and possible solutions
provided by different standards and protocols...................................................................... 11
Table 2. 2 Characteristics of various algorithms used in DSM ............................................. 19
Table 2. 3 Summary of results of the algorithms used in DSM ............................................ 24
Table 2. 4 Impacts of DSM implementation on RES integrated SG network ........................ 39
Table 3. 1 Hourly forecasted loads for different areas and electricity price [37] ................... 50
Table 3. 2 Data of residential area devices [37] ................................................................... 51
Table 3. 3 Simulation results of BPSO based load shifting [37] ........................................... 53
Table 3. 4 Data of industrial area devices [37] ..................................................................... 54
Table 3. 5 Data of commercial area devices [37] ................................................................. 56
Table 4. 1 Typical electricity tariff (average value) in Victoria, Australia ............................ 64
Table 4. 2 Appliances and power consumption pattern for households ................................ 69
Table 4. 3 Summary of the load shifting results in percentages ............................................ 76
Table A 1 Average hourly load consumption in weekday .................................................. 101
Table A 2 Average hourly load consumption in weekend .................................................. 102
x
Abbreviations
ACO Ant colony optimization
BPSO Binary particle swarm optimization
DLC Direct load control
DP Dynamic programming
DR Demand response
DSM Demand side management
EE Energy efficiency
GA Genetic algorithm
GTA Game theory algorithm
HEMS Home energy management systems
IBR Inclined block rate
LP Linear programming
MGC Micro-grid controller
MINLP Mixed-integer nonlinear programming
NLP Nonlinear programming
PAR Peak-to-average ratio
PDF Probability distribution function
PSO Particle swarm optimization
PV Photovoltaic
RES Renewable energy resources
SR Spinning reserve
TOD Time of day
TOU Time of use
WT Wind turbine
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CHAPTER 1: INTRODUCTION
1.1 Energy and sustainability
Energy is an essential element of people’s daily life. At present, social development
depends on the usage of a sufficient amount of energy, especially electricity [1,2]. The
amount of energy consumption increases along with the rapid growth of the global population
and industrial and technological development [3]. Global energy consumption primarily
depends on the fossil fuel resources such as natural gas and coal. Thus, it is quite difficult to
meet the future energy demand due to the limited resources of fossil fuels. In addition, the
fossil fuels are responsible for the high carbon emission and global warming. According to
the International Energy Agency, approximately 70% of world’s total energy is produced
through the burning of fossil fuels, primarily coal (42%) and gas (21%) [4]. Currently,
assurance of adequate electricity supply is considered one of the most challenging tasks,
required for ensuring the continuous economic and industrial development.
Environmental sustainability and energy security are associated with the amount of
energy production and consumption. A large amount of energy resources are wasted through
the unproductive use of natural resources. Moreover, some countries, such as the United
States use oil and coal-based power plants for the production of electricity. These fossil fuels-
based power plants produce a large amount of SO2, CO2, and other greenhouse gases, all of
which are considered as a threat to the environment. Because of the growing awareness of
electricity crisis and the contribution of power generation plants to climate change, the
scientific community has started to search the alternative energy options for power
generation.
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1.2 Smart grid
Electric network performance depends on the production of electricity and the
capability to meet consumers’ growing demand. In addition, the amount of energy
consumption certainly affects the energy distribution system. The situation becomes worse
when the distributed generation from renewable resources exceeds the penetration levels due
to the irregular and unpredictable characteristics of renewable energy [5,6]. This phenomenon
makes the operation of the grid unsafe and unreliable. Therefore, the variability in renewable
energy generation needs to be considered to meet the growing power demand and ensure grid
sustainability. The SG is an electric network with advanced sensing technologies, control
systems, and communication technologies that reflect the future of energy systems [7,8]. The
SG has been evolved with the effective distribution and supply of electricity. The main
characteristics of SG include the bidirectional flow of data and energy between the energy
provider and the customer [9]. Therefore, the SG opens the door for new prospects to supply
electricity to the consumer efficiently and dynamically. The SG has already been proven as a
convenient tool for reducing peak loads and energy costs [10]. The SG system consists of
several energy subsystems (Figure 1.1), communication, and security components [11].
Figure 1. 1 Smart grid conceptual model [12]
1.3 Demand side management
The issues associated with grid sustainability and reliability can be addressed by
DSM. The DSM is considered an essential mechanism in the energy management of SG,
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which provides benefits to the utilities and customers. The DSM can systematically transmit
and distribute available energy to decrease carbon emissions and peak loads, as well as
allows users to choose their preferred energy type [13]. The DSM was first devised in the
year 1970 [14]. The DSM model was introduced by the electric power generation industry to
control the time of use and level of electricity demand and to analyze the profiles of
electricity loads among users. A DSM program integrated with renewable energy sources
(RESs, e.g., solar and wind), distributed micro-generators, and energy storage devices, such
as plug-in electric vehicles and batteries can provide an optimal management system by
scheduling various smart appliances and generating renewable energy [15–17]. The price of
electricity affects the usage of energy by consumers. Consumers prefer to use less electricity
if the electricity price increases. However, the implementation of the DSM in SG can easily
handle the load patterns of the electricity market as well as can analyze and reshape load
profiles. This practice reduces the peak load demand of customers, thereby improving grid
stability, sustainability, and security; additionally reduces carbon emission levels, grid
operation costs, and electricity costs [18]. Also, effective DSM activities can easily avoid the
unnecessary construction of electrical infrastructure by controlling and managing
decentralized energy resources. These activities can manage the electricity market with
consideration of power generation, transmission, and distribution.
1.4 Algorithms for solving optimization problems
The utilization of different algorithms can solve the optimization problem of DSM in
SG, which includes different technologies, such as smart meters, advanced metering
infrastructure, and communication and control technologies. The optimization problems of
SG consider the minimization of electricity costs, aggregated power consumption, and
PAR[19]. They also consider the maximization of user comfort and the efficient integration
of RESs. For example, previous studies presented different GA-based models for reducing
Page | 4
electricity costs [20–22]. Additionally, GA, ant ACO, and BPSO were used to schedule
energy consumption and evaluate the performance of HEMS controllers [23].
1.5 Problem statement and research questions
The use of fossil fuels in electricity generation is associated with a massive amount of
greenhouse emission, which is one of the major concerns of the modern world. This situation
forces electrical engineers, researches, and policymakers to find out the way for the
optimized consumption grid electricity and utilization of renewable energy resources. Based
on the knowledge gaps identified through a detailed literature review (presented in chapter 2)
following research questions are formulated.
What will be the impact of BPSO algorithm for the optimization of load scheduling
problems when compared with the genetic algorithm?
How does the flexible pricing and time of use tariff affect the load shifting and load
profile?
How does the renewable energy integrated demand response program impact on the
households’ energy consumption and cost?
1.6 Aims and objectives
The current research work aims to minimize the electricity consumption cost of the
consumer by shifting the peak load. This can be accomplished by introducing renewable
energy systems and demand response strategy. The specific objectives of the work are as
follows:
1. Scheduling of the loads employing BPSO algorithm and its comparison with other
algorithms.
Page | 5
2. Mathematical modeling of a microgrid and analysis of its effects on the electricity
cost.
3. Investigations of the effects of different tariff rates on load scheduling and energy
consumption pattern.
1.7 Outline of the thesis
Chapter 1: This chapter presents the necessity of alternative energy resources, the
significance of the smart grid on load management, the insight of DSM, and different
algorithms for solving optimization problems.
Chapter 2: This chapter discusses DSM approaches and techniques and reviews the recent
works related to the application of demand side management in the smart grid through
discussing the techniques and algorithms and their associated challenges for effective
implementation. The chapter also details the works related to the implementation of demand
side management, including the description of their operation mode, the profile of energy
production, storage and consumption, and finally, the benefit deduced by the demand side
management implementation.
Chapter 3: The focus of this chapter is to reduce the peak load demand, electricity cost, PAR
as well as to achieve substantial cost savings using BPSO algorithm based load scheduling
technique and establish a comparison with the genetic algorithm based load scheduling
technique.
Chapter 4: This chapter proposes a home energy management model, which consisted of
microgrid framework and DSM technique. The aim of this energy management model is to
minimize the monetary expenses of electricity consumption in households by shifting the
loads to the times with lower electricity prices and utilizing energy from renewables.
Chapter 5: The major conclusions of the current thesis and future research directions are
highlighted in this chapter.
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CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
The SG is considered a new opportunity to enhance the 20th century’s power grid. The
SG has gained substantial popularity due to some features, such as distributed generation,
self-healing, digital two-way communication, self-monitoring, and universal control [24,25]
The SG can adjust renewable energy generation, create smart measurement systems, and
distribute and transmit grid power by utilizing modern information and communication
technologies [26,27]. In addition, the SG can control and manage the electricity market,
construct the infrastructure, and manage the decentralized energy resources [28,29]. The
DSM supports the SG functionalities by analyzing the short and long term status of the
electricity market, determining a cost-effective option for energy supply, and modeling and
characterizing the system load [30]. However, the capacity of SG needs to be improved to
meet the growing energy demand, which requires an installation of power generation and
transmission infrastructure [31]. The development of new infrastructure will increase not only
the complexity of the SG networks but also relevant system costs. In this situation, the
efficient implementation of DSM programs in SG can overcome complexity and high
expenses by controlling and influencing energy demand. Additionally, the DSM can improve
grid sustainability by reducing the peak load demand, reshaping load profiles, and reducing
overall costs and carbon emissions. Previous studies reported the contributions of DSM on
the reduction of carbon emissions of SG. For instance, Zhang et al. reduced the carbon
emission levels in the SG environment by incorporating electric vehicles and a DR program
[32]. Ai et al. introduced a bid-scheduling DSM method in SG to motivate consumers leading
to the reduction of carbon emission levels [33]. In this case, they developed demand side
reserve scheduling based on the DR program in the SG environment. Li et al. proposed a
carbon emission flow model to investigate and evaluate carbon emission levels from different
Page | 7
parts of an SG network [34]. DSM and supply-side management jointly take part in the
energy management program. The effects of incorporating three levels of RES utilization
were also investigated for the reduction of carbon emission.
2.2 Techniques and approaches of DSM
The DSM is a growing technique for planning, implementing, and monitoring pre-
defined activities that affect consumers’ electricity utilization patterns. These activities
mainly change the time of load consumption and the utility’s total load, thereby reducing the
expected peak loads [35]. To reduce electricity costs, the DSM manipulates customers’
electricity usage patterns and produces the preferred changes in the load profiles by altering
the load shape of the power distribution network [36,37]. Essentially, the DSM helps to avoid
excess power generation by reducing peak loads leading to a reduction in operation costs
[38]. The DSM can be categorized into six major types according to the daily and seasonal
usage of electricity (Figure 2.1). These methods include load shifting, peak clipping, strategic
load growth, valley filling, strategic conservation, and flexible load shape [39,40].
DSM
Peak clipping
Valley filling
Load shifting
Strategic conservation
Strategic load growth
Flexible load shape
Figure 2. 1 DSM techniques [41]
Peak clipping: Peak clipping is a common form of the load management technique
that decreases the peak demand of an electrical network [35]. Typically, peak clipping
controls customers’ electricity consumption through DLC, which mainly explains the
system’s peak load reduction [35,37]. The DLC can be defined as a function of the
Page | 8
DSM program by which a power supplier company regulates customers’ appliances
from a distance and shifts their peak load to off-peak hours [42,43]. The proper
scheduling of DLC is considered as a favorable way to reduce operating costs and
fossil fuel usage [35,44].
Valley filling: Valley filling aims to drop the level of load difference between the
peak load and the valley load and thereby diminish load demand by filling the valley
from a curtailed load [37,39]. Valley filling is applicable when the long-run
incremental cost is less than the average price of electricity [39].
Load shifting: The load shifting technique is mostly used in the DSM program. It is
the most effective load management technique that shifts load capacity from peak
hours to off-peak hours [37,39].
Strategic conservation: Strategic conservation diminishes overall load demand
through the application of load reduction procedures by the efficient consumption of
energy. It designs and attains the desired load shape according to the planning,
distribution, and management of the network system [37,40].
Strategic load growth: Strategic load growth motivates power companies to increase
the power generation for customers [37,39,45]. It optimizes the daily response and
changes the shape of the load with respect to the large demand beyond the valley
filling technique. The activities of strategic load growth include the amplification of
the market share of loads, the economic development of service areas, and the
guaranteeing of necessary infrastructure for handling the load demand.
Flexible load shape: The flexible load shape technique mainly secures the reliability
of SG [37,39,45]. Under this technique, an electricity generation company analyzes
the load profile to identify customers with flexible loads. The customers can get
Page | 9
various incentive awards, if they control their consumption of interruptible or
curtailable load during peak periods.
Recently, governments and utility companies have focused on the implementation of
the DSM strategies that smooth the operation of electrical systems [46–48], promote and
extend energy efficiency plans and applications, and change the behavior at the customer
level or implement dynamic demand responses [49,50]. The DSM has four strategies,
namely, DR, EE, SR, and TOU [51–54]. The main emphasis of these strategies lies in the
development and use of power-saving technologies, monetary incentives, electricity prices,
and government policies to diminish peak load demands and maintain a sophisticated
synchronization between network operators and customers.
Energy efficiency: The EE is considered a modest choice with respect to the benefits
received by energy suppliers, energy consumers, and the environment [10]. The EE is
a type of technology that provides an improved and long-lasting service when the
end-use equipment is in operation. The EE programs improve the physical
infrastructure of the electrical grid for improving electricity efficiency and reducing
peak demand [55]. The characteristics of the EE programs are utility-specific and they
can store all forms of RESs. The functions of the EE programs include the change in
policies of inefficient systems, detection and replacement of misconfigured controls,
adoption of financial incentive programs, and maintenance of a level of consumer
satisfaction [56].
Time of use: A TOU pricing strategy refers to a function of fixed tariffs that divides
24 hours into several time intervals and assigns a different price for electricity
consumption in each interval [55,57]. This strategy helps to control peak period
pricing and seasonal pricing based on different prices of energy.
Page | 10
Demand response: The DR refers to a specific tariff or program which decreases or
shifts electricity usage during peak periods with respect to time-based rates or
incentive payment programs [56,58]. The network reliability of an electrical system
becomes jeopardized due to certain conditions, such as peak period network
congestion or high prices. In this situation, the DR changes the energy usage pattern
and provides an opportunity for consumers to contribute to the operation of the
electric grid [49]. The DR programs can be classified into price-based programs and
incentive-based programs, as illustrated in Figure 2.2.
Demand response programs (DRP)
Price-based programs Incentive-based programs
Time of use Real time pricing Critical peak pricing
Emergency DRP Interruptible/curtailable services Direct load control Capacity market program Demand bidding Ancillary services market
Figure 2. 2 Classification of DR programs [59]
Spinning reserve: The reserve power connected to the grid system is activated by the
system operator to maintain the balance between load and generation in case of a
sudden drop in the generation. This interruption in the power supply is caused due to
unexpected damage in generation units, incorrect load forecasting, and scheduling
[60]. Typically, the SR is classified into primary and secondary SR [55]. In primary
SR, frequency controls the active power output, whereas, in secondary SR, the
frequency and grid state is restored with additional active power [55,61].
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2.3 Challenges of DSM implementation
The SG needs to overcome a number of difficulties related to power generation,
transmission, and distribution, as well as to the effective utilization of energy resources.
However, cybersecurity and privacy are considered as key challenges to the SG network [62].
Cyber attackers can access and misinterpret the information stored in a DSM system, which
is vulnerable to the invasion of privacy. Such information includes the software of the DSM
algorithm, load data, price signals, and users’ personal information. In addition, attackers can
easily change the load scheduling of DSM programs by introducing misinformation into the
control systems; this misinformation prompts the energy supplier to refuse to respond to the
customers’ real requests [63]. Therefore, it is necessary to provide a secure and reliable
operation between energy providers and customers. To date, a number of organizations, such
as the North American Electrical Reliability Corporation, Institute of Electrical and
Electronics Engineers, Critical Infrastructure Protection, International Society of Automation,
and US National Institute of Standards and Technology have come forward to develop
rigorous solutions for maintaining the security and privacy of SG [64,65]. Table 2.1 presents
the highlights of DSM challenges and possible solutions.
Table 2. 1 Challenges of DSM implementation in the SG network and possible solutions
provided by different standards and protocols
Challenges of DSM Possible solution
Lack of reliable
communication between
energy sources and consumers
[66].
Bluetooth or Ultra-Wide Band could be used for the
interfaces between meter and end customer devices.
IEEE 802.15.4 (ZigBee) and IEEE 802.11 (Wi-Fi) are
the technical standards which could be used for smart
meter interfaces in the home and local area network.
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Challenges of DSM Possible solution
Cellular wireless for example, GPRS, UMTS or 4G
technologies could be used for the interfaces between
meters and the central system [67,68].
Lack of interoperability
among different software
applications used by electric
utilities
MultiSpeak, an industry-wide standards developed by
the National Rural Electric Cooperative Association
improves inter-operability by defining an information
model based on a programming language scheme.
Communication protocol focuses on web services and
Simple Object Access Protocol [69].
IEC-61850, an Inter-control Centre Protocol ensures the
inter-operability by specifying the definite
communication networks and systems in substations
[70].
Identification of the overall
network architecture, service
requirements, and device
capabilities as well as ensuring
the Supervisory Control and
Data Acquisition and
Automation Systems [66,69].
Recently, the European Telecommunications Standards
Institute has developed a new committee names
machine-to-machine to deal with these issues [66].
IEEE C37.1 is an IEEE Standard which deals with the
system architectures and functions in a substation as
well as covers the protocol selections, human machine
interfaces, and implementation issues. It handles the
issues related to network performance requirements,
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Challenges of DSM Possible solution
reliability, maintainability, availability, security,
expandability, and changeability [71].
Lack of distributed resources
for the implementation of
demand response [69].
IEEE 1547 is a kind of standards that links the
distributed energy sources with the electric power
system in terms of interoperability, performance,
operation, testing, and safety [72].
DRBizNet is a DR business network which supports DR
program by monitoring electricity market operations as
well as creates a standard web service interface to
support DR applications. This network notifies the grid
operators automatically and accomplishes the specific
DR program for the market [68,69].
Open Automated Demand Response, a communication
protocol which supports the DR program by providing a
standardized information model [69].
Cybersecurity issues AMI-SEC Task Force developed a sets of security
requirements for AMI, which not only makes the
communication transparent between utility and industry
but also ensures the secure communication. These can
be achieved by the transmission of security parameters,
cryptographic key establishment, and management [73].
The North American Electric Reliability Corporation
provides a set of security requirements including critical
Page | 14
Challenges of DSM Possible solution
cyber asset identification, training in cybersecurity,
security management controls, electronic security
perimeters, incident reporting, and response planning,
information protection, recovery plans for critical cyber
assets and physical security [69].
To manages the power system and secure the
information exchange, IEC 62351 provides
cybersecurity requirements. They provide end to end
information security by algorithms and secure
manufacturing message specification. This protocol also
focuses on the security policies, access control, and key
management of the system [74].
According to the researchers, fairness is another main concern for load management
[75–79]. In order to motivate the customers to shift their load, they need to be assured that
they will pay a minimal amount for their electricity consumption or receive financial
incentives [77]. Therefore, a suitable fairness condition should be maintained to assess the
fairness of the algorithms that can help to choose an appropriate DSM program in practice.
Energy system efficiency depends on the optimization of different communication protocols,
SG applications, and control infrastructure [9]. However, due to the nature of distributed
control problems and the interdependency of different domains (i.e., power, communication,
and control), it is entirely difficult to develop an advanced communication infrastructure.
Additionally, the lack of standardization and interoperability among DSM entities inhibits the
Page | 15
possible integration of advanced applications of smart meters, smart devices, and RESs.[80]
Some DSM objectives, such as continuous interoperability, increased safety of new products
and systems, robust information security, compacted set of protocols, and information
exchange can be easily achieved through the standardization and interoperability of SG [81].
Inaccurate time measurements and automated analysis, fast control messaging, poor
visibility, slow response times, system handling under contingency, and lack of situational
awareness are also bottlenecks for effective DSM implementation [80,82,83]. Also, rising
population and demand for energy, energy storage problems, global climate change, decrease
in fossil fuel sources, equipment failures, capacity limitations of electricity generation, and
flexibility of problems are prime concerns, according to the researchers [84].
2.4 Progress of DSM models and applications of algorithms
The existing literature indicates that various types of algorithms, including single and
hybrid ones, have been developed and implemented to solve the optimization problems of the
DSM of SG. The PSO, GA, GTA, ACO, LP, NLP, and DP are the most widely studied
algorithms in the field of DSM. Recently, hybrid algorithms have gained remarkable attention
as promising methods. Table 2.2 shows the characteristics and user-defined parameters of
various algorithms used in DSM. This section provides an overview of the research and
advancement of algorithms used in DSM.
The GA has been applied to many optimization problems to achieve the desired
objectives of DSM. Table 2.3 summarizes the algorithms used for solving the DSM
optimization problems in SG. The reviews related to the application of the GA in DSM are as
follows. Logenthiran et al. proposed a day-ahead load shifting technique for the DSM of SG
[37]. They used a heuristic-based evolutionary algorithm to solve a minimization problem
and shift the load from peak hours to off-peak hours. The simulations were carried out in
residential, commercial, and industrial areas with a variety of loads. In this work, the authors
Page | 16
considered the central controller of the SG to control the DSM technique. However, if the
controller becomes disabled for any reason, this whole process will stop functioning.
Compared with other areas, residential areas showed the highest number of devices available
for control. However, the amount of reduction in operating cost was not as expected because
no incentive scheme or financial reward plan was considered to motivate the customers to
shift their load. In another study, a GA-based DSM method was proposed to solve the
objective function [85]. In this case, the method considered only an industrial load. The
authors used a load shifting technique to reduce customers’ inconvenience, energy generation
cost, and total electricity cost. Similarly, Bharathi et al. applied a heuristic-based GA to
model the DSM [86]. The proposed DSM model reshaped the load patterns and reduced
energy usage in industrial, commercial, and residential areas by using a suitable load shifting
technique. Arabali et al. introduced a GA-based SG strategy for shifting residential cooling
loads to match renewable energy production [22]. The authors recommended using the
developed approach in heating, ventilation, and air conditioning loads. Yao et al. developed a
DSM model based on a modified GA, named iterative deepening GA, to optimize the
scheduling of DLC approaches and minimize the revenue loss of electricity generation
companies [44]. They considered only the air conditioning load, which has a low impact.
Therefore, various types of loads need to be considered for further justifying the iterative
deepening GA.
The PSO algorithm-based techniques have been widely implemented by researchers
to solve energy management problems. The PSO algorithm is considered as significantly
effective in solving various optimization problems [87]. The PSO-based techniques are
reviewed in the recent study. Logenthiran et al. modeled a day-ahead load scheduling
technique that incorporates the PSO algorithm based on the customers’ inputs and forecasted
hourly electricity rates [88]. In this study, the authors considered the shiftable and non-
Page | 17
shiftable loads controlled by a central controller of the SG. The simulation was carried out in
residential, commercial, and industrial areas, and the results revealed a reduction in PAR and
an increase in electricity cost savings. However, no incentive scheme or reward plan was
applied to compensate the customers for giving up their comfort zone and shifting their load
from peak hours to off-peak hours. Nayak also developed a PSO-based DSM strategy that
considers a load shifting technique mainly for residential loads [89]. The methodology
comprises mostly population-based heuristic optimization techniques, which are used to solve
the scheduling problems and provide global optimum solutions.
Page | 19
Table 2. 2 Characteristics of various algorithms used in DSM
Algorithm type Algorithm
name
Mechanism User-defined
parameters
Characteristics Ref.
Metaheuristic and
evolutionary
algorithm
GA Inspired by the
mechanism of
natural
selection.
The size of the
population of
solutions, the number
of parents, the
probability of
crossover, the
probability of mutation
and the termination
criterion.
Genes of chromosome represent the decision
variable. This variable contains binary, continuous
or discrete values.
Genetic operators are responsible for the creation
of new solutions.
Individual chromosome provides a possible
solution and parents provide an old solution while
a new solution is provided by offspring. Elite
provides the best solution.
Population diversity and selective pressure affect
the search method.
Correction of convergence depends on the
selection of a good termination criterion and
[90–92]
Page | 20
Algorithm type Algorithm
name
Mechanism User-defined
parameters
Characteristics Ref.
optimum selective pressure.
PSO Inspired by the
social behavior
of birds flocks.
Size of the population
of solutions, the value
of the initial inertia
weight, the final value
of the inertia weight,
and the termination
criterion.
The decision variable is represented by the particle
position in each dimension.
The solution of the optimization problem is found
by the position of the particle where the position is
updated to find a new solution.
Fitness function is measured by the distance
between particle and food.
A number of iterations, selection of good
termination criteria, the improvement of the
objective function, and the run time of the
algorithm determine the confection of
convergence.
[90,93–
96]
Page | 21
Algorithm type Algorithm
name
Mechanism User-defined
parameters
Characteristics Ref.
ACO Inspired by the
collective and
searching
behavior of ant
species.
The size of the
population of
solutions, the
evaporation rate, the
control parameters of
pheromone, heuristic
information, and the
termination criterion.
Decision variables are represented by the path of
an ant.
In the case of an optimization problem, a possible
solution is determined by the tour of an ant from
nest to food.
Process of generating new solutions is accelerated
by the information-based stochastic mechanism.
ACO allocates desirability to the decision space
according to the fitness value of a solution.
Correction of convergence depends on the number
of iterations, selection of good termination criteria,
the incremental improvement of the objective
function, and the run time of the algorithm.
[90,97–
100]
Page | 22
Algorithm type Algorithm
name
Mechanism User-defined
parameters
Characteristics Ref.
Classical method LP Mathematical
programming
method where
the objective
function is
linear.
The collection of
coefficients with
respect to decision
variable, constraint,
the upper bound is the
parameter of the LP
method.
Objective functions are correspondents to a
restricted set of constraint.
It has a feasible solution and region.
The optimal solution can be found.
Multiplicity in solutions.
[101–104]
NLP Mathematical
programming
with respect to
the nonlinear
objective
function.
Parameters are defined
based on the problems
Converting a complex problem into an easy
problem.
Solving the sequence of sub-problems.
Solving of sub-problems are involved with the
unconstrained minimization function.
The optimal solution can be found.
[105–108]
Page | 23
Algorithm type Algorithm
name
Mechanism User-defined
parameters
Characteristics Ref.
DP Multistage
nature of
optimization
method.
There is no specific
parameter. Each
problem has its own
parameter.
Representing the multistage decision process.
For each stage, a policy decision is requested.
Solving multivariable optimization problem.
In order to determine the optimal solution for the
problem, the solution method is categorized.
Recursive relation is used to optimize the solution
procedure.
[108,109]
Page | 24
Table 2. 3 Summary of results of the algorithms used in DSM
Algorithm Methodology Reduction of
PAR (%)
Minimization of
energy cost (%)
Reduction
of carbon
emission
Reduction
of
operating
cost (%)
Fairness Software Remarks Ref.
Genetic
algorithm
Heuristic-
based load
shifting
technique
14.2–18.3
Customers’
savings: 5–10
Company’s
savings:15–20
× 5–10 × Real-time
simulation
The algorithm
converged
well and
globally.
[37]
Multi-
objective
particle swarm
optimization
method based
on the fuzzy
technique
Probabilistic
model-based
on incentive
payment
demand
response
programs
× × 14 21 × MATLAB Pollution
emission
factor was
considered.
[110]
Page | 25
Algorithm Methodology Reduction of
PAR (%)
Minimization of
energy cost (%)
Reduction
of carbon
emission
Reduction
of
operating
cost (%)
Fairness Software Remarks Ref.
Game theory Energy
consumption
scheduling
approach
38.1 37.8 × × × MATLAB The algorithm
converged
locally.
[111]
Game theory Distributed
energy storage
planning
31.5 22.43 × × √ MATLAB The algorithm
converged
globally with
minimum
information
exchange.
[112]
Particle swarm
optimization
Load shifting
technique
43 18 × × × MATLAB Algorithm
converged
globally and
[88]
Page | 26
Algorithm Methodology Reduction of
PAR (%)
Minimization of
energy cost (%)
Reduction
of carbon
emission
Reduction
of
operating
cost (%)
Fairness Software Remarks Ref.
required less
time interval.
Non-stationary
DSM
algorithm
based on a
repeated game
framework
Incentive
compatible
10 50 (including
discomfort and
billing cost)
× × √ Real-time
simulation
A simulation
was carried
out in the
homogeneous
and
heterogeneous
situations. The
algorithm
converged
globally.
[42]
Page | 27
Algorithm Methodology Reduction of
PAR (%)
Minimization of
energy cost (%)
Reduction
of carbon
emission
Reduction
of
operating
cost (%)
Fairness Software Remarks Ref.
Vickrey–
Clarke–Groov
Vickrey–
Clarke–Groov
pricing method
19.3 37.8 × × √ MATLAB The algorithm
converged
globally with
50 users.
Shifting and
executing the
algorithm
required a
certain amount
of time.
[113]
Distributed
algorithm
DSM scheme
based on time-
varying pricing
64.76 × × × × MATLAB
optimization
solver
The residential
load was
considered,
[114]
Page | 28
Algorithm Methodology Reduction of
PAR (%)
Minimization of
energy cost (%)
Reduction
of carbon
emission
Reduction
of
operating
cost (%)
Fairness Software Remarks Ref.
Mosek and a
simulation was
carried out on
35 appliances.
Game theory Autonomous
and distributed
DSM scheme
17 19.6 × × √ MATLAB The algorithm
converged
locally and
required
minimal
execution
time.
[115]
Non-linear
mixed-integer
Dispatching
model with DR
× Reduced × × × MATLAB The optimal
dispatching
[116]
Page | 29
Algorithm Methodology Reduction of
PAR (%)
Minimization of
energy cost (%)
Reduction
of carbon
emission
Reduction
of
operating
cost (%)
Fairness Software Remarks Ref.
linear
programming
model was
unable to
handle large
loads.
Any colony
optimization
Congestion
management
method
× Reduced × × × MATLAB The
integration of
RESs was not
considered.
[117]
Distributed
algorithm
Autonomous
energy
scheduling
scheme
24 21 × × × MATLAB The algorithm
converged
locally.
[118]
Page | 30
Algorithm Methodology Reduction of
PAR (%)
Minimization of
energy cost (%)
Reduction
of carbon
emission
Reduction
of
operating
cost (%)
Fairness Software Remarks Ref.
Sequential
Gauss–Siedel
algorithm
Parallel
autonomous
optimization
scheme with
DR framework
19.71 5.53 × × × Real-time
simulation
The algorithm
converged
locally.
[57]
Game theory Energy
consumption
and storage
optimization
method
40 19 × × × Real-time
simulation
The algorithm
converged in
parallel. The
algorithm was
able to handle
a large number
of users and
simultaneously
[119]
Page | 31
Algorithm Methodology Reduction of
PAR (%)
Minimization of
energy cost (%)
Reduction
of carbon
emission
Reduction
of
operating
cost (%)
Fairness Software Remarks Ref.
update their
strategies.
Genetic
algorithm
Load shifting
technique
23.84 × × × × MATLAB The algorithm
performed
well.
[86]
×: Not considered; √: Considered
Page | 32
The multi-objective PSO algorithm was studied by Aghajani et al. for reducing the
operating cost and emission with the integration of renewable sources in the micro-grid [110].
They recommended stochastic programming that focused on probability density functions
and was integrated with the DR model to optimize the performance of smart micro-grids.
However, the uncertain behavior of predicted power generation from wind turbines and solar
cells remarkably affects the operation cost. Therefore, some existing studies focused on the
implementation of BPSO in DSM, which is a modified form of the PSO algorithm. Kennedy
and Eberhard first employed the BPSO algorithm to schedule interruptible loads and solved a
multi-objective optimization problem [120]. Pedrasa et al. also suggested the implementation
of BPSO to optimize DSM problems [121]. A realistic scheduling mechanism based on the
BPSO for SG was suggested by Mahmood et al.[122]. They found the BPSO algorithm to be
an effective algorithm for reducing electricity costs. In this case, the appliances were
categorized according to the respective constraints and effective time of usage for increasing
the appliances’ utility. Zhou et al. proposed a real-time optimal appliance usage approach to
maintain energy usage based on the BPSO algorithm [123]. The method smoothed the peak
shaving, valley filling, and demand curve as well as reduced energy usage with the assistance
of customers and energy suppliers. Zhou and Xu also applied the BPSO algorithm to solve
the cost function of electric vehicle users, SG, and power sources [124]. The simulations for
load shifting, energy-saving, and energy supply efficiency were carried out in the MATLAB
platform.
Extensive research based on the implementation of ACO algorithm has been
performed to handle energy management optimization problems. Dethlefs et al. used a
distributed ACO-based self-optimization method for producing a day-ahead schedule [125].
In their work, only shiftable loads were considered to reduce the external purchase of energy.
The algorithm optimized the load of distributed consumers and the power generated from a
Page | 33
wind power plant. In this case, almost 10% of the power rating was used to control the
residential shiftable appliances, and the algorithm mainly adjusted the mean generation and
demand. An efficient DSM model based on the ACO was presented by Rahim et al. to control
residential energy [126]. The model reduced the peak load, PAR, and electricity costs,
considering the customers’ satisfaction levels. They also used a TOU tariff model with an
inclined block rate to avoid the peak load and complexity in the estimation of electricity bills.
Hazra et al. explored an efficient method for handling the load congestion problems in SG in
an economical way [127]. The problem was solved by using DR, ACO, and fuzzy techniques.
Their findings suggested that the method was able to reduce the electricity cost and fulfill
customers’ satisfaction through the scheduling of different generation resources. In another
study, the authors analyzed the load congestion management problems to control model cost
[117]. The problem of real-time congestion management was developed as an NLP problem.
In this study, the ACO algorithm provided a feasible solution for the problem and minimized
the electricity cost. Okonta et al. proposed an ACO-based load scheduling algorithm for a
smart home [128]. The researchers mainly focused on the total electricity bill, TOU, and the
overall increase in quality of life with the incorporation of the optimal utilization of
integrated RESs. An automated load manager based on the ACO algorithm and an interactive
web interface were used for DLC and energy management to allow users to access their home
appliances from remote areas via the internet.
Recently, the GTA algorithms have gained remarkable popularity in solving the DSM
optimization problems because of their capability of solving distributed system problems. In
addition, designing an algorithm using game theory is relatively easy [129]. Hung Khanh
Nguyen et al. applied a non-cooperative game theory model to formulate the DSM problems
[31]. The model reduced the peak demand, PAR, and total energy cost. In this case, the GTA
was not able to converge for the optimal solution in the centralized design. Nevertheless, the
Page | 34
algorithm minimized the PAR, close to the optimal solution of the centralized design. Here,
the impacts of large numbers of users with battery on system performance were not
investigated. The large numbers of users integrated with battery can influence the aggregate
cost and PAR reduction. Song et al. [42] developed an optimum non-stationary DSM model
based on a repeated game model, which was mainly an incentive-compatible model. This
model was designed to allow consumers to choose their daily consumption patterns
independently without affecting their habits, choices, and wants. Under this strategy, an
active set of consumers was selected based on the historical energy consumption patterns, and
the billing cost compensation was considered to motivate the consumers. Wang et al. studied
a DSM model integrated with cognitive radio technology based on the proficient and
trustworthy communication infrastructure in SG [112]. This model suggested a cost function
focusing on customers’ preference with a balanced payment model consisting of billing,
electricity generation, and discomfort costs. This DSM system was designed to allow users to
select an appropriate size of storage units for balancing the costs. In this study, the GTA was
applied to optimize the distributed planning storage method. The results indicated the
reduction of PAR, total energy cost, customers’ daily payment, and energy consumption. This
work also proved that the application of cognitive radio technology can effectively reduce
energy consumption in the SG communication networks. However, customers’ privacy was
not guaranteed in this case. Deng et al. analyzed the residential energy consumption
scheduling problem and formulated a couple of constrained game with respect to interactions
among customers [130]. They applied a real-time pricing approach that shifted the peak
demand to the off-peak hours to balance the energy demand. A GTA-based autonomous and
distributed DSM scheme was proposed by Mohsenian–Rad et al. [131], who focused on the
scheduling of energy consumption with the consideration of residential loads. The proposed
technique was based on incentives and thus reduced the peak load, total energy costs, and
Page | 35
customers’ daily electricity charges. In this investigation, the authors employed a new energy
cost function; however, the estimation of energy consumption and simulation was time
consuming. In another research, Nguyen et al. proposed a smart power system with an energy
storage device based on the GTA [119]. The objective of the study was to diminish the square
Euclidean distance between the instantaneous and regular electricity demands of the energy
system. The power consumption was scheduled using the energy cost-sharing model, and the
loads were synchronized by a principal controller.
Many researchers have applied the LP method to solve DSM optimization problems
and their results are summarized in the current work. Sheblt studied a load management
scheduling program using the LP method [52]. A DLC scheme was used to schedule
appliances and to increase the profit of energy suppliers according to cost or market price
function. Kurucz et al. also applied the LP model for scheduling the loads under control
periods to minimize the system peak load [132]. The LP model considered the residential,
commercial, and industrial loads to bring a specific number of customers under the model
and determine long-term and short-term control scheduling strategies. An integer LP-based
load scheduling mechanism was proposed by Zhu et al. for the DSM of SG [133]. With the
aim of reducing peak hour loads, they used the proposed mechanism to schedule home
appliances along with the optimal power and operation time according to customers’
preferences. Martins et al. proposed a multi-objective LP model to increase the power
generation capacity [134]. The total extension cost, the environmental impact associated with
energy output, and the environmental effects associated with the installed power capacity
were considered in the investigation. In order to solve the objective function, they also
considered five constraints, such as the reliability of the supply system, the availability of
generation units, and the capacity of the generation group equivalent to the DSM.
Page | 36
Many researchers have used the NLP method in DSM because it improves cost
functions and generates satisfactory results [135]. Shaaban et al. used a MINLP method for
scheduling batteries and shiftable and adjustable loads [136]. The implementation of this
energy management technique was able to reduce the operating cost under an SG network.
Wang et al. also used the MINLP method to optimize the optimal dispatching model of a
smart HEMS with distributed energy resources and intelligent domestic appliances [116].
This method reduced electricity costs and aggregated power consumption. However, the
MINLP was not able to handle many appliances because of the unpredictable, impulsive,
non-linear, and complex energy consumption patterns of consumers. Considering DR, Helal
et al. proposed a mixed-integer NLP-based energy management model to optimally schedule
the different generation technologies of AC/DC hybrid micro-grids in islands [137]. They
also suggested that the system depends on a MGC, which ensures the proper usage of energy
with minimum operating costs by controlling user appliances and water desalination units.
An optimum schedule with the minimum cost was achieved by formulating the scheduling
problems as MINLP problems.
Thus far, this model has been used in several studies to solve the DSM optimization
problems of SG. Chu et al. proposed a dynamic programming-based optimization algorithm
to determine the scheduling schemes of DLC [138]. The authors considered an air conditioner
in a commercial building for load scheduling. The method reduced the peak load according to
customers’ discomfort level. In addition, this method was found to be effective in scheduling
other appliances by determining a target load level and controlling load usage to reduce the
peak load and electricity generation costs [29]. Primarily, an analytic dynamic programming
model was used to schedule some of the appliances of residential loads. In this study, the
performance of the algorithms and the control periods of appliances were investigated in five
cases; however, the satisfaction levels of customers were not considered. Hsu et al.
Page | 37
introduced a dynamic programming-based optimization method for reducing the system’s
energy generation cost for the DLC dispatch [139]. Therefore, the DLC strategies were
integrated with a unit commitment problem, and a DP method was developed to solve
dispatch DLC and the unit commitment problem. To reduce the electricity demand in the SG
environment, Reka and Ramesh proposed a DP model with a cloud computing framework
[140], which created a small energy hub with customers and displayed the customers’
participation in DSM programs.
2.5 Integration of renewable energy sources and storage in SG
Renewable energy resource (RES) like solar, wind and their hybrid system has
become a popular means of energy supply. However, the integration of RES with the SG has
been gone through a complicated situation because of mixing a number of energy resources
and their intermittent behavior [141]. Therefore, the DSM has been incorporated in RES
integrated SG to handle the fluctuation of electricity price, the mismatch between renewable
energy generation and load demand as well as control of power transactions [142,143]. The
DSM has a significant impact on the RES and energy storage unit as the implementation of
DSM in SG shifts the loads from peak hours to off-peak hours, which allows storage unit to
store the excess power produced from the RES or in the time when grid electricity is cheap.
The stored energy can be used in the future when the energy supply is shortage and peak
periods, which can add economic value in the grid. Table 2.4 shows the summary of DSM
implementation in the RES integrated smart grid network. Quiggin et al. modeled a
residential microgrid integrated with renewable generation technologies, energy storage, and
DR systems [144]. The implementation of the DR program in this model was able to reduce
the peak demand fluctuations by 16% and optimize the energy balance between supply and
demand. Dietrich et al. analyzed the effect of the DR program on the wind energy demand
profile in terms of cost reduction in the system [145]. Incorporation of the DSM program
Page | 38
reduced the number of generation units and flattened the electricity production curve.
Aghajani et al. showed that the utilization of the DSM method reduced the effect of
uncertainty, which was caused during energy generation from solar cells and wind turbine
[110]. Çiçek and Deliç reported that the DSM method was able to achieve a steadier pattern
of social welfare, which was measured in terms of customers’ utility and energy generation
cost [146]. This method maintained the balance in the integration of the wind farm to the grid
and dealt with the issues of energy fluctuations and economic risk. Amrollahi and Bathaee
showed that the DR program maintained the energy distribution in such a way that it reduced
the required number of batteries, inverters, and PV cells as well as reduce the total cost [147].
Besides, the implementation of DSM improved the load factor and correlation factor by 57.9
and 36.8%, respectively. Wang et al. formulated a hybrid RES with DR program and applied
to a single-family residential home [148]. The implementation of DR scheme met the
consumers’ electricity demand by utilizing the available power from PV panels, wind turbine,
diesel generator, and batteries. This method improved the system efficiency when compared
with that of the traditional method. Behboodi et al. applied DSM technique with the
integration of RES to solve the multi-area electricity resource allocation problems [149]. This
method offered an uniform electricity price by maintaining a steady-state among energy
generation, transmission, and load constraints. The researchers also described an innovative
approach to solve multi-area electricity resource allocation problems considering both
intermittent renewables and DR. The method determined the hourly inter-area export/import
set that maximizes the inter-connection surplus satisfying the transmission, generation, and
load constraints.
Page | 39
Table 2. 4 Impacts of DSM implementation on RES integrated SG network
Integrated system DSM
method/technique
Operation mode Supervisory
control
Outcome Reference
Photovoltaic-battery
hybrid system
DR program with
Model predictive
control method
Grid-connected Centralized Minimized the electricity bill on
the customer side.
Maximized the use of solar
energy and battery storage.
[150]
Industrial microgrid with
wind turbine and energy
storage unit
DR scheme Grid-connected Centralized Wind turbine reduced the carbon
emission by 88% and DSM
produced 30% more reduction.
Overall electricity cost reduced by
73%
[151]
Residential microgrid
with photovoltaic panel,
wind turbine, and energy
storage unit
DR scheme with linear
programming method
Grid-connected Decentralized Energy demand was reduced by
16%.
During all hours of operation, the
reduction of CO2 emission along
[144]
Page | 40
Integrated system DSM
method/technique
Operation mode Supervisory
control
Outcome Reference
with the associated energy usage
was 10%.
During the hours of operation, the
amount of renewable supply was
reduced by 74%.
Microgrid system with
photovoltaic panels, wind
turbine, diesel generator,
battery bank, and water
supply system
DSM mechanism
along with artificial
neural network
Grid-connected Decentralized Minimized the operation cost by
3.06%.
[152]
Household dotted
with photovoltaic systems
Load scheduling
method based on
online event-triggered
energy management
Grid-connected Centralized Reduced the electricity bill as
well as ensured the user comfort
level.
[153]
Page | 41
Integrated system DSM
method/technique
Operation mode Supervisory
control
Outcome Reference
algorithm
SG network with
renewable distributed
generators
DR scheme with
parallel autonomous
optimization
Grid-connected Centralized Reduced electricity generation
costs and electricity bills.
[154]
Microgrid system with
micro turbines, wind
turbine, fuel cells,
photovoltaic panels,
storage devices and a
group of radial load
feeders
DR scheme Grid-connected Centralized The peak load was shaved from
the grid tie-line.
Achieved optimal scheduling of
batteries and diesel generators.
[155]
Microgrid with renewable
generators and energy
storage
DR scheme Isolated Centralized Achieved an optimal power
generation and peak load
dispatch.
[156]
Page | 42
Integrated system DSM
method/technique
Operation mode Supervisory
control
Outcome Reference
SG network with high
wind penetration
DR scheme Isolated Centralized Achieved 30% cost savings.
More than 56% of demand was
shifted.
[145]
SG network with the
energy storage device
Load scheduling with
game theory algorithm
Grid-connected Centralized Reduced peak load as well as
energy payment for the
consumers.
[119]
Microgrid network with
wind turbine and solar
cell
DR scheme Grid-connected Decentralized Reduced operational cost and
carbon emission.
[110]
SG network with wind
farm
DR scheme Grid-connected Centralized Achieved optimal scheduling of
energy production and
consumption for 24 hour.
[146]
Microgrid system with
photovoltaic system,
DR method with
mixed-integer linear
Isolated Decentralized Operational cost and peak load
were reduced by 17.2 and 36.8%,
[147]
Page | 43
Integrated system DSM
method/technique
Operation mode Supervisory
control
Outcome Reference
wind turbine, and battery programming respectively.
Energy system with
photovoltaic panels, wind
turbine, diesel generators,
and batteries
DR scheme Grid-connected Centralized Reduced operational cost and
environmental cost.
[148]
SG network with
photovoltaic system
DR scheme Grid-connected Decentralized Load factor is analyzed and
increased during a year.
[157]
Page | 44
CHAPTER 3: OPTIMAL SCHEDULING OF APPLIANCES IN SMART GRID ENVIRONMENT USING BPSO ALGORITHM
Abstract
In this work, BPSO algorithm is used for DSM implementation in the SG
environment. Load shifting technique is applied in the residential and industrial area and
shifted the load from peak hours to off-peak hours. Load shifting technique is mathematically
formulated and implemented as a minimization form. In this work, it has been clearly shown
that BPSO based load shifting method can be able to handle a large number of devices of
various types compared to the traditional DSM method. The focus of this work is to reduce
the peak load demand, electricity cost, PAR as well as to achieve substantial cost savings.
BPSO based load shifting method shows a better result in terms of peak load reduction when
compared to GA based DSM.
Keywords: Smart grid, DSM, Algorithms, BPSO, Load shifting.
3.1 Introduction
The electricity demand is expected to increase to almost twice the current demand by
the year 2020 because of the rapid electricity consumption as a consequence of the quick
movement of globalization and industrialization [3,158]. Therefore, effective utilization and
distribution of power supply are necessary to maintain continuous economic and industrial
development. SG brings the highest opportunities to handle the future energy system. SG is
an electric grid network with advanced sensing technologies, control methodologies, and
communication technologies, which provides bi-directional communication between the
consumers and electricity suppliers [7,8].
DSM opens the new door for the efficient supply of electricity by implementing
policies and measures for energy consumption [159]. In order to achieve an optimistic power
Page | 45
consumption curve, DSM is implemented directly or indirectly by the utility companies.
Moreover, an efficient DSM with SG helps to achieve an optimistic utilization and
distribution of electricity by varying the price tariff of electricity between the peak and off-
peak hours [86]. Generally, consumers have to spend a lot of money because of the high price
of electricity. However, the integration of DSM with SG reshapes the load profile and
provides the desired load curve to the utility companies. DSM manages the accessible
electricity in the grid from various perspectives, for example, residential, commercial, and
industrial. This situation leads to the reduction of peak load demand, electricity cost of the
customer as well as improves the grid stability, sustainability, and security.
So far, a number of algorithms, such as dynamic programming, linear programming,
and heuristic evolutionary algorithm have been employed for solving the DSM problems
[37]. For example, Kurucz et al. and Shaaban et al. proposed a linear programming algorithm
for scheduling the load and minimizing the peak load demand [132,136]. Logenthiran et al.
proposed a GA based load shifting technique. They managed the load demand in the case of
residential, commercial, and industrial areas. They were able to achieve a substantial cost
savings in terms of peak load reduction and electricity cost [37]. To minimize the system
production cost, an optimization technique based on dynamic programming was also
investigated [160]. In this paper, the DLC technique was implemented, which gave access
and permission to utilities to directly control the portion of the customers' load. Anvari-
Moghaddam et al. developed a multi-objective mixed integer nonlinear programming model
for optimal usage of electricity in a smart home [161]. They mainly focussed on the
electricity savings and comfortable lifestyle in terms of reduction of residential electricity
usage and utility bills. A multi-objective evolutionary algorithm is proposed by Muralitharan
et al. [162]. The model was presented for the DSM application for obtaining the energy cost
savings and able to minimize the appliances’ waiting time. To reduce the peak load demand,
Page | 46
Pallotti et al. proposed a GA based optimization problem. This method was able to achieve an
optimal planning of energy consumption for 246 smart homes which focuses on the energy
cost minimization and user satisfaction [163]. Kinhekar et al. presented a multi-objective
DSM problem formulation and solution based on the integer GA algorithm [164]. The DSM
solution is provided based on the forecasted load data, pool market price, and TOD tariffs.
Load shifting technique used to schedule shiftable appliances for both commercial and
industrial consumers area. To handle the home load, Rastegar et al. proposed a DR
mechanism for residential households [165]. To obtain a optimal scheduling for household
devices, the DR program was applied under the pricing scheme of TOU and IBR. This work
reduced the electricity bills and maximized the user comfort based on controllable and
uncontrollable appliances. Maximum user comfort level was achieved by focusing on the
functional hours of appliances. Multi-objective optimization problem based on MINLP was
proposed by Shirazi and Jadid [165] to obtain an optimal load scheduling for the residential
sector. The simulation was carried out for both winter and summer day in order to minimize
the energy consumption cost and maximize the customers’ comfort. Electrical and thermal
appliances were both considered for shifting the load from peak to off-peak hour. However,
these algorithms cannot be able to handle the large numbers of various types of devices
because of their system specific nature. The aim of this work is to solve a minimization
problem by shifting the load from peak hours to off-peak hours employing the BPSO
algorithm and establish a comparison with GA. In this case, residential, industrial and
commercial appliances are considered for the investigation.
3.2 Methodology
This research work presents a BPSO based load shifting technique for DSM. Here,
day-ahead load shifting technique is applied based on the forecasted load and electricity
price, where controllable and uncontrollable loads are considered for the optimization
Page | 47
problem. For all the cases (i.e., residential, commercial, and industrial area), only the
controllable loads were shifted based on the electricity cost and user preference. In this
method, residential, commercial, and industrial appliances are considered where each device
has different energy consumption. Load shifting technique is applied in a minimization form
and presented as follows.
Minimize, ∑ (𝑃(𝑡) − 𝑂(𝑡))2𝑀𝑡=1
where, 𝑃(𝑡)= actual consumption at time t.
𝑂(𝑡) = objective curve at time t.
P(t) can be expressed by the following equation.
P(t)=F(t)+C(t)-D(t) (3.1)
where, F(t)= forecasted loads at time t.
C(t)= loads connected at time t.
D(t)= loads disconnected at time t.
The PAR is calculated using the following equation.
PAR= 𝑙𝑜𝑎𝑑𝑝𝑒𝑎𝑘𝑙𝑜𝑎𝑑𝑚𝑒𝑎𝑛
(3.2)
In BPSO algorithm, the number of hours in a day was represented by a particle and
the particle was represented by a row vector with m variables. BPSO algorithm is a modified
version of the PSO algorithm as shown in Figure 3.1. In the case of BPSO, each particle of
the population has to decide whether the decision is true or false. This true/false decision was
taken place based on the binary value 0 and 1. If the decision is true, the value is 1 and if it is
false, the value is 0. For this reason, binary values are arranged into a particle in a search
space where the optimization technique is applied.
Page | 48
Start
Find and update pbest and gbest
End
Termination criteria
Yes
No
Evaluate the fitness of particles
Calculate and update the velocity of particles
Initialise particles and velocity vectors
Calculate and update the position of particles
Show gbest
Figure 3. 1 Flowchart of BPSO algorithm
The probability of the decision is true and false can be defined by the following equation
[166]:
𝑃(𝑥𝑖𝑘 = 1) = 𝑓(𝑥𝑖𝑘(𝑡 − 1), 𝑉𝑖𝑘(𝑡 − 1), 𝑝𝑖𝑘,𝑝𝑚𝑘) (3.3)
Page | 49
Here, 𝑃(𝑥𝑖𝑘 = 1) is the probability where the ith individual chooses 1 for the kth bit in the
string, which depends on 𝑝𝑖𝑘,𝑝𝑚𝑘
𝑓(𝑥𝑖𝑘(𝑡 − 1) is the function of a previous position of a bit
𝑉𝑖𝑘 defines the individual tendency to select 1 or 0.
𝑉𝑖𝑘 can be mathematically expressed by the sigmoidal function
S(𝑉𝑖𝑘) =1
1+exp (−𝑉𝑖𝑘 ) (3.4)
𝑥𝑖𝑘(𝑡) = 1 𝑤ℎ𝑒𝑛 𝑝𝑖𝑘, < 𝑆(𝑉𝑖𝑘) (3.5)
𝑥𝑖𝑘(𝑡) = 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (3.6)
𝑝𝑖𝑘 is a uniformly distributed random numbers within the range [0, 1].
3.3 Simulation Results and discussion
3.3.1 Data for simulation
Table 3.1 shows the hourly forecasted loads for residential, commercial, and industrial
areas with electricity prices [37]. These loads include both shiftable and nonshiftable loads
with different time steps. With the aim of comparing the BPSO based load shifting technique
with GA based technique, the data for the residential, industrial, and commercial appliances
were taken from the previous study [86]. This makes the comparison results more reliable.
The load shifting simulation was carried out for both residential and industrial loads in the
MATLAB software platform.
Page | 50
Table 3. 1 Hourly forecasted loads for different areas and electricity price [37]
Time (h) Price (ct/kWh) Forecasted load (kWh)
Residential Industrial Commercial
1 8.11 412.3 876.6 375.2
2 8.25 364.7 827.9 375.2
3 8.1 348.8 730.5 404
4 8.14 269.6 730.5 432.9
5 8.13 269.6 779.2 432.9
6 8.34 412.3 1120.1 432.9
7 9.35 539.1 1509.7 663.8
8 12 729.4 2045.5 923.5
9 9.19 713.5 2435.1 1154.4
10 12.27 713.5 2629.9 1443
11 20.69 808.7 2727.3 1558.4
12 26.82 824.5 2435.1 1673.9
13 27.35 761.1 2678.6 1673.9
14 13.81 745.2 2678.6 1673.9
15 17.31 681.8 2629.9 1587.3
16 16.42 666 2532.5 1558.4
17 9.83 951.4 2094.2 1673.9
18 8.63 1220.9 1704.5 1818.2
19 8.87 1331.9 1509.7 1500.7
20 8.35 1363.6 1363.6 1298.7
21 16.44 1252.6 1314.9 1096.7
22 16.19 1046.5 1120.1 923.5
23 8.87 761.1 1022.7 577.2
24 8.65 475.7 974 404
Page | 51
3.3.2 Analysis of residential appliances
In the case of residential loads, the simulation was carried out in a total of 1547
devices of 7 types, as shown in Table 3.2. Compared to industrial and commercial areas,
residential appliances have lower electricity consumption ratings and short periods of
operation. It can be seen from Figure 3.2, the implementation of DSM shifts the loads from
peak hours to off-peak hours and provides a load curve close to the objective curve. Without
DSM strategy, the value of peak demand was about 1363.6 kWh at 20th hour and reduced to
967.1 kWh when DSM was implemented. With DSM, the peak load shifted to the first hour
with a value of 1018 kWh. The hourly cost curve (Figure 3.3) depicts that the electricity cost
reduced significantly in the peak hours with DSM and remained nearly constant from the
third hour. Table 3.3 presents the comparison between the results with DSM strategy and
without DSM strategy in terms of peak demand, PAR, and electricity cost. As can be seen,
the DSM strategy reduces the PAR, peak demand, and total electricity cost by 25.41, 25.35,
and 16.87%, respectively.
Table 3. 2 Data of residential area devices [37]
Load types Power consumption of load (kWh) Number of
devices 1st
hour
2nd
hour
3rd
hour
4th
hour
5th
hour
6th
hour
Central AC 5 5 5 5 5 5 60
Well pump 8 8 8 0 0 8 430
Hair dryer 2.4 2.4 2.4 2.4 0 0 158
Dish washer 1.2 1.2 1.2 1.2 0 0 290
Vacuum
cleaner
1.2 1.2 1.2 0 0 0 248
Laptop 0.75 0.75 0.75 0.75 0 0 236
Oven 2.25 2.25 2.25 2.25 0 2.25 125
Total 1547
Page | 52
0 5 10 15 20 25200
400
600
800
1000
1200
1400
Loa
d (k
Wh)
Time (h)
Forecasted load Objective load Load after shifting
Figure 3. 2 Load curve for the residential area
0 5 10 15 20 250
5000
10000
15000
20000
25000
Cos
t ($)
Hours
Without DSM With DSM
Figure 3. 3 Hourly cost curve for the residential area
Page | 53
Table 3. 3 Simulation results of BPSO based load shifting [37]
Without DSM With DSM Reduction (%)
Residential area
PAR 1.85 1.38 25.41
Peak demand (kWh) 1363.6 1018 25.35
Total cost (US$) 2302.88 1914.4 16.87
Industrial area
PAR 1.62 1.53 5.56
Peak demand (kWh) 2727.3 2225 18.42
Total cost (US$) 5712.05 3923.48 31.31
Commercial area
PAR 1.7 1.31 22.91
Peak demand (kWh) 1818.2 1404 22.84
Total cost (US$) 3626.64 3022.3 16.66
3.3.3 Analysis of industrial appliances
A total of 133 devices of 6 types were considered for the simulation in the case of
industrial area as presented in Table 3.4. Industrial area has a lesser number of appliances
compared to residential and commercial areas. As can be seen from Figure 3.4, the load curve
after shifting closes to the objective curve except for the first 8 hours. At the 11th period, the
peak demand was 2727.3 kWh without DSM and reduced to 727.3 kWh with DSM. In the
case of the DSM strategy, the peak demand was 2225 kWh and shifted to 9th hour. Figure 3.5
shows the hourly cost reduction for the industrial area. In the peak periods, the hourly cost
with DSM significantly reduced with slight fluctuations. The results of the simulation suggest
that the proposed DSM scheme reduces the peak demand, PAR, and electricity cost by
shifting the load from peak hours to off-peak hours. In this case, the reduction of peak load,
PAR, and the total cost was 18.42%, 5.56%, and 31.31%, respectively.
Page | 54
Table 3. 4 Data of industrial area devices [37]
Load types Power consumption of load (kWh) Number of
devices 1st
hour
2nd
hour
3rd
hour
4th
hour
5th
hour
6th
hour
7th
hour
8th
hour
Heat pump
heat strips
10 10 10 0 0 0 0 0 14
Electric
furnace
10.5 10.5 25 10.5 10.5 0 0 0 20
Heat pump 0 0 0 9.77 9.77 9.77 9.77 9.77 18
Central AC 3 0 3 0 3 0 3 0 4
Electric
water heater
500 500 500 500 0 0 0 0 45
Freezer 32 32 32 32 32 0 32 32 32
Total 133
0 5 10 15 20 25500
1000
1500
2000
2500
3000
Loa
d (k
Wh)
Time (h)
Forecasted load Objective load Load after shifting
Figure 3. 4 Load curve for the industrial area
Page | 55
0 5 10 15 20 250
10000
20000
30000
40000
50000
60000
70000
80000C
ost (
$)
Hours
Without DSM With DSM
Figure 3. 5 Hourly cost curve for the industrial area
3.3.4 Analysis of commercial appliances
Simulation was carried out in a total of 702 devices of 9 types in the case of
commercial area. The commercial appliances have a higher consumption and long period of
operation compared to industrial and residential areas. The number of the controllable device
is higher than the industrial area but lower than the residential area. As can be observed from
Figure 3.6, the shifted load curve is close to the objective with a slight variation. This
variation can be seen at 1st 6 hour, 12th hour to 16th hour and 23rd hour to 24th hour.
Starting time and ending time of operation of the devices is the reason behind this variation.
Without DSM, the peak demand was 1818.2 kWh at 18th hour. After DSM implementation,
the peak demand shifted to 17th hour and the value was 1404 kWh. The amount of peak load
reduction was 414.2 kWh and the percentage of peak load reduction was 22.84%. It can be
seen from Figure 3.6 that the electricity consumption is higher where the electricity price is
Page | 56
higher. Without DSM at 11th hour to 13th hour, the average electricity price was 24.9 ct/kWh
and the total average electricity consumption was 1558.4 kWh. The total average electricity
cost in that period was 388.87 USD. After DSM implementation, the total average electricity
consumption at 11th hour to 13th hour reduced to 705.367 kWh and the total average
electricity cost reduced to 180.11 USD. So the average electricity cost reduced in 11th hour
to 13th hour by 53.68%. Without DSM, the PAR and total electricity cost was 1.7 and
3626.64 USD, respectively. With the implementation of DSM, the PAR and total electricity
cost decreased to 1.31 and 3022.3 USD, respectively. Therefore, our proposed load shifting
technique able to reduce the PAR and total cost by 22.91% and 16.66%, respectively. Figure
3.7 shows the hourly cost curve for the commercial area. In the peak periods, the hourly cost
significantly reduced after DSM implementation. However, hourly electricity cost increased
in 1st hour to 9th hour due to the shifting of loads from peak hours during these hours.
Table 3. 5 Data of commercial area devices [37]
Load types Power consumption of loads in kWh Number of
devices 1st hour
2nd hour
3rd hour
4th hour
5th hour
6th hour
7th hour
8th hour
9th hour
10th hour
Broiler 88 88 0 88 88 88 0 0 88 0 89 Dish washer 32 32 32 0 32 32 0 0 32 0 110 Roster 55 55 55 0 55 55 0 0 55 0 93 Oven (Self cleaner)
65 65 65 65 65 65 0 0 65 0 78
Coffee maker 2 2 2 2 2 2 2 2 2 2 60 Hot plate 4 4 4 4 4 4 4 4 4 4 13 Oven 26 26 26 0 26 26 0 0 26 0 128 Bottle warmer 0 7 7 0 7 7 0 0 7 0 52 Trash compactor
29 29 29 0 29 29 0 0 29 29 79
Total 702
Page | 57
0 5 10 15 20 25200
400
600
800
1000
1200
1400
1600
1800
2000
Loa
d (k
Wh)
Time (h)
Forecasted load Objective load Load after shifting
Figure 3. 6 Load curve for the commercial area
0 5 10 15 20 250
10000
20000
30000
40000
50000
Cos
t ($)
Hours
Without DSM With DSM
Figure 3. 7 Hourly cost curve for the commercial area
Page | 58
3.3.5 Comparative analysis with GA-DSM
Figure 3.8 shows the comparison between the proposed BPSO based and GA based
DSM method. GA based DSM method used the “flexible load shape” technique while BPSO
method used the “load shifting” technique. It can be observed from Fig. 5 that the GA based
DSM method reduced peak demand by 23.81%, 17.49%, and 19.29% for residential,
industrial, and commercial areas, respectively [86]. On the other hand, the proposed BPSO
based DSM method reduced the peak demand by 25.35%, 18.42%, and 22.84% for
residential, industrial, and commercial areas, respectively. Therefore, the BPSO based DSM
method improves the performance by 1.54%, 0.93%, and 3.55% for residential, industrial,
and commercial areas, respectively. In addition, the proposed BPSO based load shifting
method is simple in mathematical formulation and provides the maximum efficiency and
successive rate compared to GA based approach. However, BPSO is associated with some
limitations for instances, difficulties in chossing appropriate parameters for optimization,
sometimes it gets stuck in local optimum solution.
Residential Industrial Commercial0
5
10
15
20
25
30
Peak
load
red
eact
ion
(%)
BPSO GA
Figure 3. 8 Comparison between BPSO and GA based DSM
Page | 59
3.4 Conclusions
In this work, a BPSO based load shifting strategy is proposed, which has the potential
to bring the benefit for both customers and suppliers. This method is able to provide a stable
cost reduction curve. Simulation is carried out with a number of appliances of various types
in the residential, commercial, and industrial areas. It has been shown that the proposed
BPSO based method finds an optimal load schedule in terms of reduction of peak demand,
electricity price, and PAR. In addition, a comparative study has been carried out with the GA
based DSM method and found that the proposed BPSO based DSM method performs better.
Page | 60
CHAPTER 4: OPTIMAL MANAGEMENT OF HOME LOADS WITH RENEWABLE ENERGY INTEGRATION AND DEMAND RESPONSE STRATEGY
Abstract: The implementation of proper energy management techniques and utilization of
renewable energy resources enhance the energy efficiency and stability of future grid
systems. This research proposed a home energy management model consisting of microgrid
framework and demand side management (DSM) technique. To reduce peak load, peak to
average, and energy cost, households’ loads were shifted on the basis of price-based tariff
such as flexible and time of use tariff. Simulation was carried out using binary particle swarm
optimization algorithm in MATLAB. The microgrid was mathematically modelled, and the
impacts of DSM integrated microgrid were analysed for different households in terms of
electricity cost reduction. Simulations suggested that DSM implementation significantly
reduced peak loads and renewable resources produced significant trade-off. The proposed
integrated approach reduced 90%–100% of the total electricity cost of households.
Keywords: Demand side management; Demand response; Load shifting technique; BPSO
algorithm; Energy tariff; Renewable energy generation
Page | 61
4.1 Introduction
The increase in energy consumption as well as the rapid growth of population and the
lack of implementation of proper management techniques result in an extreme spike in
energy demand [3,167]. Existing electrical grid worsens the situation due to their old-
fashioned design as well as redundant and overstressed infrastructure [168]. Recently
environmental pollution gains much attention among the scientists and environmentalists
because of public consciousness of reducing carbon emission and political pressure [169].
About 85% of the total global energy consumption depends on fossil fuels [170]. The
excessive usage of fossil fuels is associated with the release of substantial CO2 emission. The
integration of RES in power generation is the most effective and feasible way to promote
sustainable development and reduce environmental pollutions [171].
The utilization and optimization of RES lead to the concept of microgrid as a
replacement for fossil energy sources [172]. Recently, the use of microgrid system has gained
significant popularity in finding ways to increase the stability of energy supply by integrating
distributed energy resources, such as wind turbines and solar panels, and distributed energy
storage like batteries [173]. Moreover, microgrid has distinct features, including reliability,
low investment costs, and regulations of different distributed generator units’ output voltage
and current [174]. Additionally, microgrid can be incorporated with different DSM
frameworks and operated in grid connected and off-grid modes.
DSM refers to the amendments of consumers’ energy consumption pattern to enhance
the efficiency of electrical energy systems and network [175,176]. This technique modifies
daily energy consumption pattern to achieve a desired load profile [177]. Numerous scholars
studied the implementation of DSM in residential loads management [178]. For instance,
Gottwalt et al. carried out a simulation for shifting residential loads on the basis of TOU tariff
[179]. Ma et al., Ozkan, Steen et al., Lu et al., Missaoui et al., and Ogunjuyigbe et al. also
Page | 62
developed DSM model to reduce peak load and energy cost [180–185]. Bharathi et al. solved
DSM optimization problems for residential loads management using genetic algorithm to
reduce peak load [86]. In our previous work, we analyzed the load shifting problem for
residential loads using the BPSO algorithm [186].
Modeling of microgrid with the application of DSM can play a pivotal role in
reducing peak load, energy inefficiency, and operational cost of electricity provider, thus
helping to reduce carbon foot prints. This integrated approach can reduce the amount of
energy required to buy from the grid. Recent works were dedicated to the implementation of
DSM along with the integration of renewable energy for achieving a balance between energy
generation and consumption. Quiggin et al. implemented the demand response program in a
residential microgrid with the integration of solar photovoltaic, wind turbine, and energy
storage [144]. This method optimized the energy balance between supply and demand by
reducing peak demand fluctuations by 16%, thus significantly reducing CO2. The effect of
DSM technique was also investigated in an industrial microgrid by Blake and O'Sullivan
[151], who proposed a method that reduced the overall electricity cost by 73% with almost
88% CO2 reduction. Palma-Behnke et al. modelled a microgrid system with solar
photovoltaic, wind turbine, diesel generator, battery bank, and water supply system [152].
The DSM mechanism along with artificial neural network was used for determining optimal
operation and reducing costs . Shen et al. carried out an incentive-based demand response
program in a microgrid, which consisted of micro turbines, wind turbine, fuel cells, solar
photovoltaic, storage devices, and controllable load [187]. The authors solved an operational
scheduling problem to achieve an optimal scheduling of the batteries and diesel generators.
Nunna and Doolla proposed an intelligent energy management system with demand response
program to reduce peak load demand in the microgrid, and they performed simulation by
using Java Agent Development framework [188]. To reduce electricity cost, an incentive
Page | 63
method was recommended by the authors to motivate the consumers for participating in the
demand response program. Philippou et al. investigated a price-based DSM mechanism and
performed sensitivity analysis to reduce peak load during summer and winter [157].
A comprehensive case study was carried out in the current work, where a price-based
(i.e., flexible and time of use tariff) demand response was applied for households’ load
scheduling. The DSM optimization problem was solved using BPSO algorithm in MATLAB.
Additionally, the work integrated renewable energy resources with DSM to reduce electricity
cost and maximize renewable energy use.
4.2 Load modeling and DSM implementation for multiobjective optimization
DSM implementation aims to reduce load demand during peak hours, reduce
electricity bill, and maximize the use of renewable energy as well as reduce the usage of
electricity from the main distribution grid. A BPSO-based load scheduling mechanism was
applied to manage residential load demand. In BPSO algorithm, each hour in a day was
denoted by row vector. The proposed DSM technique scheduled households’ shiftable
appliances. Each of the household devices considered in this study consumed different
amounts of energy with various power ratings. In the BPSO algorithm, a set of household
devices is presented by D= {d1, d2,……,dn}.
For each household, the scheduling vector of energy consumption of the appliances
can be presented by 𝐷𝑛=[𝑑11, ……… , 𝑑𝑛𝑡 ], where 𝑑𝑛1 is the energy consumption of appliances
n, scheduled for 1 hour. The total energy consumption for the household is estimated by the
following expression:
𝐸𝑛 =∑𝑑𝑛𝑡
24
𝑡=1
(4.1)
Page | 64
The devices were scheduled based on their daily energy consumption. The scheduled
energy consumption of the appliances can be expressed as follows:
𝐸𝑛 = ∑ 𝑑𝑛𝑡
𝐸𝑇
𝑡=𝑆𝑇
(4.2)
where ST and ET are the schedule start and end time of the appliances, respectively.
The cost function 𝐶𝑇𝑂𝑈, which represents the cost of energy in each hour, was
calculated based on flexible pricing tariff and time of use (TOU) tariff provided by the utility
company. Flexible pricing tariff introduced three periods, such as peak, off-peak, and
shoulder while, TOU tariff considered only peak and off-peak periods. Table 1 shows the
typical TOU tariff and flexible pricing tariff of Victoria, Australia
[https://www.canstarblue.com.au/electricity/victoria-electricity-tariffs/]. The price of
electricity at peak hour is higher as compared to price at off-peak hour due to the high
demand at peak hour. The peak and off-peak hours for TOU tariff are the same for weekdays
and weekends. In the case of flexible tariff, no peak period for weekends exists. However,
shoulder periods are extended from 7am to 10pm.
Table 4. 1 Typical electricity tariff (average value) in Victoria, Australia
Flexible TOU
Peak Off-peak Shoulder Peak Off-peak
Time 3pm–9pm 10pm–7am 9pm–10pm and 7am–3pm 7am–11pm 11pm–7am
Rate (ct/kWh) 45 19.5 35.5 36.5 17
The time span of 24 hours was divided into equal time slots, where t ∈ 𝑇. The cost is
the function of amount of energy consumed. The cost function (CTOU) for TOU tariff is
presented by the following equation:
Page | 65
𝐶𝑇𝑂𝑈 =
{
∑ 𝐸𝑛
𝑇 ∗ 𝑀𝑇𝑂, 𝑜𝑓𝑓 − 𝑝𝑒𝑎𝑘 ℎ𝑜𝑢𝑟
𝑇𝑜
𝑇=23
∑𝐸𝑛𝑇 ∗ 𝑀𝑇𝑃,
𝑇𝑝
𝑇=7
𝑝𝑒𝑎𝑘 ℎ𝑜𝑢𝑟𝑠
(4.3)
where MTP and MTO are the costs of electricity per unit for TOU tariff during the peak
and off-peak periods, respectively. In the case of TOU tariff, the total cost (CTT) for 24 hours
duration is the summation of cost during peak and off-peak hours, as shown in the following
equation:
𝐶𝑇𝑇 = 𝐶𝑇𝑃 + 𝐶𝑇𝑂 (4.4)
where CTP and CTO are the energy consumption cost during peak and off-peak hours,
respectively. In the case of flexible pricing tariff, the cost function (CFLP) is given as follows.
𝐶𝐹𝐿𝑃 =
{
∑ 𝐸𝑛
𝑇 ∗ 𝑀𝐹𝑂, 𝑜𝑓𝑓 − 𝑝𝑒𝑎𝑘 𝑝𝑒𝑟𝑖𝑜𝑑
𝑇𝑜
𝑇=22
∑ 𝐸𝑛𝑇 ∗ 𝑀𝐹𝑆, 𝑆ℎ𝑜𝑢𝑙𝑑𝑒𝑟 𝑝𝑒𝑟𝑖𝑜𝑑
𝑇𝑠
𝑇=21,7
∑ 𝐸𝑛𝑇 ∗ 𝑀𝐹𝑃 , 𝑝𝑒𝑎𝑘 𝑝𝑒𝑟𝑖𝑜𝑑
𝑇𝑝
𝑇=15
(4.5)
where MFP, MFS, and MFO are the per unit electricity price for flexible pricing tariff
during peak, shoulder, and off-peak hours, respectively. For flexible pricing tariff, the total
cost of energy consumption (CFT) is the summation of cost during peak, shoulder, and off-
peak hours and expressed as follows:
𝐶𝐹𝑇 = 𝐶𝐹𝑃 + 𝐶𝐹𝑆 + 𝐶𝐹𝑂 (4.6)
The load scheduling was formulated as a minimization problem, and the optimal
scheduling of the household appliances was obtained by solving the minimization problem as
Page | 66
follows, where, CT represents the total electricity cost, which can be either based on the
flexible pricing tariff or TOU pricing tariff.
Minimize∑𝐶𝑇
𝑇
𝑡=1
In BPSO algorithm, the particles are initialized randomly with the binary value
between 0 and 1. The status of load defined by 𝑥𝑖 = [𝑥1,𝑥2, …… . . 𝑥𝑛], ∀𝑥𝑖𝜖 (0,1), where 𝑥𝑖 =
1 represents the connection of the load, and 𝑥𝑖 = 0 indicates the disconnection of the load.
The BPSO algorithm optimized the objective function (i.e., minimization of cost function) by
updating the position and the velocity of the particles. The velocity of the particle is
expressed by 𝑥𝑖(𝑡𝐵𝑃𝑆𝑂+1) = 𝑥𝑖
𝑡𝐵𝑃𝑆𝑂 + 𝑉𝑖(𝑡𝐵𝑃𝑆𝑂+1), and the velocity of the particle is presented
as follows:
𝑉𝑖(𝑡𝐵𝑃𝑆𝑂+1) = 𝑤. 𝑉𝑖
𝑡𝐵𝑃𝑆𝑂 + 𝐶1. 𝑟𝑎𝑛𝑑(). (𝑥𝑝𝑏𝑒𝑠𝑡.𝑖𝑡𝐵𝑃𝑆𝑂 − 𝑥𝑖
𝑡𝐵𝑃𝑆𝑂) + 𝐶2 . 𝑟𝑎𝑛𝑑(). (𝑥𝑔𝑏𝑒𝑠𝑡.𝑖𝑡𝐵𝑃𝑆𝑂 − 𝑥𝑖
𝑡𝐵𝑃𝑆𝑂) (4.7)
where C1 and C2 are the cognitive constant, and w is the weighting function. To
schedule the household appliances, the objective function is fed to the BPSO algorithm on
basis of electricity price.
4.3 Microgrid Modeling
The proposed microgrid consists of three subsystems, namely, energy generation,
residential load demand, and energy distribution subsystems. The schematic diagram of a
hybrid microgrid is shown in Figure 4.1. The WT and solar PV panels worked as renewable
energy generators. In our proposed model, residential load profile was used as a demand
subsystem. The microgrid was connected to the grid through AC bus, and whole microgrid
system worked as a power distribution subsystem. DSM technique was applied in the
Page | 67
residential loads to schedule the load demand. The energy generated from the renewable
sources was used to meet the scheduled load demand on an hourly basis.
Wind turbine
Solar PV
Power electronic interface
Residential loads
AC Bus
DC/AC
Main grid
Renewable energy sources
DSM implementation
Figure 4. 1 Conceptual design of proposed microgrid model
4.3.1 PV system
The PV system produces electrical energy from solar energy, and the output of the PV
system depends on solar irradiance, efficiency of the PV panel, and atmospheric temperature
[189]. The output power from solar PV was calculated by the following equation:
PVout = PVr ∗ 𝐹𝑃𝑉 (4.8)
where PVout is the output power of solar PV. PVr and FPV indicate the rated power of
solar PV (kW) and performance of the solar PV (%), respectively. The performance of the
solar PV data was based on the Victorian state performance on the particular date [https://pv-
map.apvi.org.au/live].
4.3.2 Wind turbine
The wind power system converts the wind speed into electrical power. The output
power of the WT energy system depends on the hourly wind speed [190]. In this study, the
hourly wind speed data for the study area was collected from timeanddate.com
Page | 68
[https://www.timeanddate.com/weather/australia/melbourne/historic?month=11&year=2019].
The output power from WT can be calculated by the following equation [190,191]:
𝑃𝑊𝑇𝑂𝑈𝑇 =
{
0, 𝐶 < 𝐶𝐼𝑎𝑛𝑑 𝐶 ≥ 𝐶𝑜
𝑃𝑊𝑇𝑅 ∗𝐶 − 𝐶𝐼𝐶𝑅 − 𝐶𝐼
, 𝐶𝐼 ≤ 𝐶 < 𝐶𝑅
𝑃𝑊𝑇𝑅 , 𝐶𝑅 ≤ 𝐶 < 𝐶0
(4.9)
where PWTOUT and PWTR indicate the output power from WT and rated power of
WT, respectively. C, CR, CI, and CO represent the real time wind speed, rated wind speed,
cut-in speed, and cut-out speed, respectively.
4.3.3 Energy savings from renewables
The hourly power generated from the solar PV and WT is compared with the
scheduled load demand for each of the households. The hourly surplus energy (i.e., excess
energy from renewables after fulfilling the demand) and energy deficit (i.e., energy required
to purchase from grid after consuming the energy from renewables) were estimated using the
following equation:
𝐸𝑆/𝐷 = 𝑃𝑉𝑂𝑈𝑇𝑡 + 𝑃𝑊𝑇𝑂𝑈𝑇
𝑡 − 𝐸𝑛𝑡 (4.10)
where ES/D is amount of energy surplus or deficit in an hour. The positive value of
ES/D indicates energy surplus, whereas negative value represents energy deficit. The monthly
revenue generation by selling surplus energy can be estimated using the following equation,
where FRAV is the average feed-in-tariff in Victoria:
𝑅𝑀 = [∑(∑(𝑃𝑉𝑂𝑈𝑇𝑡 + 𝑃𝑊𝑇𝑂𝑈𝑇
𝑡 − 𝐸𝑛𝑡)
24
𝑡=1
)
30
𝑑=1
] ∗ 𝐹𝑅𝐴𝑉; 𝑃𝑉𝑂𝑈𝑇𝑡 + 𝑃𝑊𝑇𝑂𝑈𝑇
𝑡 > 𝐸𝑛𝑡 (4.11)
Page | 69
4.4 Results and discussion
The performance of the proposed microgrid model was investigated for a case study.
The simulation was carried out in four households in Victoria, Australia. The electricity
consumption of each of the households and different set of appliances are listed in Table 4.2.
In this mode, shiftable and nonshiftable loads are considered for the estimation of overall
energy requirement and cost. However, only shiftable loads were considered for load shifting
simulation. Apart form this, high-power appliances such as vacuum cleaner, washing
machine and iron was used in weekends. The electricity consumption of these devices were
included in the weekends’ load profile.
Table 4. 2 Appliances and power consumption pattern for households
Device Preferred time of use Hourly consumption (kW) Power rating (kW) 1 h 2 h 3 h
Household 1 Washing machine 10am–5pm 0.25 0.25 0 0.25 Vacuum cleaner 12pm–3pm 0.9 0 0 1.8 Oven 6am–9am and 12:30pm–
1:30pm 0.37 0.37 0 1.1
Iron 7pm–9pm 0.34 0 0 1 Rice cooker 5pm–7pm 0.25 0.25 0 0.5 Induction cooker 5pm–8pm 0.2 0.2 0.1 0.2 Room heater 6pm–11pm 0.95 0.95 0.95 1.9 Toaster 7am–9 am 0.17 0 0 0.7 Household 2 Washing machine 11am–2pm 1.25 0.625 0 1.25 Vacuum cleaner 10am–1pm 1 0 0 2 Iron 5pm–10pm 0.67 0 0 2 Rice cooker 6am–9am and 7pm–9pm 0.35 0.35 0 0.7 Dish washer 5pm–7pm 1.8 0 0 1.8 Room heater 7pm–10pm 2 1 2 2 Toaster 7am–9am 0.29 0 0 1.75 Hair dryer 7am–8am 0.36 0 0 2.2 Air Fryer 5pm–7pm 0.5 0 0 1.5 Oven 6am–9am 0.25 0.25 0.25 1.5 Household 3 Washing machine 9am–1pm 0.25 0.041 0 0.25 Vacuum cleaner 10am–12pm 1 0 0 2 Blender 6am–9am and 6pm–8pm 0.16 0 0 0.5 Iron 7pm–10pm 0.66 0 0 2
Page | 70
Rice cooker 7am–9am and 6pm–9pm 0.35 0.35 0 0.7 Room heater 6pm–10pm 1.9 1.9 0.9 1.9 Toaster 7am–9am 0.13 0.13 0 0.8 Oven 8am–9am 0.23 0.23 0.23 1.4 Household 4 Vacuum cleaner 9am–4pm 1.2 0 0 1.2 Blender 4pm–6pm 0.1 0.1 0 0.3 Iron 9pm–10pm 0.8 0 0 2.4 Rice-cooker 6pm–8pm 0.35 0.35 0 0.7 Double hot plate 6pm–9pm 0.7 0.7 0 1.4 Room heater 7pm–11pm 1.9 1.9 1.9 1.9 Oven 7am–10am 0.2 0.2 0.2 1.2
4.4.1 Load profiles and scheduling of the loads
Figure 4.2 shows the weekday’s average load profiles for four households and their
comparison with the load profiles after DSM implementation under the flexible pricing and
TOU tariff scheme. The weekdays load data was taken from one random weekday of each
week for the whole last year (i.e., in 2018). Then the load profile was developed using the
average of 52 weeks’ data for each household. The loads were shifted based on electricity
price and consumers’ preference keeping the total load demand same. Figure 4.2 shows that
the each of the households’ hourly electricity consumption was different because of variation
in preference and behaviour of energy consumption. Whether the consumers used the flexible
pricing tariff or TOU tariff scheme, they were not getting any benefit from the tariff scheme
because most of the electricity was consumed during peak periods. In the case of flexible
pricing tariff, the peak period was between 15th hour to 21th hour, whereas the peak period
was between 7th hour to 23rd hour for TOU pricing tariff. In the peak period, the electricity
providers charged a higher price for per unit of electricity compared to off-peak and shoulder
periods. In addition,, the total electricity demand in peak period was about 8.24 kWh for the
household 1 under flexible pricing tariff. After the implementation of DSM, the total energy
demand during the peak period was reduced to 6.41 kWh due to the shifting of loads from
peak to off-peak and shoulder hours. Therefore, almost 22.2% (1.83 kWh) of loads in peak
Page | 71
periods was shifted from peak periods to off-peak and shoulder periods, where the per unit
electricity price was lower than that during peak periods. Similarly, the implementation of
DSM shifted approximately 18.2%, 23.4%, and 20.6% of loads in peak periods from peak
periods to off-peak and shoulder periods for households 2, 3, and 4, respectively.
Additionally, the individual peak load for all the households was also reduced after DSM
implementation, as shown in Figure 4.2. In the case of TOU pricing tariff, the energy
providers offer only peak and off-peak hour pricing scheme. Most of the hours of the day
(7am–11pm) are considered peak period, where consumers usually prefer to use their loads.
Therefore, no significant load shifting was observed in the case of TOU pricing tariff due to
the users’ preference of load utilization, nature of loads, and extended hours of peak periods.
However, the implementation of DSM reduced the individual peak load and distributed the
reduced amount of load in the entire peak periods for all the households, as presented in
Figure 4.2.
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000.0
0.5
1.0
1.5
2.0
Loa
d (k
Wh)
Time (h)
Without DSM Flexible price Time of use
Household 1
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
Loa
d (k
Wh)
Time (h)
Without DSM Flexible price Time of use
Household 2
Page | 72
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75L
oad
(kW
h)
Time (h)
Without DSM Flexible price Time of use
Household 3
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
Loa
d (k
Wh)
Time (h)
Without DSM Flexible price Time of use
Household 4
Figure 4. 2 Average hourly load profile of households during weekday
Figure 4.3 displays the weekend’s average hourly electricity consumption for the
households. The weekends load data was taken from one random weekend of each week for
the whole last year (i.e., in 2018). Then the load profile was developed using the average of
52 weeks’ data for each household. The average electricity consumption in the weekend for
all the households was higher than in weekdays. In the case of flexible tariff, DSM was not
applied for weekend because of the absence of peak period for weekend. Moreover, a
shoulder rate is charged most of the day (7am–10pm). Similar to weekday, peak and off-peak
rates were charged in the weekend for TOU pricing tariff. Hence, DSM was applied to shift
the loads from peak periods. However, no notable load shifting was obtained for all the cases
because of the similar reasons, as described earlier. When DSM was applied, the individual
peak load was reduced significantly and distributed throughout peak periods. However, the
individual peak load remained in the same hour for all the households, as illustrated in Figure
4.3.
Page | 73
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000.0
0.5
1.0
1.5
2.0
2.5
3.0L
oad
(kW
h)
Time (h)
Without DSM TOU
Household 1
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:00
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Loa
d (k
Wh)
Time (h)
Without DSM TOU
Household 2
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000.0
0.5
1.0
1.5
2.0
2.5
3.0
Loa
d (k
Wh)
Time (h)
Without DSM TOU
Household 3
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000.0
0.5
1.0
1.5
2.0
2.5
3.0
Loa
d (k
Wh)
Time (h)
Without DSM TOU
Household 4
Figure 4. 3 Average hourly load profile of households during weekend
4.4.2 Performance gain in terms of energy and cost
Figure 4.4 depicts the effects of DSM implementation on the reduction of electricity
cost in weekday for all the households considered. The DSM allows the consumers to shift
the load from peak hours to off-peak and shoulder hours as discussed earlier, causing
substantial electricity cost (Figure 4.4). The reduction of hourly electricity cost depends on
the type of selected appliances and the flexibility of the operation time, which were provided
by the consumers for load scheduling. The hourly electricity cost presented in Figure 4.4
shows that the electricity cost in peak periods was about 65.1% of the total cost for household
1, which was reduced to approximately 50.7% after DSM application. Thus, the total
electricity consumption was significantly reduced during peak periods. In the case of
Page | 74
household 1, TOU and flexible pricing tariff showed higher electricity expense in the 20th
hour due to the peak demand in that hour. Similarly, the electricity cost in peak periods was
reduced from 59.7%, 52.7%, and 51.7% to 51.6%, 42.3%, and 43.2% of the total cost under
flexible pricing tariff for households 1, 2, and 3, respectively. In the case of TOU pricing
tariff, a slight reduction in electricity cost during peak periods was observed for all the
households.
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000
20
40
60
80
100 Flexible price without DSM Flexible price with DSM TOU without DSM TOU with DSM
Ele
ctri
city
cos
t (ct
)
Time (h)
Household 1
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000
16
32
48
64
80 Flexible price without DSM Flexible price with DSM TOU without DSM TOU with DSM
Ele
ctri
city
cos
t (ct
)
Time (h)
Household 2
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000
15
30
45
60
75 Flexible price without DSM Flexible price with DSM TOU without DSM TOU with DSM
Ele
ctri
city
cos
t (ct
)
Time (h)
Household 3
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000
15
30
45
60
75 Flexible price without DSM Flexible price with DSM TOU without DSM TOU with DSM
Ele
ctri
city
cos
t (ct
)
Time (h)
Household 4
Figure 4. 4 Hourly cost curves for average load of households during weekday
Figure 4.5 shows the hourly electricity cost reduction during weekend after load
scheduling, particularly under TOU pricing tariff. In the case of flexible pricing tariff, no load
was scheduled. Figure 4.5 shows that the pattern of hourly electricity cost of unscheduled
loads under flexible pricing tariff was almost similar to that of unscheduled loads under TOU
Page | 75
pricing tariff. When TOU tariff rate is considered, most of the household appliances were
operated during the peak periods. The cost of electricity consumed in peak periods under
TOU tariff was about 94.9%, 94.3%, 97.9%, and 97.5% of total electricity cost for
households 1, 2, 3, and 4, respectively. However, with the application of DSM, the cost of
electricity in peak periods for the households was reduced to 92.1%, 93.5%, 95.9%, and
95.5% of total electricity cost, respectively.
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000
20
40
60
80
100 Flexible price without DSM TOU without DSM TOU with DSM
Ele
ctri
city
cos
t (ct
)
Time (h)
Household 1
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000
20
40
60
80
100 Flexible price without DSM TOU without DSM TOU with DSM
Ele
ctri
city
cos
t (ct
)
Time (h)
Household 2
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000
20
40
60
80
100
120 Flexible price without DSM TOU without DSM TOU with DSM
Elec
tric
ity c
ost (
ct)
Time (h)
Household 3
3:00 6:00 9:00 12:00 15:00 18:00 21:00 24:000
20
40
60
80
100
120 Flexible price without DSM TOU without DSM TOU with DSM
Elec
tric
ity c
ost (
ct)
Time (h)
Household 4
Figure 4. 5 Hourly cost curves for average load of households during weekend
The user always wants to reduce their electricity cost and prefers to maintain a
balance of load with a low PAR. In the present case study, the application of DSM showed a
significant reduction in electricity cost and PAR. Table 4.3 shows the comparison between
the flexible pricing and TOU pricing tariff scheme for load scheduling in terms of peak load
Page | 76
reduction, total electricity cost reduction, and PAR reduction. In weekdays, the PAR and
electricity cost reduction were 12.8% to 27.1% and 5.4% to 6.6% under flexible pricing tariff,
whereas those for TOU tariff were 12.8% to 20.4% and 2.0% to 2.9%, respectively, due to
the application of DSM. In the weekend, the load scheduling under TOU tariff reduced the
PAR and electricity cost by 14.9% to 20.1% and 1.0% to 2.9%, respectively.
Table 4. 3 Summary of the load shifting results in percentages
Consumers Flexible price tariff (Weekday) TOU tariff (Weekday) TOU tariff (Weekend)
PAR PL Cost PAR PL Cost PAR PL Cost
Household 1 27.1 27.1 6.6 16.1 16.1 2.7 19.0 19.0 2.9
Household 2 15.9 15.9 6.3 20.4 20.4 2.3 20.1 20.1 1.0
Household 3 20.1 20.1 5.4 12.8 12.8 2.9 14.9 14.9 2.2
Household 4 12.8 12.8 5.9 14.9 14.9 2.0 16.0 16.0 2.1
PAR- Peak to average ratio; PL- Peak load
4.4.3 Renewable energy integration
We considered residential microgrid, which can be connected to the main grid to
import and export power for each of the households. The proposed microgrid model contains
a 3 kW solar panel and a 1.5 kW wind turbine. The hourly power output from solar PV was
estimated using the Victorian hourly performance data of solar PV. According to the
technical specifications of the 1.5 kW wind turbine widely used in Australia
[https://www.australianwindandsolar.com/aws-hc-wind-turbines], the rated, cut-in, and cut-
out speeds are 10.5 m/s, 2.7 m/s, and 12.1 m/s, respectively. Hence, the hourly wind power
output was predicted based on the hourly wind speed. Figure 4.6 presents the average hourly
predicted power generation from the renewable energy sources for weekday and weekend.
The hourly power output for weekday and weekend showed almost the similar trend. The
higher renewable output predicted during the mid-day for both weekday and weekend was
Page | 77
due to the higher PV output, which was linked to the higher intensity of solar irradiance
during those periods.
0 5 10 15 20 250.0
0.5
1.0
1.5
2.0
Pow
er o
utpu
t (kW
)
Time (h)
Wind turbine Solar PV Total renewable
(A)
0 5 10 15 20 250.0
0.5
1.0
1.5
2.0
2.5(B) Solar PV
Wind turbine Total renewable
Pow
er o
utpu
t (kW
)Time (h)
Figure 4. 6 Average power output from renewable energy sources (A) Weekday (B) Weekend
Figure 4.7 illustrates the average hourly energy surplus and deficit with the
integration of renewable generation for each of the households after load shifting. In the case
of weekday, energy deficit was observed during 6am–9am and 18pm–22pm for all the
households under flexible and TOU pricing tariff due to the use of more loads and less
generation from renewables. The higher surplus energy was obtained during the mid-day for
both tariff cases associated with the higher renewable energy generation and less energy
consumption in those periods, as shown in Figure 4.7 (A) and (B). On the contrary, the
energy deficit for weekend occurred during 9am–10am and 15pm–23pm. Similar to weekday,
weekend also showed maximum energy surplus during mid-day. Therefore, approximately
7.65–11.51 kW of surplus energy was obtained per day for the studied households by the
incorporation of DSM and renewable energy resources. Accordingly, the estimated monthly
surplus energy was in the range between 223 kW and 275 kW which can be sold into grid to
generate revenue.
Page | 78
0 5 10 15 20 25
-1.0
-0.5
0.0
0.5
1.0
1.5
Deficit energy
Pow
er (k
W)
Time (h)
Household 1 Household 2 Household 3 Household 4
Surplus energy
(A)
0 5 10 15 20 25
-1.0
-0.5
0.0
0.5
1.0
1.5
Deficit energy
Pow
er (k
W)
Time (h)
Household 1 Household 2 Household 3 Household 4
Surplus energy
(B)
0 5 10 15 20 25
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Deficit energy
Pow
er (k
W)
Time (h)
Household 1 Household 2 Household 3 Household 4
Surplus energy
(C)
0 5 10 15 20 25-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Deficit energy
Pow
er (k
W)
Time (h)
Household 1 Household 2 Household 3 Household 4
Surplus energy
(D)
Figure 4. 7 Hourly energy surplus and deficit for each of the households after load shifting
(A) flexible pricing weekday, (B) TOU weekday, (C) flexible pricing weekend and (D) TOU weekend
4.4.4 Trade off from DSM integrated with microgrid
Figure 4.8 shows the monthly electricity cost and benefits gained from the DSM
implementation and renewable integration. Each of the households generated revenue by
selling surplus energy, and the value was estimated based on the average Victorian feed-in-
tariff of 10 ct/kWh [https://www.canstarblue.com.au/electricity/victoria-electricity-tariffs/].
Figure 4.8 (A) shows that the monthly electricity cost was reduced by 5.5%–6.1% for flexible
pricing tariff when some of the total household’s loads were shifted from peak hours to off-
peak and shoulder hours based on the electricity price and consumers’ preference. On the
contrary, the maximum electricity cost reduction in the case of TOU pricing tariff was only
just above 2.5%, as shown in Figure 4.8 (B). In the case of TOU pricing tariff, an
Page | 79
insignificant amount of load was shifted from peak hours to off-peak hours due to users’
preference and nature of loads, which were linked to a small reduction in electricity cost.
Analysis of flexible and TOU pricing tariff shows that users’ monthly electricity cost was
reduced significantly for both tariff cases (Figure 4.8). For instance, over 88% of base
electricity cost (i.e., electricity cost without DSM) reduction was obtained for household 4
under flexible pricing tariff due to the combined effects of load shifting and renewable
integration. Additionally, the monthly revenue generation from surplus energy was more than
15% of the electricity cost without DSM, which can compensate the users’ remaining
electricity cost after DSM implementation and renewable integration. Therefore, considering
all the scenarios i.e., load shifting, power generation from renewables and revenue from
surplus energy, the households considered for the current case study the need to spend from
no to only 9.5% of the total electricity cost without DSM.
Household 1 Household 2 Household 3 Household 40
20
40
60
80
100
120
140
160
180 Cost without DSM Cost with DSM Cost with DSM and renewables Revenue from renewables
Cur
renc
y (A
U$)
(A)
Household 1 Household 2 Household 3 Household 40
20
40
60
80
100
120
140
160
180 Cost without DSM Cost with DSM Cost with DSM and renewables Revenue from renewables
Cur
renc
y (A
U$)
(B)
Figure 4. 8 Monthly cost analysis under different scenarios (A) flexible pricing tariff (B) TOU pricing tariff
4.5 Conclusions
The current work aims to minimize the monetary expenses of electricity consumption
in households by shifting the loads to the times with lower electricity price and utilizing
energy from renewables. A residential household load management model was proposed
based on the price-based demand response along with the integration of renewable energy
Page | 80
resources. Simulation and case studies were conducted in different households to analyze the
effectiveness of the proposed model (i.e., DSM integration with renewables) under flexible
and TOU pricing schemes using BPSO algorithm in MATLAB. Results showed the potential
benefits of DSM implementation in reducing peak load and electricity cost. As a result of
demand response program and renewable energy integration, the consumers used a minimal
amount of electricity from grid, and they could sell surplus energy to the grid. This practice
greatly impacts the reduction of the households’ monthly electricity cost.
Page | 81
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS
Conclusions:
This thesis focuses on the management of household load demand in the context of
SG and microgrid. In order to achieve an optimal schedule, load shifting and DR program
were implemented along with renewable energy integration based on real time data. The
scheduling problem was optimized in terms of reduction of peak load, electricity bill, and
PAR under various pricing schemes. BPSO algorithm was used for optimizing the proposed
load scheduling model in MATLAB. Microgrid was modelled mathematically for residential
households. The simulation was carried out to validate the proposed model under the
Victorian tariff rate. The following specific conclusions can be drawn from the simulation
results.
The BPSO based method finds an optimal load schedule in terms of reduction of peak
demand, electricity cost, and PAR for residential, industrial, and commercial loads.
The proposed BPSO based DSM method performs better over GA based approach in
terms of optimization for residential, industrial, and commercial loads scheduling.
DSM implementation provides potential benefits in terms of reduction of peak load
and electricity cost for residential households.
As a result of both demand response program and renewable energy integration, the
consumers used a minimal amount of electricity from the grid and they could sell
surplus energy to the grid.
Future work:
In this thesis, various problems of energy management have been investigated.
Various approaches have been suggested to provide a solution by attaining an optimal load
schedule in terms of cost and peak load reduction. But still, there are some prospective
Page | 82
directions to extend the current research work. Future research work can be extended as
follows:
In order to reduce the operational cost and emission of a smart grid, a probabilistic
model can be further investigated. Wind and solar energy are stochastic in nature and
always uncertain to predict the accurate generation. Due to this issue, a probability
distribution function can be introduced in microgrid modeling to predict the behavior
of renewable energy. Incentive based DR program could be another possible solution
to remove the uncertainties in SG.
Further extensive study can be performed considering a large number of smart homes
along with energy storage systems and large renewable energy system.
Adding electric vehicles to the home energy management system as a big load is
realistic nowadays, which regulates voltage and frequency. However, more research is
required on various aspects particularly, on the improvement of battery lifetime as
battery requires to undergo frequent charging and discharging, which causes
wearing.The customers can be classified into different subgroups for analyzing the
potential countermeasure of peak loads based on the various pricing schemes.
Hybrid algorithms can be developed and implemented for solving the DSM
optimization problems and establishment of comparison with existing algorithms.
Such investigations would be extremely useful to make the load management system more
additive and to achieve the desired goal.
Page | 83
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Appendix
Table A 1 Average hourly load consumption in weekday
Time (h) Load (kWh)
Household1 Household 2 Household 3 Household 4
0:00 0.56 0.31 0.12 0.11
1:00 0.64 0.22 0.09 0.11
2:00 0.11 0.09 0.1 0.17
3:00 0.09 0.09 0.1 0.21
4:00 0.12 0.1 0.5 0.29
5:00 0.09 0.16 0.1 0.35
6:00 0.11 0.14 0.1 0.49
7:00 0.25 0.09 0.86 0.59
8:00 0.46 0.08 0.09 0.66
9:00 0.12 0.86 0.16 0.71
10:00 0.15 1.03 0.19 0.58
11:00 0.48 0.83 1.36 0.68
12:00 0.71 0.91 0.79 0.51
13:00 1.01 0.65 1.64 0.95
14:00 0.68 0.87 1.08 0.81
15:00 2.18 0.93 0.09 1.48
16:00 1.08 1.57 0.61 0.73
17:00 1.13 1.06 1.44 1.05
18:00 1.06 1.09 1.24 0.88
19:00 0.98 0.91 1.11 0.55
20:00 1.09 0.79 0.84 0.63
21:00 0.72 0.81 0.48 0.56
22:00 0.61 0.16 0.14 0.51
23:00 0.53 0.13 0.13 0.51
Page | 102
Table A 2 Average hourly load consumption in weekend
Time (h) Load (kWh)
Household1 Household 2 Household 3 Household 4
0:00 0.11 0.16 0.12 0.12
1:00 0.12 0.16 0.1 0.11
2:00 0.56 0.13 0.1 0.13
3:00 0.11 0.16 0.08 0.11
4:00 0.12 0.16 0.08 0.11
5:00 0.58 0.16 0.09 0.16
6:00 0.11 0.75 0.1 0.1
7:00 0.1 0.14 0.54 0.1
8:00 0.35 0.14 0.27 1.06
9:00 0.96 1.01 1.51 1.46
10:00 1.1 0.21 2.48 1.83
11:00 0.29 0.11 1.09 0.74
12:00 0.11 0.58 1.77 1.7
13:00 0.61 0.15 0.14 1.31
14:00 0.65 0.87 0.91 2.13
15:00 2.46 1.74 0.58 0.51
16:00 1.38 2.54 0.11 0.29
17:00 1.22 2.89 0.61 0.79
18:00 2.79 0.85 0.83 1.03
19:00 0.29 0.16 1.6 0.98
20:00 0.82 0.65 0.81 0.11
21:00 0.66 0.16 0.78 0.65
22:00 0.28 0.32 0.13 0.13
23:00 0.61 0.45 0.19 0.13
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