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Smart Home Energy Management System


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With the development of smart grid technology, residents can schedule their power consumption patterns in their homes to minimize electricity expenses, reducing peak-to-average ratio (PAR) and peak load demand. The two-way flow of information between electric utilities and consumers in the smart grid opened new areas of applications. In this chapter, the general architectures of the home energy management systems (HEMS) are introduced in a home area network (HAN) based on the smart grid scenario. Efficient scheduling methods for home power usage are discussed. The energy management controller (EMC) receives the demand response (DR) information indicating the Time-of-use electricity price (TOUP) through the home gateway (HG). With the DR signal, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG.
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Chapter 11
DOI: 10.4018/978-1-7998-1230-2.ch011
With the development of smart grid technology, residents can schedule their power consumption pat-
tern in their home to minimize electricity expense, reducing peak-to-average ratio (PAR) and peak load
demand. The two-way flow of information between electric utilities and consumers in smart grid opened
new areas of applications. In this chapter, the general architectures of the home energy management
systems (HEMS) are introduced in a home area network (HAN) based on the smart grid scenario. Efficient
scheduling methods for home power usage are discussed. The energy management controller (EMC)
receives the demand response (DR) information indicating the Time-of use electricity price (TOUP)
through the home gateway (HG). With the DR signal, the EMC achieves an optimal power scheduling
scheme that can be delivered to each electric appliance by the HG.
Due to the ever-growing demand of electrical energy, the percentage of renewable energy generation
is greatly raised along with the increased energy prices. Various environmental constraints also posed
a limit on energy generation from conventional energy sources (Khomami and Javidi, 2013). All these
challenges motivated power industry to shift their focus on smart demand side management techniques.
Electricity usage report in United States suggests that at least 30% of electric power is wasted from 72%
of energy consumed by residential and commercial users (Geng and Lu, 2016). In Europe, as target by
2020, 20% share of renewable energy production and 20% of energy efficiency have been fixed to be
met. The advancement of information and communication technologies (ICTs) increased the demand
of reliable and quality power supply (Zhao et al., 2013; Khan et al., 2019; Khan et al., 2018; Khan et
al., 2017; Banteywalu et al., 2019; Anteneh et al., 2019; Molla et al., 2019, Molla et al., 2018, Jariso
et al. 2018). These ICTs are important part of smart grid system that transfers information from one
system to another one. This information transfer is very helpful to control and coordinate various smart
Smart Home Energy
Management System
Tesfahun Molla
Hawassa University, Hawassa, Ethiopia
Smart Home Energy Management System
grid technologies to respond immediately under varying demand conditions. Smart grids can use local
renewable energies like wind and solar energy to solve environmental problems, increase reliability of
equipment and system and reduce the costs of infrastructure (Dogaheh and Dogaheh, 2017). Further to
control the demand from consumer premises, these ICT systems are very helpful. These are the integral
part of smart home appliances energy management system, which is one aspect of smart grid (Moham-
madi et al., 2013).
Today, the concept of micro-grid is being used for the purpose of helping the environment through
using several renewable and available resources such as wind and solar energy, along with other energy
generators such as micro-turbines and fuel cells to reduce generation costs and reduce environmental
pollutions. The resources are not only interrelated, but also the interaction continues in higher levels
such as distribution networks (Alhelou et al., 2019; Makdisie et al., 2018; Alhelou et al., 2018; Alhelou
et al., 2016; Haes Alhelou et al., 2019; Njenda et al., 2018). Restriction of fossil fuels and increasing
growth of demand for energy, enhanced living standards, global warming and environmental problems
have led to increasing advancements in technology and use of modern and cleaner energies. The idea of
smart grid was begun with the idea of Advanced Metering Infrastructure (AMI) to develop demand side
management, increase energy efficiency and a self-healing electric grid, so that it could improve reliability
and respond to natural disasters or deliberate sabotage (Dogaheh and Dogaheh, 2017). Advancement of
smart grid system allows consumers to reduce their energy consumption through proper scheduling of
different appliances (Khan et al., 2014; Khan et al., 2013; Khan et al., 2012; Negash et al., 2017; Negash
et al., 2016; Jariso et al., 2017; Kifle et al., 2018; Yeshalem et al., 2017; Singh et al., 2017; Gupta et
al., 2015). This is possible with the information of different electricity pricing techniques such as real
time electricity pricing (RTEP) and time of use pricing (TOUP). The demand side management (DSM)
techniques are very old in power system that incorporates DR techniques with load shifting, energy ef-
ficiency and conservation program. Shifting of consumers’ load from peak hours to off peak hours is
the main function of DR techniques. For that purpose incentives are offered to consumers.
DR programs transfer customers’ load during periods of high-demand to off-peak periods by offer-
ing them incentives and can reduce critical peak demand or daily peak demand (Qayyum et al. 2015).
In smart grid environment there are different DR pricing schemes. The most common pricing schemes
include real-time pricing (RTP), time of use pricing (TOUP) and critical peak pricing (CPP). Within the
concept of demand-side energy management, residential energy management recently evokes increas-
ing desire from the research community. The traditional grid has demand response programs for only
large-scale consumers such as commercial buildings and industrial plants. However, for the residential
consumers it does not have a similar mechanism due to two main reasons (Fini et al., 2016; Alhelou
et al., 2018; Zamani et al., 2018; Alhelou et al., 2015; Njenda et al., 2018; Haes Alhelou et al., 2018;
Haes Alhelou et al., 2019). First, it has been difficult to handle a large number of residential customers
without efficient automation tools, sensors and communication. The second reason is, compared with
their implementation cost; the impact of demand response programs has been considered to be relatively
small. However, in smart grid system, smart appliances, low-cost sensors and efficient communication
system set a stage for efficient energy management technique with the interaction between the utility
grid, the users and electrical devices.
Smart Home Energy Management System
Background of Smart Grid
Traditional power grid is insufficient to meet the electricity demand of modern society. In order to meet
these challenges the concept of smart grid was introduced. Smart grid increases efficiency of the power,
storage capacity, grid sustainability and customer engagement (Hassan et al. 2018). The traditional
power grid is known to be service provider and centrally controlled and networked system. It allows the
transmission of power in one way from the generating station to the consumers. In developed countries to
satisfy the fluctuating demand of the consumers, the electricity suppliers supply sufficient power and the
consumers utilize without consumption limit. When the power demand and fluctuation of consumption
increases abruptly, it could be problematic to operate without using the feedback technology. For this
reason, it is important to change the power grid into a much smarter level. There is no common defini-
tion for a smart grid. Generally, smart grid can be defined as; it is a modern electric power grid that is
sustainable and technically superior to that of traditional power grid. A smart grid is an autonomous
electricity environment which delivers electricity in a smarter and much controlled way from the point
of generating station into the consumers. It is integrated with power electronic devices, sensors, actuators
and communication devices. The consumers are known to be the integral part of a smart grid because
they can affect the pattern of electricity consumption behavior based on the information received and
the incentives provided by the electric utility. Smart grid allows two-way communications between cus-
tomers and electricity utility companies; smart appliances, smart meters and renewable energy sources
(RES) helps to achieve reliable, cost effective and intelligent control of the Utility power grid. Figure 1
presented the overview of Smart grid system.
Smart grid has four common features:
An indoor home-in-display (HID) and feedback interface to display the information, controlling
and schedule the appliances in remote way using tablets or PCs.
A smart meter for monitoring the energy consumed by the customer and the energy sell back to
the grid when there is DG connected to the grid.
A distribution network for bidirectional communication between the utility and the consumer.
An interface in the utility side
Smart Building
A smart building is an automated type of building, which monitors the electrical operations of the build-
ing such as lighting, heating, ventilation, air conditioning, internal and external security and other related
systems automatically. In order to collect data and monitor the entire system and services based on the
desire of the users, the building uses different sensors, microchips and actuators. The owners, facility
managers and operators are benefited from this type of infrastructure due to maximization of reliability
and performance, optimized energy usage and reduction of the environmental impact on the building.
Traditional buildings are not integrated with automation technologies, sensors and communication
system. It used as shelters and provide temperature control and safety with the same efficiency level for
years. But, the oldest traditional structures and newer buildings have been converted in to smart build-
ings, are changing constantly. Smart buildings connected to highly adaptable and intelligent software
environment makes the way of living easy, increase comfort of occupants, increase security and have
Smart Home Energy Management System
low impact on environment than buildings which are not connected. There are different smart buildings
around the world. Those include educational facilities, hospitals, stadiums and others.
According to the Navigant research it was estimated that the market share of smart building technol-
ogy will generate $8.5 billion revenue in 2020 up from $4.7 billion in 2016 which was growing with a
compound annual growth rate of 15.9% over the forecasted period (SB, nd). Making buildings smart
begins by interconnecting the core systems like lighting systems, heating systems, water pumps, fire
alarms etc. with sensors and efficient advanced control system. In this thesis, the main focus is limited to
lowering cost of energy consumption of appliances without highly affecting comfort level of occupants,
reduction of electricity billing and regulating the peak load demand while maintaining the horizontal
load distribution within a day by using demand side management (DSM) technique based on the demand
response signal received from the electric utility company.
Levels of Energy Management under Smart Grid
Communication Network (SGCN)
There are different levels of communication network in smart grid which helps to effectively monitor
energy management system and transfer information between electric utility company and consumers.
Those networks include Local area network (LAN), Neighborhood Area network (NAN), Wide Area
Network (WAN).
Consumers under Home Area Network (HAN)
HAN is a type of local area network, which is located at buildings and facilitates the communication or
exchange of information between EMC and home appliances. This enables the EMC to identify each
schedulable home appliances based on its’ address and efficiently monitor their operation within a day. A
feedback signal which shows the electric pricing data and billing cost are displayed via home in display.
The electricity consumption data are sent to the utility grid via smart meter after receiving the informa-
tion from each appliance. Therefore, HAN is a backbone of the communication between schedulable
appliances at home and the smart meter.
Figure 1. Overview of smart grid (Hassan et al. 2018)
Smart Home Energy Management System
Aggregator Under Neighborhood Area Network (NAN)
The exchange of information between utility companies’ WANs and smart metering communications
on customer premises are facilitated by neighborhood area network (NAN). It covers from the start
point of smart meters on customer side which is the most important element known as heart of smart
grid revolution up to Utilities’ WAN. The smart meter records real time or nearly real time data. The
net energy which is the difference between energy consumed from utility grid and energy exported to
utility grid recorded by smart meters is sent to Utility Company. Similarly, a feedback signal like real
time energy pricing data and other important information sent to the customer from utility company
via smart meter or home in display (HID) to view the information received. NAN acts as an aggregator
between the smart meter domain and WAN domain to facilitate the two-way communication or exchange
of information. NAN is the most crucial element in smart grid communication system which transfers a
huge amount of data and distributing control signals among electric utility companies and an abundant
volume of devices installed at customers’ premises.
Electric Power Utility Under Wide Area Network (WAN)
A WAN is used as an aggregator of data from multiple NANs and sends it to the private network owned
by the electric utility company. It covers long-haul distances from NAN to a control center. WAN ap-
plications, including wide-area control, wide-area monitoring and protection, have been identified as the
next-generation solution to achieve power system operation, power system planning and power system
protection in the smart grid Wide-area monitoring, protection and control applications which provides
higher data resolution and shorter response time than classical supervisory control and data acquisition
(SCADA) and energy management (EMS) systems (Kuzlu et al., 2014).
Demand Response
Demand response is a technique in which end-users are involved in demand side management of electri-
cal power consumption based on electrical pricing signal received from electric power utility company.
When end-users are creating awareness about their energy consumption, they tend to manage their total
usage. The demand response program in traditional grid system mostly applied for large scale consumers
such as commercial buildings and industrial plants; however, such mechanism does not exist for resi-
dential users mainly due to two reasons. The first reason is it is too difficult to handle a large number of
home appliances without using automation technology, sensors and communication system. The second
reason is, compared to their implementation cost the impact of demand response program is relatively
small (Qayyum et al. 2015). Studies have suggested that by employing automated energy management
strategies, it is possible to let the users to participate in demand response program and control their load
consumption pattern based on the pricing information. In order to implement this strategy, different
techniques can be adopted, for example by using internet and controlling the energy consumption via
software installed on PC. Demand response can be categorized into two. The first demand response
program is price-based demand response such as time-of-use (TOU) pricing, critical peak pricing (CPP)
and real-time pricing (RTP), which provides time-varying rates of electricity consumption at different
periods of time. This encourages customers to use less electricity when the price of electricity is high
and they tend to shift their loads to non-peak hours when electricity pricing is low. The second type of
Smart Home Energy Management System
demand response programs are incentive-based demand response program in which the utility company
pays the customers who are participating in the program to reduce their loads in the time requested by
the utility company or program sponsor, which is triggered by reliability problem in the grid system or
highest electricity prices.
Demand response techniques uses smart appliances, sensors, actuators, smart meters and two-way
communication. So, it is considered to be part of smart grid technologies.
Demand Side Management
In demand side management (DSM), customers will contribute to:
Reduction of peak load in peak-hours of the day and reduction of pollutant power plants by using
renewable energy sources, thus minimizing operational and maintenance cost.
Integration into the main distribution grids of micro generators (MG) and distributed generators
(DG) which are mostly exposed to generation volatility.
Maximizing stability of the main distribution network and, thus minimizing interruption problems
of power supply, grid maintenance and operational cost.
DSM consists of all activities that manage time of energy consumption. One of the most important
goals of DSM is to reduce the peak load. The most frequently used techniques in demand side management
are the following (Qayyum et al., 2015). Figure 2 presented the Demand side management techniques.
1. Peak Clipping: It refers to demand reduction or load cutting during heavy load periods of the day
using a method of direct load control (DLC) of customer appliances by the utility via remotely
controllable switches. The duration for which peak occurs is reduced by methods like distributed
generation and shutting down the consumers equipment.
2. Valley Filling: refers when the cost of production is lower compared to peak hours, it promotes
the consumption of energy during off-peak hours. So, low demand periods are filled by building
off-peak capacities. There are various incentives like discounts that motivate consumers to change
their habits of energy consumption.
3. Strategic Conservation: refers to the reduction of seasonal energy consumption mainly by mini-
mizing the energy wastage, to increase the efficiency of energy consumption. In this method, load
shape optimization is achieved by applying demand reduction methods on customer premises.
4. Strategic Load Growth: It monitors the maximum consumption of electrical energy in seasonal
pattern. This technique optimizes the significant daily demand response when the demand exceeds
beyond valley filling technique. The method is based on maximizing the market share of loads
which is supported by a combination of energy conversion technique and energy storage systems
(ESS) or distributed energy resources (DG).
5. Load Shifting: It shifts the energy usage from the peak period to off-peak periods during which
the consumption of energy is lower on recurring basis. This is often done by using the energy
stored during off-peak periods and using it during peak hours. It may also be supported by using
distributed generation.
Smart Home Energy Management System
6. Flexible Modeling: It consists of actions, following an integrated plan between the consumer and
concessionaire, according to the need of moment. Through installation of the load-limiting devices,
it limits the energy use of a consumer during certain moments without affecting the actual security
Time Based Pricing Programs
Time based pricing programs also known as time-based rate programs are prices incurred on customers
that vary on different times of the days. In traditional grid system it consisting of small commercial and
residential customers and those customers are subjected to two types of pricing schemes known as flat
rates and tiered rates. In flat rates, customers are charged with the same rate for their consumption during
a given period of time (e.g., during one month or 30 days of billing cycle). In tiered rates, certain blocks
of consumption on a given period of time (e.g., first 100 kWh vs. next 100 kWh) are charged based
on different pricing (this might be season to season, day to day or hour to hour). Time based pricing is
generally grouped into the following types (TBRP, nd):
Time of Use Pricing (TOU): This type of pricing is applied to a certain broad blocks of hours.
For example in a typical summer week day on-peak 6 hours; off-peak hours in other hours in sum-
mer days of the month.
Real Time Pricing (RTP): The pricing rates are applied to consumption on an hourly basis.
Variable Peak Pricing (VPP): This is the combination of time of use and real time pricing.
Critical Peak Pricing (CPP): when utility companies anticipate or consider a high wholesale
market prices or emergency conditions in power system, for a specified period of time they may
call critical events (e.g., on a hot summer weekday 4 p.m. to 6 p.m.), during these time, the electric
price may be substantially raised.
Figure 2. Demand side management techniques (Gaur et al. 2017)
Smart Home Energy Management System
Except TOU pricing, the pricing is not known with certainty ahead of time. So, all the electricity
pricing listed excluding TOU pricing scheme are known as Dynamic pricing.
The Structure of Home Energy Management System in Smart Grid
It is assumed that a smart home consists of various appliances having adequate and proper communica-
tion interfaces in order to facilitate the exchange of information with the EMC. It is also assumed that
each of the appliances communicate with the EMC and can also communicate with each other in the
most sophisticated and effective scheduling. Figure 3 presents the structure of home energy management
system with different data communication domains, those domains include the internet domain, home
area network (HAN) domain and the smart meter domain (AMI).
The internet domain enables consumers to monitor and control their power consumption profile,
the scheduling of their appliance power consumption and etc. through an in-home display (IHD) which
consists of computer, tablet or smart phone. The smart meter domain includes a large number of intercon-
nected smart meters called “Automatic metering infrastructure (AMI)” which is installed and monitored
by utility companies in order to transmit load information and DR signals between the smart homes and
power market. Based on the pricing signals received 24 hours day a head, the home energy management
controller (EMC) schedules the electricity usage pattern for the next day.
Load Type Categorization
In energy management system of residential building, scheduling of different loads can be achieved by
specifying the type of load to be scheduled and characteristics of the given load. Those characteristics
include the operation duration and the average energy consumption of each appliance. Generally, Home
appliances are classified into three major groups as follows (Ruan et al. 2014).
Figure 3. Overview of home energy management system
Smart Home Energy Management System
1. Base LINE LOADS (NON-SHIFT ABLe): Those are type of appliances which operate immedi-
ately when the residents are requested. Such appliance includes Light, Television, air conditioner,
computer, etc. the energy supplied to such appliances are considered to be non-schedulable.
2. Uninterruptible Flexible Loads: Those types of home appliances are operated continuously until
the completion of the task. The starting and ending time of such appliances can be set flexibly.
Such appliances include washing machine, Refrigerator, dish washer and Electric Oven.
3. Interruptible Flexible Loads: It refers to the appliances which are allowed to operates continu-
ously and can be interrupted when the user desire to shut down in the specified interval of time.
Such appliances include clothes dryer, pool pump and floor cleaning robot.
In the home area network (HAN) domain the Electric vehicles (EV) and Energy storage system
(ESS) may be available and are integrated into the system. The EV can be used as a load during charg-
ing mode or grid to vehicle (G2V) modes of operation and it can also be used as energy storage system
during discharging mode or vehicle to grid (V2G) modes of operation. To maximize the comfort level
of the user, baseline loads are left uncontrollable in-home energy management system problem. It is
also assumed that all the home appliances operate at their rated power during their working periods of
time and all constraints needs to be met.
Mathematical Model of HEMS in Smart Grid
The total power consumption of household appliances can be calculated as:
. τ.Xa
t = Ea. (1)
Appliance operation duration;
t = [
a ] (2)
Length of operation time for an appliance for non-interruptible appliances;
1 (3)
Length of operation time for an appliance for interruptible appliances;
a (4)
Where, Xa
t represents the on and off status of an appliance n. Xa
t =1 represents the appliance is on
and Xa
t = 0 represents the appliance is off. P
t represents power consumption value of household ap-
Smart Home Energy Management System
pliances and Ea is the maximum power consumption limit of appliances in kWh; [
a] is the bound-
ary limit for the operation of an appliance which is set by the user. La is the length of operation time
for an appliance a (hour). Τ is the resolution time in minutes. The priority of operation for each house-
hold appliance is set by the user to switched on or off at the same time. This helps to maximize user
satisfaction. Assuming that, the objective functions are minimizing users’ cost of electricity consumption
and minimization of customer’s dissatisfaction value due to scheduling;
1. 1. Minimizing users’ cost of electricity consumption:
minC =
grid w grid
. . ,
=C P
TOU w grid
Sold w grid
0 (6)
Where Cgrid the grid electricity cost and P
w grid, is the power supplied by the grid at time slot t (kW),
is time of use electricity pricing ($ /kWh) and
Sold is the cost of electricity sold back to the grid.
2. Minimization of customer’s dissatisfaction value due to scheduling
One of the objectives of this paper is to minimize customer’s discomfort level while minimizing the
cost of electricity and reducing the peak load. In order to model and quantify user dissatisfaction, a delay
time rate function is introduced.
minimize Aa f
Where, fsn is the discomfort associated with a shiftable appliance which is obtained by defining the
delay time rate (DTR) of shift able home appliances as shown in equation (8) (Zhao et al., 2013):
la a
a a
a a
β α
a and
a are the start and end time limits to finish operation of appliance which is set by
the user. ‘la’ is length of operation duration for a given appliance and
is the actual starting time of a
given appliance. If the appliance starts operation later than the actual operation starting time (ta), the
DTR would increase. The Value of DTR for an appliance varies between 0 and 1. Assume ta ϵ [
a, βa]
and LOT is la, if the appliance started its’ operation at time slot
a the DTR could be 0. This is because,
there is no any delay time to start operation. If the appliance start its’ operation at time slot
, the
Smart Home Energy Management System
value of DTR could be 1. Further a delay parameter >1 may be introduced to equate fsn as DTR. Thus,
discomfort associated with shift able appliance would be as follows:
By satisfying energy and timing constraints the above objective functions would be solved easily.
Demand side management is the most effective technique used in optimization of appliance load sched-
uling in smart grid environment to minimize cost of electricity billing and peak to average ratio with
minimum peak load and nearly optimum horizontal load distribution within 24 hours of the day. There-
fore, this chapter discussed the various DSM techniques with the formulation of optimization problem
for Residential Demand response.
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Demand Side Management (DSM): These techniques are very old in power system that incorporates
DR techniques with load shifting, energy efficiency and conservation program. Shifting of consumers’
load from peak hours to off peak hours is the main function of DR techniques.
Smart Building: It is an automated type of building, which monitors the electrical operations of the
building such as lighting, heating, ventilation, air conditioning, internal and external security and other
related systems automatically.
Smart Grid: It can be defined as; it is a modern electric power grid that is sustainable and techni-
cally superior to that of traditional power grid. A smart grid is an autonomous electricity environment
which delivers electricity in a smarter and much controlled way from the point of generating station in
to the consumers.
... For DSM services to exist, HEM and SM technologies must be present in a scalable and secure way in an AMI [26]. However, the introduction of these technologies to provide HyDSMaaS also presents new operational and technical challenges [27], and the main one is which technology and communication topology to use to satisfy this data exchange between customers and the EPC, or vice versa. ...
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Seeking to solve problems in the power electric system (PES) related to exacerbated and uncontrolled energy consumption by final consumers such as residences, condominiums, public buildings and industries, electric power companies (EPC) are increasingly seeking new information and communication technologies (ICTs) to transform traditional electric power distribution networks into smart grids (SG). With this implementation, PES will be able to remotely control electric power consumption as well as monitor data generated by smart meters (SM). However, Internet-of-Things (IoT) technologies will enable all this to happen quickly and at low cost, since they are low-cost devices that can be deployed quickly and at scale in these scenarios. With this in mind, this work aimed to study, propose, and implement a hybrid communication infrastructure with LoRaWAN and LoraMesh for the demand-side management as a service (HyDSMaaS) using IoT devices such as long range (LoRa) to provide an advanced metering infrastructure (AMI) capable of performing all these applications as a service offered by EPC to end consumers. Additionally, services such as demand-side management (DSMaaS) can be used in this infrastructure. From the preliminary results it was found that the LoRaWAN network achieved a range of up to 2.35 km distance and the LoRaMESH one of 600 m; thus, the latter is more suitable for scenarios where there is little interference and the SMs are at long distances, while the other is used for scenarios with greater agglomeration of nearby SMs. Considering the hybridized scenario between LoraWAN and LoRaMESH, it can be seen that the implementation possibilities increase, since its range was approximately 3 km considering only one hop, and it can reach 1023 devices present in a mesh network. Thus, it was possible to propose the actual implementation of LoRaWAN and LoRaMESH protocols as well as the hybridization of the two protocols for HyDSMaaS. Additionally, the results obtained are exclusively from Radioenge’s LoRa technology, which can be further improved in the case of using more powerful equipment.
The pace of energy use has significantly grown during the previous several years. In order to reduce energy consumption and demand, energy management systems (EMS) are required in households, workplaces, structures, industries, etc. Newly developing technologies like artificial intelligence (AI), the Internet of Things (IoT), big data, machine learning (ML), deep learning (DL), etc., may assist with this. This helps the users to achieve a very new, sustainable, and advanced life experiences in their homes. This paper aims to discuss smart home energy consumption and weather conditions which affects the demand and consumption of energy in any particular environment. In this research work, a smart home dataset which has different parameters of energy consumption and weather conditions is taken from the online repositories. This dataset is preprocessed using different machine learning techniques. After the preprocessing, the best suited model for the predictive modeling of the energy consumption in smart homes is obtained. A comparative analysis is carried out to find the best techniques among the existing techniques with the batter results and less error rate. This paper aims to perform the predictive modeling of the energy consumption dataset and find out the best suited technique with less error rate.KeywordsMachine learningInternet of ThingsBig dataArtificial intelligenceSmart homeEnergy managementSustainability
Conference Paper
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Efficient energy demand management plays an essential role in smart grid, sustainable and smart cities applications and efforts to reduce CO2 emissions. In this paper, we propose a framework for describing the household daily energy consumption and how it can be used to help residential households to perform appliance rescheduling to reduce energy consumption and hence reducing their energy bills while keeping resident's comfort. In this paper, heuristic optimization techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) are used for solving the load scheduling problem. Due to its ability to deal with computational complex scenarios in less computational time using less and less computational resources, Heuristic optimization techniques are used. In the proposed model, dynamic pricing is adopted where the objective is to minimize the overall cost of electricity consumption and payments by scheduling different devices in a way that fulfil each individual's constraints and preferences. Here, MATLAB was used as the simulation platform. Simulation results showed that GA and PSO can optimize energy consumption and bills and at the same time fulfils needs and preferences of each individual customer.
One of the main innovations of the intelligent grid is the use of clean resources and energy storage of delivery systems in the smart home. A primary resource of energy storage schemes is market-based control. Instead of the public network, the intelligent grid design has been frequently envisioned in suburban communities. The smart home renewable energy management (SHREM) system has been proposed, and this system provides high efficiency and high-quality solar panel for power generation. The proposed SHREM system manages smart home energy needs by installing renewable energy and planning and controlling electricity flow during peak and off-peak periods by optimization techniques. Besides, the two-way communication protocol has been designed to help maximize the flow of resources and usage efficiency for the homeowner and service provider. SHREM framework is supposed to optimize home energy consumption and save home energy costs by considering energy consumption and generation. The statistical analysis shows the highest performance accuracy of 89.2% and a lower average electrical load shifting ratio of 0.68 (within the threshold level) than existing models.
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Efficient job scheduling reduces energy consumption and enhances the performance of machines in data centers and battery-based computing devices. Practically important online non-clairvoyant job scheduling is studied less extensively than other algorithms. In this paper, an online non-clairvoyant scheduling algorithm Highest Scaled Importance First (HSIF) is proposed, where HSIF selects an active job with the highest scaled importance. The objective considered is to minimize the scaled importance based flow time plus energy. The processor's speed is proportional to the total scaled importance of all active jobs. The performance of HSIF is evaluated by using the potential analysis against an optimal offline adversary and simulating the execution of a set of jobs by using traditional power function. HSIF is 2-competitive under the arbitrary power function and dynamic speed scaling. The competitive ratio obtained by HSIF is the least to date among non-clairvoyant scheduling. The simulation analysis reflects that the performance of HSIF is best among the online non-clairvoyant job scheduling algorithms.
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In the event of a generator loss or disturbance, the power system frequency declines quickly and overall system stability is at risk. During these scenarios, under frequency load shedding is triggered to restore the power system frequency. The main stage of modern adaptive under frequency load shedding techniques is disturbance estimation. However, the swing equation is widely used in disturbance estimation but has some critical estimation errors. In this paper, instead of using the swing equation we proposed the use of a disturbance observer to estimate the curtailed power. By making use of wide area measurements, a system frequency response model, which is a representative of the whole power system, can be realized in real time. Using different power system states of the developed model, a disturbance observer can be designed as well. The main advantage of the disturbance observer is that it can accurately estimate the disturbance magnitude and its location in a very short time. Further investigations show that by using the disturbance observer disturbances, which occur at the same time or at different times in different areas regardless of the magnitude or size, accurate estimations can be made. To ascertain the efficiency of the proposed scheme, simulations are done for a four-area power system using Matlab/Simulink.
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Power systems are the most complex systems and have great importance in modern life. They have direct impacts on the modernization, economic, political and social aspects. To operate such systems in a stable mode, several control and protection techniques are required. However, modern systems are equipped with several protection schemes with the aim of avoiding the unpredicted events and power outages, power systems are still encountering emergency and mal-operation situations. The most severe emergencies put the whole or at least a part of the system in danger. If the emergency is not well managed, the power system is likely to have cascading failures that might lead to a blackout. Due to the consequences, many countries around the world have research and expert teams who work to avoid blackouts on their systems. In this paper, a comprehensive review on the major blackouts and cascading events that have occurred in the last decade are introduced. A particular focus is given on the US power system outages and their causes since it is one of the leading power producers in the world and it is also due to the ready availability of data for the past events. The paper also highlights the root causes of different blackouts around the globe. Furthermore, blackout and cascading analysis methods and the consequences of blackouts are surveyed. Moreover, the challenges in the existing protective schemes and research gaps in the topic of power system blackout and cascading events are marked out. Research directions and issues to be considered in future power system blackout studies are also proposed.
This paper proposes a novel, fully decentralized load frequency control (LFC) approach based on dynamic state estimation (DSE). The proposed approach employs an unknown input observer (UIO) for each power system area for tracking the dynamic states in real time operation. In order to achieve the fully decentralized control approach, the demand fluctuation and tie-line power deviations are modeled as unknown inputs to the UIO of each area. An optimal state feedback control method based on the observed states is used next to control each area separately. This approach reduces the complexity of the designed observer, achieves fully decentralized control, improves the robustness of the controllers, and makes the estimation process highly efficient, accurate, and easy to implement. The applicability of the proposed method is shown on a representative model of four areas interconnected via various transmission links. Simulation results demonstrate the efficiency and accuracy of the designed observer and verify the superiority and robustness of the proposed control approach compared to alternative approaches.
Conference Paper
With the increase in world population and the advancement of industries, demand for power and energy has also sharply increased. Consequently, power systems are usually loaded very close to the steady state stability limit to meet this growing demand. This threatens the secure operation of power systems, therefore schemes that are more intelligent are needed to effectively handle power systems disturbances. In the event of a generation-demand imbalance caused by generator outage under frequency load shedding (UFLS) schemes are implemented. In this paper we propose an UFLS scheme based on the minimum predicted frequency and the extrapolated disturbance magnitude. Based on polynomial curve fitting techniques the future behavior of the system frequency can be predicted. A few data points soon after the disturbance are required to predict the minimum frequency the system will reach. The load to be shed can then be determined from the linear relationship between the frequency drops and load to be shed. Index Terms-Frequency prediction, under frequency load shedding (UFLS), disturbance extrapolation.
Conference Paper
The use of wide area measurements at a central monitoring and control center has brought greater flexibility in the monitoring, control and protection of modern power systems. Under frequency load shedding (UFLS) is one of the many areas experiencing the advantages involved with using wide area measurement systems (WAMS). Conventional UFLS techniques follow a pre-set and rigid threshold of frequency to shed loads at dedicated load buses in the network without considering the magnitude of the disturbance. In this paper, based on the wide area phasor measurements, the actual disturbance in the system is determined a few seconds before performing load shedding. Time domain simulations on an analytic system frequency response model of wide area power system verify the efficiency of the proposed method. Index Terms-Disturbance estimation, system frequency response model (SFR), under frequency load shedding (UFLS), wide area measurement systems (WAMS).
Reliable electrical distribution system is the primary requirement of smart grid. Further, with the integration of intermittent renewable energy sources (RESs), reliability assessment is very vital. Various deterministic and probabilistic methods are utilized to assess the reliability of distribution system. This review study is about distribution system reliability assessment (DSRA) with and without renewable energy generation technologies such as micro grid, distributed generation, solar and wind. For that purpose, DSRA methods such as Monte Carlo simulation (MCS) and other DSRA methods are discussed. The distribution system reliability is considered by using the renewable energy generation techniques. The stochastic features of the parameters in the designing process defined the type of MCS simulation technique. These techniques are utilized to provide reliability assessment of compact system due to huge computational time associated with them. It can be restricted by restricting number of lumped equipments for a given renewable energy source. Further, numerous states can also be used to describe the arbitrariness in the renewable energy generation, because of the stochastic behavior of the resources and the mechanical degradation of the system.
Integration of the distributed generators (DGs) to power system is growing thanks to their technical, economic and environment benefits. The high penetration of DGs however raises a number of technical problems. One of these problems is islanding, which occurs when one or more DGs continue to energize a portion of the power system which has been isolated from the main grid. Timely islanding detection and discrimination between islanding and non-islanding disturbance conditions are very important for the safety of the personnel and the safe operation of equipment. This paper proposes a hybrid islanding detection method for distribution systems containing synchronous distributed generation (SDG) based on two active and reactive power control loops and a signal processing technique. The operation of the control loops accelerates islanding detection at early stages of instability by processing the terminal voltage of SDG. The proposed method is validated by extensive simulations. The results demonstrate that the proposed method is able to timely and precisely detect islanding situations and can clearly distinguish between islanding and non-islanding events, even if active and reactive power between generation and demand are very close. In addition, the proposed method has negligible impacts on power quality and is capable to allow the islanded system to operate in autonomous mode after separation from the main grid.
Smart grid enables consumers to control and schedule the consumption pattern of their appliances, minimize energy cost, peak-to-average ratio (PAR) and peak load demand. In this paper, a general architecture of home energy management system (HEMS) is developed in smart grid scenario with novel restricted and multi-restricted scheduling methods for the residential customers. The optimization problem is developed under time of use pricing (TOUP) scheme. To optimize the formulated problem, powerful meta-heuristic algorithm called grey wolf optimizer (GWO) is utilized, which is compared with particle swarm optimization (PSO) algorithm to show its effectiveness. A rooftop photovoltaic (PV) system is integrated with the system to show the cost effectiveness of the appliances. For analysis, eight different cases are considered under various time scheduling algorithms.