Smart homes: Potentials and challenges

Abstract

Decentralized distributed clean-energy sources have become an essential need for smart grids to reduce the harmful effects of conventional power plants. Smart homes with a suitable sizing process and proper energy-management schemes can share in reducing the whole grid demand and even sell clean energy to the utility. Smart homes have been introduced recently as an alternative solution to classical power-system problems, such as the emissions of thermal plants and blackout hazards due to bulk plants/transmission outages. The appliances, sources and energy storage of smart homes should be coordinated with the requirements of homeowners via a suitable energy-management scheme. Energy-management systems are the main key to optimizing both home sources and the operation of loads to maximize home-economic benefits while keeping a comfortable lifestyle. The intermittent uncertain nature of smart homes may badly affect the whole grid performance. The prospective high penetration of smart homes on a smart power grid will introduce new, unusual scenarios in both generation and loading. In this paper, the main features and requirements of smart homes are defined. This review aims also to address recent proposed smart-home energy-management schemes. Moreover, smart-grid challenges with a high penetration of smart-home power are discussed.

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Received: 26 January 2021; Accepted: 6 April 2021
© The Author(s) 2021. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy
R A
Smart homes: potentials and challenges
RashaEl-Azab*
Electrical Power and Machines Department, Faculty of Engineering at Helwan University, Cairo, Egypt
*Corresponding author. E-mail: r_m_elazab@yahoo.com
Abstract
Decentralized distributed clean-energy sources have become an essential need for smart grids to reduce the harmful
effects of conventional power plants. Smart homes with a suitable sizing process and proper energy-management
schemes can share in reducing the whole grid demand and even sell clean energy to the utility. Smart homes have been
introduced recently as an alternative solution to classical power-system problems, such as the emissions of thermal
plants and blackout hazards due to bulk plants/transmission outages. The appliances, sources and energy storage
of smart homes should be coordinated with the requirements of homeowners via a suitable energy-management
scheme. Energy-management systems are the main key to optimizing both home sources and the operation of loads to
maximize home-economic benets while keeping a comfortable lifestyle. The intermittent uncertain nature of smart
homes may badly affect the whole grid performance. The prospective high penetration of smart homes on a smart
power grid will introduce new, unusual scenarios in both generation and loading. In this paper, the main features and
requirements of smart homes are dened. This review aims also to address recent proposed smart-home energy-
management schemes. Moreover, smart-grid challenges with a high penetration of smart-home power are discussed.
Graphical Abstract
Residential
ESS
Residential
PV system
Micro-Wind
turbine
Info flow
SHEMS
Smart
Microgrid
Smart
Meter
Smart
Appliances
EV
Po
wer flow
Keywords: smart homes; energy-management system; electrical tariff; smart-home infrastructure; load
scheduling; power-quality control
Clean Energy, 2021, 302–315
doi: 10.1093/ce/zkab010
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Introduction
Smart homes provide comfortable, fully controlled and se-
cure lifestyles to their occupants. Moreover, smart homes
can save energy and money with the possibility of prof-
iting from selling clean renewable energy to the grid. On
the other hand, the probable decrease in total domestic-
energy loads encourages many governments to support
promising smart-home technologies. Some countries have
already put out many rules, laws and subsidy programmes
to encourage the integration of smart homes, such as
encouraging the optimization of the heating system, sup-
porting building energy storage and/or deploying smart
meters. For instance, the European Standard EN 15232 [1]
and the Energy Performance of Building Directive 2010/31/
EU [2], which is in line with Directive 2009/72/EC as well as
the Energy Road Map 2050 [3], encourage the integration
of smart-home technologies to decrease power demand in
residentialareas.
To control the environment, a smart home is auto-
mated by controlling some appliances, such as those used
for lighting and heating, based on different climatic condi-
tions. Now, recent control schemes adapt many functions
besides classical switching ones. They can monitor the in-
ternal environment and the activities of the home occu-
pants. They also can independently take pre-programmed
actions and operate devices in set predened patterns,
independently or according to the user’s requirements.
Besides the ease of life, smart homes conrm efcient
usage of electricity, lowering peak load, reducing energy
bills and minimizing greenhouse-gas emissions [4, 5].
Smart homes can be studied from many points of view.
The communication systems [6], social impacts [7], thermal
characteristics [8], technologies and trends of smart homes
[9] are reviewed individually. Moreover, the monitoring and
modelling of smart-home appliances via smart meters
are reviewed for accurate load forecasting, as in [10, 11].
Recently, power-grid authorities have modied residential
electrical tariffs to encourage proper demand-side man-
agement by homeowners. Different from previous reviews,
this paper introduces smart homes from the electrical/eco-
nomic point of view. It also discusses smart-home energy-
management systems (SHEMS) in two different modes,
ofine load scheduling and real-time management. The
prospective impacts of unusual smart-home power proles
on future smart grids are also summarized.
After this introductory section, Section 1 describes the
different denitions of smart homes within the last two
decades. Smart-home communication schemes and other
infrastructures of smart homes are discussed in Section
2.Section 3 discusses in more detail the existing functions
of SHEMS, their pre-proposed optimization techniques
and related technical/economical objective functions.
The impacts of smart homes on modern grids are also
discussed in Section 4.Finally, in Section 5, the main con-
clusions and contributions of the paper are highlighted.
1 Smart-home denition
The term ‘smart home’ has been commonly used for
about two decades to describe houses with controlled en-
ergy schemes. This automation scheme conrms easier
lifestyles for homeowners than normal un-automated
homes, especially for elderly or disabled persons.
Recently, the concept of ‘smart home’ has a wider de-
scription to include many applications of technologies
in oneplace.
Sowah etal. [12] dene smart homes as: ‘Houses that
provide their occupants a comfortable, secure, and energy
efcient environment with minimum possible costs re-
gardless their occupants.’ The Smart Homes Association
denes a smart home as: ‘The integration of technology
and services through home networking for a better quality
of living’ [13].
Makhadmeh et al. dene them as: ‘Incorporated resi-
dential houses with smart technology to improve the
comfort level of users (residents) by enhancing safety and
healthcare and optimizing power consumption. Users can
control and monitor smart-home appliances remotely
through the home energy-management system (HEMS),
which provides a remote monitoring system that uses
telecommunication technology’ [14].
Smart homes can be dened as: any residential build-
ings using different communication schemes and opti-
mization algorithms to predict, analyse, optimize and
control its energy-consumption patterns according to
preset users’ preferences to maximize home-economic
benets while preserving predened conditions of a com-
fortable lifestyle.
Distributed clean energy generated by smart homes
provides many benets for prospective smart grids.
Consequently, the effects of smart homes on future power
grids should be extensively studied. In the near future,
smart homes will play a major role as a power supplier in
modern grids, not only as a power consumer.
2 Smart-home infrastructures
The general infrastructure of smart homes consists of
control centres, resources of electricity, smart meters and
communication tools, as shown in Fig. 1. Each component
of the smart-home model will be discussed in the fol-
lowing subsections.
2.1 The controlcentre
The control centre provides home users with proper units
to monitor and control different home appliances [15]. All
real-time data are collected by SHEMS to optimize the de-
mand/generation coordination and verify the predened
objectives. The main functions of the control centre can be
summarized as follows [15]:
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(i) collecting data from different meters, homeowners’
commands and grid utility via a proper communica-
tion system;
(ii) providing proper monitoring and analysing of home-
energy consumption for homeowners;
(iii) coordinating between different appliances and re-
sources to satisfy the optimal solution for predened
objectives.
2.2 Smartmeter
The smart meter receives a demand-response signal from
power utilities as an input to the SHEMS system [16, 17].
Recently, advanced smart-metering infrastructures can
monitor many home features such as electrical consump-
tion, gas, water and heating [18].
2.3 Appliances
Smart-home loads can be divided according to their
operating nature into two categories: schedulable and non-
schedulable loads. Non-schedulable loads are operated occa-
sionally according to the homeowner’s desires without any
predictable operating patterns, such as printers, televisions
and hairdryers, whereas schedulable loads have a predict-
able operating pattern that can be shifted or controlled via
SHEMS, such as washing machines and air conditioners [19].
According to [19], controllable devices are also clas-
sied into interruptible and non-interruptible load ac-
cording to the effect of supply interruption on their tasks.
Electric vehicles (EVs) can be considered as an exceptional
load [20, 21]. EVs have two operating modes: charging and
discharging. Therefore, EVs are interruptible schedulable
loads during the charging mode. Moreover, EV battery
energy can also be discharged to supply power to the grid
during critical events, which is known as vehicle-to-grid
[22]. By SHEMS, EVs can participate in supplying loads
during high-priced power periods. In low-priced power
periods, EVs restore their energy from the grid [23, 24].
2.4 Resources of electricity
Solar and wind plants are the most mature renewable-
energy sources in modern grids. Nowadays, many build-
ings have installed photovoltaic (PV) modules, thermal
solar heaters or micro wind turbines. For smart homes,
various functions can be supplied by solar energy besides
generating electricity, such as a solar water heater (SWH),
solar dryer and solar cooler [25]. Moreover, PV plants are
cheap with low requirements of maintenance [26], whereas
hot water produced by SWHs can be used in many home
functions, such as washing and cooking, which increases
the home-energy efciency [27].
Energy storage may be considered as the cornerstone
for any SHEMS. SHEMS are usually installed with energy-
storage systems (ESSs) to manage their stored energy ac-
cording to predened objectives. Many energy-storage
technologies are available in the power markets. Batteries
and fuel cells are the most compatible energy-storage
types of smart-home applications [28]. Afuel-cell structure
is very similar to a battery. During the charging process,
hydrogen fuel cells use electricity to produce hydrogen.
Hydrogen feeds the fuel cell to create electricity during
the discharging process. Fuel cells have relatively low ef-
ciency compared to batteries. Fuel cells provide extra clean
storage environments with the capability of storing extra
hydrogen tanks. That perfectly matches isolated homes in
remote areas [29].
Electic Vechicl
e
Cloud
WI-FI
Router
WI-FI
WI-FI
Energy Stroage System
WI-FI
Smart Home Controller
Smart meter
Electrical
connection
Cloud
Cloud
PV panel
Wind Turbine
Grid
80% 100%
Non-schedulable HA Full-schedulable HA
Fig. 1. Infrastructure of SHEMS source
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Although wind energy is more economical for large-scale
plants, it has a very limited market for micro wind turbines
in homes. Typically, micro wind turbines require at least
a wind speed of 2.7 m/s to generate minimum power, 25
m/s for rated power and 40 m/s for continuous generated
power [30]. Amicro wind turbine is relatively expensive,
intermittent and needs special maintenance requirements
and constraints compared to a solar plant [31].
Recently, biomass energy has been a promising renew-
able resource alternative for smart homes. Many pieces of
research have recommended biomass energy for different
types of buildings [32]. Heating is the main function of
biomass in smart homes, as discussed in [33, 34]. In add-
ition, a biomass-fuelled generation system is examined for
many buildings [35, 36].
2.5 Communicationschemes
Recently, communication systems are installed as built-in
modules in smart homes. Both home users and grid op-
erators will be able to monitor and control several home
appliances in the near future to satisfy the optimum
home-energy prole while preserving a comfortable life-
style. Therefore, both wired and wireless communication
schemes are utilized, which is known as a home area
network (HAN), to cover remote-control signals as home
occupants’ ones. Fig. 1 shows an example of a HAN that
consists of Wi-Fi and cloud computing networks for both
indoor and outdoor data exchange, respectively [37, 38].
Energy-management systems for homes require three
main components: the computational embedded control-
lers, the local-area network communication middleware
and the transmission control protocol/internet protocol
(TCP/IP) communication for wide-area integration with
the utility company using wide-area network communi-
cation [37].
According to home characteristics, many wired com-
munication schemes can be selected, such as power-line
communication (PLC), inter-integrated circuit (I2C) and
serial peripheral interface or wireless technologies such
as Zigbee, Wi-Fi, radio-frequency identication (RFID) and
the Internet of Things (IoT) to develop HANs. Afew of the
most common techniques will be discussed briey in the
following subsections [38].
2.5.1 PLC
PLC is a technique that uses power lines to transmit both
power and data via the same cable to customers simul-
taneously. Such wired schemes provide fast communica-
tion with low interference of data. Moreover, PLC provides
many communication terminals, as all power plugs can be
used for data transferring. As all electrical home devices
are connected by power cables, PLC can communicate with
all these devices via the samecable.
PLC set-up has a low cost, as it uses pre-installed
power cables with minimum hardware requirements.
With a PLC communication scheme, home controllers
can also be integrated easily with a high speed of data
transfer. On the other hand, PLC has a high probability of
data-signal attenuation. Furthermore, data signals suffer
from electromagnetic interference of transmitted power
signals.
2.5.2 Zigbee
Zigbee is a wireless communication technique [37–46].
Zigbee follows the IEEE 802.15.4 standard as a radio-
frequency wireless communication scheme. It does not
require any licenses for limited zones such as homes
[37]. Also, Zigbee is a low-power-consuming technique.
Therefore, it is suitable for basic home appliances, such as
lighting, alarm systems and air conditioners [39, 40]. Zigbee
usually considers all home devices as slaves with a master
coordinator/controller, which is known as a master–slave
architecture.
Zigbee provides highly secured transferred data [38,
41] with high reliability and capacity [42]. It also has self-
organizing capabilities [42]. Conversely, Zigbee is relatively
expensive due to special hardware requirements with low
data-transfer rates. Moreover, Zigbee is not compatible
with many other protocols, such as internet-supported
protocols and Wi-Fi.
2.5.3 Wi-Fi technology
Wi-Fi is a wireless communication technique that follows
the IEEE 802.11 standard. Wi-Fi provides high-rate data
transfer that is compatible with many information-based
devices such as computers, laptops, etc. [43, 44].
Wi-Fi is a highly secured scheme with many of the fa-
miliar internet capabilities and low data-transfer delays
(<3ms) [45]. On the contrary, it is a relatively high-power-
consuming scheme compared to Zigbee schemes [45]. Also,
home devices can affect transmitted data signals by their
emitted electromagnetic elds [46]. Wi-Fi can also suffer
from interference from other communication protocols
such as Zigbee and Bluetooth [43].
2.5.4 RFID
RFID is a wireless communication technique that con-
forms to the electronic product code protocol [47–52]. It
can coincide with other communication schemes such as
Wi-Fi and Zigbee. It can be utilized for a relatively wide-
spread range of frequencies, from 120kHz to 10 GHz. It
also covers a wide range of distances, from 10cm to 200
m [48]. Many researchers are investigating RFID home ap-
plications, such as energy-management systems [49], door
locks [50] and lighting controls [51].
RFID operates on tags and reader-identication systems
with a high data-transfer rate. Nevertheless, RFID has ex-
pensive chips with low bandwidth. The possibility of tag
collision within the same zone decreases the accuracy of
the RFID scheme.
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2.5.5 IoT
This scheme connects home devices, users and grid op-
erators via the internet to monitor and manage smart
homes [6, 38, 53–65]. Consequently, the IoT and cloud com-
puting have proven to be cheap, popular and easy services
for smart homes. Moreover, IoT schemes are compat-
ible with many other communication protocols, such as
Zigbee, Bluetooth, etc., as listed in Table 1. Internet hacking
is the main problem with IoT schemes. System security
and privacy are critical challenges for such internet-based
schemes.
3 Smart-home
energy-managementscheme
Today, building energy-management systems (BEMS) are
utilized within residential, commercial, administration
and industrial buildings. Moreover, the integration of vari-
able renewable-energy sources with proper ESSs deployed
in buildings represents an essential need for reliable,
efcientBEMS.
For small-scale residential buildings or ‘homes’, BEMS
should deal with variable uncertain load behaviours ac-
cording to the home occupants’ desires and requirements,
which is known as SHEMS. Throughout recent decades,
many SHEMS have been presented and dened in many
research studies.
In [66], SHEMS are dened as services that efciently
monitor and manage electricity generation, storage and
consumption in smart houses. Nazabal etal. [67] include
a collaborative exchange between smart homes and the
utility as a main function of SHEMS. In [68], SHEMS are de-
ned from the electrical-grid point of view as important
tools that provide several benets such as attening the
load curve, a reduction in peak demand and meeting the
demand-side requirements.
3.1 Functions ofSHEMS
Adaptive SHEMS are required to conserve power, espe-
cially with the increasing evolution in home loads. SHEMS
should control both home appliances and available energy
resources according to the real-time tariff and home user’s
requirements [4]. Home-management schemes should pro-
vide an interface platform between home occupants and the
home controller to readjust occasionally the load priority [5].
As shown in Fig. 2, the majority of smart-home centres
can be summarized as having ve main functions [5], as
follows:
(i) Monitoring: provides home residents with visual
instantaneous information about the consumed
power of different appliances and the status of sev-
eral home parameters such as temperature, lights,
etc. Furthermore, it can guide users to available
alternatives for saving energy according to the ex-
isting operating modes of different home appliances.
(ii) Logging: collects and saves data pertaining to the
amount of electricity consumed by each appliance,
generated out of energy-conservation states. This
functionality includes analysing the demand response
for real-time prices.
(iii) Control: both direct and remote-control schemes can
be implemented in smart homes. Different home
appliances are controlled directly by SHEMS to match
the home users’ desires, whereas other management
functions are controlled remotely via cell phones or
laptops, such as logging and controlling the power
consumption of interruptible devices.
(iv) Management: the main function of SHEMS. It concerns
the coordination between installed energy sources
such as PV modules, micro wind turbines, energy
storage and home appliances to optimize the total
system efciency and/or increase economic benets.
Table 1: IoT protocols features
Protocol Advantages Disadvantages
5G [59] Reliable with high speed and capable to
manage a lot of devices simultaneously
Expensive with many problems related to security
and privacy
Z-Wave [6, 38, 54–56] Reliable, low data-transfer delay and
without any interference with other
communication schemes
Limited ranges and needs special networking
requirements
6LoWPAN [57] Low power consumer with large data-
exchange capability
Complicated with low data-transfer rate
Zigbee [58, 59] Low power consumer, simple and cheap Limited range and incompatible with other
communication schemes
Wireless HART [60–62] Robust Insecure with low data-transfer rate
Bluetooth [63] Low power consumer Insecure with low data-transfer rate. It can be
interfered with by other IEEE 802.11 WLANs
Bluetooth Low
Energy (BLE) [63]
Simple, cheap with very low
power-consuming rate
Limited range and low amount of data handling
Narrowband IoT (NB-IoT)
[64, 65]
Simple, cheap with very low
power-consuming rate
Low speed with high data-transfer delay
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(v) Alarms: SHEMS should respond to specic threats or
faults by generating proper alarms according to fault
locations, types, etc.
3.2 Economic analysis
Economic factors affecting home-management systems
are classied into two classes. First, sizing costs include
expanses of smart-home planning. Second, operating
costs consist of bills of consumed energy. These costs de-
pend mainly on the electricaltariff.
3.2.1 Sizingcosts
These include capital, maintenance and replacement
costs of smart-home infrastructures, such as PV sys-
tems, wind turbines, batteries/fuel cells and communi-
cation systems. In most previous SHEMS, such planning
costs usually are not taken into consideration, as man-
agement schemes usually concern the daily operating
costs only [69].
3.2.2 Operatingcosts
The electricity tariff is the main factor that gives an indi-
cation of the value of saving energy, according to the gov-
ernmental authority; there are many types of tariffs, as
follows [70–74]:
(i) Flat tariffs: the cost of consumed energy is constant
regardless of the continuous change in the load.
Load-rescheduling schemes do not affect the elec-
tricity bills in this scheme. Therefore, homeowners are
not encouraged to rearrange their consumed energy,
as they have no any economic benets from managing
the consumption of their appliances.
(ii) Block-rate tariffs: in this scheme, the monthly
consumed energy price is classied into different
categories. Each category has its own at-rate price.
Therefore, the main target of SHEMS is minimizing the
total monthly consumed energy to avoid the risk of
high-priced categories.
(iii) Seasonal tariffs: in this scheme, the total grid-demand
load is changed signicantly from one season to an-
other. Therefore, the utility grid applies a high at-rate
tariff in high-demand seasons and vice versa. SHEMS
should minimize the total consumption in such high-
priced seasons and get the benet of consumption in
low-priced seasons.
(iv) Time-of-use (TOU) tariff: there are two or three pre-
dened categories of tariffs daily in this scheme. First,
a high-priced-hours tariff is applied during high-
demand hours, which is known as a peak-hours tariff.
Second, an off-peak-hours tariff is applied during low-
demand hours with low prices for energy consump-
tion. Sometimes, three levels of pricing are dened
by the utility grid during the day, i.e. off-, middle- and
high-peak costs, as discussed in [75]. SHEMS shift in-
terruptible loads with low priority to off-peak hours to
minimize the bill.
Alarm
Management
Control
SHEMS control center
Monitor Logging
Fig. 2: Functions of SHEMS
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(v) Super peak TOU: this can be considered as a special
case of the previously described TOU tariff but with a
short peak-hours period of ~4 hours daily.
(vi) Critical peak pricing (CPP): the utility grid uses this
tariff scheme during expected critical events of
increasing the gap between generation and power
demand. The price is increased exceptionally during
these critical events by a constant predened rate.
(vii) Variable peak pricing: this is a subcategory of the CPP
tariff in which the exceptional increase in the tariff
is variable. The utility grid informs consumers of the
exceptional dynamic price increase according to its
initial expectations.
(viii) Real-time pricing (RTP): the price is changing contin-
uously during pre-identied intervals that range from
several minutes to an hour. This tariff is the riskiest
pricing scheme for homeowners. The electricity bill
can increase signicantly without a proper manage-
ment system. SHEMS should communicate with grid
utility and reschedule both home appliances, sources
and energy storage continuously to minimize the
total bill.
(viii) Peak-time rebates (PTRs): a proper price discount is
considered for low-consumption loads during peak
hours, which can be refunded later by the grid.
Depending on the electricity tariff, SHEMS complexity
varies dramatically. In the case of using a at-rate tariff,
the algorithm becomes simpler, as one value is recorded
for selling or buying the electricity. Tariffs may be pub-
lished from the proper authority or predicted according
to historical data. Prediction of the dynamic tariff is a
main step in any SHEMS. Many time frames of tariff pre-
diction are proposed that vary from hourly, daily or even
a yearly prediction. Many optimization techniques with
various objective functions are proposed to handle dif-
ferent features of both smart-home infrastructures and
electricity tariffs, as will be discussed in the following
section.
3.3 Pre-proposedSHEMS
Different SHEMS may be classied according to four fea-
tures: operational planning of load-scheduling techniques,
system objective functions, optimization techniques and
smart-home model characteristics, as will be discussed in
the following subsections.
3.3.1 Load-scheduling techniques
SHEMS concern the generation/load power balance to pro-
vide a comfortable lifestyle with the minimum possible
costs. Scheduling loads according to their priority and the
periods of renewable energy (solar, wind and EV state) can
help in reducing the overall energy consumption daily.
According to data collected by the management system,
an initial load schedule is suggested daily to minimize the
daily cost of consumed energy [76].
By using a proper optimal scheduling algorithm, electri-
city bills can be reduced by shifting loads from high-priced
to low-priced intervals [77, 78]. Many techniques have been
proposed for home load scheduling, as will be discussed in
the following subsections:
(i) Rule-based scheduling: in this algorithm, all home
appliances and resources are connected to smart
data-collector taps. By processing the collected data,
different appliances are scheduled according to their
priorities and based on the if/then rule. Also, some
high-priority loads are supplied by home renewable
sources/storage to maintain their function during
predicted peak hours [79, 80].
(ii) Articial intelligence (AI): many AI controllers have
been proposed for home load scheduling, such as ar-
ticial neural networks (ANNs), fuzzy logic (FL) and
adaptive neural fuzzy inference systems (ANFISs).
Table 2 compares between the three types of sched-
uling scheme based on AI.
3.3.2 Objective functions
(i) Single-objective techniques: in these schemes, only
one criterion is minimized or maximized according
to the home-user requirements. Several minimization
objective functions were proposed, as follows:
– lifetime degradation [47–49];
– life-cycle costs [93];
– gas emissions [94–96];
– both active and reactive losses [97, 98].
On the other hand, some research dened other single
maximizing objective functions, such as:
– net present value [96].
– economic prots [97, 98].
– increased system reliability: according to many
well-known reliability indices, such as loss of
power supply probability, loss of load probability
and others [99, 100].
– generated power [101, 102].
– loadability [103];
(ii) Multi-objective techniques: homeowners may have
several criteria to be optimized together. Multi-
objective optimization (MOO) problems consider
many functions simultaneously. MOO nds a proper
coordination that moderately satises the considered
objectives. In [102], SHEMS with MOO techniques are
summarized. Table 3 lists some examples of such
multi-objective functions.
3.3.3 Optimization techniques
Optimization techniques aim usually to identify the best
coordination taking into consideration predened con-
straints. Many approaches are available for addressing op-
timization problems. These approaches can be classied
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into two categories: classical and AI-based techniques.
Table 4 lists various SHEMS optimization techniques and
their main features.
Classical methods, especially linear programming
types, have been usually applied in the last decade for
smart homes with limited objective functions and simple
model characteristics of tariff and home appliances.
Recently, AI-based techniques have been proposed to
cover more complicated models of smart homes with
multi-objective functions with high levels of comfort-
able lifestyles.
3.3.4 Home-model characteristics
The smart-home model differs signicantly according to
three factors: installed variable energy sources, applied
tariff and EV deployment. PV systems have been applied
for nearly all studied smart homes due to their low price,
simplicity of installation, low maintenance requirements
and easily predicted daily power prole. On the other hand,
a few pieces of research have considered micro wind tur-
bines in their home models, such as [120]. Wind turbines
are limited by high-wind-speed zones that are usually lo-
cated in rural areas. In addition, homeowners usually do
not prefer wind turbines due to their high prices, mech-
anical maintenance requirements and the unpredictable
variation in windpower.
Dynamic tariffs are applied in most smart-home re-
search. Specically, the TOU tariff is analysed in a lot of
studies, such as [121, 122], whereas little research uses RTP,
such as [123, 124]. EV is studied as an energy source in the
parking period or vehicle-to-grid (V2G) mode. In [75, 125],
EV in V2G mode reduces the electricity bill in peak hours,
whereas, in [126–130], ESSs are managed only to reduce the
electricity usage from the grid.
4 Technical challenges of smarthomes
Many technical challenges arise for modern grids due to
the increasing mutual exchange between smart homes
and utility grids, especially power-quality control. Electric-
power-quality studies usually conrm the acceptable be-
haviour of electrical sources such as voltage limits and
harmonics analysis. Recently, smart power grids have di-
verse generation sources from different technologies that
depend mainly on power electronics devices that increase
the difculty in power-quality control. Power-quality con-
straints should be taken into consideration for any energy-
management systems to provide harmony between
modern sources andloads.
On the other hand, power-quality issues should not
form an additional obstacle against the integration of new
technologies in modern grids. Therefore, both advanced
communication schemes and AI-based techniques make
modern grids ‘smart’ enough to cope with selective power-
quality management. Smart homes exchange power with
utility grids. With the prospective increase in such smart
homes, the effect of their behaviour should be studied and
controlled. Smart homes affect the grid-power quality in
three different areas, as will be discussed in the following
paragraphs [154–156].
4.1 Generating equipment
Integrated micro generation schemes in smart homes are
mainly single-phase sources based on inverters with high
switching frequencies that reach to many kHz. Low-order
harmonics of such a generation type can usually be disre-
garded. However, with the expected continuous increase
in such micro generators, the harmonics of low-voltage
networks may shift into a range of higher frequencies,
perhaps from 2 to 9 kHz [157]. Therefore, more research
is needed to re-evaluate the appropriate limits for gener-
ation equipment in smart homes. Moreover, single-phase
Table 2: Optimization techniques for load scheduling
ANN [81–83] FL [84–91] ANFIS [92]
Complicated design Easy design Normal design
Normal structure Simple structure Complex structure
Its behaviour depends on training data and
selected appliances and number of sources
Its behaviour depends on rule-based
algorithm parameters and selected
membership functions
Its behaviour depends on training data
and selected membership functions
Learning process is required Learning process is not required Learning process is required
Table 3: Multi-objective functions of SHEMS
First objective Second objective
Economic-prot
maximizing
Emissions minimizing [104]
Reliability maximizing [105]
Electricity-bills
minimizing
Reliability maximizing [106,
107]
Emissions minimizing [108,
109]
Lifetime maximizing [110, 111]
Loadability maximizing [112]
Economic-prot maximizing
[113, 114]
Investment-costs
minimizing
Reliability maximizing [115,
116]
Emissions minimizing [117,
118]
Fuel-consumption minimizing
[93]
Electricity-bills minimizing
[119]
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generation increases the risk of an unbalanced voltage in
low-voltage grids. Therefore, negative-sequence voltage
limits should be re-evaluated particularly for weak distri-
bution networks. Also, a need for zero-sequence voltage
limits may arise [154].
4.2 Home appliances
Modern home appliances depend mainly on electronic
devices, such as newer LED lighting systems, EV battery
chargers, etc., with relatively low fundamental current
and high harmonic contents compared to traditional ones.
According to many power-system analysers, many har-
monics will increase signicantly to risky levels, particu-
larly fth-harmonic voltage, with increase in such new
electronic appliances [155].
4.3 Distributionnetwork
In future grids, signicant unusual operating scenarios may
be possible with high penetration of domestic generation,
especially with the possibility of an islanded (self-balanced)
operation of smart homes. Short-circuit power will differ
signicantly during different operating conditions compared
to classical grids. Moreover, low-voltage networks may suffer
from damping-stability problems due to the continuous de-
crease in resistive loads, in conjunction with the increase in
capacitive loads of electronic equipment. In addition, reson-
ance problems may occur with low frequencies according to
the continuous change in the nature of the load [156].
Although smart homes have bad impacts on utility
grids, there are no charges applied from the grid authority
to homeowners based on their buildings’ effects on grid-
power quality. Therefore, home planners and SHEMS de-
signers are usually concerned only with the economic
benets of their proposed schemes.
5 Conclusion
Smart homes, using new revolutions in communication
systems and AI, provide residential houses with electrical
power of a dual nature, i.e. as producer and consumer or
‘prosumer’. The energy-management system includes
many components that mainly depend on a suitable
communication scheme to coordinate between available
sources, loads and users’ desire. Among many proposed
communication systems, the IoT has many advantages
Table 4: Optimization techniques in SHEMS
Method Objectives Advantage Drawbacks
Classic Geometric programming [131] Electricity consumption
and minimizing bills
Simple Difcult for users
Quadratic programming [132, 133] Optimal operation for battery
and engine
Fast Limited real‐time
usage
Convex programming [134–136] Maximizing economic benets
with preserving comfortable
lifestyle
High efciency with
real‐ time operation
capability
Complicated
Linear programming [137] Battery-charging cost
minimizing
Real‐time operation
capability
Valid for only one
linear variable
MILP [138, 139] Operating-cost minimizing High accuracy Sensitive to selected
models
MINLP [140–144] Optimizing battery-charging/
discharging processes
Simple modelling
capability
Slow with low
accuracy
Markov decision [145] Minimizing consumption with
preserving comfortable
lifestyle
Good decision
maker
Valid only for linear
variable
Articial
intelli-
gence
ANN [146] Simple load control Suitable for
forecasting
Limited number of
nodes
Genetic algorithm [147, 148] Minimizing emission and
operating cost
Easy Long computational
time
Particle swarm
algorithm [148]
Minimizing operating cost Easy with limited
required inputs
Long computational
time
Articial bee colony [149] Minimizing operating cost Robust and exible Complicated
Simulated annealing [150] Minimizing operating cost Fast Unreliable
Fuzzy [151] Optimizing battery-charging/
discharging processes and
minimizing operating cost
Simple and exible Long computational
time
Model predictive control [152] Minimizing emission and
operating cost
Excellent predictive
capabilities
Expensive and
complicated
Robust [153] Maximizing energy trading Flexible with
disturbances
Complicated for
real-time use
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and was chosen in many studies. Besides the popularity
of the IoT, it does not need any special equipment installa-
tion and is compatible with many other communications
protocols.
Many functions are applied by management systems
such as monitoring and logging to facilitate a proper inter-
action between home occupants and the management
scheme. Home security also should be conrmed via the
management scheme by using different alarms corres-
ponding to preset threats. Home users control different
home appliances according their desires by SHEMS and via
cell phones or manually.
The electricity tariff plays an important role in dening
management-system characteristics. Tariffs vary from
simple xed at rates to complicated variable dynamic
ones according to the electrical-grid authority’s rules for
residential loads. According to the tariff and selected ob-
jective functions, pre-proposed optimization techniques
vary signicantly from simple classical linear program-
ming to sophisticated AIones.
Modern electronic-based home appliances increase
power-grid-quality problems, such as high harmonic con-
tents, unbalanced loading and unpredictable short-circuit
currents. On the other hand, power-grid authorities do
not charge homeowners according to their buildings’ ef-
fects on the power quality. Therefore, all proposed energy-
management systems are concerned mainly with the
economic prots from reducing electricity consumption
or even selling electrical power to the utility grids. In the
future, price-based power-quality constraints should be
dened by the grid authorities to conrm proper power
exchange between both smart homes and grids. A pos-
sible future direction is behaviour modelling of aggregated
smart homes/smart cities in different operating scenarios
to conclude probable power-grid scenarios for stability and
quality.
Funding
This work was supported by the project entitled ‘Smart Homes
Energy Management Strategies’, Project ID: 4915, JESOR-2015-
Cycle 4, which is sponsored by the Egyptian Academy of Scientic
Research and Technology (ASRT), Cairo, Egypt.
Conict of Interest
None declared.
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