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<reference>
ORIGINAL RESEARCH
published: 16 November 2021
doi: 10.3389/fenrg.2021.772027

Investigation of Smart Home Energy
Management System for Demand
Response Application
Yunlong Ma 1, Xiao Chen 1, Liming Wang 2 and Jianlan Yang 3*
1
State Grid Jiangsu Electric Power Co., Ltd., Nanjing, China, 2Jiangsu Frontier Electric Power Technology, Co., Ltd., Nanjing,
China, 3College of Electrical and Information Engineering, Hunan University, Changsha, China

Edited by:
Jian Zhao,
Shanghai University of Electric Power,
China
Reviewed by:
Changyun Li,
Shandong University of Science and
Technology, China
Hongshun Liu,
Shandong University, China
*Correspondence:
Jianlan Yang
Yangjianlan@hnu.edu.cn
Specialty section:
This article was submitted to
Process and Energy Systems
Engineering,
a section of the journal
Frontiers in Energy Research
Received: 07 September 2021
Accepted: 28 September 2021
Published: 16 November 2021
Citation:
Ma Y, Chen X, Wang L and Yang J
(2021) Investigation of Smart Home
Energy Management System for
Demand Response Application.
Front. Energy Res. 9:772027.
doi: 10.3389/fenrg.2021.772027

Electricity market reform provides the conditions for demand-side load resources to be
incorporated into the supply–demand regulation, and the increase of residential-side
electriﬁcation level makes residential load resources a high-quality resource for demand
response (DR). Resident home appliances participate in the “two-way interaction” of the
power grid in the form of DR, which can effectively alleviate the tension of power supply and
consume clean energy, to improve the safe and stable operation of the power system.
Firstly, this article summarizes the structure and functions of the home energy
management system (HEMS). Secondly, it discusses the key technologies of the
HEMS, starting from an advanced metering infrastructure (AMI) and DR technology.
Finally, it analyzes the control strategies of the HEMS, including component models
and various optimal scheduling algorithms, and describes the challenges of the HEMS.
Keywords: home energy management system, demand response, residential load, control strategy, advanced
metering infrastructure

INTRODUCTION
In May 2009, the State Grid Corporation of China proposed to build a strong smart grid with
the characteristics of informatization, digitization, automation, and interaction (Liu et al.,
2009). Smart grids can realize real-time monitoring and analysis of user power consumption
information, and smart power services have gradually begun to realize the extensive
participation of users and autonomous responses to demands (Tian et al., 2014). The
reform and development of electricity marketization have gradually diversiﬁed the
stakeholders of the power system, and demand-side resources have emerged as important,
so demand response (DR) has emerged. Since 2011, residential household energy consumption
has grown faster than that in the industrial sector, and households have become one of the most
critical factors inﬂuencing the management of sustainable development. In 2019, the electricity
consumption of the whole society in China was 7,225.5 billion kWh, an increase of 4.5% yearon-year, and the electricity consumption of urban and rural residents was 1,025 billion kWh,
accounting for 14.2% of the total electricity consumption of the society (National Energy
Administr, 2019). Residential load resources on the demand side have become important
resources for DR.
Stamminger et al. (2008) pointed out that residential and commercial electricity consumption in
the United States accounted for about 72%, of which at least 30% of electricity consumption can be
avoided. In China, with the development and popularization of electric vehicles and distributed
power generation, the energy within the family will gradually become diversiﬁed and complex in the
future, and a large part of energy waste can be avoided under reasonable planning. Reasonable

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TABLE 1 | Overview of the development of the HEMS.
Country
USA
Germany
Japan
Italy

Britain
Netherlands
Denmark
China

Speciﬁc situation
In 2009, the ﬁrst smart grid city in the USA was built in Colorado, with more than 15,000 smart meters installed (Tang et al.,
2009), and the ﬁrst HEMS application was launched in Texas in 2011.
Installed smart meters in 2001 and established a smart metering network.
The Yokohama Smart City Project developed a variety of energy management systems.
Indesit Company successfully developed smart home appliances and cooperated with the University of Parma to develop a
home appliance networking communication solution. In 2011, all power users across the country used 36 million smart
electricity expressions (Renner and Heinemann, 2011).
Britain energy providers piloted experiments with up to 58,000 homes in 2010–2011.
The Smart City Construction Project was launched in 2008.
Carried out the largest vehicle-to-grid (V2G) project from 2009 to 2011.
Production of the ﬁrst batch of smart meters started in 2009, and since then, full-scale work on building a strong smart grid
has been carried out (Liu et al., 2009; Huang et al., 2013).

The energy ﬂow relationship between the various components
of the HEMS and between it and the external power grid is shown
in Figure 3. The HEMS components include the electricity
consumption load, the energy storage device, and the
distributed power supply. The collection module uploads the
collected electricity consumption information to the home host.
By collecting electricity consumption information, tariff
information from power-related departments, user setting
information, etc., the home host realizes optimal
management of the entire system operation and gives users
the most economical and comfortable scheduling strategy.
Users can read the power consumption of speciﬁc powerusing devices, view the real-time status of distributed power
and energy storage devices through cell phones, personal
computers, etc., and perform relevant operations according
to speciﬁc needs.
The interaction between the HEMS and power-related
departments is reﬂected in the exchange of energy and
information. Users can receive information sent by the
power department through wired and wireless transmission
to arrange daily electricity consumption; the HEMS can upload
the user’s electricity consumption information to facilitate the
relevant power departments to grasp the user’s electricity
consumption information in real time and arrange
reasonable electricity production and transmission through
this analysis.

arrangement of the orderly work of household electrical
equipment through the home energy management system
(HEMS), which can assist users in controlling the energy ﬂow
and load dispatching in the system, helps in mobilizing users’
enthusiasm for participating in DR and accelerating the on-site
distribution of distributed energy consumption (Zhang et al.,
2021).
The development of smart grid technology and the rise of
residential energy consumption provide opportunities and
challenges for HEMS research, and some countries, such as
the United States and Germany, have started to conduct indepth research on intelligent power utilization and HEMS. Due to
the different background environment, development technology,
and storage capacity of new energy sources in each country, each
country presents different development characteristics, as shown
in Table 1 (Chen et al., 2019).
At present, many researchers and scientiﬁc research
institutions worldwide are studying the HEMS participating in
DR. This article ﬁrst summarizes the structure and function of the
HEMS. Second, it discusses the key technologies of the HEMS,
starting from the two aspects of AMI and DR technology. The
article ﬁnally analyzes the control strategies of the HEMS,
including component models, various optimization scheduling
algorithms, and a description of the challenges for the HEMS.

STRUCTURE AND FUNCTION OF HEMS

Functions of HEMS
The HEMS mainly includes the following ﬁve functional
modules: monitoring, recording, control, management, and
alarm, as shown in Figure 4, and the speciﬁc description is as
follows (Son and Moon, 2010; Li, 2016).
(1) Monitoring module. It monitors the energy
consumption in real time and displays the working mode
and energy status of household appliances. (2) Recording
module. It saves electricity consumption data such as home
appliances, distributed energy, and energy storage status. (3)
Control module. It is divided into direct control and remote
control. Direct control includes control equipment and
control system, and remote control means that users can
access the usage mode and equipment status of home
appliances online. (4) Alarm module. If there is an

HEMS Network Structure
In the traditional power consumption mode, there is only a oneway ﬂow from the grid to the user side, and there are
disadvantages to varying degrees on both the user side and the
power supply side, as shown in Figure 1. Compared with the
traditional model, the HEMS is an intelligent network control
system that can integrate all power generation, power
consumption, and energy storage equipment in the home for
control and management, which can improve the power
efﬁciency of the user, change the power consumption habits of
the user, reduce the user’s electricity bill, and realize two-way
communication with the grid, two-way energy ﬂow, etc. (Li et al.,
2020; Zhang et al., 2016), and the network structure is shown in
Figure 2 (Li et al., 2015a).

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KEY TECHNOLOGY OF HEMS

abnormal situation, an alarm will be generated and sent to the
HEMS center. (5) Management module. It contains various
services to improve the optimized operation and power usage
efﬁciency of residential households.

Advanced Measurement System
AMI Structure
The advanced metering infrastructure (AMI) is a set of control
processing systems used to collect, measure, analyze, and store
user electricity consumption information and grid electricity
price information, including four main components: smart
meter, communication network, meter data management
system (MDMS), and home area network (HAN), and its
typical structure is shown in Figure 5, which can realize twoway interaction between household energy information and the
grid (Zhao et al., 2010; Yang, 2015; Peng et al., 2017).
Various components of the AMI are connected through the
network to realize the two-way transmission and power control of
user electricity information and electricity price information
necessary in the HEMS and realize the automation and
intelligence of DR.
Composition of AMI
1) Smart meter. It is equivalent to a sensor installed on the user
side. Based on the measurement, communication, and

FIGURE 1 | Traditional electricity mode.

FIGURE 2 | HEMS network structure.

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important part of the advanced measurement system (Luan
et al., 2014).
The smart meter can be used as a communication gateway
between the power company and the user’s indoor network,
allowing users to view electricity consumption information
and receive electricity price information in near real time,
which is of great signiﬁcance to the HEMS. The schematic
diagram of the smart meter function is shown in Figure 6,
which has the following functions: two-way metering function,
two-way communication function, and user load control
function, in addition to anti-theft detection, dynamic
display of electricity consumption information, remote time
synchronization update, software upgrade, and other
functions.
2) Communication network. A safe and stable communication
network serves as a basic bridge for information interaction
between power companies, users, and controllable power
loads. The AMI uses a ﬁxed two-way communication
network, which is divided into remote channels and local
channels. The remote channel realizes the transmission of
residential electricity information to the power company and
connects the information of the data concentrator with the
data center. The commonly used methods are mainly optical
ﬁber and telephone lines, radio waves, etc. The local channel is
the communication line between the data concentrator and
the smart meter, and the main methods used are power line
carrier, wireless, and RS485.
3) MDMS. It is a database with analysis tools, which is the
“nerve center” of AMI. It is used in conjunction with the

FIGURE 3 | HEMS energy ﬂow diagram.

calculation, it can realize not only traditional functions such as
power recording but also real-time collection, measurement,
display, and storage of power information, real-time
bidirectional metering, automatic billing, monitoring of
power quality and power supply reliability, analysis of
faults for prediction, remote connection and disconnection,
power theft detection, and other functions. It is also an

FIGURE 4 | Five functional modules of the HEMS.

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FIGURE 5 | Typical structure of the AMI.

ZigBee has greater advantages in power consumption, cost,
and networking, and its market acceptance is higher.

Demand Response
DR means that electricity users respond to electricity price
guidance or incentive mechanisms and adjust electricity
consumption methods to achieve peak shaving and valley
ﬁlling, so that the load presents ﬂexible characteristics, to
promote the optimal allocation of power resources (Li et al.,
2005; Yang et al., 2014; Yang et al., 2016). The Federal Energy
Regulatory Commission classiﬁes DR into two types: price-based
demand response (PDR) and incentive-based demand response
(IDR), as shown in Figure 7.
Due to the huge impact of price on consumer behavior, the
price mechanism is the most sensitive mechanism in the
market. The market side publishes the electricity price, and
the end-user side responds to the electricity price to change the
time and electricity consumption of household loads to obtain
economic beneﬁts. PDR technology has three types: time-ofuse pricing (TOU), real-time pricing (RTP), and critical peak
pricing (CPP).
IDR usually refers to an incentive measure formulated by
independent operators or power companies to incentivize
power users under power shortage or other special
circumstances to reduce the operation of home appliances,
thereby reducing the operating pressure of the power system.
This type of compensation is usually implemented utilizing
direct economic compensation or preferential electricity
prices. Therefore, this kind of control measure avoids the
possibility of large-scale power outages and brings economic
beneﬁts to power users.
IDR strategies are divided into plan-based incentives and
market-based incentives. Plan-based incentives can be
subdivided into direct load control (DLC) and interruptible
load (IL); market-based incentives can be further subdivided
into demand side bidding (DSB), emergency demand
response (EDR), capacity market program (CMP), and
ancillary service market program (ASMP). Due to the
United States’ advantages in mechanism, technology, and

FIGURE 6 | Functional schematic diagram of a smart meter (Luan,
2009).

AMI automatic data collection system through an
enterprise service bus to obtain and store the metering
value of the electricity meter, to realize network
reconstruction, power theft analysis, and fault prediction.
Stable operation, asset management, and other advanced
applications provide a reliable guarantee.
4) HAN. It is a bridge that realizes the information interaction
between indoor intelligent interactive terminals, intelligent
electrical energy meters, and household electrical
equipment, making the entire HEMS into a whole. Due
to the relatively random placement of indoor equipment,
which is not suitable for wired communication, and the
variety of household loads and the wide range of residents’
lives, it is more appropriate to use a wireless network in the
HEMS. The communication technology suitable for the
HAN is very large. The commonly used wireless
communication methods include ZigBee, WiFi, and
Bluetooth. Table 2 shows a comparison of representative
communication technologies (Wu et al., 2011; Ji, 2017).

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TABLE 2 | Comparison of wireless communication techniques.
Communication protocol

Signal
frequency band (MHz)

Transmission distance (m)

Transmission speed (Mb·s−1)

Network structure

ZigBee
WiFi
M-Bus
Bluetooth
LonWorks
WiMAX

868–915/2.4 × 103
2.4 × 103/5.0 × 103
868
2.402 × 103–2.480 × 103
433.9
2.5 × 103–2.7 × 103 (USA)

≤100
50–100 m
600
10–100
2.7 × 103
50 × 103

31.25
>6.75
0.1
1–3
1.25
74.81

Star, mesh, cluster
Star
Star, ring, bus type
Star
Bus type, star, free topology
Star, mesh

implementation time, all types of DR projects have been
implemented in the United States.

own electricity consumption behavior according to the needs
of the grid within a certain range (Li et al., 2015b). The rigid
load is an uncontrollable load. Flexible load resources usually
adopt load dispatching methods of peak shifting and peak
avoiding. According to load response characteristics, the
peak shifting load includes the shiftable load and
transferable load. Users stagger their own power
consumption peak from the peak of the power grid load,
to realize peak cutting and valley ﬁlling. The peak avoiding
load is a load that can be reduced to reduce power
consumption during peak hours.
Air conditioners and water heaters are typical ﬂexible load
resources. As far as the air conditioner load is concerned, the
time-varying model equation of room temperature can be
obtained by deriving and solving the ﬁrst-order equivalent
thermal parameter model:

HEMS CONTROL STRATEGY
Equivalent Model of HEMS
Photovoltaic Cell Model
The output power of photovoltaic cells is related to solar
irradiance and temperature:
G(t)
⎪
⎧
⎪
⎨ Pp (t)  P
[1 + k(T(t) − TSTC )] max,
GSTC
⎪
⎪
⎩
T(t)  Tair (t) + 0.0138G(t)(1 + 0.031Tair (t))(1 − 0.042VW ),
(1)
where PPV(t) is the photovoltaic output power; Pmax is the
maximum output power under standard test conditions; G(t)
is the current solar irradiance; GSTC is the rated solar irradiance; k
is the temperature coefﬁcient; T(t) is the temperature of the
battery assembly at the current moment; Tair(t) is the ambient
temperature; TSTC is the rated reference temperature; and VW is
the current wind speed.

Tin (t + 1)  Tout (t + 1) ∓ QtAC · R − Tout (t + 1) ∓ QtAC · R
Δt

− Tin (t)e− RC ,
(3)
where Tin(t+1) is the indoor temperature at time t+1, °C; Tin(t) is
the indoor temperature at time t, °C; Tout(t+1) is the outdoor
temperature at time t+1, °C; R is the equivalent thermal resistance,
°C/kW; C is the equivalent speciﬁc heat capacity, J/°C; Δt is the
time period, h; and QtAC is the cooling capacity of the air
conditioner in the time period t, kW. When the air
conditioner is working in the cooling mode, QtAC the sign
before QtAC is the symbol “-,” and when the air conditioner is
working in the heating mode, the sign before QtAC is the
symbol “+.”
If the air conditioner works in the cooling mode, the on–off
state of the air conditioner at time t can be expressed as

Energy Storage Device and Electric Vehicle Model
The energy storage device participates in scheduling through
charging and discharging, and the change in the state of charge is
used to reﬂect the remaining capacity of the energy storage device:


η PBc (t)SB (t)Δt
⎪
⎧
Bc
⎪
SOCB (t) +
⎪
⎪
⎨
EB
,
(2)
SOCB (t + 1)  ⎪
⎪
P
⎪
Bd (t)SB (t)Δt
⎪
,
⎩ SOCB (t) −
ηBd EB

⎪
⎧
⎨0
SAC (t)  ⎪ 1
⎩
SAC (t − 1)

where SOCB(t) is the state of charge of the energy storage device
during the t period; ηBc, PBc(t) are the charging efﬁciency and
power of the energy storage device, respectively; ηBd, PBd(t) are the
discharge efﬁciency and power of the energy storage device,
respectively; and SB(t) and EB are the switching state and rated
capacity of the energy storage device, respectively. The electric
vehicle (EV) model is similar to the battery, so we will not repeat
it here.

(4)

where ΔTAC is the temperature setting range of the air
conditioner; SAC (t-1) is the on/off state of the air conditioner
at t-1; and Ts(t) is the temperature value set by the user.
As far as the water heater is concerned, consider all the
water in the tank as a single unit with a uniform temperature.
The temperature change model of the water heater is as
follows:

Load Model
Resident loads can be divided into ﬂexible loads and rigid
loads. The ﬂexible load refers to the load that can change their

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Tin (t) < Ts (t)
Tin (t) > Ts (t) + ΔTAC
,
Ts (t) ≤ Tin (t) ≤ Ts (t) + ΔTAC

Tinside (t + 1)  k · Tinside (t) + Ψ,

6

(5)

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FIGURE 7 | DR classiﬁcation.

Vtank − fhot Δt Atank Δt
−
,
(6)
Vtank
cρVtank
fhot Δt
PEWH (t)ηEWH SEWH (t)Δt Atank Δt
Ψ
Tinlet +
+
Tamp , (7)
cρVtank
Vtank
cρVtank

optimization algorithm, dragonﬂy optimization algorithm,
simulated annealing algorithm, and tabu search algorithm.
Optimal scheduling objectives can be divided into two aspects.
On the one hand, it is considered from the perspective of users.
The main purpose is to reduce user electricity costs and improve
user comfort. On the other hand, it is considered from the power
system, including increasing the utilization rate of renewable
energy and reducing the peak ratio during peak electricity
consumption.

k

where c is the speciﬁc heat capacity of water, J/kg·°C; ρ is the
density of water, kg/m3; Vtank is the volume of the water heater
tank, mL; Tinside(t) is the water temperature in the water tank at
time t, °C; Tinlet is the temperature of cold water ﬂowing into the
water tank, °C; fhot is the hot water outﬂow rate in the water tank,
mL/s; Δt is the duration of each time slot, min; PEWH(t) is the
operating power of the water heater at time t; ηEWH is the
operating efﬁciency of the water heater; SEWH(t) is the on–off
state of the water heater at time t, whose value is 0 when it is
closed and 1 when it is open; Atank is the surface area of the water
heater, m2; REWH is the thermal resistance of the water heater,
m2·°C /W; and Tamb is the indoor ambient temperature, °C.
The on–off state of the water heater at time t can be
expressed as
⎪
⎧
⎨ 0,
SEWH (t)  ⎪ 1,
⎩
SEWH (t − 1),

Reducing Residential Electricity Costs
1) Predicted dynamic electricity prices and residential electricity
consumption habits are used as indicators to adjust the
operation of electricity-using equipment to reduce
customers’ electricity costs. Kim and Poor (2011)
formulated the dispatching problem as a Markov decision
process based on past and current electricity prices so as to
derive the optimal strategy and used algorithms to derive price
thresholds for each time period that could bring economic
beneﬁts to electricity consumers. Lee et al. (2013) predicted
the user’s home appliance usage habits through the user’s
power consumption data and Bayesian theorem, so that the
user can reasonably reduce the electrical appliance energy
consumption, so as to reduce the power consumption cost.
2) According to the output status of renewable energy power
generation, coordinated control of electrical equipment
increases the utilization of low-grade renewable energy and
reduces residential electricity costs. Wang et al. (2015)
proposed a household energy–coordinated scheduling
strategy including photovoltaic and energy storage
equipment, including photovoltaic forecasting and
household load forecasting, and the use of particle swarm
algorithms to optimize the energy dispatch of users with the
goal of maximum household energy proﬁtability. Table 3
shows that this strategy increases the proportion of local
photovoltaic consumption while increasing user revenue.
Xu et al. (2017) considered the power grid, photovoltaic
power generation, and energy storage as energy sources,

Tinside (t) > Tset (t)
Tinside (t) < Tset (t) − D
,
Tset (t) − D ≤ Tinside (t) ≤ Tset (t)
(8)

where D is the heat preservation interval of the water heater;
SEWH(t-1) is the on–off state of the water heater at t-1; and Tset(t)
is the temperature value set by the user, Tmin ≤ Tset(t)-D&Tset(t)≤
Tmax, with Tmin and Tmax being the upper and lower temperature
limits of the water heater.

Optimal Scheduling Algorithm of HEMS
The family energy management strategy aims to optimize the
load usage in the family. Household energy management is a
mixed-integer non-linear programming problem. Different
mathematical methods or intelligent algorithms can be used to
solve household energy scheduling problems, such as the artiﬁcial
bee colony algorithm, genetic algorithm, particle swarm

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TABLE 3 | Electricity consumption model revenue data (Wang et al., 2015).
Electricity mode

Proportion of
photovoltaic spontaneous
self-use (%)

Total investment
cost (yuan)

Estimated annual
income (yuan)

Cost recovery
period (year)

Life cycle
income (yuan)

21
67
46
92

20,000
25,000
20,000
23,000

746
941
851
1046

10.4
10.4
8.7
8.4

36,150
36,025
38,775
42,650

Photovoltaic directly connected to the grid
Photovoltaic/energy storage grid-connected
Active load transfer
Cooperative scheduling strategy

established a multi-objective mixed-integer non-linear
optimization model, and proposed an intelligent solution
method based on the adaptive particle swarm optimization
algorithm (APSOA), which can minimize household power
consumption.

be arranged reasonably, and the utilization rate of
photovoltaic energy can be improved.

Reducing Peak Ratios During Peak Electricity
Consumption Periods
Latif et al. (2018) proposed a bat genetic algorithm (BGA) based on
TOU, which shifts household appliances from the period of high
peak electricity prices to the period of low electricity prices; it has an
obvious effect on reducing the peak ratio and residential power cost
in the peak period. Tu et al. (2019) took the incentive mechanism of
residents’ load participating in power grid peak shaving as the starting
point and designed the incentive model and optimization strategy of
peak shaving and valley ﬁlling, which can reduce the peak ratio in the
peak period while considering the user’s power consumption cost
and realize the friendly interaction of “network load.”

3) Other situations include collaborative scheduling of EVs
and energy storage equipment. Yao et al. (2020) proposed
two modes of ground control strategies for cooperative
dispatching of EVs and energy storage devices. Mode 1 is a
charging-only storage device for EVs; mode 2 adopts the
control strategy of “charging before discharging” for EVs,
which further improves the economy and ﬂexibility. In
addition, the HEMS can also improve the economy of user
energy consumption by reducing the idle loss of load and
transfer the working time of some loads from a “high price
period” to a “low price period.”

Challenges for HEMS
At present, there have been many studies on HEMS control
strategies, but the application and promotion of the HEMS are
still in the process of exploration:

Improving User Comfort
Chen et al. (2012) proposed the concept of operational comfort
level (OCL) and developed a minimum load scheduling
algorithm, which can achieve peak load transfer and maximize
the residents’ OCL. Liu et al. (2015) proposed an optimal
scheduling model for home appliances based on real-time
electricity prices, which can achieve scheduling including
satisfaction and economy, as well as scheduling with the least
carbon dioxide emissions. Nguyen and Le (2014) discussed the
energy dispatch optimization problem of household appliances
such as EVs, air conditioners, and water heaters, which can realize
the integration of user comfort and electricity cost.

1) The penetration rate of energy equipment in households is
low, and how to promote household energy equipment and
transform the existing household power supply system on a
large scale is also a problem to be solved.
2) Household energy consumption information is lacking, and DR
is insufﬁcient. In the residential DR, accurate demand response
and behavior analysis are still issues that need attention. On the
premise of ensuring the comfort of power consumption,
providing users with power-saving measures and effective
energy consumption suggestions will help mobilize the
enthusiasm of residential users to participate in the demandside response and enhance the friendly interaction between the
power grid and users, which is of great signiﬁcance.
3) A reasonable incentive mechanism is lacking. Although there are
many incentive mechanisms in China, including energy efﬁciency
subsidies, differential tariffs, tiered tariffs, peak and valley tariffs,
and temporary incentives for peak power load reduction, the
formulation of subsidies lacks a market-based mechanism, and it is
difﬁcult to optimize the allocation of resources when subsidy
standards are given. The development of the HEMS in China is
still in the primary stage, and more policies, software and hardware
facilities, and related supporting technologies are needed to
promote the establishment of home intelligent networks.

Improving the Utilization of Renewable Energy
Huang et al. (2015) researched household users including
wind power and photovoltaic power generation. Based on
TOU, fully considering the economy of electricity
consumption and user comfort, a demand-side response
method was proposed, which greatly improved the
utilization rate of wind power. Zong et al. (2013) proposed
a distributed active demand-side management controller
based on an artiﬁcial neural network. The system includes
photovoltaic, energy storage, and home automation systems,
which can coordinate user preferences and predict power
generation. Therefore, the operation of home appliances can

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Home Energy Management System

CONCLUSION

purposes of reducing user electricity costs, improving
user comfort, improving renewable energy utilization,
and reducing peak power consumption during peak
periods. Finally, the challenges to the development of
the HEMS are pointed out.

The conclusions are drawn as follows:
1) The HEMS integrates all power generation, electricity
consumption, and energy storage equipment in the home.
The core equipment in the HEMS structure is the home host,
which is the key to realizing the two-way interaction between
the residential load and the power grid. It includes ﬁve
functional modules: monitoring, recording, control,
management, and alarm.
2) The AMI and DR technology in the smart grid are the core
technology and foundation of the HEMS. The HEMS connects
various home appliances, distributed power sources, energy
storage, and other equipment through the AMI to monitor
and manage the use of various electrical equipment and
realizes the optimal management of electrical equipment in
combination with DR technology. The development and
implementation of these technologies have made household
energy management on the residential side become of great
signiﬁcance, helping to save energy, reduce emissions, and cut
peaks and ﬁll valleys.

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in
the article/Supplementary Material, and further inquiries can be
directed to the corresponding author.

AUTHOR CONTRIBUTIONS
YM and XC proposed the concepts and ideas. LW analyzed the
results. JY wrote this paper and revised the contents of this
manuscript.

FUNDING

3) The component model of the HEMS is established. From
the perspective of residents and the power grid, it
discussed household energy optimization for the

This work was ﬁnancially supported by the Jiangsu Electric Power
Company Research Foundation under Grant No.
SGJS0000YXJS2001039.

REFERENCES

Li, Y. P., Zhou, J., Ju, P., and Ding, M. (2015). Quantitative Assessment Method for
Interactive Impact of Flexible Load. Automation Electric Power Syst. 39 (17),
26–32+67. doi:10.7500/AEPS20140417002
Li, Y., Wang, B. B., and Li, F. X. (2015). Outlook and Thinking of Flexible and
Interactive Utilization of Intelligent Power. Automation Electric Power Syst. 39
(17), 2–9. doi:10.7500/AEPS20150730004
Li, Y., Wang, B. B., and Song, H. K. (2005). Demand Side Response and its
Application. Power DSM 7 (6), 13–15+18. doi:10.3969/j.issn.10091831.2005.06.008
Liu, J. H., He, R., and Li, R. F. (2015). Optimal Scheduling Model for home
Energy Management System Based on Real-Time Electricity Pricing.
Appl. Res. Comput. 32 (1), 132–137+160. doi:10.3969/j.issn.10013695.2015.01.030
Liu, Z. Z., Wang, M. J., and Yang, X. S. (2009). Research Status and Development
Trend of Smart Grid. Power Syst. Technol. 33 (13), 1–11.
Luan, W. P. (2009). Advanced Metering Infrastructure. South. Power Syst. Technol.
3 (2), 6–10.
Luan, W. P., Wang, G., and Xu, D. Q. (2014). Advanced Metering Infrastructure
Solution Supporting Multiple Services and Business Integration. Proc. CSEE 34
(29), 5088–5095.
National Energy Administration, National Energy Administration Releases Total
Electricity Consumption in 2019. Available at: http://www.nea.gov.cn/2020-01/
20/c_138720877.htm.
Nguyen, D. T., and Le, L. B. (2014). Joint Optimization of Electric Vehicle and
Home Energy Scheduling Considering User Comfort Preference. IEEE Trans.
Smart Grid 5 (1), 188–199. doi:10.1109/tsg.2013.2274521
Peng, X. G., Li, Z. M., Deng, X. K., and Liu, Y. (2017). Research on Advanced
Metering Infrastructure under Smart Grid Framework. Guangdong Electric
Power 30 (12), 7–14.
Renner, S., and Heinemann, C. (2011). European Smart Metering Landscape
Report.
Son, Y., and Moon, K. (2010).Home Energy Management System Based on Power
Line Communication, 2010 Digest of Technical Papers International
Conference on Consumer Electronics (ICCE), IEEE, 9 Jan 2010, Las Vegas,
NV, USA, 115–116 .

Chen, M., Guan, X., Meng, J., and Wu, W. X. (2019). Review on Home Energy
Management System for Smart Grid. Building Energy Efﬁciency 47 (10),
117–121. doi:10.3969/j.issn.1673-7237.2019.10.023j
Chen, Y., Liu, R. P., Wang, C., de Groot, M., and Zeng, Z. (2012). IEEE,
1–6.Consumer Operational Comfort Level Based Power Demand
Management in the Smart Grid, 2012 3rd IEEE PES Innovative Smart Grid
Technologies Europe (ISGT Europe), 14 Oct. 2012, Berlin, Germany.
Huang, L., Wei, Z. N., Yan, Y. F., Sun, G. Q., Sun, Y. H., Liu, J. S., et al. (2013).
A Survey on Interactive System and Operation Patterns of Intelligent Power
Utilization. Power Syst. Technol. 37 (18), 2230–2237. doi:10.13335/j.10003673.pst.2013.08.005
Huang, T., Ma, X. Y., Lei, J. Y., Xu, A. D., Guo, X. B., Li, P., et al. (2015). Optimal
Operation of Household User-Side Microgrid Considering Time-Of-Use Price
and Demand Response. South. Power Syst. Technol. 9 (4), 47–53. doi:10.13648/
j.cnki.issn1674-0629.2015.04.008
Ji, S. Y. (2017). Research on Home Energy Management for Intelligent Power
Utilization. Hefei: Hefei University of Technology.
Kim, T. T., and Poor, H. V. (2011). Scheduling Power Consumption with Price
Uncertainty. IEEE Trans. Smart Grid 2 (3), 519–527. doi:10.1109/tsg.2011.2159279
Latif, U., Javaid, N., Zarin, S. S., Naz, M., Jamal, A., and Mateen, A. (2018).,
667–677. Cost Optimization in Home Energy Management System Using
Genetic Algorithm, Bat Algorithm and Hybrid Bat Genetic Algorithm, 2018
IEEE 32nd International Conference on Advanced Information Networking
and Applications (AINA), IEEE, 16 May 2018. Krakow, Poland.
Lee, S., Ryu, G., Chon, Y., Ha, R., and Cha, H. (2013). Automatic Standby Power
Management Using Usage Proﬁling and Prediction. IEEE Trans. Human-mach.
Syst. 43 (6), 535–546. doi:10.1109/thms.2013.2285921
Li, Q., Wang, Z. X., Yan, S., Wang, C. M., Bao, L. X., and Qin, H. (2020). Optimal
Operation of Home Energy Management System Considering User Comfort
Preference. Acta Energiae Solaris Sinica 41 (10), 51–58.
Li, W. T. (2016). Research on Load Power Strategy of Home Energy Management
Systems. Changsha: Hunan Unversity.

Frontiers in Energy Research | www.frontiersin.org

9

November 2021 | Volume 9 | Article 772027

Ma et al.

Home Energy Management System

System for Hydrogen Fueling Stations. IEEE Trans. Ind. Applicat., 1.
doi:10.1109/TIA.2021.3093841
Zhang, Y. Y., Zeng, P., and Zang, C. Z. (2016). A Scheduling Algorithm for home
Energy Management System in Smart Grid. Power Syst. Prot. Control. 44 (2),
18–26.
Zhao, H. T., Zhou, J. Y., and Y, E. K. (2010). Advanced Metering
Infrastructure Supporting Effective Demand Response. Power Syst.
Technol. 34 (9), 13–20.
Zong, Y., Mihetpopa, L., Kullmann, D., Thavlov, A., Gehrke, O., and Bindner, H.
W. (2013). Model Predictive Controller for Active Demand Side Management
with PV Self-Consumption in an Intelligent Building, IEEE PES International
Conference & Exhibition on Innovative Smart Grid Technologies, IEEE, 14 Oct.
2012, Berlin, Germany.

Stamminger, R., Broil, G., Pakula, C., Jungbecker, C., Braun, M., Ruidenauer, L.,
et al. (2008). Synergy Potential of Smart Appliances. Europe: Report of the
Smart-A project.
Tang, Y., Pipattanasomporn, M., Shao, S. N., Liu, H. M., and Rahman, S. (2009).
Comparative Study on Smart Grid Related R&D in China, the United States and
the European Union. Power Syst. Technol.
Tian, S. M., Wang, B. B., and Zhang, J. (2014). Key Technologies for Demand
Response in Smart Grid. Proc. CSEE 34 (22), 3576–3589.
Tu, J., Zhou, M., Song, X. F., Luan, K. M., and Li, G. Y. (2019). Research on
Incentive Mechanism and Optimal Power Consumption Strategy for
Residential Users’ Participation in Peak Shaving of Power Grid”. Power Syst.
Technol. 43 (2), 443–453.
Wang, S. X., Sun, Z. Q., and Liu, Z. (2015). Co-scheduling Strategy of Home
Energy for Smart Power Utilization. Automation Electric Power Syst. 39
(17), 108–113.
Wu, L., Xin, J. Q., and Wang, S. (2011). Advanced Metering Infrastructure and
its Application in Demand Response. Water Resour. Power 29 (12),
170–173+216.
Xu, J. J., Wang, B. E., Yan, L. M., and Li, Z. (2017). The Strategy of the Smart Home Energy
Optimization Control of the Hybrid Energy Coordinated Control. Trans. China
Electrotechnical Soc. 32 (12), 214–223. doi:10.1109/tec.2017.2696979
Yang, C. S. (2015). Design on Communications and Information System of Grid and
Users Bidirectional Interaction. Beijing: School of Electrical and Electronic
Engineering.
Yang, S. C., Liu, J. T., Yao, Y. G., Ding, H. F., Wang, K., and Li, Y. P. (2014). Model
and Strategy for Multi-Time Scale Coordinated Flexible Load Interactive
Scheduling. Proc. CSEE 34 (22), 3664–3673.
Yang, X. Y., Zhou, M., and Li, G. Y. (2016). Survey on Demand Response
Mechanism and Modeling in Smart Grid. Power Syst. Technol. 40 (1),
220–226.
Yao, G., Mao, Z. L., Zhou, L. D., and Li, D. (2020). Home Energy Management
Strategy for Co-scheduling of Electric Vehicle and Energy Storage Device. Proc.
CSU-EPSA 32 (4), 35–41+50.
Zhang, K., Zhou, B., Or, S. W., Li, C., Chung, C. Y., and Voropai, N. I. (2021).
Optimal Coordinated Control of Multi-Renewable-To-Hydrogen Production

Frontiers in Energy Research | www.frontiersin.org

Conﬂict of Interest: The authors YM and XC were employed by the company State
Grid Jiangsu Electric Power Co., Ltd. The author LW was employed by the
company Jiangsu Frontier Electric Power Technology Co., Ltd.
The remaining author declares that the research was conducted in the absence of
any commercial or ﬁnancial relationships that could be construed as a potential
conﬂict of interest.
Publisher’s Note: All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their afﬁliated organizations, or those of
the publisher, the editors, and the reviewers. Any product that may be evaluated in
this article, or claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Copyright © 2021 Ma, Chen, Wang and Yang. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC
BY). The use, distribution or reproduction in other forums is permitted, provided the
original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academic practice.
No use, distribution or reproduction is permitted which does not comply with
these terms.

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</reference>

<statements>
1. Academic and industrial research consistently identifies Home Energy Management Systems as a core architectural component of future smart homes, responsible for monitoring and optimizing generation, storage, and consumption. HEMS coordinate smart meters, smart plugs, distributed energy resources (DERs) such as rooftop PV and battery energy storage systems (BESS), and controllable loads like appliances and HVAC under demand‑side management and time‑of‑use tariffs.
2. Studies show that IoT‑enabled HEMS can substantially reduce residential electricity costs and peak loads by shifting consumption to off‑peak hours, integrating local generation, and orchestrating storage. For example, one real‑time HEMS implementation integrating PV and BESS reported up to a 90% reduction in power costs for a single dwelling alongside improved user comfort and significant peak‑to‑average ratio reductions. Such results are encouraging ongoing deployment of cloud‑backed HEMS architectures that use scalable ingestion, storage, and analytics layers to support clusters of homes and multi‑level energy communities.
3. As utilities roll out demand‑response programs and dynamic tariffs, home energy platforms and EV charging products are increasingly expected to provide integrated load management, real‑time monitoring, and grid‑responsive capabilities. This positions smart EV chargers, V2H/V2G‑capable bidirectional chargers, and integrated solar‑battery‑EV orchestration systems as important future product categories within the smart home.
</statements>

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