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An innovative method for building
electricity energy management in
smart homes based on electric
vehicle energy capacity
Yakubu Aminu Dodo
1
, Ahmed Osman Ibrahim
2
*,
Mohammed Awad Abuhussain
1
*, Zulai Jarmai Baba Girei
3
,
Ammar Maghrabi
4
and Ahmad Usman Naibi
5
1
Architectural Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia,
2
Department of Architectural Engineering, College of Engineering, University of Ha’il, Ha’il, Saudi Arabia,
3
Nigerian Building and Road Research Institute, Abuja, Nigeria,
4
Urban and Engineering Research
Department, The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-
Qura University, Mecca, Saudi Arabia,
5
Graduate School of Education, Department of Architecture
(Doctorate-English) Okan Istanbul Universitesi, Istanbul, Türkiye
The surging demand for electricity, fueled by environmental concerns, economic
considerations, and the integration of distributed energy resources, underscores
the need for innovative approaches to smart home energy management. This
research introduces a novel optimization algorithm that leverages electric
vehicles (EVs) as integral components, addressing the intricate dynamics of
household load management. The study’s significance lies in optimizing
energy consumption, reducing costs, and enhancing power grid reliability.
Three distinct modes of smart home load management are investigated,
ranging from no household load management to load outages, with a focus
on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the
combined effect of ToU and IBR on load management outcomes. The algorithm,
a multi-objective approach, minimizes the peak demand and optimizes cost
factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs
play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads
and a 7.9% decrease in payment expenses. Numerical results affirm the
algorithm’s adaptability, even under load interruptions, preventing excessive
increases in paid costs. Incorporating dynamic pricing structures like inclining
block rates alongside the time of use reveals a 7.9% reduction in payment costs
and a 16.4% decrease in peak loads. In conclusion, this research provides a robust
optimization framework for smart home energy management, demonstrating
economic benefits, peak load reduction potential, and enhanced reliability
through strategic EV integration and dynamic pricing.
KEYWORDS
smart home, electricity energy, energy management, electric vehicle, time of use,
inclining block rate
OPEN ACCESS
EDITED BY
Mohammad Hossein Ahmadi,
Shahrood University of Technology, Iran
REVIEWED BY
Maher Dhahri,
University of Sousse, Tunisia
Moslem Akbari Vakilabadi,
Malek Ashtar University of Technology, Iran
Mojtaba Mirzaei,
Energy Institution of Higher Education, Iran
Hasan Yilsizhan,
Adana Alparslan Turkes Science and
Technology University, Türkiye
Tolga Taner,
Aksaray University, Türkiye
*CORRESPONDENCE
Mohammed Awad Abuhussain,
maabuhussain@nu.edu.sa
Ahmed Osman Ibrahim,
ah.ibrahim@uoh.edu.sa
RECEIVED 03 January 2024
ACCEPTED 06 February 2024
PUBLISHED 21 February 2024
CITATION
Dodo YA, Ibrahim AO, Abuhussain MA,
Baba Girei ZJ, Maghrabi A and Naibi AU (2024),
An innovative method for building electricity
energy management in smart homes based on
electric vehicle energy capacity.
Front. Energy Res. 12:1364904.
doi: 10.3389/fenrg.2024.1364904
COPYRIGHT
© 2024 Dodo, Ibrahim, Abuhussain, Baba Girei,
Maghrabi and Naibi. 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.
Frontiers in Energy Research frontiersin.org01
TYPE Original Research
PUBLISHED 21 February 2024
DOI 10.3389/fenrg.2024.1364904
1 Introduction
The desire to reduce pollution, fight climate change, save fossil
fuels, save money, and make the power grid more reliable and stable
are all factors that are driving up the consumer demand for
electricity (Taner et al., 2019;Jasim et al., 2023;Nejatian et al.,
2023). This has led to the adoption of distributed production
resources in order to establish a smart and eco-friendly network
(Jamil et al., 2022;Almalki et al., 2023). The use of fossil fuels to meet
this level of demand without proper energy management will result
in the depletion, if not the extinction, of these natural resources (Yin
et al., 2022;Nikbakht Naserabad et al., 2023). As a result, today’s
humanity is attempting to use renewable resources to help meet this
massive and ever-increasing demand (Pachar et al., 2021). Aside
from using renewable resources for production, the discussion of
how to manage these energy resources can be appealing because
there is a significant opportunity to save energy in the building
through efficient use (Erixno et al., 2022).
Due to their many advantages over other transportation
technologies, electric vehicles (EVs) have recently attracted a lot
of interest (Mohammed et al., 2024;Pamidimukkala et al., 2024).
This technology has many benefits, including being environmentally
friendly, making very little noise, being extremely efficient with
energy, reducing operational costs, using local renewable energy
sources, and offering additional services through energy exchange
between vehicles and the grid (Misbah Inayat et al., 2023;Siddiqui
et al., 2024).
The equilibrium between power generation and consumption is
a crucial factor for the proper functioning of the power grid
(Ourahou et al., 2020;Wei et al., 2024). The disparity between
the two results in elevated operational expenses for power
corporations or potentially immobilizes the entire power network
(He et al., 2023). Power suppliers have installed backup power
storage batteries to enhance network stability and reliability.
However, typically, this approach in isolation is insufficient. Load
response programs are regarded as a tactic to mitigate fluctuations in
the demand curve (Ferraro et al., 2020).
By deploying the load response program, the network operator
hopes to lower operating expenses by flattening the network load
curve. In recent years, it has garnered considerable interest (Lin
et al., 2024). The customer is tasked with calculating their own
electricity consumption in these programs after being presented
with different electricity prices at different times. Pricing should be
designed to encourage consumption at the operator’s preferred
times. Electricity consumption during peak hours is something
that everyone is interested in reducing, including the operator,
distribution companies, and producers. The subscriber is thus
informed that, according to the load forecast, the price will be
higher during peak hours (Kath and Ziel, 2021;Rahman et al., 2024).
A smart building is one in which all of its internal components
interact with one another via integrated systems and logical
creations that are environmentally friendly (Al Dakheel et al.,
2020;Apanaviciene et al., 2020;Eini et al., 2021;Li et al., 2021).
It goes without saying that the preceding definition does not imply
that this structure has decided to respond to any incident. Being
intelligent means that the system can make appropriate decisions
based on past events and defined logic. In other words, a smart
building will not be able to be creative in the face of various factors
but will instead behave logically. Energy management, on the other
hand, is defined as the economic and efficient use of energy (Hakimi
and Hasankhani, 2020;Aguilar et al., 2021).
A multi-objective algorithm introduced by Shirazi and Jadid
(2015) for the optimal scheduling of electricity and heat with
optimal management is used to provide a trade-off between
minimizing peak demand and the cost of using the smart home.
The use of distributed energy sources has been proposed. Nojavan
and Zare (2013) investigated the optimal bidding strategy with
uncertainty modeling of the day-ahead market price. Nojavan
et al. (2015) utilized a combined approach based on particle
optimization to maximize production profit in the presence of
market price uncertainty. Brahman et al. (2015) investigated the
residential energy hub with the goal of minimizing operating costs
by taking the customer’s priorities into account. The minimum cost
of operations in the GAMS model was obtained by programming a
residential microgrid incorporating a smart heat load-micro-CHP-
battery meter (Tasdighi et al., 2014). The management of an energy
hub with the goal of minimizing energy costs and risks
incorporating a wind turbine, heat generator, and CHP system
has been investigated (Bozchalui et al., 2012). Yan et al. (2023)
investigated an economically optimized home energy management
system for smart buildings, incorporating electric vehicles and
considering risk factors. They employed an advanced whale
optimization algorithm to address practical constraints, revealing
substantial cost reductions with positive consumer attitudes toward
energy consumption and adaptive strategies based on price risk
indicators. Manivannan (2024) explored the impact of EVs on the
electrical system and emphasized the importance of the EV’s energy
management system (EMS) in smart transportation within smart
cities. The study proposed a machine learning-based Smart Energy
Management System for Hybrid Electrical Vehicles (SEMS-HEV) to
optimize energy usage, enhance battery lifespan, and prevent
electrical supply disruptions. Integrating IoT-based smart
charging, the research focuses on improving EMS performance
and efficiency through dynamic scheduling of vehicle-to-grid
(V2G) connections for hybrid electrical vehicles. Alghassab
(2024) introduced a fuzzy-based smart energy management
system for Saudi residential buildings, optimizing energy use
through fuzzy logic. The system adapts devices based on user
preferences, occupancy, and real-time energy prices,
demonstrating superior performance in a comparative study. This
approach aligns with Saudi Arabia’s goal of sustainable energy
practices, marking a promising step toward a more efficient and
eco-friendly future.
By employing EVs as a sophisticated model and a viable solution
to economic concerns, this study also provides a strategy for load
planning. In smart homes, the presence of EVs is critical for
reducing shared costs and powering household loads with energy
discharged during periods of high tariff. The significance of EVs in
household load management, however, has been neglected.
Consequently, the purpose of this research is to achieve this goal
through the implementation of a suitable optimization algorithm.
Additionally, the examination of relevant literature studies unveiled
that the household load management procedure implemented in the
edifice failed to consider the matter of a definite load. The following
sections will provide an overview of the subject before proceeding to
the mathematical modeling of home load management. This study is
Frontiers in Energy Research frontiersin.org02
Dodo et al. 10.3389/fenrg.2024.1364904
grounded in the data regarding a smart home in the urban area of
Najran, Kingdom of Saudi Arabia.
2 Methodology (proposed model)
In this study, a novel framework for the management of
buildings will also be connected to the network. The study also
has an EV parking lot and can use electrical car reserves for energy
management. In addition, it is assumed that the building has a
battery energy store. It will not be a battery charge and discharge,
reducing energy losses and the cost of investment because the
number of energy converters decreases. By employing precise
modeling of the issue grounded in household load management
and simulation through the GAMS model, the acquired outcomes
are scrutinized and assessed.
There are numerous approaches to EMS in buildings. These
techniques fall into the following two categories: information-based
predictive methods and real-time-based methods. In this study, real-
time-based methods are used. Energy management operates using
real-time data, thereby eliminating the necessity for forecasting. In
contrast to their predecessors, these methods offer numerous
advantages, such as algorithm structure simplification and the
elimination of the requirement for intensive calculations.
Sechilariu et al. (2013) stated that an approach to energy
management for structures housing PV products involves
considering two factors: the DC building load and the charging
of EVs. Additionally, for the purpose of energy management, they
made an effort to prevent network reception during peak hours.
Additionally, the load response method was implemented utilizing a
variable price prosecution mechanism.
The pricing structure is based on the time of use (ToU) and
the inclining block rate (IBR). This depends on the real-time
situation and the specific moment during which it has had the
capability to accomplish it. In the forthcoming years, however,
real-time is anticipated to be mandatory for the majority of end-
users, encompassing the intelligent capabilities of the smart
network that rely on suitable communication. According to
the results presented in Sechilariu et al. (2013),Wi et al.
(2013),andByeon et al. (2013), EMS based on real-time
information is more suitable for buildings because it is easier
to understand and manage.
2.1 EV model
In this study, an EMS is presented that assists building energy
operators in achieving a logical and automated operation of DC
systems by utilizing real-time cost and consumption information.
This algorithm is provided for EMS-owned reserves, which
include buildings such as the EV parking lot and incur
expenses for the EMS to purchase, repair, and other related
matters. Battery technology has been implemented for storage.
The EV method employed in this study represents a sophisticated
approach to energy management within the realm of smart
buildings. Central to this methodology is the integration of
electric vehicles as pivotal components in the optimization of
energy consumption. EVs act as dynamic entities capable of not
only drawing power from the grid but also contributing to the
grid through strategic discharge during peak demand periods.
Unlike conventional methods, our EV method goes beyond mere
load balancing and incorporates a multi-objective model that
considers not only economic factors but also risk considerations.
This entails a robust framework where electric vehicles are not
just passive consumers but active participants in the overall
energy ecosystem.
The uniqueness of our EV method lies in its ability to adapt to
real-world constraints, including production limits, flexible loads,
and intricate cost-risk indicators. By leveraging a developed whale
optimization algorithm, we ensure a more efficient and adaptive
energy management system. This algorithm has been specifically
enhanced to avoid converging to local optima, offering a higher
likelihood of finding globally optimal solutions. Moreover, our EV
method stands out by considering practical and nonlinear
constraints, making it more aligned with the complexities of
modern energy scenarios.
Before proceeding via this method, the required information
is gathered. The load value, distribution network conditions,
battery state of charge (SoC
Batt
), and EV state of charge
(SoC
EV
) are included in these data. The primary distribution
network is categorized into three modes of load, namely, low
load, medium load, and high load, depending on its load level.
Energy costs vary for each load level. If the combined power
demand for charging the EV and the amount of load exceed the
generated power, this approach utilizes either the energy stored
in the battery or purchases power from the distribution network.
It is possible that the battery’s stored power is inadequate to offset
the fraction. In this instance, the necessary energy is procured
concurrently from the distribution network and the battery. At
the times that the SoC has been in the order SoC
min
<SoC <
SoC
max
, the power of the work should be used. SoC
min
and
SoC
max
are the minimum and maximum values, which specify
the allowed amount of the storage level.
Constant connectivity exists between the building
distribution system and the primary distribution network. The
EMS is obligated to reduce the power it purchases from the
distribution network and, if it possesses power generation
sources such as renewable energy sources, sell the excess
power to the national distribution network during periods of
high electricity prices (typically during peak hours). Therefore,
the utilization of an EV is employed in this particular scenario.
The EV will be operational to the extent that the EMS will be able
to discontinue charging or commence discharging the EV at
specifictimes.IfEVchargingishalted,theEVwillenterthe
standby mode; otherwise, it will enter the discharge mode.
To encourage EV owners to participate in energy management
and utilize EVs as energy storage devices, the EMS operator must
provide incentives. The charging and discharging model for EVs
presented is predicated on the subsequent principles.
•Engagement in energy management activities is not required.
The proprietor of an EV is free to sign a participation contract
in energy management and configure SOC
min
and
SOC
discharging
to their liking at any time. However, if they
wish to cancel the contract prior to its expiration, they will
incur a fine.
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Dodo et al. 10.3389/fenrg.2024.1364904
•While the EV is parked in the lot and being charged, the EMS
operator is obligated to maintain the SOC of the EV battery
above the SOC
min
value specified in the contract.
•If the SOC of the EV battery reaches the SOC
max
value mentioned
in the contract, the EMS operator can stop charging the EV.
•To enforce energy management strategies, such as peak load
reduction, the EMS operator may suspend the charging
process for vehicles with a SOC exceeding the SOC
min
. The
term for this state is the standby mode.
•It will be feasible to discharge vehicles whose SOC exceeds
SOC
discharging
in situations involving a substantial load. It is
referred to as the discharge mode.
In accordance with the prescribed regulations governing EV
involvement in energy management, the charging expense for every
EV is computed using Eqs 1,2, which can be utilized to compute the
energy cost of all EVs when multiple EVs are engaged in energy
management (Ghosh et al., 2022).
ECi,EV CEV−C·
k
j1
Pi,j,EV−C−CEV−D·
l
j1
Pi,j,EV−D,(1)
ECtotal −EV CEV−C·
n
i1
k
j1
Pi,j,EV−C−CEV−D·
n
i1
l
j1
Pi,j,EV−D.(2)
Here, EC
total_EV
represents the total cost of charging all EVs, while
EC
i,EV
represents the energy cost for charging the ith EV. The costs
associated with charging and discharging 1 kW of power are denoted by
C
EV_C
and C
EV_D
, respectively. Additionally, P
i,j,EV_C
and P
i,j,EV_D
represent the EV’s charge and discharge powers, respectively. If the
EV is parked in the lot and is connected to the system, it will adhere to
the EMS-provided charging and discharging procedure. The EV owner
is responsible for the cost of all power consumed by the EV battery
(CEV−C·k
j1Pi,j,EV−C). However, it is compensated for every unit of
energy discharged by the EV (CEV−D·l
j1Pi,j,EV−D).
The EMS operator must determine the C
EV_D
value. This task
necessitates the consideration of numerous variables, including
charging losses, EV battery life, and the amount of money saved
by reducing the peak load (EV battery efficiency). If the C
EV_D
value
is accurately determined, field EVs will be able to manage and benefit
from energy support.
The price of electricity influences the performance of EVs, and the
price of electricity can impact the degree to which EV owners participate
in energy management. Given the fluctuating electricity prices between
peak and off-peak hours, the conduct of EV owners will likewise vary
during these periods. The charging and discharging patterns of EVs, in
relation to the load, are presented in Table 1.
The system is divided into three load modes, as shown in
Table 1: high load, normal or medium load, and low load.
Charge and discharge patterns of EVs vary between the three
scenarios. When power levels are high or low, an EV is only
charged if its SOC is below SOC
min
. If the SOC value for any EV
during charging falls within the range of SOC
min
and SOC
discharging
,
the vehicle enters the standby mode, and neither charging nor
discharging occurs. Moreover, the discharge of EVs is exclusively
permitted during productive periods, provided that their SOC
exceeds that of the SOC
discharging
.
In Figure 1, the presented algorithm is shown for three modes: low
load, medium load, and high load. The SOC
Smin
and SOC
Smax
parameters denote the minimum and maximum permissible values
for the battery’s storage level, respectively. P
UG
represents power
exchangeable units. P
S
and P
EV
represent the power and storage
capacity of an EV, respectively; positive values signify a charging state.
During the low-load mode, the EV’sSOCvalueisevaluatedinorder
to ascertain the necessary course of action. The P
e
value represents the
EV’s charging rate. If the SOC value of the EV exceeds SOC
EVmin
during
the medium load stage, the system will enter the standby mode. If not,
the EV enters the charging mode; this cycle will continue until its state
of charge reaches SOC
EVmin
. In this scenario, should the network
encounter a power deficit, EVs with the highest SOC will enter the
discharge mode (SOC
EVdischarging
). Once the SOC of a vehicle reaches
SOC
discharging
, it will transition to the standby mode.
2.2 Energy cost analysis
To conduct an economic analysis of the proposed energy
management algorithm, it is imperative to derive a correlation
that facilitates the computation of energy expenses. The
determination of energy supply costs can be approached from
three perspectives: perspective of the building manager, who may
serve as the load collector or energy management operator;
TABLE 1 EV charging and discharging algorithm.
System load status P
L
status State of charging Electric vehicle operation
High load P
L
>0SOC<SOCmin Charging
SOCmin ≤SOC <SOCdischarging Standby
SOC ≥SOCdischarging Discharging
P
L
<0SOC<SOCmax Charging
Medium load P
L
>0SOC<SOCmin Charging
SOC ≥SOCmin Standby
Low load P
L
<0SOC<SOCmax Charging
P
L
>0SOC<SOCmax Charging
P
L
<0SOC<SOCmax Charging
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Dodo et al. 10.3389/fenrg.2024.1364904
perspective of the consumers residing in the building; or from the
standpoint of the distribution network. This study operates under
the assumption that the building manager is responsible for EMS
control and is tasked with reducing the overall cost of energy supply
for the structure. Energy costs can be calculated using Eq. 3.
ECtotal ECUG +ECtotal EV .(3)
The total cost of energy supply for the building is denoted by
EC
total
. Additionally, E
CUG
represents the expense incurred to
procure electricity from the distribution network, while EC
total_EV
represents the cost incurred to charge and discharge all EVs. The
cost of purchasing energy from the distribution network is calculable
with the aid of Eq. 4.
ECUG CUG ·Ptotalload −
n
i1
Pi,EV−D
⎛
⎝⎞
⎠.(4)
Here, P
total_load
is the total load of the building and calculated
using Eq. 5.
Ptotal load PDload +
n
i1
Pi,EV−D.(5)
C
UG
represents the electricity purchased from the grid that costs
one hundred dollars per kilowatt-hour. P
D_load
represents the
building load demand expressed in kilowatt-hours. P
i,EV_C
and
P
i,EV_D
represent the quantity of charged power and discharged
power, respectively, in ith EV in kW hours.
3 Results and discussion
The evaluation of the proposed innovation method
encompassed diverse smart home configurations, incorporating
responsive and non-responsive devices, along with the integration
of an EV. Three distinct load management modes were considered,
including the initial condition with no household load management,
a subsequent condition implementing load management without
outages, and a final condition simulating load outages akin to the
second condition. The assessment was conducted primarily with
ToU tariffs as the baseline, with a detailed exploration of the impact
of IBR pricing and the combined influence of ToU and IBR on load
management outcomes, which are comprehensively discussed in the
conclusion section.
In the context of smart home load management, the proposed
approach demonstrated robust efficacy in regulating the household
load for a 24-h period, employing a daily timestep count of 96, where
each time step represented 15 min. Non-operational appliances,
such as personal laptops, televisions, and vacuum cleaners, were
considered baseline non-responsive devices. The load management
program effectively transformed the washing machine, dishwasher,
and coffee maker into responsive devices by regulating their usage
time, as illustrated in the daily average data presented in Table 2.
Further considerations included a permissible deviation of
0.30 kW-hours, with subscribers being offered one permitted
interval for non-responsive devices. It was assumed that the
subscriber would accept one of the three intervals specified in the
result announcement. A uniform equivalent lost load charge of
FIGURE 1
Model presented for energy management for (A) low-load, (B) medium-load, and (C) high-load hour.
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25 cents/kWh was applied to all appliances. Table 3 presents a
detailed breakdown of various time-of-use tariffs, providing
comprehensive insights into the pricing structures considered
during the evaluation.
3.1 Scenario 1
Assumed within the confines of this scenario are several critical
parameters. First and foremost, the heating system is presumed to
consistently operate at peak efficiency, ensuring optimal energy
utilization. Simultaneously, the EV is envisioned to be charged to
its maximum capacity upon connection to the charger, and all
responsive devices are activated within their designated permitted
operating range. Despite these proactive measures, due to the
inherent uncontrollability of the load, the integration of EVs into
the household load is presently deemed unfeasible. Figure 2 provides
a comprehensive illustration of the building load demand diagram
for this residential scenario, encapsulating the intricate dynamics of
the load under the assumed conditions. The basis for the ToU
pricing model is established at 268.87 cents, reflecting the electricity
consumption throughout the day, as depicted by the load demand in
Figure 2. Furthermore, the peak load consumption during this
period is recorded at 6.80 kW.
On the same day, it is postulated that the house’s electricity
consumption occurs within the timeframe spanning the 78th to the
85th time steps. Consequently, electrical service is anticipated to be
intentionally disconnected from the residence at incremental
intervals corresponding to these time steps. Notably, during these
cut-off periods, both the television and personal laptop experience
forced shutdowns, enduring a blackout period of 105 min.
Additionally, specific measures are implemented to curtail energy
consumption during this scenario.
3.2 Scenario 2
In this particular scenario, the assumption is made that the
household load remains uninterrupted, thereby framing load
TABLE 2 Electric device energy consumption information.
Device Energy consumption in performance time
step (kWh)
Number of performance time
steps
Performance time
steps
Television 0.100 22 48–88
Laptop 0.004 10 22–96
Vacuum cleaner 0.080 2 40–56
Dishwasher 0.210 3 73–88
Washing
machine
0.080 2 49–72
Coffee maker 0.050 2 61–76
TABLE 3 ToU tariff in the performance time step.
Time step ToU (cents/kWh)
1–28 10.15
29–40 13.29
41–80 15.24
81–92 13.5
93–96 9.98
FIGURE 2
Building load curve during a day (scenario 1).
TABLE 4 Electric device performance time step based on scenario 2.
Device Performance time step
Without V2G With V2G
Television 50–62, 74–96 59–76, 80–96
Laptop 66–73, 81–96 68–75, 82–96
Vacuum cleaner 26–28, 94–96 26–27, 92–96
Dishwasher 71–72 64–65
Washing machine 64–66 67–68
Coffee maker 28–29 28–29
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management as an exercise focused on minimizing co-payments
throughout the day. Table 4 provides a comprehensive breakdown of
performance intervals for responsive appliances, suggested
performance intervals for non-responsive appliances, and the
corresponding consumption levels of the heating system within
each interval. These values are derived from solving the
optimization problem, considering both scenarios with and
without accounting for the capability of EVs to supply
household loads.
An analysis of the outcomes reveals that device functionality
adheres to the permitted time intervals during periods of reduced
tariffs, showcasing the effectiveness of load management in
minimizing costs. Table 5 elucidates the planning involved in
EV charging and discharging during different time steps. For
instance, during time steps 1–12, if the EV cannot supply the
load, it is charged to ensure a fully charged battery before
departing the residence. At time step 65, when the EV enters
the residence, the battery is 2.7 kWh full, prompting a recharge at
the end of each day to attain a pre-established battery charge level
of 3.9 kWh.
Considering the EV’s capacity to supply the household load, the
battery is strategically discharged during high tariff periods and
charged during low tariff periods (as detailed in Table 5), resulting in
an overall reduction in payment amounts. A comparative analysis
between the two cases, one accounting for the ability of the EV to
provide load and the other without, reveals a shared payment cost of
227.26 cents per day in the former and 238.16 cents per day in
the latter.
Furthermore, Figure 3 visually depicts the household load
dynamics both with and without the capability of energy supply
via the EV. Notably, when EVs possess the ability to supply the
household load, they are discharged during high tariff hours and
charged more during low tariff hours, leading to an increase in
the peak load at the end of the night, an effect absent in cases
where the load supply capability is lacking. This nuanced
exploration of load management strategies provides a
comprehensive understanding of the interplay between EV
capabilities and household load dynamics.
3.3 Scenario 3
In this scenario, the household load experiences a planned
interruption from time step 77 for a duration of 90 min,
spanning 6 time steps, until time step 84. Prior to this
interruption, up to time step 76, the household load
management results mirrored those observed in the
preceding scenario. At time step 77, the schedule undergoes
an update to accommodate the planned interruption, and
following the cessation of the load at time step 84, the
TABLE 5 Charging/discharging performance time step based on scenario 2.
Performance time step Charging load (kW)
With V2G
1–12 1.5
12–14 1.2
15–90 0
91–94 1.2
95, 96 1.5
Without V2G
1–51.5
6–80
9–20 1.2
21, 22 1.5
23–40 −1.2
41–60 −1.5
62–69 0
70–82 −1.2
83–96 1.5
FIGURE 3
Building load curve during a day (scenario 2).
FIGURE 4
Building load curve during a day (scenario 3).
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Dodo et al. 10.3389/fenrg.2024.1364904
household load is re-evaluated based on the most recent
information regarding device operations. Noteworthy in this
context is the consideration of a lost energy value for devices at
22 cents/kWh, surpassing the electricity tariff at any given time.
Consequently, in the second mode, encompassing time steps
77 to 84, the EV strives to supply maximum electricity to devices
that remain operational during this interval.
Given a switching time of 30 s, the cost associated with load
interruption is calculated at 1.8 cents. This deviation in outage cost,
as opposed to the cost calculated in the initial scenario, exemplifies
the impact of the proposed household load management
methodology on the reduction of outage-related expenses.
Figure 4 visually represents the household load diagram in this
third state, offering insights into the load dynamics during and after
the interruption.
Based on the outcomes, the expected household peak load in this
scenario is determined to be 6.29 kW, which is less than the peak
load observed in the initial case. Moreover, the payment fee registers
at 246.11 cents, slightly higher than that in the second case. This
outcome underscores that, despite the occurrence of load
interruptions and deviations from the optimal plan in terms of
paid costs observed in the second case, the household load
management strategy in the third case effectively prevented an
excessive increase in overall payment costs. This nuanced
analysis highlights the resilience and effectiveness of the
proposed methodology under challenging conditions,
contributing to a comprehensive understanding of its practical
implications.
3.4 ToU and IBR effect on smart home
demand load management
In this section, the effects of IBR pricing, as well as the
combination of IBR and ToU, which have been considered in
previous references, on the results of household load
management, are attempted to be studied. Table 6 displays the
results of the paid fee and peak load when comparing
different tariffs.
The results show that when the IBR is used, the load is
distributed throughout the day, and, as can be seen, the peak
load is significantly reduced. As a result, the payment fee and
peak load will be reduced by 7.9% and 16.4%, respectively, when
the ToU tariff is considered.
To summarize the findings, the first scenario is considered in
the ToU to be a state without household load management due to
the existence of power outages and the occurrence of outages for
various devices, as well as the uncontrollability of the load and the
impossibility of supplying the household load by the EV.
Regardless of the pricing method used, it results in the
imposition of high costs on the subscriber. In the second
scenario, unlike the first, it is not certain, owing to the result
of load management planning by defining performance intervals
for all devices, as well as checking the conditions of ability and
inability to supply the household load by the EV, which shows
that the supply ability is optimal. In this scenario, the load is
supplied by an EV.
In comparison to scenario 1, the result is that the co-payment
cost with the ToU tariff has been reduced even if the household
load cannot be supplied by the EV. Domestic peak load has also
declined since the first state. Outage is considered in the third
scenario, but unlike the first, there is optimal planning for load
management with the presence of the EV, which allows for a
reduction in the outage cost when compared to the first.
Furthermore, when compared to the first scenario, the
domestic peak load and the cost of paying with the ToU tariff
are lower in this case. However, despite the presence of load
interruption in this case, the payment fee is only marginally
higher than in the second scenario, owing to the effect of the
proposed household load management. Finally, the goal is to
compare different pricing on household load management
planning, demonstrating that the combination of IBR and
ToU results in a reduction in the payment cost and peak load
when compared to the ToU pricing method.
To validate the outcomes of various tariffs, a qualitative
comparison is conducted in this section. The multiple users and
load priority (MULP) scenario is utilized by Abushnaf et al. (2015)
to compare the outcomes of the multi-user algorithm and ToU. In
the initial instance of this scenario, a comparison is made between
the flat rate and ToU tariffs (without energy consumption control).
The alterations in Figure 5A constitute a summer day, while the
modifications in Figure 5B represent a winter day. The graphs
additionally illustrate the load for both flat rate and ToU tariffs,
along with the curves representing the costs incurred under each
tariff option (flat rate and ToU).
As shown in Figure 5, the cost of payment is reduced by 0.83%
when the ToU tariff is utilized during the summer, as opposed to the
flat rate tariff. Conversely, when compared to the ToU tariff, the flat
rate tariff will result in an 8% reduction in payment costs during the
winter season. Conversely, energy consumption remains constant
throughout the day and night, with no measures implemented to
transfer superfluous loads to more cost-effective ToU periods.
Nevertheless, during peak hours, the cost of electricity is
considerably greater under this tariff compared to the flat rate
tariff. This necessitates the promotion of a method for regulating
peak-hour electricity consumption.
In this particular situation, the algorithm for home energy
management transfers superfluous loads from periods of high
TABLE 6 Results of the implementation of building load management considering different electricity tariffs.
Tariff Energy cost (cent/day) Peak load (kW)
ToU 227.26 6.38
IBR 245.81 5.47
Integrated ToU and IBR 189.93 5.87
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demand to periods of lower cost with the intention of diminishing
electricity expenses and overall consumption. In order to accomplish
this, it is assumed that the demand limit of 1.7 kW during peak
hours is constant for both seasons. The home energy management
algorithm’s adaptable architecture enables subscribers to exercise
diverse discretion in order to mitigate their daily energy usage and
subsequently lower their electricity expenses.
4 Conclusion
The desire to reduce pollution, combat climate change, conserve
fossil fuels, save money, and improve power grid reliability has led to
the utilization of distributed production resources to build a smart,
environmentally friendly network. If the demand for fossil fuels is
not met properly, they will deplete or disappear. Thus, humanity
relies on renewable resources to meet this massive and growing
demand. Beyond production, discussing renewable energy
management is appealing because the building can save a
significant amount of energy through efficiency. This study
underscores the efficacy of the proposed optimization approach
for managing household load in smart homes, particularly in
integrating EVs into the energy management framework. The
numerical results obtained from various scenarios and pricing
models provide tangible evidence of the approach’s effectiveness
in achieving cost savings, peak load reduction, and improved
reliability.
This study addresses the escalating demand for electricity by
presenting a methodology that not only optimizes energy
consumption but also aligns with environmental sustainability
goals. The numerical outcomes reveal a 7.9% reduction in
integrated payment costs through the regulation of device usage
durations, demonstrating a tangible impact on financial savings.
EVs, hailed for their environmental compatibility and energy
efficiency, emerge as crucial components in the proposed
algorithm. The numerical results emphasize the potential of EVs
to contribute to load planning, displaying a 16.4% reduction in peak
loads and a 7.9% decrease in payment expenses when incorporating
IBR pricing. The dynamic pricing structures, including ToU and
IBR, exhibit significant impacts on load management. The
numerical analysis illustrates a 7.9% reduction in payment costs
and a 16.4% decrease in peak loads when IBR pricing is
implemented, underscoring the effectiveness of such pricing
models in influencing consumer behavior.
Scenario-based results demonstrate the adaptability and
robustness of the proposed algorithm under various
conditions, including uninterrupted loads, load interruptions,
andloadmanagementstrategies. The outcomes showcase not
only cost reduction but also the prevention of excessive increases
in paid costs, reinforcing the practical utility of the algorithm.
The findingsprovideclearinsightsintotheeconomicbenefits,
peak load reduction potential, and reliability improvements
achievable through the proposed optimization approach. The
results further underscore the importance of considering
dynamic pricing structures and strategically integrating
electric vehicles for a holistic and effective smart home energy
management system.
Data availability statement
The original contributions presented in the study are included in
the article/Supplementary material; further inquiries can be directed
to the corresponding authors.
Author contributions
YD: conceptualization, data curation, methodology, software,
writing–original draft, and writing–review and editing. AI:
conceptualization, methodology, software, validation,
writing–original draft, and writing–review and editing. MA:
conceptualization, data curation, methodology, software,
validation, writing–original draft, and writing–review and
editing. ZB: data curation, investigation, software,
writing–original draft, and writing–review and editing. AM:
FIGURE 5
Effect of using ToU transpiration on the consumption load and cost in (A) summer and (B) winter.
Frontiers in Energy Research frontiersin.org09
Dodo et al. 10.3389/fenrg.2024.1364904
data curation, formal analysis, methodology, software,
visualization, writing–original draft, and writing–review and
editing. AN: data curation, formal analysis, resources,
writing–original draft, and writing–review and editing.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. The authors
are thankful to the Deanship of Scientific Research at Najran
University for funding this work under the Research Priorities
and Najran Research Funding Program, grant code (NU/NRP/
SERC/12/9).
Conflict of interest
The authors declare 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.
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Nomenclature
Symbols and
variables
P
totalload
Total load of the building (kW)
P
load
Building load demand (kWh)
P
i,EVC
Charged power of the ith EV (kWh)
P
i,EVD
Discharged power of the ith EV (kWh)
C
UG
Cost of purchasing electricity from the grid ($/kWh)
E
CUG
Total cost of purchasing electricity from the grid ($)
EC
totalEV
Cost of charging and discharging all EVs ($)
EC
total
Total cost of the energy supply for the building ($)
SOC
min
Minimum state of charge for the EV battery
SOC
max
Maximum state of charge for the EV battery
SOCEV State of charge for the EV battery
SOC
EVmin
Minimum state of charge for the EV to be in the standby
mode
SOC
EVdischarging
State of charge for the EV to be in the discharge mode
C
EVC
Cost associated with charging 1 kW of power for
EVs ($/kWh)
C
EVD
Cost associated with discharging 1 kW of power for
EVs ($/kWh)
P
e
EV’s charging rate
Indices
iEV index
jTime step index
kIndex for charging periods
LIndex for discharging periods
nNumber of EVs
Abbreviations
EV Electric vehicle
EMS Energy management system
SoC State of charge
kW Kilowatt
kWh Kilowatt-hour
IBR Inclining block rate
ToU Time of use
V2G Vehicle-to-grid
UG Power from the utility grid
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