Economic Value Assessment of Vehicle-to-Home (V2H) Operation under Various Environmental Conditions

Abstract

The rise of electric vehicles (EVs) has initiated a significant transformation in both the transportation and energy sectors. With the increasing adoption of EVs, their interaction with the power grid is becoming more critical. A notable and innovative concept emerging in this context is Vehicle-to-Home (V2H) operations, which utilize the battery storage capabilities of EVs to meet residential energy demands. Our research provides a way of economically evaluating V2H operations under various environmental conditions including pricing, seasonal differences, and EV operations. The proposed model aids in understanding the optimal operation of V2H and identifying the factors that encourage its adoption. Furthermore, optimizing V2H use can promote renewable energy utilization, providing an additional solution to address its intermittent nature. The findings highlight the potential of V2H operations to contribute to more economically efficient energy systems, provided that supportive policies and adaptive technologies are in place.

1. Introduction

In recent years, the global electric vehicle (EV) population has seen unprecedented growth, driven by advancements in battery technology, increasing environmental awareness, and supportive government policies. According to recent reports [1,2], EV sales have surged, with millions of new electric cars being added to the roads each year. This growth is particularly notable in countries like China, the United States, and several European nations, where substantial investments in charging infrastructure and incentives for EV buyers have been implemented. The expansion of the EV market is not limited to passenger vehicles; electric buses, trucks, and two-wheelers are also becoming more prevalent. This rapid increase in EV adoption is a crucial step towards reducing greenhouse gas emissions and transitioning to a more sustainable transportation system. As automakers continue to innovate and economies of scale drive down costs, the EV population is anticipated to grow even more swiftly in the coming years, reinforcing its crucial role in the global effort to combat climate change [3,4].

The rise of EVs has initiated a significant transformation in both the transportation and energy sectors. With the increasing adoption of EVs, their interaction with the power grid is becoming more critical. A notable and innovative concept emerging in this context is Vehicle-to-Home (V2H) operations, which utilize the battery storage capabilities of EVs to meet residential energy demands. V2H technology enables bidirectional energy transfer between electric vehicles and residential buildings. This integration transforms an EV from a simple mode of transportation into a multifaceted energy resource. In V2H systems, EV batteries can be charged using electricity from the grid or renewable energy sources such as solar panels. The stored energy can then be discharged to power the home during peak demand periods, grid outages, or when electricity prices are high. This functionality not only enhances energy security and resilience for homeowners but also offers potential economic benefits by lowering electricity bills and optimizing energy consumption. V2H is often considered the first or simplest application of V2X technology. Unlike Vehicle-to-Grid (V2G), it does not require coordination among multiple vehicles or with a grid management entity. It also does not need a sophisticated building owner or energy management system, as Vehicle-to-Building (V2B) does [5]. Due to its relative simplicity, V2H technology should be more widely adopted by homeowners compared to other V2X technologies, promoting diverse uses of EV batteries.

Integrating V2H systems with homes equipped with photovoltaic (PV) installations presents a powerful synergy for sustainable energy management. In such setups, the PV system generates renewable energy during daylight hours, which can be used to charge the EV battery. The V2H system allows for the stored energy in the EV battery to be utilized within the household during periods of low solar generation, such as at nighttime or cloudy days, or during peak electricity demand periods. This dual capability not only maximizes the utilization of clean energy but also enhances energy resilience and independence for homeowners. By leveraging both solar power and V2H technology, households can significantly reduce their reliance on the grid, lower electricity bills, and minimize their carbon footprint. Additionally, this integration provides a reliable backup power source during grid outages, further ensuring energy security. As the adoption of PV and EV technologies continues to increase, integrated V2H and PV systems are poised to become fundamental to modern, sustainable home energy solutions.

The implementation of V2H operations provides several additional advantages [6]. First, it provides a decentralized energy storage solution that can mitigate grid instability and support the integration of renewable energy sources. Second, it enhances the energy independence of households by allowing them to rely on their own energy resources during grid failures or emergencies. Third, it offers a practical approach to managing peak load demands, contributing to a more balanced and efficient power grid while avoiding the need to construct large-scale power plants.

Despite its promising potential, the widespread adoption of V2H technology faces several challenges, even though it may be easier to implement than V2G technology. Technical barriers include the development of efficient bidirectional charging systems and the integration of V2H systems with existing home energy management systems. Additionally, regulatory and policy frameworks need to evolve to support the deployment of V2H operations, addressing issues such as grid compatibility, energy tariffs, and incentives for users [7]. Furthermore, consumer acceptance and awareness play a crucial role in the adoption of V2H technology, necessitating educational initiatives and demonstrative projects to showcase its benefits. In this work, we propose a method to optimize V2H operations across various environments from an economic perspective. This approach can aid policymakers in designing incentive policies or energy tariffs to promote V2H operations and the use of distributed energies, ultimately helping to reduce the costs associated with building large power generation plants. The contributions of this paper can be summarized as follows.

• It proposes a method for economically evaluating V2H operations, applicable to various environmental conditions.
• It suggests a way to operate V2H systems under various conditions, such as different pricing structures, PV generation levels, and peak demands, making it adaptable to different regions worldwide.
• Based on this method, economic benefits from government incentives and energy tariffs can be anticipated, thereby facilitating the effective design of policies that promote V2H operations.

The structure of this paper is as follows. Section 2 presents a literature review. Section 3 introduces the proposed V2H operation optimization model, and Section 4 describes the simulation model using this optimization. Section 5 presents the results, and Section 6 discusses the conclusions of this study.

2. Literature Review

Vehicle-to-Home (V2H) technology, a subset of Vehicle-to-Grid (V2G) systems, enables EVs to provide power to residential homes. This capability leverages the battery storage of EVs to optimize energy use, enhance grid stability, and potentially reduce energy costs. The optimal operation of V2H systems is critical to maximize benefits, considering various factors such as battery degradation, energy demand patterns, and economic incentives [8,9,10,11,12]. However, in this work, battery degradation is not considered because, while V2G and V2H operations lead to additional cycling, their overall impact on battery health is minor compared to the significant operational and economic benefits these systems offer [13,14]. This study focuses more on how to effectively assess the economic benefits under various conditions, including different tariff policies, billing systems, and demand patterns.

2.1. Energy Management and Optimization Strategies for V2H Operations

Several studies have focused on developing optimal energy management strategies for V2H systems. One significant approach involves the use of advanced optimization algorithms to manage the bidirectional energy flow between the EV and the home. These algorithms aim to minimize energy costs while considering constraints such as battery life, charging efficiency, and user driving needs. For instance, Guille and Gross [15] proposed a decentralized control strategy that optimizes V2H operations by predicting household energy demand and EV availability. This model employs a rolling-horizon optimization technique to adjust charging and discharging cycles dynamically. The V2H mode of operation is discussed in various studies for its high efficiency and ability to enhance the utilization of renewable energy sources, such as photovoltaic (PV) systems, in smart homes [16,17]. However, the efficacy of the V2H mode of operation has been validated primarily for linear loads. Smart homes, which include various appliances acting as constant power loads, predominantly feature nonlinear loads. To address this gap, the study in [18] examines nonlinear loads for the V2H operation of an EV charger. In this paper, we specifically consider the electric demand profile, which exhibits nonlinear patterns. The V2H system can also be utilized for peak shaving in load demand, as discussed in various studies [19,20]. However, it is unlikely that homeowners who operate V2H systems will pursue peak shaving without any economic benefits. To this end, a tariff system should be established to promote energy conservation during peak times by implementing policies such as time-based rates and tax incentives. In this context, further research is needed to effectively design incentive or tariff policies that promote the application of V2H systems. In our study, we aim to provide an effective method for evaluating new policies for V2H systems under various environmental conditions.

2.2. Smart Homes

The comprehensive review in [6] examines the integration of rooftop solar panels with EVs in smart homes. The study focuses on how V2H technology can be utilized to maximize the use of solar energy, reduce energy costs, and enhance energy independence. It also discusses the benefits of combining V2H with an ESS to create a more resilient and efficient energy system for residential applications. Villante et al. [21] explore the potential benefits of V2H charging systems using a dynamic simulation and optimization tool. The study highlights how V2H can significantly improve energy efficiency and reduce costs by optimizing the use of renewable energy sources and managing the energy flow between EVs and homes. The research also emphasizes the role of V2H in enhancing grid stability and supporting smart home energy management systems. Since V2H enables bidirectional power flow between the EV battery and the home’s electrical system, the EV battery can serve as a backup for the ESS [4,22,23]. This innovative design optimizes energy benefits, such as reducing electricity costs, by efficiently managing the charging and discharging process. The V2H mode is particularly valuable during periods of operational uncertainty caused by renewable energy sources such as PV and wind, as well as during peak hours, as the EV can discharge excess electricity to power the home. However, there is a lack of research on standard methods for economically evaluating new smart home designs. This study aims to propose such a standard method.

2.3. Technological and Market Barriers in V2H Operations

The uncertainty of the V2H system is primarily due to three factors: the variability of solar energy generation from rooftop installations, fluctuations in power demand, and the unpredictable operation of EVs. Auza et al. [3] provide a detailed examination of how uncertainties, such as fluctuating renewable energy output and varying household energy demands, are managed in V2H systems. They review various strategies and models used to predict and mitigate these uncertainties, ensuring efficient energy distribution and storage in smart homes equipped with V2H technology. In [24], effective management of power purchases from the grid during times of fluctuating electricity prices can significantly lower energy costs. A smart house equipped with renewable generation, as well as controllable and uncontrollable loads, is examined in [25]. The study models uncertainty in renewable generation and loads across various scenarios. In [26], the optimization of power generation management in a smart home is achieved through demand response (DR) and multi-objective optimization, considering the uncertainties in consumption and renewable generation. Meanwhile, Martinez-Pabon et al. [27] employ mixed-integer linear programming (MILP) to analyze the impact of utilizing controllable loads and electric vehicles in a home. Despite the promising benefits, several barriers hinder the widespread adoption of V2H systems. Technological challenges include the need for standardized communication protocols and bidirectional chargers capable of efficiently managing energy flows. Additionally, market barriers such as regulatory constraints, high initial costs, and limited consumer awareness pose significant hurdles. Adnan et al. [7] emphasize the importance of addressing these barriers through a combination of technological advancements, policy support, and consumer education. They argue that a comprehensive approach that includes stakeholder engagement from the government, industry, and consumers is crucial for the successful commercialization of V2H technology. Although many studies agree that various policy supports are necessary to overcome these challenges, there appears to be a lack of research on standard methods for evaluating such policies. Addressing these issues, we present a novel approach for assessing the economic benefits of V2H operations under various conditions, designed to help policymakers and consumers discuss effective policies for promoting V2H applications.

3. Modeling

The V2H system under examination in this study is fundamentally grounded in the periodic review framework, a common approach in inventory management. At each review period, two essential actions are considered: power exchange from an ESS and EV battery. Specifically, this framework necessitates the optimization of both grid power procurement and electricity allocation within the Energy Storage System (ESS) for each discrete time segment denoted as t. The primary aim of this optimization endeavor is to minimize expenditure on electricity, leveraging the capacity of EV batteries and separately installed ESS units within residential buildings for energy storage. Assuming there are no barriers to the charging and discharging power between the ESS and the EV battery, the EV battery can be regarded as an additional ESS when connected to the V2H system. The decision variables at time t are delineated as follows:

• Decision Variables

  -

  $E_t^g$: the amount of electricity purchased from the grid. In general, it is assumed not to be negative because electricity cannot be delivered to the grid in this model.

  -

  $a_t$: the amount of electricity charged or discharged from the EV.

  -

  $b_t$: the amount of electricity charged or discharged from the ESS.

At each time t, the state variables are considered as follows:

• State Variables

  -

  $x_t$: EV battery status of charge at t (kWh)

  -

  $y_t$: ESS status at t (kWh)

Here, we focus solely on the operational cost resulting from purchasing electricity from the utility grid. Therefore, the immediate cost function $c_t$ at time t is defined as follows:

$$c_t=E_t^g·r_t,$$

(1)

where $r_t$ is the price of electricity procured from the grid at t. Additional energy sources utilized to meet the electricity demand $D_t$ include PV generation $P{V}_t$ generated by solar panels installed on the roof, along with the discharge/charge of electricity from the EV and ESS, denoted as $a_t$, and $b_t$, respectively. In this model, we assume that the power generated by PV is not fed into the grid, representing an off-grid PV system. This model can be easily extended to include grid feeding by allowing negative values of $E_t^g$. However, our focus remains on the V2H system without considering profits from arbitrage. Since we do not account for outages, it is imperative to ensure that the demand $D_t$ is met. Thus, the demand constraint can be expressed as follows:

$$D_t=a_t+b_t+E_t^g+P{V}_t.$$

(2)

Considering the actions at time t, $a_t$, and $b_t$, the state variables in the subsequent period $(t+1)$ are determined while taking into account the efficiency rate, ${\rho }_v$ and ${\rho }_s$, both within the rage of $[0,1]$.

$$x_{t+1}=x_t-a_t/{\rho }_v:(\mathrm{EV}\mathrm{battery}\mathrm{constraint})$$

(3)

$$y_{t+1}=y_t-b_t/{\rho }_s:(\mathrm{ESS}\mathrm{change})$$

(4)

The EV battery and ESS each have defined minimum and maximum levels determined by their capacity. Additionally, their constant discharging and charging rates are crucial factors, representing the maximum power that can be extracted from and stored into the EV battery and ESS within a given time frame. The actions for the ESS and EV battery, $a_t$ and $b_t$, are restricted as follows:

$$max\left(x_t-\overline{x},-C_t^v\right)\le {\displaystyle \frac{a_t}{{\rho }_v}}\le min\left(x_t-\underline{x},C_t^v\right)$$

(5)

$$max\left(y_t-\overline{y},-C_t^s\right)\le {\displaystyle \frac{b_t}{{\rho }_s}}\le min\left(y_t-\underline{y},C_t^s\right)$$

(6)

where $\overline{x}$ and $\overline{y}$ represent the maximum levels, while $\underline{x}$ and $\underline{y}$ denote the minimum levels for the EV battery and ESS, respectively. Additionally, $C_t^v$ and $C_t^s$ stand for the maximum charging/discharging capacity for the EV battery and ESS. Note that negative values of $a_t$ and $b_t$ indicate the charging of electricity into the EV and ESS. The optimal value function at time t, denoted by $V_t(x_t,y_t)$, can be derived by considering dynamic programming recursion:

$$V_t(x_t,y_t):=\underset{a_t,b_t}{min}\left[c_t+V_{t+1}(x_{t+1},y_{t+1})\right]$$

(7)

This formulation is interpreted as follows: in the final stage (T), only discharging electricity from the EV and ESS is allowed, meaning $a_T>0$ and $b_T>0$. In all other stages, both charging and discharging actions are available and can occur concurrently. The optimal value function can be easily computed through standard backward recursion.

4. Numerical Study

This section assesses the managerial significance of electricity purchasing actions through computational analysis within the context of household electricity consumption in the V2H system. Based on data from Republic of Korea, we test the appropriateness of our model for assessing the V2H system from an economic perspective. Since environmental conditions are used as input resources in this model, data from other regions around the world can be easily applied to analyze V2H systems in these areas [28,29]. We investigate three issues: (1) how an EV battery can be effectively used to save on the electricity bill considering different scenarios of EV use for transportation; (2) the optimal size of the installed ESS when an EV battery is also utilized; and (3) the effectiveness of the installed PV in the V2H system.

4.1. Power Generated by PV Systems

To model hourly PV output, we consider various factors, including the size of the solar panels and monthly and hourly effects. Most homeowners who utilize PV systems choose a place on the roof. We consider 12 panels, each with a size of 1.6 m² and a capacity of 250 W, resulting in a total PV capacity of 3 kW, which is the most popular size installed on Jeju Island, Republic of Korea. The latitude and longitude of Jeju are 33.4996° N 126.5312° E, respectively [30]. To model the hourly PV output for each month, monthly and hourly changes are examined.

Figure 1 shows the average daily PV outputs of a single solar panel, which is used to obtain data for the monthly factor. The hourly factor for each month can be derived from real data on PV generation in Jeju Island in 2023, as shown in Figure 2. The higher values in April and October compared to July are due to more rainy days in July 2023 and the installation of more PV systems in September 2023. Weather conditions and changes in the capacity of PV systems can affect the PV outputs, and these are some reasons why historical data cannot be directly used for this numerical study.

We use the data from Figure 2 to obtain the hourly factor and the data from Figure 1 to obtain the monthly factor. First, we find the profile of hourly changes for each month, and then we consider the monthly factor to obtain the PV hourly output profile for each month as follows:

$$P{V}_t^m=h_t^m(n\times p_m),$$

(8)

$$h_t^m:={\displaystyle \frac{\mathrm{Average}\mathrm{hourly}\mathrm{PV}\mathrm{at}\mathrm{time}t\mathrm{of}\mathrm{month}m}{\mathrm{Average}\mathrm{daily}\mathrm{PVs}\mathrm{for}\mathrm{each}\mathrm{month}m}},$$

(9)

where n is the number of installed solar panels and $p_m$ is the average of daily PV output shown in Figure 1. Thus, the value of $h_t^m$ can be regarded as the relative ratio for hourly PV outputs of each month m.

In this numerical study, we assume a PV system with a capacity of 3 kW, consisting of 12 solar panels. The resulting PV output profiles for several months are displayed in Figure 3. Note that the maximum PV generations in Figure 3 are less than 2.5 kWh, while those in Figure 1 are around 250 kWh. Consistent with the data presented in Figure 1, higher PV generation and longer periods of PV production (greater than 0 kW) are observed during the spring and summer seasons.

4.2. A Model for Household Electricity Consumption

Assuming that the V2H system does not affect household electricity demand, the simulation data for household electricity consumption are derived from real data. Initially, we estimate the average monthly electricity consumption for a year. Subsequently, we analyze the monthly and hourly effects using real data to determine the relative ratios for each hour and each month. The hourly demand for each month and each hour is then calculated as follows:

$$\begin{array}{c}\hfill D_t^m={\displaystyle \frac{1}{30}}·\mu ·l^m·r_t^m\end{array}$$

(10)

where $\mu$ represents the mean monthly electricity consumption for a year, $l^m$ denotes the relative ratio of the monthly electricity consumption for the corresponding month m, and $r_t^m$ is the relative ratio of the hourly electricity consumption for the corresponding month m and hour t. We collected the electricity consumption data of households on Jeju Island in 2022 to determine the relative ratio $l^m$ for each month. Since this simulation considers a single-detached family house, the ratio is simply multiplied by the mean monthly consumption value to obtain the monthly consumption as follows:

$$\begin{array}{cc}\hfill l^m:=& {\displaystyle \frac{\mathrm{household}\mathrm{electricity}\mathrm{consumption}\mathrm{of}\mathrm{month}\mathrm{m}\mathrm{in}\mathrm{Jeju}\mathrm{island}}{\mathrm{total}\mathrm{household}\mathrm{electricity}\mathrm{consumption}\mathrm{in}2022}}\hfill \\ \hfill \mu ·l^m:=& \mathrm{electricity}\mathrm{consumption}\mathrm{for}\mathrm{month}m\mathrm{of}\mathrm{a}\mathrm{house}\hfill \end{array}$$

(11)

In this experiment, the average monthly consumption of a single house is set to 800 kWh (i.e., $\mu$ = 800 kW), which approximates the average electricity consumption of a house in the U.S. We choose U.S. data because most residences in Republic of Korea are apartment complexes while V2H systems are more suitable to single-detached homes. The ratio $r_t^m$ is calculated by dividing the average electricity consumption of each hour t by the hourly electricity consumption of month m.

Figure 4 illustrates the electricity consumption profile of a house. As anticipated, more electricity is consumed during the summer and winter seasons due to air conditioning. In Republic of Korea, gas heating is more prevalent, resulting in slightly lower electricity consumption in winter compared to summer. The hourly pattern of household electricity consumption typically varies throughout the day and is influenced by factors such as lifestyle, season, and location. Generally, a higher demand occurs early in the morning as people prepare for the day. There is lower electricity use in the afternoon when the house is often unoccupied. In the evening, electricity usage peaks due to the high use of lighting, electronic devices, and appliances such as TVs, computers, laundry machines, and dishwashers.

4.3. Electricity Price—Time of Use (TOU) Billing System

The time of use (TOU) billing system is used for this study. In Republic of Korea, the common practice is a progressive billing system, where electricity consumption is divided into multiple tiers, each with a different rate. Higher tiers incur a higher cost per kilowatt-hour (kWh), meaning the more electricity you use, the higher your average cost per unit of electricity. However, the progressive billing system does not support the promotion of V2H systems because the billing rate remains the same regardless of the time of use. In other words, unless the total monthly electricity consumption for a month changes, storing electricity for future use does not offer any cost benefits. Therefore, the TOU billing system, which varies rates based on the time of day and season, is more suitable for V2H systems. The TOU billing system used in this study is shown in

Table 1. The time of use (TOU) billing system in Republic of Korea.

Time of Use Period		Winter	Summer	Spring/Fall
Peak (6 h)	Time	09~12, 16~19	11~12, 13~18	11~12, 13~18
	Rate	$0.16/kWh	$0.16/kWh	$0.13/kWh
Standard (8 h)	Time	08~09, 12~16, 19~22	8~11, 12~13, 18~22	8~11, 12~13, 18~22
	Rate	$0.14/kWh	$0.14/kWh	$0.11/kWh
Off-Peak (10 h)	Time	22~08	22~08	22~ 08
	Rate	$0.10/kWh	$0.10/kWh	$0.09/kWh

.

4.4. Additional Factors and Considered Scenarios

An Energy Storage System (ESS) is also integrated with PV systems to more effectively utilize the intermittently generated electricity. In this experiment, the ESS has a capacity of 15 kWh, and the EV is equipped with a 40 kWh battery. Both the ESS and the EV battery have a charging and discharging capacity of 3 kW. If the EV is not used for travel, the combined ESS capacity available to the house totals 55 kWh. We consider several scenarios for the EV’s availability for V2H:

• Scenario 1: The EV is available for V2H from evening (7 p.m.) to morning (8 a.m.) (13 h).
• Scenario 2: The EV is available for V2H from 1 p.m. to 8 a.m. (19 h a day).
• Scenario 3: The EV is available for V2H from 7 p.m. to 1 p.m. (17 h a day).
• Scenario 4: The EV is available for V2H all day.

To model the status of an EV battery during travel, we assume that the EV requires a full charge to begin the journey and utilizes half of its battery capacity during the trip. In this experiment, the EV battery is fully charged to 40 kWh at the start of the journey and has 20 kWh remaining after completing the trip.

5. Numerical Results

To obtain the one-day results using V2H operations, we use the following setup: At 8:00 am, the initial time for this simulation, the EV battery is set to a full charge of 40 kWh, and the ESS is charged to half its capacity, which is 7.5 kWh. After the EV has completed its travel, we assume that the state of charge (SOC) of the EV battery is $50\%$ [29]. In most cases where electric vehicles are charged at home, the battery is typically charged overnight to ensure it is fully charged by morning, minimizing charging costs. Consequently, in this simulation, the SOC of the EV battery starts at 100% and returns to 100% after 24 h. In this section, we demonstrate how V2H systems operate under different driving patterns, weather conditions, and ESS capacities. Note that our model allows for the consideration of various environmental conditions in the economic evaluation of the V2H system.

5.1. Scenario-Based Results

In Figure 5, Figure 6, Figure 7 and Figure 8, the numerical results for four different scenarios in July are compared. Two lines represent PV generation and electricity demand, each in a different color. The line (orange) showing higher values during the daytime represents the hourly PV generation, while the line (blue) with relatively higher values in the evening indicates the electricity demand. The bar charts indicate electricity purchase (green) as well as charging or discharging of the EV (blue) and ESS (purple). Note that bar charts with values below zero indicate the charging of the ESS or EV.

Since this operation aims to minimize the cost of purchasing electricity from the grid, Figure 8 shows no electricity being purchased while the ESS and EV battery are used to meet the electricity demand. The daily electricity demand is around 30 kWh, and PV generation is around 20 kWh per day. Therefore, an additional 10 kWh of electricity is needed on average to meet the daily demand. This should be purchased during non-peak times to reduce costs and optimize the use of the ESS and EV. Figure 5 shows the results when the EV is fully utilized during the daytime (8 a.m. to 7 p.m.). The EV battery is not used for V2H because it needs to be charged for the next day’s trip. Figure 6 and Figure 7 present the results for EV trips during the afternoon and morning, respectively. Interestingly, the EV battery is not used for V2H in Figure 6, whereas V2H is utilized in the morning in Figure 7. This is because, in Scenario 3, there is no morning trip, allowing enough time for V2H operations and charging with PV generation before the next trip. In contrast, in Scenario 2, the EV is used for a morning trip and returns home at 1 pm with the battery at half capacity, requiring overnight charging for the next day, which results in no V2H operations. In all scenarios, the EV battery is primarily charged at night by purchasing electricity from the grid due to lower prices, while the ESS is mainly charged when PV generation exceeds electricity demand.

The positive bars in the graphs for the ESS and the EV battery represent discharging power to the house, illustrating feeding from EV to home. It is noted that while the ESS aids in cost reduction, the EV battery is seldom used for V2H operations to save costs, as evidenced by the infrequent positive bars for the EV. The findings can be summarized as follows:

• When the ESS is utilized and its capacity is sufficient to meet daily demand, V2H, which involves using power from EV batteries, is not necessary.
• Charging during periods of excess solar power generation or when electricity prices are lowest can maximize the efficiency of V2H operation.
• Without time-based rates, such as a TOU billing system for electricity, the use of ESS and EV batteries is primarily beneficial for storing excess solar power beyond demand, but this benefit is limited. Conversely, the ESS can be more effectively utilized to reduce costs with a TOU billing system.

5.2. Seasonal Effect

This section examines the seasonal impact on V2H operations, taking into account varying electricity demand and PV generation. By analyzing the four different scenarios across different seasons, we compare the cost reductions achieved through V2H operations. The cost reduction ($CR$) for a day from V2H operations can be calculated as follows:

$$CR=p_f\times \left(\sum _{t=1}^{24}{D}_t+\left|\sum _{t=1}^{24}{a}_t^{-}\right|\right)-\sum _{t=1}^{24}{r}_t{E}_t^g$$

(12)

where $p_f$ represents a fixed price, specifically the average hourly price of TOU rates, and $a_t^{-}=min[a_t,0]$. The first term represents the payment for electricity demand at a fixed rate, the second term accounts for the charging cost of the EV, and the last term covers the cost of using V2H operations. In other words, the first and second terms can be seen as the daily traditional costs without PV generation and the V2H system, while the third term is the daily cost with the V2H system. Therefore, the cost reduction is the difference between the cost of purchasing the entire daily electricity demand from the grid and the daily cost of V2H operations, excluding the initial investment cost.

Figure 9 illustrates the monthly cost reductions ($CRs$), calculated simply by $30\times CR$, for four scenarios across four seasons: January, April, July, and October. The results for April and October are similar because the electricity prices, PV generation, and demand patterns are close to each other. The slightly greater reduction in April is due to the higher PV generation compared to October, as shown in Figure 1.

In April and October, Scenario 3 results in greater cost reduction because it has a shorter travel duration and more time to store excess PV generation compared to Scenarios 1 and 2. Additionally, unlike Scenario 4, Scenario 3 allows for smart charging of the EV (charging at night or in the morning), providing further cost savings that Scenario 4 cannot achieve with its charging method.

In January, due to very limited PV generation as shown in Figure 3, Scenario 4, which involves the full use of the ESS and EV battery without any trips, results in a greater cost reduction. This is because the electricity stored at night can be used to meet a larger portion of the household electricity demand. In other words, in Equation (13), even though the second term is zero, the third term is comparatively low because more electricity is purchased and stored at a lower rate. The results will be similar to those in the scenario without PV generation.

In July, the high PV generation (shown in Figure 3) allows the EV battery and ESS to be charged more with surplus PV energy, as depicted in Figure 6 and Figure 7. Additionally, the price difference throughout the day is greater than in April and October, leading to a greater cost reduction. Compared to Scenario 4, Scenario 3 achieves a greater cost reduction because the EV battery is primarily charged at lower rates or with surplus PV energy.

5.3. ESS Effect

Considering different ESS sizes and scenarios, Figure 10 compares the monthly cost reductions to see the effect of ESS sizes. First, without an ESS, only the EV battery can be utilized to store electricity when PV generation exceeds demand and electricity prices are relatively lower. In this case, Scenario 1 (traveling from 8 a.m. to 7 p.m.) results in the lowest cost reduction due to the lack of time to store surplus PV generation. However, as ESS size increases, the cost reduction in Scenario 1 becomes greater than in Scenario 4 (no traveling) because EV batteries are not used for V2H operations in Scenario 4. For all the scenarios, when the capacity exceeds 15 kWh, the cost reduction does not increase because additional storage capacity is unnecessary. In fact, the ESS charges up to a maximum of approximately 14.8 kWh.

Similar to the results of the previous section, Scenario 3 results in the greatest cost reduction due to the relatively short EV trip, providing more opportunities to utilize V2H operations and store surplus PV generation. The low cost reduction in Scenario 4 is because the EV battery becomes unnecessary with sufficient ESS capacity. As a result, it is found that the ESS is more effective for home energy management (HEM) than V2H operations for achieving cost reduction. It is also observed that driving patterns significantly impact the effectiveness of V2H operations.

6. Discussion and Conclusions

In this research, we present a methodology for economically evaluating V2H operations under varying conditions including pricing, weather conditions, different sizes of EV batteries, and diverse EV operations. Our approach is designed to address the complexities and dynamic nature of V2H systems, offering a comprehensive analysis that integrates economic, technical, and regulatory factors.

Our findings demonstrate that V2H operations can significantly enhance economic efficiency for homeowners by reducing electricity costs and optimizing energy consumption patterns. By leveraging the bidirectional flow of energy between the home and the EV, households can benefit from lower electricity bills, particularly during peak pricing periods and high PV generation periods. The economic viability of V2H is highly influenced by the local electricity pricing structure, the availability of renewable energy sources, and the initial cost of V2H infrastructure. Our model does not consider the transfer of PV-generated power to the grid, which would enable homeowners to increase their profits by selling the power. In such models, an ESS and EV batteries for surplus PV generation are unnecessary, as the PV generation will be sold or consumed for profit through arbitrage. If a grid-connected system is to be considered, V2G or microgrid systems should be examined instead of V2H systems [31,32].

The economic advantages of V2H operations vary significantly by region and season due to differing electricity prices and climate conditions. Seasons with elevated electricity costs and frequent power outages benefit most from V2H implementations. Conversely, areas with low electricity costs or substantial investments in renewable energy infrastructure may experience less pronounced economic gains. Our analysis highlights the importance of tailoring V2H operations—considering PV capacity, demand pattern, ESS capacity, and electricity billing system—to regional characteristics to optimize their economic potential.

Policy frameworks and regulatory environments play a crucial role in the adoption and effectiveness of V2H operations. Incentives such as tax credits, subsidies, and feed-in tariffs can significantly enhance the attractiveness of V2H systems. On the other hand, regulatory obstacles and lack of standardization can hinder the deployment of V2H technologies. Our research emphasizes the need for supportive policies that encourage V2H adoption, simplify regulatory processes, and promote the integration of V2H systems with existing energy infrastructures.

Different EV operations, including varying driving patterns, battery capacities, and charging behaviors, have a profound impact on the economic performance of V2H systems. EVs with larger battery capacities and predictable driving schedules offer greater flexibility and potential for economic savings. Conversely, irregular driving patterns and smaller battery capacities may limit the benefits of V2H operations. Our novel approach accounts for these variables, providing a robust framework for evaluating the economic implications of diverse EV operations.

With advancements in battery technology, smart grid integration, and renewable energy adoption expected to further enhance their economic viability, the future of V2H operations is promising. Future research is needed to explore the long-term impacts of V2H systems on grid stability, energy markets, and environmental sustainability. Additionally, efforts should focus on finding ways to attract homeowners to participate in V2H systems.

Our research provides a comprehensive understanding of the economic evaluation of V2H operations. By considering a wide range of factors, including pricing, seasonal differences, and EV operations, we present a robust framework that can guide future developments in V2H deployment. The findings highlight the potential of V2H operations to contribute to more sustainable and economically efficient energy systems, provided that supportive policies and adaptive technologies are in place. However, if an ESS is installed and has sufficient capacity to meet the daily demand, the effectiveness of V2H operations becomes limited.

This work has several limitations that should be addressed in future research, such as not accounting for uncertainties in PV generation and EV driving patterns. Other environmental risk factors, such as battery degradation, grid reliability, and technical compatibility, also need to be considered. Additionally, the use of solar power generation and the charging and discharging of electric vehicle power were assumed in a simplified manner. More complex processes and additional constraints should be considered. A more detailed model is required to accurately represent the actual system. Network congestion issues, such as insufficient infrastructure and localized congestion, should also be taken into account [33,34]. Moreover, a long-term economic analysis considering initial investment cost and operating and maintenance expenses (O&M) is necessary to further advance V2H technology.