Evaluation of Technological Configurations of Residential Energy Systems Considering Bidirectional Power Supply by Vehicles in Japan

Abstract

To reduce CO₂ emissions in the residential and transportation sectors, distributed energy technologies, such as photovoltaic power generation (PV), stationary storage batteries (SBs), battery electric vehicles (BEVs), and vehicle-to-home (V2H) systems, are expected to be introduced. The objective of this study was to analyze the impact of the installed capacity of PV and SB, the type of vehicle, and their combination on the economic and environmental performance of the total energy consumption of residences and vehicles. Thus, this study developed a model to optimize the technological configuration of residential energy systems, including various vehicle types and driving patterns. The simulation results showed that it is more economically and environmentally efficient to install a BEV and a V2H system in households with longer parking times at the residence and to install an SB in addition to these technologies in households with shorter parking times at the residence. Furthermore, comparing a gasoline vehicle and an SB, the most economical combination, with a BEV and a V2H system and with a BEV, a V2H system, and an SB, estimated the carbon tax rate necessary for cost equivalence. The result indicated that the carbon tax rate needs to be increased from its current level.

1. Introduction

As floods, droughts, extreme heat, torrential rains, and other weather disasters have occurred in many parts of the world in recent years, climate change has become an important issue of concern. To mitigate the negative impacts of climate change, Japan and many other countries are supporting the development of new technologies to achieve carbon neutrality. In the transportation sector, in addition to improving the fuel efficiency of gasoline vehicles (GVs), clean energy vehicles such as hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel cell vehicles (FCVs) are being developed and introduced to the market. Furthermore, the use of distributed energy technologies, such as decentralized power sources and power storage devices, is expected in the residential sector as a measure to improve energy use efficiency and reduce CO₂ emissions. Photovoltaic power generation (PV) and stationary storage batteries (SBs) have already begun to be introduced into residences. The introduction of BEVs as an energy storage technology into residences has been attracting attention in recent years. Moreover, the introduction of a vehicle-to-home (V2H) system using a dedicated power conditioner that connects the BEV to the distribution board of the residence will enable a bidirectional power supply between the BEV and the residence, allowing the BEV to be used as a residential power storage system. This is expected to reduce CO₂ emissions through more efficient energy use in the residential and transportation sectors.

Several studies have evaluated the effects of PVs, BEVs, and V2H systems on the efficiency of electricity use in residences. Osawa et al. [1] calculated and compared the self-sufficiency rate, self-consumption rate, and CO₂ emissions in five cases, i.e., GVs, BEVs, GVs and SBs, BEVs and SBs, and BEVs and V2H systems. The self-sufficiency rate indicates the percentage of energy consumption of the residence that is covered by the amount of PV electricity generated, whereas the self-consumption rate indicates the percentage of PV electricity generated that is consumed by the residence. The comparison results indicated that the introduction of a BEV and V2H system has the potential to reduce CO₂ emissions compared with the case of BEVs alone. In addition, Akimoto et al. [2] investigated SB charging, BEV charging, and daytime operation of heat pump water heaters as operational methods for the self-consumption of PV surplus electricity and compared the effects of installing each device. The results showed that households with less frequent use of vehicles and shorter driving distances have higher self-consumption of PV surplus power and lower CO₂ emissions and annual costs, whereas households with more frequent use of vehicles and longer driving distances cannot fully consume PV surplus power. Kobashi et al. [3] assumed a cost decline in PVs, SBs, and BEVs until 2030 in Kyoto, Japan, and Shenzhen, China, and evaluated the economic and environmental advantages of PVs and V2H systems over PVs and SBs in Japan in the future. Nishioeda et al. [4] surveyed the PV generation capacity, private vehicle usage patterns, and electricity demand of residences in Tagajo, Japan, through questionnaires and on-site surveys. Then, based on those results, they estimated the amount of PV-generated surplus electricity and the amount of surplus electricity charged to BEVs.

Several studies have examined the optimal configuration of equipment installed in residences to improve the efficiency of residential energy systems. Higashitani et al. [5,6] optimized residential equipment configuration and operation, such as with SBs and gas water heaters, to minimize the annual costs in each case of GVs, BEVs, and combined BEVs and V2H installation. The results showed that it is more economical to combine V2H systems and SBs in households where BEVs are absent for long periods of time. New installations of heat pump water heaters were also shown to be effective. Erdinc et al. [7] developed a model to optimize PV and SB capacities in a residence with a BEV and V2H system. Wu et al. [8] optimized the SB size and battery operation strategy in a household with PVs and a BEV. The results showed that V2H systems have the potential to reduce daily electricity costs.

As described above, existing studies have evaluated the effectiveness of BEVs and V2H systems from various perspectives. However, these studies have focused on BEVs and have yet to analyze the implications of the introduction of other types of vehicles, such as HEVs, PHEVs, and FCVs. Moreover, vehicle types have not been included as design variables for optimization. A comprehensive study to optimize the technological configuration of vehicle types and PV and SB installed capacities, with or without V2H installation, considering the variety of vehicle types and the future price declines in these technologies has not yet been conducted. Various powertrains are being developed with the goal of achieving carbon neutrality and satisfying diverse consumer preferences, and consideration of the characteristics of these various vehicle types is important for the design of future energy systems.

Several previous studies have focused solely on the transportation sector, without considering the residential sector, and have examined optimal plans for the introduction of clean energy vehicles [9,10,11,12,13]. These studies have shown that the characteristics of each vehicle type, such as the energy source, CO₂ emissions, and cost, affect future installation plans. On the other hand, the introduction and operational effects of clean energy vehicles in conjunction with the transportation and residential sectors, such as V2H systems, have not been considered. Assuming a wide variety of vehicle options, there is currently no comprehensive discussion about whether they should be implemented alone or in combination with SBs or V2H systems. Such information would be valuable for developing future scenarios for vehicles and residential energy systems for more efficient energy use.

In this study, a model was developed to evaluate and optimize the technological configuration of residential energy systems that include various vehicle types. The objective of this study was to analyze the impact of the installed capacity of PVs and SBs, the type of vehicle, and their combination on the economic and environmental performance of the total energy consumption of residences and vehicles. A unique feature of this study is that it considered various types of vehicles with low environmental impacts in addition to the introduction of PVs, SBs, and V2H systems into residences. Each vehicle type has different battery capacities and energy sources, resulting in different operational effectiveness and optimal PV and SB capacities. Moreover, the cost and CO₂ emissions of the residential energy system for each technological configuration were calculated, and a multi-objective optimization model considering both economic and environmental aspects was developed. In recent years, consumer preferences have diversified, and some consumers are concerned not only with economic efficiency but also with environmental efficiency. In addition, the government of Japan needs to reduce CO₂ emissions to achieve carbon neutrality. When considering future environmental and energy policies for the diffusion of distributed energy technologies, it would be beneficial to develop a multi-objective optimization method that considers both environmental and economic aspects and provides results. Furthermore, multiple patterns of vehicle usage were assumed. The study examined how the time of vehicle absence during the day would affect the effectiveness of V2H systems and SB implementation.

Section 2 of this paper describes the new model for evaluating and optimizing the technological configuration of residential energy systems. Section 3 elaborates on the various prerequisite data for the model. Section 4 presents and explains the simulation results generated by the model, and Section 5 presents the conclusions of this study and discusses future prospects based on the discoveries made herein.

2. Methods

2.1. Framework

An overview of the analytical model constructed in this study is shown in Figure 1. The study consists of three categories of inputs: residential data, vehicle data, and equipment data. To define the residential data, the first step is to select the target region. In this study, the Kanto region of Japan was selected as a case study. Based on the selected target region, residential data such as electricity demand, solar radiation, and electricity prices were established. In addition, vehicle data and equipment data such as price, charge/discharge capacity, and durability years were used. Because vehicle absence time is considered an important parameter that can affect the economic and environmental performance of V2H systems, three patterns were established. Then, based on these inputs, the charging and discharging of electricity and the total energy consumption of the residence and the vehicle were calculated. As there are various possible configurations of vehicle type, PV capacity, SB capacity, and V2H installations, it is important to consider which types of technological configurations will be required in the future based on economic and environmental aspects. It is then necessary to provide information that will be valuable in making policy decisions for improving energy efficiency. Therefore, this study developed an optimization model to calculate the optimal configuration of vehicle type, PV capacity, SB capacity, and V2H installation with two objective functions: minimization of annual costs and minimization of annual CO₂ emissions. In this study, the genetic algorithm (NSGA-II) of Pymoo (version 0.6.1) [14] was used to perform multi-objective optimization. The mathematical formulas for the optimization model are described in the following section.

A flow chart of the residential energy system in the analytical model developed in this study is shown in Figure 2. In this model, electricity demand at residences and vehicle driving demand were considered. Several configurations of PV capacity, SB capacity, and vehicle type were assumed. The model allows for the analysis of changes in electricity demand and supply for each configuration. The time resolution of the model is 1 h. Operation on a total of 48 representative days by month, weekday/holiday, and weather (sunny/other) is considered, and this is multiplied by the number of days in each category for each region to calculate values for one year. Considering the vehicle types currently on the market that are compatible with the V2H system, we set two vehicle types capable of using the V2H system: PHEVs and BEVs. The electricity supply from these two vehicle types to the residence was assumed to be rechargeable and dischargeable only when the vehicles were at the residence.

2.2. Optimization Model

2.2.1. Objective Function

In this study, two objective functions were established, i.e., annual costs and annual CO₂ emissions (Equations (1) and (2), respectively).

Annual costs consisted of three costs: the installation cost of vehicles and equipment, the energy cost, and the carbon cost for CO₂ emissions (Equation (1)). The installation cost of vehicles and equipment, which is a fixed cost, was converted to a one-year cost by using the durable years and the interest rate (Equation (3)). The interest rate was set at 3%. The energy cost was defined as the sum of the annual purchase costs of electricity, gasoline, and hydrogen (Equation (4)). The electricity cost was defined as the difference between the cost of purchasing grid power and the profit from the sale of surplus power generated by PVs. The time resolution of the model is 1 h. Based on the operation on a total of 48 representative days by month, weekday/holiday, and weather (sunny/other), the values were multiplied by the number of days in each category and calculated for one year (Equations (4)–(8)). Electricity prices were set as amounts for daytime (7–22) and nighttime (23–6), respectively. In addition, electricity prices were set according to the energy mix of the Kanto region, based on the business scope of the major electric power companies in Japan. Many countries have introduced or are considering carbon taxes to achieve carbon neutrality. In Japan, a carbon tax has been partially introduced as a “tax for global warming countermeasures” [15]. Therefore, the carbon tax rate was multiplied by the CO₂ emissions in each case of technological configuration and added to the annual cost.

Annual CO₂ emissions were defined as the annual purchases of grid power, gasoline, and hydrogen multiplied by their respective CO₂ intensity (Equations (9)–(12)). The CO₂ intensity of grid power was set based on the energy mix of the Kanto region. The sale of surplus electricity to the grid was not considered a CO₂ reduction effect (Equation (10)).

$$min{AC}_k\left(T_{ik},{PVA}_{ik},{SBC}_{ik}\right)=\sum _i{T}_{ik}\left({TC}_{ik}+{EC}_{ik}+{CC}_{ik}\right)$$

(1)

$${minACE}_k\left(T_{ik},{PVA}_{ik},{SBC}_{ik}\right)=\sum _i{T}_{ik}\left({ELCE}_{ik}+{GACE}_{ik}+{HYCE}_{ik}\right)$$

(2)

$${TC}_{ik}={VTC}_{ik}\frac{r{\left(1+r\right)}^{{vtd}_i}}{{\left(1+r\right)}^{{vtd}_i}-1}+{PVA}_{ik}ID\frac{r{\left(1+r\right)}^{pvd}}{{\left(1+r\right)}^{pvd}-1}+{SBC}_{ik}\frac{r{\left(1+r\right)}^{sbd}}{{\left(1+r\right)}^{sbd}-1}$$

(3)

$${EC}_{ik}=\sum _{m,d,w}{ND}_{kmdw}\left({ELC}_{ikmdw}+{GAC}_{ikmdw}+{HYC}_{ikmdw}\right)$$

(4)

$${ELC}_{ikmdw}=\sum _h{ELU}_{ikhmdw}{ELP}_{kh}$$

(5)

$${ELU}_{ikhmdw}={GTH}_{ikhmdw}+{AEC}_{ikhmdw}$$

(6)

$${GAC}_{ikmdw}=\sum _h{AGC}_{ikhmdw}{GP}_k$$

(7)

$${HYC}_{ikmdw}=\sum _h{AHC}_{ikhmdw}{HP}_k$$

(8)

$${CC}_{ik}=CP\left({ELCE}_{ik}+{GACE}_{ik}+{HYCE}_{ik}\right)$$

(9)

$${ELCE}_{ik}={ELCI}_k\sum _{h,m,d,w}{ND}_{kmdw}\left(\left\{\begin{array}{c}{ELU}_{ikhmdw},{GTH}_{ikhmdw}\ge 0\\ {AEC}_{ikhmdw},{GTH}_{ikhmdw}<0\end{array}\right.\right)$$

(10)

$${GACE}_{ik}=GACI\sum _{h,m,d,w}{ND}_{kmdw}{AGC}_{ikhmdw}$$

(11)

$${HYCE}_{ik}={HYCI}_k\sum _{h,m,d,w}{ND}_{kmdw}{AHC}_{ikhmdw}$$

(12)

In the above, i is the technological configuration type [GV, HEV, PHEV, BEV, FCV, GV_SB_PV, HEV_SB_PV, PHEV_SB_PV, BEV_SB_PV, FCV_SB_PV, PHEV_V2H_PV, BEV_V2H_PV, PHEV_V2H_SB_PV, BEV_V2H_SB_PV]; k is the target year [2025, 2030]; h is the hour [0–23]; m is the month [1–12]; d is the day category [weekday, holiday]; w is the weather category [sunny, other]; r is the discount rate [%]; vtd is the durable years of each vehicle type [years]; pvd is the durable years of PV power [years]; sbd is the durable years of the SB [years]; T is the sales volume of vehicle type [units]; PVA is the PV installation area [m²]; SBC is the storage capacity [kWh]; AC is the annual costs [million yen]; ACE is the annual CO₂ emissions [t-CO₂]; TC is the installation cost of vehicles and equipment [million yen]; EC is the energy cost [million yen]; CC is the carbon cost for CO₂ emissions [million yen]; ELCE is the CO₂ emissions from electricity [t-CO₂]; GACE is the CO₂ emissions from gasoline [t-CO₂]; HYCE is the CO₂ emissions from hydrogen [t-CO₂]; VTC is the installation cost of each vehicle type [million yen]; ID is the installation density [kW/m²]; ND is the number of days in each category [days]; ELC is the electricity cost [million yen]; GAC is the gasoline cost [million yen]; HYC is the hydrogen cost [million yen]; ELU is the electricity usage [MJ]; ELP is the electricity price [yen/MJ]; AEC is the amount of electricity for additional charging while driving [MJ]; AGC is the consumption of gasoline used for driving [MJ]; GP is the gasoline price [yen/MJ]; AHC is the consumption of hydrogen used for driving [MJ]; HP is the hydrogen price [yen/MJ]; CP is the carbon tax rate for CO₂ emissions [yen/t-CO₂]; ELCI is the CO₂ intensity of electricity [g-CO₂/MJ]; GACI is the CO₂ intensity of gasoline [g-CO₂/MJ]; HYCI is the CO₂ intensity of hydrogen [g-CO₂/MJ]; and GTH is the amount of electricity purchased or sold between the residence and the grid power [MJ].

Equation (13) shows the energy balance. The hourly residential electricity demand and supply coincide. Considering residential electricity consumption, PV generation, vehicle charging/discharging, and SB charging/discharging, the system was set up to purchase electricity from the grid when demand exceeds supply and sell electricity to the grid when demand falls below supply. Moreover, based on previous research [16], hourly PV generation was calculated by multiplying the amount of solar radiation by the installed area, module conversion efficiency, temperature loss factor, and integration factor (Equation (14)).

$$\begin{array}{c}{GTH}_{ikhmdw}=\left({ELD}_{khmdw}+{HTV}_{ikhmdw}+{HTS}_{ikhmdw}\right)\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}-\left({PTH}_{ikhmdw}+{VTH}_{ikhmdw}+{STH}_{ikhmdw}\right)\end{array}$$

(13)

$${PTH}_{ikhmdw}={SR}_{khmdw}{PVA}_{ik}CE{TL}_{m}IF$$

(14)

In the equations, ELD is the amount of electricity consumed by a residence [MJ]; HTV is the amount of electricity flowing from the residence to the vehicle to charge the vehicle [MJ]; HTS is the amount of electricity flowing from the residence to the SB to charge the SB [MJ]; PTH is the amount of electricity generated by PVs [MJ]; VTH is the amount of electricity flowing from the vehicle to the residence due to the vehicle’s discharge [MJ]; STH is the amount of electricity flowing from the SB to the residence due to the SB discharge [MJ]; SR is the amount of solar radiation [MJ/m²]; CE is the module conversion efficiency [-]; TL is the temperature loss factor [-]; and IF is the integration factor [-].

2.2.2. Constraints

First, constraints were set for V2H and SB operations. Electricity generated by PVs is prioritized for consumption in the residence; if there is a surplus, it is charged to a SB or vehicle. Even after charging a SB or vehicle, if there is a surplus, electricity is sold to the grid. If the consumption in the residence exceeds the electricity generated by PVs, electricity is supplied from a SB or vehicle. If there is still a shortage after power supply from a SB or vehicle, electricity is purchased from the grid. When both V2H systems and SBs are introduced, priority is given to the V2H system. In other words, after charging/discharging from the vehicle by the V2H system, if there is still a surplus/deficiency of electricity, SB charging/discharging is performed. Moreover, the purchase of electricity from the grid and the sale of electricity to the grid cannot occur at the same time. Similarly, it is not possible to charge a SB and vehicle and supply power from a SB and vehicle at the same time. The two types of vehicles that could supply power to the residence were PHEVs and BEVs. The vehicles were designed to be able to charge and discharge electricity only when parked at a residence.

Additionally, the amount of electricity stored in the batteries of SBs, PHEVs, and BEVs must be less than or equal to their storage capacity (Equations (15) and (16)). The system was set to stop discharging to the residence when the amount of charge fell below the lower limit. However, during driving, there is no lower limit of discharge, and the system is set to charge the battery externally if the amount of charge is insufficient. With reference to previous studies [1,17,18], the lower limit of the charge for BEVs and PHEVs was set at 40% of the storage capacity. The lower limit of the charge for SBs was set at 20% of the storage capacity. The lower limit was set with the strategy of not affecting driving in the case of vehicles and of allowing use in an emergency in the case of SBs. Furthermore, the amount of electricity entering and leaving the storage battery each hour coincides with the change in the state of charge (SOC) of the storage battery (Equations (17) and (18)):

$${VSOC}_{ikhmdw}\le {VBC}_{ik}$$

(15)

$${SSOC}_{ikhmdw}\le {SBC}_{ik}$$

(16)

$${VSOC}_{ikhmdw}={VSOC}_{ikh-1mdw}+\left({HTV}_{ikhmdw}-{VTH}_{ikhmdw}+{AEC}_{ikhmdw}\right)/cmk$$

(17)

$${SSOC}_{ikhmdw}={SSOC}_{ikh-1mdw}+\left({HTS}_{ikhmdw}-{STH}_{ikhmdw}\right)/cmk$$

(18)

where VSOC is the SOC of the battery of each vehicle type [kWh]; VBC is the battery capacity of each vehicle type [kWh]; SSOC is the SOC of the SB [kWh]; and cmk is the conversion factor between megajoules and kilowatt–hours [-].

Then, two constraint conditions were set: the number of units and the PV area installed. In this study, the number of vehicles owned per household was assumed to be one (Equation (19)). Furthermore, the area available for PV installation depends on the area of the detached houses, which varies from prefecture to prefecture. Therefore, the area available for installation in the Kanto region was calculated by multiplying the average area of a detached house in each prefecture of the Kanto region (Ibaraki, Tochigi, Gunma, Saitama, Chiba, Tokyo, Kanagawa, and Yamanashi Prefecture) by the installable area ratio. The value was then used as the upper limit for the PV installed area (Equation (20)):

$$\sum _i{T}_{ik}=1$$

(19)

$${PVA}_{ik}\le PVUL$$

(20)

where PVUL is the upper limit for the installed PV area [m²].

3. Prerequisite Data

3.1. Technology Parameters

The values of the parameters for vehicles and equipment that do not change from year to year are shown in

Table 1. Technology parameters that do not change from year to year.

Technology	Parameter
Vehicle	Durability [year]	14
	Charging capacity [kWh]	6 (V2H), 3 (Others)
	Discharging capacity [kWh]	6 (V2H)
	Lower limit rate of discharge [%]	40
	Rate of initial SOC [%]	50
PV	Durability [year]	25
	Installation area [m2]	6, 11, 16, 21, 26, 31, 36, 41, 46, 48
	Maximum installable area (Kanto region) [m2]	37
	Module conversion efficiency [-]	0.18
	Temperature loss factor [-]	0.9 (December–February) 0.8 (June–August) 0.85 (Others)
	Integration factor [-]	0.7
SB	Price [million yen/kWh]	0.042
	Durability [year]	15
	Storage capacity [kWh]	4, 6, 8, 10, 12
	Charging and discharging capacity [kWh]	2.5
	Lower limit rate of discharge [%]	20
	Rate of initial SOC [%]	50

.

Considering the vehicle types currently on the market that are compatible with the V2H system, we set two vehicle types capable of using a V2H system: PHEVs and BEVs. Then, based on previous studies [5,6], the charging and discharging capacity was set to 6 kWh when the V2H system was used. Conversely, when charging without the V2H system, the charging capacity was set to 3 kWh.

Previous studies [17,18] have found that many users recharge BEVs when their SOC is approximately 30–35% because of concerns about their driving range. Therefore, 40% was set as the lower limit rate of discharge. If the SOC falls below 40%, discharge is stopped, and the battery is recharged when it is parked at the residence the next time. When driving, there is no lower limit of discharge. If the battery capacity becomes insufficient while driving, the vehicle is recharged externally using electricity or gasoline. In addition, as a response to emergencies, a lower limit discharge rate of 20% was also set for SBs.

The optimal levels of PV installation area and SB capacity are expected to vary depending on the technological configuration, such as which type of vehicle is used and whether a V2H system is used. Therefore, the PV installation area and SB capacity were considered as design variables, consisting of 10 and 5 alternatives, respectively.

Furthermore, distributed energy technologies are still in the early stages of market penetration, and prices tend to be relatively high, but the cost is projected to decrease in the future. Therefore, the prices of each vehicle type and PV generation were set based on the projected values in the target year based on previous research [9,19]. On the other hand, GVs and SBs were considered to have less price volatility than other technologies and thus were assumed to have fixed values [5,9]. Additionally, the fuel economy and battery capacity of each vehicle type were set based on previous studies [9,20], considering changes over time.

3.2. Solar Radiation Data and Electricity Demand Data

Data on the hourly values of the total solar radiation and weather for January–December 2018–2022 were obtained from the Japan Meteorological Agency [21]. Average values were then calculated by month, weekday/holiday, and weather category (sunny/other). Data for the Kanto region were taken from Tokyo, where the headquarters of Tokyo Electric Power Company, one of Japan’s major power companies operating in the Kanto region, is located.

For electricity demand and total floor area of houses, we used data actually measured in detached houses from the “Database of Energy Consumption in Houses” created by the Research and Study Committee on Energy Consumption in Houses of the Architectural Institute of Japan [22]. The study used data from nine residences in the Kanto region between October 2002 and March 2005. We also obtained data from the Japan Meteorological Agency [21] on total solar radiation and weather for hourly values at the same time of year when electricity demand was actually measured. The median electricity demand per area by month, weekday/holiday, and weather category was then calculated. The median electricity demand per area was then multiplied by the average of the total floor area in the Kanto region [23] to calculate the electricity demand of the average household by month, weekday/holiday, and weather category. The weather category for each future day was randomly assigned based on the percentage of weather categories for each month in 2018–2022. It was assumed that the incidence of each weather category in the future would be approximately the same as in the past. In this study, the total floor area was considered a characteristic of each detached house, and electricity demand per area was used. On the other hand, from the perspective of considering the overall trend in electricity demand for detached houses, this study did not analyze other characteristics in detail, such as the number of people in the household. Although these data are not necessarily the same as current data, this study used median values that exclude outliers, which we believe is useful for observing overall trends in the effectiveness of the linkage between vehicles and residences. It is also possible to change the input data as the survey data are updated. Thus, we do not believe that this circumstance will affect the usefulness of the model itself.

3.3. Energy Parameters

The energy sources are gasoline for GVs, HEVs, and PHEVs, electricity for BEVs and PHEVs, and hydrogen for FCVs. For PHEVs, electricity is used for BEV driving, and gasoline is used for HEV driving. This study also considered CO₂ emissions during both the driving phase of the vehicle and the production phase of the energy source.

Based on previous studies [9,24], the price of gasoline was calculated based on the price of crude oil plus petroleum, coal, and gasoline taxes and refining and distribution margins. With reference to the study of Osawa [9], the CO₂ intensity of gasoline was set to approximately 85.

The price and CO₂ intensity of hydrogen were set based on the assumption that blue hydrogen will be mainly used until 2029 and that green hydrogen will be mainly used after 2030. The price and CO₂ intensity of hydrogen vary depending on whether it is produced domestically or abroad. However, hydrogen is still a technology in the development and demonstration stages, and the cost and CO₂ intensity of hydrogen produced abroad and transported to Japan will vary greatly depending on the country of production. Therefore, in this study, the price and CO₂ intensity were set assuming domestic production and transportation [25,26,27,28,29].

The purchase price of electricity is affected by the mixture of power sources in the region. Based on previous work [24,30], this study calculated the power supply mixture for the Kanto region in 2020 and multiplied it by the cost of each power source to determine the cost of electricity generation. The cost of electricity generation was then deducted from the current daytime and nighttime electricity purchase prices, and the respective margins were calculated. The future electricity purchase price was then set by multiplying the Kanto region’s energy mix by the future cost of each power source and adding each margin. Based on a previous study [6], the sale price of surplus electricity was fixed at 8.5 yen/kWh. The CO₂ intensity of electricity was also calculated by multiplying the energy mix by the CO₂ intensity of each power source, based on previous work [31].

3.4. Vehicle Driving Patterns

It is assumed that the time spent parked at the residence affects the effectiveness of the V2H system. Therefore, based on previous studies [1,5,6], three driving patterns were developed for this study (

Table 2. Vehicle driving patterns.

Pattern	Day Category	Use	Parking Time at Residence	Mileage [km/h]
A	Weekdays	Shopping	0–9 12–23	10
	Holidays	Shopping	0–9 13–23
B	Weekdays	Commuting	0–7 18–23	10
	Holidays	Shopping	0–9 13–23
C	Weekdays	Shopping	0–9 12–23	10
	Holidays	Long-distance driving	0–9 17–23

). Pattern A assumes that the main purpose of use is shopping and that users make short-distance trips. Pattern B assumes that the main purpose of use is commuting on weekdays and shopping on weekends and holidays. The SOC is assumed to remain unchanged (constant) during the time the vehicle is parked at the office. Pattern C assumes that the main purpose of use is shopping on weekdays and long-distance driving on weekends and holidays, and that the vehicle is used for long-distance travel. The annual mileage values in 2025 calculated from the hourly mileage were approximately 8490 km for driving pattern A, 10,950 km for driving pattern B, and 13,250 km for driving pattern C. Osawa [9] estimated the average annual mileage to be 9601 km, which is close to the mileage values used in this study; thus, the values are considered to be a reasonable setting.

4. Results and Discussion

4.1. Optimal Technological Configuration

This section describes the calculation results of the optimal technological configuration for residential energy systems that include a variety of vehicle types.

Figure 3 shows the Pareto optimal solution for each driving pattern in 2025 and 2030. The Pareto optimal solution shown in the figure reveals that the curve shifts to the lower left from 2025 to 2030. This indicates that the reduction potential of annual costs and annual CO₂ emissions improves as the cost of vehicles and PVs declines and their installation becomes easier. By driving pattern, the curve shifts to the upper right for B and C compared to A. This indicates that for driving pattern A, in which vehicles spend more time parked at the residence, a bidirectional power supply could improve both annual costs and annual CO₂ emissions.

Table 3. Optimal technological configurations in 2025 and 2030.

Driving Pattern	Year	Technological Configuration Type	Installation Area of PVs [m2]	Storage Capacity of an SB [kWh]
A	2025	BEV_V2H_PV	31	-
	2030	BEV_V2H_PV	31	-
B	2025	HEV_SB_PV	21	12
	2030	BEV_V2H_SB_PV	36	4
C	2025	HEV_SB_PV	21	12
	2030	BEV_V2H_SB_PV	31	10

shows the technological configuration of the optimal solutions that consider both annual costs and annual CO₂ emissions in a balanced manner, which are shown in the rhombus in Figure 3. This optimal solution describes the solution with the lowest sum of values of each objective function of the Pareto optimal solution normalized and multiplied by an equal weight (0.5 each), respectively. For the technological configurations in each optimal solution in Table 3, the combination of BEVs and V2H systems was chosen in both 2025 and 2030 for driving pattern A, which assumes more time parked at the residence. It is assumed that PV power will not be deployed to the maximum extent because of its limited ability to consume and store the amount of electricity it generates. This is attributed to the fact that the BEV is not at the residence for some time. For driving pattern B, which is used for commuting on weekdays, and driving pattern C, which is used for long-distance driving on weekends and holidays, the combination of HEVs and SBs was selected for 2025. On the other hand, the combination of BEVs, V2H systems, and SBs was selected for 2030. This suggests that a V2H system would not be fully effective in 2025 because of the short parking time at the residence and that it would be more beneficial to introduce SBs in addition to HEVs. HEVs also have better fuel economy than GVs, which is advantageous when the driving distance is long. For 2030, BEVs were chosen instead of HEVs, and V2H systems and SBs were introduced. An SB was added as a way to charge the surplus electricity generated by PVs and discharge it for residential electricity consumption when the BEV is not at the residence. Furthermore, the price of PV power will decrease in the future. With the introduction of SBs, the area of PV installation increases, indicating that more energy can be produced and used at the residence.

Table 4. Optimal technological configurations for driving pattern A in 2030.

Case	Technological Configuration Type	Installation Area of PV [m2]	Storage Capacity of SB [kWh]
Minimized annual costs	GV_SB_PV	26	6
Minimized annual CO2 emissions	BEV_V2H_SB_PV	36	10

shows the technological configurations of the two optimal solutions for driving pattern A in 2030: the case with minimized annual costs and the case with minimized annual CO₂ emissions. In the case of minimizing annual costs, a combination of GVs and SBs was chosen. The area of the PV installation was 26 m², and the installed SB capacity was 6 kWh. The system is designed to generate and use only the amount of electricity needed for residential consumption, which is similar to the current residential energy system. On the other hand, in the case of minimizing annual CO₂ emissions, a combination of BEVs, V2H systems, and SBs was chosen. The installed PV area was 36 m², and the installed SB capacity was 10 kWh. It is assumed that the maximum amount of electricity is generated at the residence and then stored and used. The introduction of an SB is accompanied by an increase in the installed area of PVs compared with the case of BEVs and V2H systems in driving pattern A in 2030 (Table 3).

Figure 4 illustrates the comparison of annual costs and annual CO₂ emissions of GV, HEV, and PHEV_V2H_PV cases with the technological configurations of each optimal solution for driving pattern A in 2030. In all cases, the costs of installing vehicles, PVs, and SBs account for a large proportion of the total costs. The energy cost is lowered significantly by utilizing V2H systems and SBs. Furthermore, the combination of BEVs and V2H systems can significantly reduce annual CO₂ emissions compared with GVs and HEVs. Even when a GV is used, the combination of a GV and an SB results in a significant reduction in annual CO₂ emissions. When a BEV is replaced by a PHEV under the same condition of PV use, a BEV is superior in both annual costs and annual CO₂ emissions.

To achieve carbon neutrality, the government of Japan plans to change the energy source of vehicles from gasoline to electricity or hydrogen. However, the result in Figure 4 shows that it is reasonable for consumers to choose a combination of GVs and SBs in addition to PV installation when cost is a priority. To change the energy source, it is necessary to set up a carbon tax that would cost the same amount as the combination of GVs and SBs. The carbon tax rates at which the combinations of BEVs and V2H systems and BEVs, V2H systems, and SBs in driving pattern A in 2030 would cost the same as in the combination of GVs and SBs are approximately 24,000 yen/t-CO₂ and 46,000 yen/t-CO₂, respectively. The current tax rate of the “tax for global warming countermeasures” introduced in Japan is 289 yen/t-CO₂. Furthermore, the carbon tax rate at which the BEVs, V2H systems, and SBs in driving patterns B and C in 2030 would cost about the same amount as the GVs and SBs was more than around 60,000 yen/t-CO₂. It is clear that the operation of vehicles has a significant impact on the design of the carbon tax. Thus, it is essential to reform the existing carbon tax rate.

4.2. Supply and Demand for Electricity

This section describes the daily electricity supply and demand for several optimal solutions and their characteristics.

Figure 5 shows the daily electricity supply and demand in two cases in Table 3 and Table 4: a combination of BEVs and V2H systems and a combination of BEVs, V2H systems, and SBs in driving pattern A in 2030. First, in the case of BEVs and V2H systems, it is clear that most of the electricity is supplied by the BEV at night. During the day, the BEV is charged with surplus power according to the amount of PV electricity generated. However, during the time when the vehicle is out of the residence, the PV power generated is not fully consumed and is sold to the grid. In contrast, in the case of BEVs, V2H systems, and SBs, the surplus PV power is charged to the SB, even when the BEV is out of the residence. As a result, the entire amount of PV generation can be consumed in residence or charged to a vehicle or SB. No electricity is purchased from the grid, which contributes to the reduction in CO₂ emissions. On the other hand, the SB charges the surplus PV power generated during the day, becomes fully charged, and then does not operate. This occurs because the BEV discharges at night, utilizing its large-capacity battery to provide the electricity consumed by the residence.

Figure 6a shows the daily electricity supply and demand for BEVs, V2H systems, and SBs in driving pattern C in 2030. As in driving pattern A, the nighttime electricity consumption is covered by the discharge from the BEV. On the other hand, in driving pattern C, the driver goes for a long drive during the day on holiday, and the SB absorbs the surplus power from the PV generation system. The electricity required to charge the BEV after driving is partly covered by the discharge from the SB, which is charged during the day. It reveals that the economic efficiency of the SB improves when the daytime driving distance is long. On the other hand, due to the upper limit of the SB’s discharging capacity, additional electricity is purchased from the grid to recharge the BEV.

In the case of BEVs (Figure 5), there is ample capacity in the storage battery of the BEV. Given that the battery capacity of BEVs is approximately 50 kWh, it is clear that the battery capacity of the BEV is not fully utilized. Therefore, the electricity supply and demand were calculated for the case in which the BEV in Figure 5a is replaced by a PHEV (Figure 6b). As a result, it was found that the battery capacity of the PHEV is approximately 13 kWh, which is insufficient to cover the nighttime electricity demand or absorb all of the surplus PV electricity generated during the daytime. In other words, assuming the residential energy system alone, the battery capacity of the PHEV is insufficient. Thus, increasing the battery capacity of PHEVs would be beneficial for more efficient energy use in residential energy systems. When introducing BEVs, it is also important to consider schemes to utilize their battery capacity more effectively, such as by implementing a bidirectional power supply to not only residences but also offices and commercial facilities where people commute and shop.

5. Conclusions

This study developed a model to evaluate and optimize the technological configuration of residential energy systems, considering a variety of distributed energy technology options. The objective of this study was to use the model to analyze the impacts of the installed capacity of PVs and SBs, the type of vehicle, and their combination on the economic and environmental performance of the total energy consumption of residences and vehicles. The overall results showed that introducing V2H systems and SBs is expected to reduce annual costs and CO₂ emissions. This study underscores the importance of understanding the relationship between vehicle type, the capacity of SBs and PVs, and the effective combination of these technologies. The impact of residents’ vehicle driving patterns on choosing optimal technological configurations highlights the necessity for customized energy solutions tailored to individual lifestyles and needs. The extension of the model to households with different geographical and socio-economic backgrounds is expected to broaden our understanding of residential energy system optimization across various contexts. A detailed analysis revealed the following key findings:

• The optimal technological configuration, considering the balance between annual costs and annual CO₂ emissions, varied depending on the vehicle driving patterns. For residences with long parking times, the combination of BEVs and V2H systems was most effective in 2025 and 2030. In contrast, with long times outside the residence, the combination of HEVs and SBs was selected for 2025, and the combination of BEVs, V2H systems, and SBs was selected for 2030. In addition to BEVs, the introduction of SBs has increased the installed PV area and improved energy efficiency.
• When cost is a priority, it is reasonable for consumers to choose a combination of GVs and SBs in addition to PV power. For residences with long parking times, the carbon tax rates at which the combinations of BEVs and V2H systems and BEVs, V2H systems, and SBs in 2030 would cost the same amount as GVs and SBs are approximately 24,000 yen/t-CO₂ and 46,000 yen/t-CO₂, respectively. The current tax rate of the “tax for global warming countermeasures” introduced in Japan is 289 yen/t-CO₂. Increasing the carbon tax rate could be effective.
• Considering the daily electricity supply and demand, when the parking time at the residence is long, a large amount of electricity can be made self-sufficient by installing BEVs and V2H systems, or BEVs, V2H systems, and SBs in addition to PV power. Furthermore, even for households that spend a lot of time outside the residence, by introducing SBs, a portion of the electricity used to charge the BEV after driving could be covered by discharging electricity from the SB, which is charged with surplus PV electricity during the day. The introduction of additional SBs has increased flexibility in electricity supply and demand.
• PHEVs, while cheaper to purchase than BEVs, have less battery capacity, which was found to be insufficient for covering nighttime electricity demands or absorbing all the surplus electricity generated by PV systems during the daytime.

Furthermore, in contrast with previous studies, this study optimized for a variety of vehicle types in addition to PV installed area, SB capacity, and whether a V2H system is used. The additional installation of SBs showed the potential to increase energy efficiency by allowing an increased installed PV area and utilizing its energy for post-drive charging of BEVs. The study also clarified the carbon tax rate at which BEVs and V2H systems and BEVs, V2H systems, and SBs would have cost levels comparable with those of the most economically rational combination, GVs and SBs.

The optimization model of this study provides valuable insights for developing future scenarios for vehicles and residential energy systems for more efficient energy use. The study also contributes to the development of sustainable energy systems in relation to goals 7 and 13 of the Sustainable Development Goals (SDGs) [32]. However, it should be noted that there are uncertainties in the prerequisite data, such as fuel and vehicle prices. In this study, data on demand patterns for electricity were used for a specific region. However, electricity demand patterns may vary from region to region. In addition, although electricity demand per floor area was used as the basis for this study, the number of people in the household and other factors may also affect electricity demand. Vehicle driving patterns may also differ between urban and rural areas. A future issue for this study is to consider the diversity of households. In addition, electricity supply and demand, such as PV generation, and vehicle driving patterns are uncertain, and the model needs to be improved to account for these unexpected fluctuations. It is important to carry out further research to gain a deeper understanding of the impact of the optimal technological configuration of each residence on the electricity system at the local level. Furthermore, raising people’s awareness of environmental and energy issues and changing their behavior are also important. Educational programs have been developed at some universities in recent years, and their effectiveness has been confirmed [33,34,35]. Another challenge is using the findings of this research to educate potential personnel involved in the development of sustainable energy systems.