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Effects of electric vehicles (EV) on environmental loads with consideration of regional differences of electric power generation and charging characteristic of EV users in Japan
Applied Energy 71 (2002) 111–125 www.elsevier.com/locate/apenergy
Effects of electric vehicles (EV) on environmental loads with consideration of regional differences of electric power generation and charging characteristic of EV users in Japan Keisuke Nansaia,*, Susumu Tohnob, Motoki Konoc, Mikio Kasaharab a
Endocrine Disruptors and Dioxin Research Project, National Institute for Environmental Studies, 16-2 Onogawa Tsukuba Ibaraki, 305-8506 Japan b Graduate School of Energy Science, Kyoto University, Gokasho Uji Kyoto, 611-0011 Japan c Toshiba International Fuel Cells Corporation, 4-1 Ukishima-cho Kawasaki-ku Kawasaki Kanagawa, 210-0862 Japan
Abstract In order to evaluate the reduction effect of electric vehicles (EVs) on various atmospheric environmental loads, we have performed a life-cycle inventory analysis (LCI), including the installation of charging stations and regional, seasonal and temporal difference of the energy mix of electricity generation. For an EV converted from a small gasoline vehicle, a regional LCI analysis was carried out in the following steps: (1) location of the charging stations, (2) modeling of charging characteristics of station users, (3) calculation of temporal life-cycle emission intensities of CO2, NOx and SOx by region, season and day. Assuming that total traveling distance is 100,000 km, the electricity consumption rate is 0.119 kWh/km and the charging/discharging efficiency is 70%, the average life-cycle emission of CO2 for that EV was 3.6 t-C throughout Japan. However, if we took regional difference into account, the emission ranged over 70–160% of the average amount. It was revealed that the regional difference of the primary energy mix significantly affected the emissions of EVs during the operation phase. # 2002 Published by Elsevier Science Ltd. All rights reserved. Keywords: Life-cycle inventory; Electric vehicle; Regional characteristic
1. Introduction Air pollutants from an automobile contribute to not only regional environmental problems such as human-health effects but global environmental issues like climate * Corresponding author. Tel.: +81-298-50-2889; fax: +81-298-50-2880. E-mail address: [email protected] (K. Nansai). 0306-2619/02/$ - see front matter # 2002 Published by Elsevier Science Ltd. All rights reserved. PII: S0306-2619(01)00046-0
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change due to CO2 emissions. In Japan the number of automobiles is increasing due to their convenience, and the consumptions of gasoline and kerosene are growing even after the collapse of the bubble economy of 1990s. The transportation efficiency (fuel consumption per passenger-km) continues to go down since the late 1980s because of the increase of body weight [1]. This is attributed to the popularity of automatic cars and RVs (recreational vehicles), additional safety devices and so on. Currently the contribution of the transportation sector to the total CO2 emissions is approximately 20% in Japan [2]. Especially, emissions generated from passenger and freight cars dominates nearly 90% of the sector [2]. Therefore, an immediate reduction of CO2 emissions from automobiles should be essential for Japan to achieve the goal of greenhouse gases reduction as in the Kyoto protocol. At present, most carmakers are developing good mileage cars and ZEVs (Zero tailpipe emission vehicles), such as EVs (electric vehicles) or fuel cell vehicles. Unfortunately, a rapid spread of ZEVs is not expected in the short term because of their higher cost and poor performance in comparison with ICVs (internal combustion vehicles). However, their improvement effect on the atmospheric environment is very hopeful. LCA is a useful tool to evaluate quantitatively the reduction effect of EVs on environmental loads. To date, several LCA case studies about EVs have been reported [3–6]. It has been demonstrated that an indirect emission from electric-power plants on the charging phase accounts for 50% of its life cycle CO2 emission [6]. The emission factor related to electricity generation on the charging phase was usually based on the annual average energy mix. In Japan, ten electric companies supply electricity to their regions and have their own power stations to generate electricity. Therefore differences in the energy mix by distribution area result in regional differences of environmental loads related to electricity generation. Furthermore, the energy mix varies with time during a day even for an electric company. We have performed a LCI (life-cycle inventory analysis) of EVs considering the regional, seasonal and temporal differences of environmental loads (emission factors) during the charging phase of EVs.
2. Methodology An EV’s life-cycle is separated into four phases as follows; (1) manufacturing of the EV, (2) installation of a charging station, (3) charging at the station and home and (4) disposal at the end of its useful life. Environmental loads (CO2, NOx and SOx) at each step were estimated using LCI data calculated by the process and input–output approaches. We categorized EV users into groups and modeled the charging characteristics for each group. Emissions on the EV charging stage were calculated with the life-cycle emission factors (emissions per kWh) of the electricity-generation system and modeled charging characteristics of the users. We estimated the emission factors from the operation phase of power plants as well as those for the plant construction, management and maintenance. The regional, seasonal and temporal differences of the energy mix were considered for the operation phase.
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Quantitative analysis was also made for the influence of mid-night charging on life-cycle emissions of EVs. The details of the method are discussed in the following sections. 2.1. Manufacturing, installation of the charging station and scrapping Life-cycle CO2, NOx and SOx emissions of EV’s manufacturing and scrapping stage were presented in our previous studies [7, 8]. The emissions from installing two types of charging stations were estimated according to our previous procedure [6]. One station is equipped with a charger, stand and batteries to save electricity during the night and another one is without batteries. Table 1 summarizes the emission factors of each stage. 2.2. Modeling of the charging characteristic by EV-user groups 2.2.1. Electric-power demand by charging A home charge (charging of an EV at the user’s home during the night) is expected to be the most appropriate charging style. However, public charging during the daytime is indispensable to promote switching of ICV to EV. Then, the charging characteristics will become diversified. In earlier studies of modeling, the charging patterns, charging characteristics were assumed to follow the Gaussian distribution [9] or were modeled by applying the actual measurement data of current charging-stations [10]. We presented a charging characteristic model, taking into account differences of the start time of charging and the electric-power demand by EV users at public charging stations. The details of the modeling are schematically shown in Fig. 1. It was assumed that public charging stations are for charging 2 EVs for about 3 h [6]. Accordingly, the location of high usability is generally one where a driver can spend an hour or more doing something else while receiving a charge. For example, shopping centers, entertainment locations, etc. were chosen as eligible sites. We categorized EV users into 4 groups according to their travel purposes; (1) group-workers for commuting from home to workplace and vice versa, (2) group-workers for business activity, (3) group-homemakers for daily work like shopping, and (4) group-others Table 1 Emission intensities of EV manufacturing, scrapping and installation of charging stations for EVs Emission intensities Stage category
CO2 (t-C/EV)
NOx (kg/EV)
SOx (kg/EV)
EV manufacturing Scrappinga Charging stationb Charging stationc
1.09 0.002 0.45 0.17
5.28 0.006 2.06 1.00
4.33 0.005 1.77 0.69
a b c
Same as a compact passenger car. With a storage battery. Without a storage battery.
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Fig. 1. Modeling flow of daily electric power demand for charging an EV at a public charging station.
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for recreation or other purposes. Charging facilities were installed at parking lots of the workplace for groups (1) and (2), shopping centers, municipal offices or hospitals etc. for group (3) and amusement parks, art museums or hotels, etc., for group (4). In order to determine electric-power demand at a charging station, first, we investigated the temporal pattern of our daily activity from a report [11] and estimated the number of visits to the facility with a charging station at time x for 10 regions. With the assumption that a charging site was used by a group and the users charge their EVs batteries whenever they visit the facility, we determined the total number u(x) of visits at the sites by each group. Then a probability p(x) of starting to charge at time x is given by Eq. (1) for each group. The regions consisting of prefectures correspond to the distribution areas of 10 electric companies, as shown in Table 2. ð 24 pðxÞ ¼ uðxÞ=
uðxÞdx
ð0 4 x 4 24Þ:
ð1Þ
0
The probability N(t) of starting to charge until time t is determined as (see Fig. 1A) ðt NðtÞ ¼ pðxÞdx: ð2Þ 0
N(t) also shows the number fraction of EVs. Accordingly, the probability Nc(t) on charging is expressed as Nc ðtÞ ¼ NðtÞ Nðt TÞ;
ð3Þ
where T represents a charging time (see Fig. 1B). An electric-power demand D(t) at a charging station is calculated by multiplying Nc(t) by the supply power f(kW) (see Fig. 1C). DðtÞ ¼ fNc ðtÞ:
ð4Þ
Table 2 Ten regions used in this study and their prefectures Region
Prefecture
Hokkaido Tohoku Kanto Chubu Hokuriku Kansai Chugoku Shikoku Kyushu Okinawa
Hokkaido Aomori, Iwate, Miyagi, Akita, Yamagata, Fukushima, Niigata Ibaraki, Tochigi, Gunma, Saitama, Chiba, Tokyo, Kanagawa, Yamanashi Nagano, Gifu, Shizuoka, Aichi, Mie Toyama, Ishikawa, Fukui Shiga, Kyoto, Osaka, Nara, Hyogo, Wakayama Tottori, Shimane, Okayama, Hiroshima, Yamaguchi Tokushima, Kagawa, Aichi, Kochi Fukuoka, Saga, Nagasaki, Kumamoto, Ohita, Miyazaki, Kagoshima Okinawa
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D(t) was determined in the 10 regions for weekdays and holidays according to 4 EV-user groups. Then 80 patterns of D(t) were presented in total. On the other hand, we assumed that the home charge begins at 11 pm in every region and denote an electric-power demand at home as H(t). 2.2.2. Setting a charging characteristic We set a charging time, charging frequency and electric-power demand at a charging station according to EV-user types. The charging time was set as follows. A daily travel distance for the EV was given by a mileage (km/l) times daily fuel consumption (kl) of ICV with the assumption that there is little difference in the distance travelled between EV and ICV. The daily travel distance of group-workers (commuters), group-homemakers and group-others was set at 36.75 km according to the statistics [12]. The actual data on the daily trip distance L0 of the EVs for business use in an urban town [13] were applied to estimate the distance L travelled by group workers (business). L was calculated as 53.45 km by Eq. (5) under the assumption that it relates to the magnitude of an inhabited city area. pffiffiffiffiffiffiffiffiffiffi ð5Þ L ¼ L0 S=S0 ; where S represents the mean value for inhabited city areas throughout the country and S0 is the city area where L0 was measured. The specific distance and charging efficiency of the EVs were set at 0.119 kWh/km and 70%, respectively [14]. The electric-power demand per charge was calculated from the travel distance, mileage and charging efficiency. Charging time by groups was determined by dividing supplied electric power (12 kW=200 V60 A) [15] of a charging station into each user’s power-demand. The charging frequency, that is, how often an EV user utilizes a charging station per week, was given by a census [11]. We also assumed that a home charge was started from 11 pm of the day when a charging station had not been used and the supplied electric power was 1.5 kW (=100 V15 A). Here, we summarize the charging characteristics according to EV-user groups. A distribution loss of each electric company was taken into account in converting electric power on the user side to that on the supplier side [16]. Group-workers (commuters)—they use a charging station at their companies on weekdays and do home charges only on weekends. Group-workers (business)—this group charges EV batteries at only the charging stations of the companies. Due to the cheaper price of off-peak electric power than that during daytime, it is expected that they charge up the batteries during the nighttime. Therefore, we assumed the required daily power demand was fulfilled with both daytime charging and only nighttime on weekdays. Group-homemakers, group-others—weekly frequency of using charging stations is provided by data [11] indicating how many times these groups visit the facilities eligible for charging sites. They use home charges on the days when they do not visit the charging stations.
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These charging characteristics are shown in Table 3 and daily variations of electric-power demands for charging (excluding home charges) on weekdays in the Kansai area are illustrated in Fig. 2 according to the above 4 groups. 2.3. Emission intensity at the charging stage We compiled the emission intensities based on net electricity distributed to users e(t) (emission/kWh) of air pollutants during the charging phase with consideration of the differences between regions, seasons, day and time. Table 3 Parameters of the charge conditions by EV-user groups EV user group Parameters
Workers (commute)
Workers (business)
Homemakers
Others
Travel distance (km/day) Electric-power demand (kWh/day) Charging time at a charging station (h/day) Frequency of charging station use (times/week) Frequency of home charging (times/week) Charging time at home (h/day)
36.75 6.25 0.52 5 2 4.17
53.45 9.09 0.38 5 5a 3.03b
36.75 6.25 0.52 3.37 3.63 4.17
36.75 6.25 0.52 2.58 4.42 4.17
a b
Charge up at company during night-time. By supplied electric power.
Fig. 2. Electric-power demand curve for weekdays at charging stations in the Kansai area.
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First of all, typical 80-day load curves by 10 regions, 4 seasons and weekday– weekends were selected by statistics [16] and an interview with the electric companies. Next, direct air pollutant emission intensity ak from fuel burning of the power plant k (coal, petroleum, LNG; ak of hydro and nuclear power plants are zero) and indirect emission intensity bk with construction and maintenance of the plant (coal, petroleum, LNG, hydro, nuclear) were calculated using the method [17,18] and coefficients [8] of the earlier studies. Sums of the direct and indirect emission intensities by plants were used to estimate total hourly emission intensities. Finally, according to region, e(t) was determined by dividing the total of the hourly electric-power demand qk(t) of each plant into the sum of direct and indirect emissions of it, as in Eq. (7). eð t Þ ¼
5 5 X X ðak þ bk Þqk ðtÞ= qk ðtÞ: k¼1
ð7Þ
k¼1
There is some exchange of electricity between the 10 electric companies and other independent electric companies which generate and supply electricity to the 10 electric companies. The effect of the exchange of electricity was included in estimating e(t).
3. Results and discussion 3.1. Regional and temporal difference of emission intensity Regional CO2 emission intensities e(t) during charging on a summer weekday are illustrated in Fig. 3. Air pollutant emissions with regard to construction and
Fig. 3. Regional comparison of CO2 emission intensities for charging during summer weekdays.
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maintenance of power plants are shown in Table 4 and the seasonal weighted averages of the emission intensities are provided in Table 5a–c (weight of 5 for a weekday, that of 2 for a weekend). Regional differences of e(t) were responsible for the energy mix, fuel type and operating condition in each region. Annual average of CO2 emission intensity obtained in this study was comparable with that of Ref. [17]. Values of CO2-e(t) of the Okinawa and Chugoku areas are higher than those of others, since there are only fossil-fuel fired plants in Okinawa and the contribution of fossil-fuel power to total electricity generation is relatively large in Chugoku. In the Tohoku area, the exchange of electricity with middle-scale electric companies is higher than in other areas. The high CO2-e(t) of the Tohoku area is attributable to the operation of fossil-fuel power generations in most of the middle-scale electric companies. It was confirmed that there was about 3.6 times difference in CO2 emission intensities among the 10 regions. Table 4 Air pollutant emissions as a result of construction and maintenance of each power plant Emission intensity
CO2 (g-C/kWh) NOx (g/kWh) SOx (g/kWh)
Fossil fuel power generation Coal
Petroleum
LNG
25.9 0.47 0.21
12.1 0.20 0.08
41.1 0.07 0.02
Nuclear
Hydro
5.6 0.002 0.001
4.7 0.03 0.01
Table 5 Regional and seasonal air pollutant emission intensities during the charging phase Seasons
Hokkaido Tohoku Kanto Chubu Hokuriku Kansai Chugoku Shikoku Kyushu Okinawa
a. CO2 emission intensity (g-C/kWh) Spring 109.6 136.9 82.8 Summer 121.4 155.3 87.1 Autumn 136.3 147.5 91.9 Winter 134.2 148.6 95.0 Annual average 125.4 147.1 89.2
101.5 120.0 99.4 130.9 113.0
105.7 100.4 116.8 148.0 117.7
66.7 81.1 65.9 76.0 72.4
159.1 176.7 167.2 179.1 170.5
64.5 88.0 76.7 126.3 88.9
80.2 109.6 86.2 118.5 98.6
265.5 253.0 254.8 257.8 257.8
b. NOx emission intensity (g/kWh) Spring 0.52 0.42 Summer 0.57 0.49 Autumn 0.64 0.47 Winter 0.63 0.46 Annual average 0.59 0.46
0.17 0.18 0.19 0.20 0.18
0.16 0.19 0.16 0.21 0.18
0.31 0.29 0.34 0.43 0.34
0.12 0.15 0.13 0.13 0.13
0.44 0.50 0.47 0.51 0.48
0.30 0.41 0.36 0.59 0.41
0.18 0.24 0.19 0.26 0.22
0.77 0.74 0.74 0.75 0.75
c. SOx emission intensity (g/kWh) Spring 0.59 0.32 Summer 0.65 0.38 Autumn 0.71 0.36 Winter 0.71 0.35 Annual average 0.66 0.35
0.12 0.13 0.14 0.14 0.13
0.14 0.17 0.14 0.18 0.16
0.32 0.30 0.36 0.46 0.36
0.08 0.10 0.09 0.09 0.09
0.33 0.38 0.36 0.40 0.37
0.29 0.41 0.35 0.59 0.41
0.18 0.26 0.20 0.28 0.23
1.86 1.84 1.84 1.85 1.85
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The CO2-emission intensity in the Kansai and Kanto areas became smaller at night, because nuclear-power generation contributed a high share in these regions. For example, the contributions of nuclear power to annual electric-power generation in the Kansai and Kanto areas were 52 and 46%, respectively in 1998. The maximum of e(t) in the Kansai area was 1.7 times higher than the minimum in a day. The nuclear-power share was approximately 45% on a summer weekday in the Shikoku area and the contribution of fossil-fuel power became small during nights. Accordingly, e(t) of the Shikoku area had about a 2.3 times difference in a day. The Hokuriku area showed the same temporal variation as the Shikoku area, because of a higher contribution of hydropower. However, e(t) of some regions like Chugoku, Tohoku, Chubu or Hokkaido showed only a small temporal change. The share of coal-fired power generation at night was higher than in the daytime in the Okinawa area. Hence, nighttime electricity use in those areas is not necessarily linked to mitigating the CO2 emission. In the cases of NOx and SOx emission intensities, regional and temporal differences depended on the energy mix, fuel type and removal technology (i.e. cover ratio of desulfurizers and denitrification facilities in power stations and their efficiencies). Annual average of NOx emission intensities varied by a factor of 5.8 among the regions, while the difference of SOx emission intensity was extended to 20.3 times. This result can be explained by the following facts. There was no LNG thermal power plant in the Hokkaido and Shikoku areas and domestic coals with a higher sulfur content are used in the Hokkaido area. Therefore, SOx emission intensities of these areas were higher than those of other areas. Regarding temporal variations of NOx and SOx emission intensities, the Shikoku area had the largest difference of them, that is, they were 2.4 times different during a day. 3.2. Emissions during the charging phase by user type Daily air-pollutant emissions Es and Eh from EVs, charging at a charging station and home respectively, were calculated from D(t), H(t) and e(t) as in the following equations. ð 24 DðtÞeðtÞdt:
Es ¼
ð8Þ
0
ð 24 Eh ¼
HðtÞeðtÞdt:
ð9Þ
0
For instance, if we assume that EV users drive 100,000 km in the Kansai area and charge EV batteries according to the charge conditions as shown in Table 3, the lifetime CO2 emissions of group-workers (commuters) on the charging stage was 1.4 t-C. This was the largest value among 4 groups, while the emission of group-others was 1.2 tC of the minimum. It was found that the difference of charging characteristics between groups gave a variation of lifetime emissions during the driving phase even if the mileage and lifetime driving distance were common to all users. On a percentage basis, CO2, NOx
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and SOx emissions on the charging phase had 15, 15 and 13% differences among users, respectively. 3.3. Regional comparison of LCIs of EVs Figs. 4–6 show regional LCIs of EVs including emissions during the driving phase and also for car body production, installation of the charging station and scrapping the car. Emissions during the driving phase were arithmetic means of each user’s emission in each region. Emissions of gasoline vehicles (GVs) were adopted from our previous study [6] and emissions for the mining and transportation phase of petroleum were added to cover the same system boundary as for EVs. The average life cycle CO2 emission of an EV was estimated to be 3.6 t-C. The emission amount ranged from 72 to 165% of the average among the 10 regions. The least emission region was the Kansai area i.e. 2.6 t-C, while the highest-emission area was Okinawa at 5.9 t-C due to the operation there of only thermal-power plants. The amounts of CO2 emissions in the Chugoku and Tohoku areas were large among regions having non-thermal power plants because the ratio of thermal generation to total power generation was high. Life-cycle CO2 emission of a GV was about 9.3 t-C and it was confirmed that the emission of EVs was in the range of 26–64% of GV emissions. In the case of NOx, the average life-cycle emission was 13.1 kg/EV and the regional difference was greater than that for CO2. The difference with the average emission ranged from 66% of the Kansai area to 151% of the Okinawa area. Although the share of thermal-power generation in the Hokkaido area was lower than those in the Chugoku and Tohoku areas, Hokkaido was the area of the second largest NOx
Fig. 4. Regional comparison of life cycle CO2 emission of EVs.
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Fig. 5. Regional comparison of life cycle NOx emissions of EVs.
Fig. 6. Regional comparison of life cycle SOx emissions of EVs.
emission with EVs. There was no LNG power plant in the Hokkaido area and the share of coal power plants was high. Therefore, the NOx-emission intensity was larger than for other areas as shown in Table 5. Life-cycle NOx emission of EVs was in the range of 20 (8.6 kg/EV) to 45 (19.8 kg/EV) % of GV emissions (43.9 kg/GV).
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Fig. 7. Variations of life-cycle and charging-phase emissions by using off-peak electric power.
The regions of higher SOx emission were Okinawa, Hokkaido and Shikoku. The large SOx-emission intensity of the Shikoku area is attributable to no LNG thermal power plants in Shikoku as well as Hokkaido. The average life-cycle SOx emissions of EVs were 12.2 kg with the regional difference of 51 (6.2 kg)–312% (38.1 kg) of the mean. It was confirmed that the life-cycle emission of EVs was larger than that of GVs in some regions. This result suggests the possibility of a SOx emission increase by spreading EVs in a specific region. The replacement of a diesel vehicle (DV) by an EV is effective in mitigating SOx emissions. The regional difference of EV emissions was 11–64% of DV emissions (59.1 kg/DV). 3.4. Effect of using off-peak electric power on emission reduction It was reported that the use of off-peak electric power for charging EV batteries contributed to a constant electric power demand [10]. Assuming that a charging station utilizes electric power saved in a storage battery during the night, we examined the effect of using off-peak electric power on air pollutant emissions. Emissions from the installation of a storage battery caused the increase of each environmental load of a charging station as shown in Table 1. Fig. 7 shows that the ratios of environmental loads with the use of off-peak power to those without it during the driving phase and on the life cycle basis in the 10 regions. It was revealed that off-peak power use decreased the emission amounts during the driving phase in every region except for the Okinawa area, where a share of coal-fired power plants in the electricity generation is high during the night.
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Especially, in the Hokuriku and Kansai areas, the emissions with the use of off-peak power went down to 80% of the emissions without it. However, from the life-cycle point of view, some regions showed that the emission amount with use of off-peak power was larger than the emission without it due to the increase of emission from the installation of a charging stations. This result greatly depends on the methodology to allocate emission from building the infrastructure to an EV. We applied the method in our previous paper [6]. Improvement of the allocation method should be required to achieve a better comparison of environmental advantages between GVs and EVs.
Acknowledgements The authors would like to thank 10 electric companies in Japan for furnishing data. This work is supported by the ‘‘Distributed Autonomous Urban Energy System for Mitigating Environmental Impact’’ Project (JSPS-RFTF97P01002) under the ’’Research for the Future‘‘ Program by the Japan Society for Promotion of Science.
References [1] Moriguchi Y, Kondo Y, Nansai K, Tohno S. Proceeding of the 16th Energy System, Economics and Environment Conference, Tokyo, January 2000. pp. 73–8 [in Japanese]. [2] Ministry of Environment, Kankyo Hakusho (Environmental white paper), 1997 [in Japanese]. [3] Kondo Y, Moriguchi Y, Shimizu H. Proceeding of the 12th Energy System, Economics and Environment Conference, Tokyo, January 1996. pp. 529–34 [in Japanese]. [4] Nishimura K, Uchiyama Y. Proceedings of the Third International Conference on EcoBalance, Tsukuba, November 1998. pp.295–8. [5] Matsuhashi R, Ishitani H, Suga M, Yoshioka K. Life-cycle assessments of gasoline and electric vehicles. J Japan Institute of Energy 1998;77:1184–92 [in Japanese]. [6] Nansai K, Tohno S, Kono M, Kasahara M, Moriguchi Y. Life-cycle inventory analysis of infrastructure for electric vehicles. Applied Energy 2001;70:251–65. [7] Nansai K, Tohno S, Kasahara M, Moriguchi Y. Proceedings of the Third International Conference on EcoBalance, Tsukuba, November 1998. pp. 299–302. [8] Nansai K, Tohno S, Kasahara M, Moriguchi Y. Proceeding of the 14th Energy System, Economics and Environment Conference, Tokyo, January 1998 pp. 203–208 [in Japanese]. [9] Koyanagi F, Uriu Y. Modeling of electric vehicle’s demand and its impact on the daily load. Trans IEE Japan 1997;177-B:41–6 [in Japanese]. [10] Ishihara K, Nanahara T, Kurihara I, Nishiyama K, Urata H, Nishino S. Report of Central Research Institute of Electric-Power Industry, T97514, 1998 [in Japanese]. [11] National time-use survey 1995 report. NHK Broadcasting Culture Research Institute, NHK Service Center, 1996. [12] The annual report of air transport. Ministry of Land, Infrastructure and Transport, 1996 [in Japanese]. [13] Private communications with electric companies. [14] Shimizu H. Denki Jidosha no Subete (Book of electric vehicle). Nikkan Kougyo Shinbunsha, 1997 [in Japanese]. [15] http://www.epri.com.
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Effects of electric vehicles (EV) on environmental loads with consideration of regional differences of electric power generation and charging characteristic of EV users in Japan Keisuke Nansaia,*, Susumu Tohnob, Motoki Konoc, Mikio Kasaharab a
Endocrine Disruptors and Dioxin Research Project, National Institute for Environmental Studies, 16-2 Onogawa Tsukuba Ibaraki, 305-8506 Japan b Graduate School of Energy Science, Kyoto University, Gokasho Uji Kyoto, 611-0011 Japan c Toshiba International Fuel Cells Corporation, 4-1 Ukishima-cho Kawasaki-ku Kawasaki Kanagawa, 210-0862 Japan
Abstract In order to evaluate the reduction effect of electric vehicles (EVs) on various atmospheric environmental loads, we have performed a life-cycle inventory analysis (LCI), including the installation of charging stations and regional, seasonal and temporal difference of the energy mix of electricity generation. For an EV converted from a small gasoline vehicle, a regional LCI analysis was carried out in the following steps: (1) location of the charging stations, (2) modeling of charging characteristics of station users, (3) calculation of temporal life-cycle emission intensities of CO2, NOx and SOx by region, season and day. Assuming that total traveling distance is 100,000 km, the electricity consumption rate is 0.119 kWh/km and the charging/discharging efficiency is 70%, the average life-cycle emission of CO2 for that EV was 3.6 t-C throughout Japan. However, if we took regional difference into account, the emission ranged over 70–160% of the average amount. It was revealed that the regional difference of the primary energy mix significantly affected the emissions of EVs during the operation phase. # 2002 Published by Elsevier Science Ltd. All rights reserved. Keywords: Life-cycle inventory; Electric vehicle; Regional characteristic
1. Introduction Air pollutants from an automobile contribute to not only regional environmental problems such as human-health effects but global environmental issues like climate * Corresponding author. Tel.: +81-298-50-2889; fax: +81-298-50-2880. E-mail address: [email protected] (K. Nansai). 0306-2619/02/$ - see front matter # 2002 Published by Elsevier Science Ltd. All rights reserved. PII: S0306-2619(01)00046-0
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change due to CO2 emissions. In Japan the number of automobiles is increasing due to their convenience, and the consumptions of gasoline and kerosene are growing even after the collapse of the bubble economy of 1990s. The transportation efficiency (fuel consumption per passenger-km) continues to go down since the late 1980s because of the increase of body weight [1]. This is attributed to the popularity of automatic cars and RVs (recreational vehicles), additional safety devices and so on. Currently the contribution of the transportation sector to the total CO2 emissions is approximately 20% in Japan [2]. Especially, emissions generated from passenger and freight cars dominates nearly 90% of the sector [2]. Therefore, an immediate reduction of CO2 emissions from automobiles should be essential for Japan to achieve the goal of greenhouse gases reduction as in the Kyoto protocol. At present, most carmakers are developing good mileage cars and ZEVs (Zero tailpipe emission vehicles), such as EVs (electric vehicles) or fuel cell vehicles. Unfortunately, a rapid spread of ZEVs is not expected in the short term because of their higher cost and poor performance in comparison with ICVs (internal combustion vehicles). However, their improvement effect on the atmospheric environment is very hopeful. LCA is a useful tool to evaluate quantitatively the reduction effect of EVs on environmental loads. To date, several LCA case studies about EVs have been reported [3–6]. It has been demonstrated that an indirect emission from electric-power plants on the charging phase accounts for 50% of its life cycle CO2 emission [6]. The emission factor related to electricity generation on the charging phase was usually based on the annual average energy mix. In Japan, ten electric companies supply electricity to their regions and have their own power stations to generate electricity. Therefore differences in the energy mix by distribution area result in regional differences of environmental loads related to electricity generation. Furthermore, the energy mix varies with time during a day even for an electric company. We have performed a LCI (life-cycle inventory analysis) of EVs considering the regional, seasonal and temporal differences of environmental loads (emission factors) during the charging phase of EVs.
2. Methodology An EV’s life-cycle is separated into four phases as follows; (1) manufacturing of the EV, (2) installation of a charging station, (3) charging at the station and home and (4) disposal at the end of its useful life. Environmental loads (CO2, NOx and SOx) at each step were estimated using LCI data calculated by the process and input–output approaches. We categorized EV users into groups and modeled the charging characteristics for each group. Emissions on the EV charging stage were calculated with the life-cycle emission factors (emissions per kWh) of the electricity-generation system and modeled charging characteristics of the users. We estimated the emission factors from the operation phase of power plants as well as those for the plant construction, management and maintenance. The regional, seasonal and temporal differences of the energy mix were considered for the operation phase.
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Quantitative analysis was also made for the influence of mid-night charging on life-cycle emissions of EVs. The details of the method are discussed in the following sections. 2.1. Manufacturing, installation of the charging station and scrapping Life-cycle CO2, NOx and SOx emissions of EV’s manufacturing and scrapping stage were presented in our previous studies [7, 8]. The emissions from installing two types of charging stations were estimated according to our previous procedure [6]. One station is equipped with a charger, stand and batteries to save electricity during the night and another one is without batteries. Table 1 summarizes the emission factors of each stage. 2.2. Modeling of the charging characteristic by EV-user groups 2.2.1. Electric-power demand by charging A home charge (charging of an EV at the user’s home during the night) is expected to be the most appropriate charging style. However, public charging during the daytime is indispensable to promote switching of ICV to EV. Then, the charging characteristics will become diversified. In earlier studies of modeling, the charging patterns, charging characteristics were assumed to follow the Gaussian distribution [9] or were modeled by applying the actual measurement data of current charging-stations [10]. We presented a charging characteristic model, taking into account differences of the start time of charging and the electric-power demand by EV users at public charging stations. The details of the modeling are schematically shown in Fig. 1. It was assumed that public charging stations are for charging 2 EVs for about 3 h [6]. Accordingly, the location of high usability is generally one where a driver can spend an hour or more doing something else while receiving a charge. For example, shopping centers, entertainment locations, etc. were chosen as eligible sites. We categorized EV users into 4 groups according to their travel purposes; (1) group-workers for commuting from home to workplace and vice versa, (2) group-workers for business activity, (3) group-homemakers for daily work like shopping, and (4) group-others Table 1 Emission intensities of EV manufacturing, scrapping and installation of charging stations for EVs Emission intensities Stage category
CO2 (t-C/EV)
NOx (kg/EV)
SOx (kg/EV)
EV manufacturing Scrappinga Charging stationb Charging stationc
1.09 0.002 0.45 0.17
5.28 0.006 2.06 1.00
4.33 0.005 1.77 0.69
a b c
Same as a compact passenger car. With a storage battery. Without a storage battery.
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Fig. 1. Modeling flow of daily electric power demand for charging an EV at a public charging station.
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for recreation or other purposes. Charging facilities were installed at parking lots of the workplace for groups (1) and (2), shopping centers, municipal offices or hospitals etc. for group (3) and amusement parks, art museums or hotels, etc., for group (4). In order to determine electric-power demand at a charging station, first, we investigated the temporal pattern of our daily activity from a report [11] and estimated the number of visits to the facility with a charging station at time x for 10 regions. With the assumption that a charging site was used by a group and the users charge their EVs batteries whenever they visit the facility, we determined the total number u(x) of visits at the sites by each group. Then a probability p(x) of starting to charge at time x is given by Eq. (1) for each group. The regions consisting of prefectures correspond to the distribution areas of 10 electric companies, as shown in Table 2. ð 24 pðxÞ ¼ uðxÞ=
uðxÞdx
ð0 4 x 4 24Þ:
ð1Þ
0
The probability N(t) of starting to charge until time t is determined as (see Fig. 1A) ðt NðtÞ ¼ pðxÞdx: ð2Þ 0
N(t) also shows the number fraction of EVs. Accordingly, the probability Nc(t) on charging is expressed as Nc ðtÞ ¼ NðtÞ Nðt TÞ;
ð3Þ
where T represents a charging time (see Fig. 1B). An electric-power demand D(t) at a charging station is calculated by multiplying Nc(t) by the supply power f(kW) (see Fig. 1C). DðtÞ ¼ fNc ðtÞ:
ð4Þ
Table 2 Ten regions used in this study and their prefectures Region
Prefecture
Hokkaido Tohoku Kanto Chubu Hokuriku Kansai Chugoku Shikoku Kyushu Okinawa
Hokkaido Aomori, Iwate, Miyagi, Akita, Yamagata, Fukushima, Niigata Ibaraki, Tochigi, Gunma, Saitama, Chiba, Tokyo, Kanagawa, Yamanashi Nagano, Gifu, Shizuoka, Aichi, Mie Toyama, Ishikawa, Fukui Shiga, Kyoto, Osaka, Nara, Hyogo, Wakayama Tottori, Shimane, Okayama, Hiroshima, Yamaguchi Tokushima, Kagawa, Aichi, Kochi Fukuoka, Saga, Nagasaki, Kumamoto, Ohita, Miyazaki, Kagoshima Okinawa
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D(t) was determined in the 10 regions for weekdays and holidays according to 4 EV-user groups. Then 80 patterns of D(t) were presented in total. On the other hand, we assumed that the home charge begins at 11 pm in every region and denote an electric-power demand at home as H(t). 2.2.2. Setting a charging characteristic We set a charging time, charging frequency and electric-power demand at a charging station according to EV-user types. The charging time was set as follows. A daily travel distance for the EV was given by a mileage (km/l) times daily fuel consumption (kl) of ICV with the assumption that there is little difference in the distance travelled between EV and ICV. The daily travel distance of group-workers (commuters), group-homemakers and group-others was set at 36.75 km according to the statistics [12]. The actual data on the daily trip distance L0 of the EVs for business use in an urban town [13] were applied to estimate the distance L travelled by group workers (business). L was calculated as 53.45 km by Eq. (5) under the assumption that it relates to the magnitude of an inhabited city area. pffiffiffiffiffiffiffiffiffiffi ð5Þ L ¼ L0 S=S0 ; where S represents the mean value for inhabited city areas throughout the country and S0 is the city area where L0 was measured. The specific distance and charging efficiency of the EVs were set at 0.119 kWh/km and 70%, respectively [14]. The electric-power demand per charge was calculated from the travel distance, mileage and charging efficiency. Charging time by groups was determined by dividing supplied electric power (12 kW=200 V60 A) [15] of a charging station into each user’s power-demand. The charging frequency, that is, how often an EV user utilizes a charging station per week, was given by a census [11]. We also assumed that a home charge was started from 11 pm of the day when a charging station had not been used and the supplied electric power was 1.5 kW (=100 V15 A). Here, we summarize the charging characteristics according to EV-user groups. A distribution loss of each electric company was taken into account in converting electric power on the user side to that on the supplier side [16]. Group-workers (commuters)—they use a charging station at their companies on weekdays and do home charges only on weekends. Group-workers (business)—this group charges EV batteries at only the charging stations of the companies. Due to the cheaper price of off-peak electric power than that during daytime, it is expected that they charge up the batteries during the nighttime. Therefore, we assumed the required daily power demand was fulfilled with both daytime charging and only nighttime on weekdays. Group-homemakers, group-others—weekly frequency of using charging stations is provided by data [11] indicating how many times these groups visit the facilities eligible for charging sites. They use home charges on the days when they do not visit the charging stations.
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These charging characteristics are shown in Table 3 and daily variations of electric-power demands for charging (excluding home charges) on weekdays in the Kansai area are illustrated in Fig. 2 according to the above 4 groups. 2.3. Emission intensity at the charging stage We compiled the emission intensities based on net electricity distributed to users e(t) (emission/kWh) of air pollutants during the charging phase with consideration of the differences between regions, seasons, day and time. Table 3 Parameters of the charge conditions by EV-user groups EV user group Parameters
Workers (commute)
Workers (business)
Homemakers
Others
Travel distance (km/day) Electric-power demand (kWh/day) Charging time at a charging station (h/day) Frequency of charging station use (times/week) Frequency of home charging (times/week) Charging time at home (h/day)
36.75 6.25 0.52 5 2 4.17
53.45 9.09 0.38 5 5a 3.03b
36.75 6.25 0.52 3.37 3.63 4.17
36.75 6.25 0.52 2.58 4.42 4.17
a b
Charge up at company during night-time. By supplied electric power.
Fig. 2. Electric-power demand curve for weekdays at charging stations in the Kansai area.
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First of all, typical 80-day load curves by 10 regions, 4 seasons and weekday– weekends were selected by statistics [16] and an interview with the electric companies. Next, direct air pollutant emission intensity ak from fuel burning of the power plant k (coal, petroleum, LNG; ak of hydro and nuclear power plants are zero) and indirect emission intensity bk with construction and maintenance of the plant (coal, petroleum, LNG, hydro, nuclear) were calculated using the method [17,18] and coefficients [8] of the earlier studies. Sums of the direct and indirect emission intensities by plants were used to estimate total hourly emission intensities. Finally, according to region, e(t) was determined by dividing the total of the hourly electric-power demand qk(t) of each plant into the sum of direct and indirect emissions of it, as in Eq. (7). eð t Þ ¼
5 5 X X ðak þ bk Þqk ðtÞ= qk ðtÞ: k¼1
ð7Þ
k¼1
There is some exchange of electricity between the 10 electric companies and other independent electric companies which generate and supply electricity to the 10 electric companies. The effect of the exchange of electricity was included in estimating e(t).
3. Results and discussion 3.1. Regional and temporal difference of emission intensity Regional CO2 emission intensities e(t) during charging on a summer weekday are illustrated in Fig. 3. Air pollutant emissions with regard to construction and
Fig. 3. Regional comparison of CO2 emission intensities for charging during summer weekdays.
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maintenance of power plants are shown in Table 4 and the seasonal weighted averages of the emission intensities are provided in Table 5a–c (weight of 5 for a weekday, that of 2 for a weekend). Regional differences of e(t) were responsible for the energy mix, fuel type and operating condition in each region. Annual average of CO2 emission intensity obtained in this study was comparable with that of Ref. [17]. Values of CO2-e(t) of the Okinawa and Chugoku areas are higher than those of others, since there are only fossil-fuel fired plants in Okinawa and the contribution of fossil-fuel power to total electricity generation is relatively large in Chugoku. In the Tohoku area, the exchange of electricity with middle-scale electric companies is higher than in other areas. The high CO2-e(t) of the Tohoku area is attributable to the operation of fossil-fuel power generations in most of the middle-scale electric companies. It was confirmed that there was about 3.6 times difference in CO2 emission intensities among the 10 regions. Table 4 Air pollutant emissions as a result of construction and maintenance of each power plant Emission intensity
CO2 (g-C/kWh) NOx (g/kWh) SOx (g/kWh)
Fossil fuel power generation Coal
Petroleum
LNG
25.9 0.47 0.21
12.1 0.20 0.08
41.1 0.07 0.02
Nuclear
Hydro
5.6 0.002 0.001
4.7 0.03 0.01
Table 5 Regional and seasonal air pollutant emission intensities during the charging phase Seasons
Hokkaido Tohoku Kanto Chubu Hokuriku Kansai Chugoku Shikoku Kyushu Okinawa
a. CO2 emission intensity (g-C/kWh) Spring 109.6 136.9 82.8 Summer 121.4 155.3 87.1 Autumn 136.3 147.5 91.9 Winter 134.2 148.6 95.0 Annual average 125.4 147.1 89.2
101.5 120.0 99.4 130.9 113.0
105.7 100.4 116.8 148.0 117.7
66.7 81.1 65.9 76.0 72.4
159.1 176.7 167.2 179.1 170.5
64.5 88.0 76.7 126.3 88.9
80.2 109.6 86.2 118.5 98.6
265.5 253.0 254.8 257.8 257.8
b. NOx emission intensity (g/kWh) Spring 0.52 0.42 Summer 0.57 0.49 Autumn 0.64 0.47 Winter 0.63 0.46 Annual average 0.59 0.46
0.17 0.18 0.19 0.20 0.18
0.16 0.19 0.16 0.21 0.18
0.31 0.29 0.34 0.43 0.34
0.12 0.15 0.13 0.13 0.13
0.44 0.50 0.47 0.51 0.48
0.30 0.41 0.36 0.59 0.41
0.18 0.24 0.19 0.26 0.22
0.77 0.74 0.74 0.75 0.75
c. SOx emission intensity (g/kWh) Spring 0.59 0.32 Summer 0.65 0.38 Autumn 0.71 0.36 Winter 0.71 0.35 Annual average 0.66 0.35
0.12 0.13 0.14 0.14 0.13
0.14 0.17 0.14 0.18 0.16
0.32 0.30 0.36 0.46 0.36
0.08 0.10 0.09 0.09 0.09
0.33 0.38 0.36 0.40 0.37
0.29 0.41 0.35 0.59 0.41
0.18 0.26 0.20 0.28 0.23
1.86 1.84 1.84 1.85 1.85
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The CO2-emission intensity in the Kansai and Kanto areas became smaller at night, because nuclear-power generation contributed a high share in these regions. For example, the contributions of nuclear power to annual electric-power generation in the Kansai and Kanto areas were 52 and 46%, respectively in 1998. The maximum of e(t) in the Kansai area was 1.7 times higher than the minimum in a day. The nuclear-power share was approximately 45% on a summer weekday in the Shikoku area and the contribution of fossil-fuel power became small during nights. Accordingly, e(t) of the Shikoku area had about a 2.3 times difference in a day. The Hokuriku area showed the same temporal variation as the Shikoku area, because of a higher contribution of hydropower. However, e(t) of some regions like Chugoku, Tohoku, Chubu or Hokkaido showed only a small temporal change. The share of coal-fired power generation at night was higher than in the daytime in the Okinawa area. Hence, nighttime electricity use in those areas is not necessarily linked to mitigating the CO2 emission. In the cases of NOx and SOx emission intensities, regional and temporal differences depended on the energy mix, fuel type and removal technology (i.e. cover ratio of desulfurizers and denitrification facilities in power stations and their efficiencies). Annual average of NOx emission intensities varied by a factor of 5.8 among the regions, while the difference of SOx emission intensity was extended to 20.3 times. This result can be explained by the following facts. There was no LNG thermal power plant in the Hokkaido and Shikoku areas and domestic coals with a higher sulfur content are used in the Hokkaido area. Therefore, SOx emission intensities of these areas were higher than those of other areas. Regarding temporal variations of NOx and SOx emission intensities, the Shikoku area had the largest difference of them, that is, they were 2.4 times different during a day. 3.2. Emissions during the charging phase by user type Daily air-pollutant emissions Es and Eh from EVs, charging at a charging station and home respectively, were calculated from D(t), H(t) and e(t) as in the following equations. ð 24 DðtÞeðtÞdt:
Es ¼
ð8Þ
0
ð 24 Eh ¼
HðtÞeðtÞdt:
ð9Þ
0
For instance, if we assume that EV users drive 100,000 km in the Kansai area and charge EV batteries according to the charge conditions as shown in Table 3, the lifetime CO2 emissions of group-workers (commuters) on the charging stage was 1.4 t-C. This was the largest value among 4 groups, while the emission of group-others was 1.2 tC of the minimum. It was found that the difference of charging characteristics between groups gave a variation of lifetime emissions during the driving phase even if the mileage and lifetime driving distance were common to all users. On a percentage basis, CO2, NOx
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and SOx emissions on the charging phase had 15, 15 and 13% differences among users, respectively. 3.3. Regional comparison of LCIs of EVs Figs. 4–6 show regional LCIs of EVs including emissions during the driving phase and also for car body production, installation of the charging station and scrapping the car. Emissions during the driving phase were arithmetic means of each user’s emission in each region. Emissions of gasoline vehicles (GVs) were adopted from our previous study [6] and emissions for the mining and transportation phase of petroleum were added to cover the same system boundary as for EVs. The average life cycle CO2 emission of an EV was estimated to be 3.6 t-C. The emission amount ranged from 72 to 165% of the average among the 10 regions. The least emission region was the Kansai area i.e. 2.6 t-C, while the highest-emission area was Okinawa at 5.9 t-C due to the operation there of only thermal-power plants. The amounts of CO2 emissions in the Chugoku and Tohoku areas were large among regions having non-thermal power plants because the ratio of thermal generation to total power generation was high. Life-cycle CO2 emission of a GV was about 9.3 t-C and it was confirmed that the emission of EVs was in the range of 26–64% of GV emissions. In the case of NOx, the average life-cycle emission was 13.1 kg/EV and the regional difference was greater than that for CO2. The difference with the average emission ranged from 66% of the Kansai area to 151% of the Okinawa area. Although the share of thermal-power generation in the Hokkaido area was lower than those in the Chugoku and Tohoku areas, Hokkaido was the area of the second largest NOx
Fig. 4. Regional comparison of life cycle CO2 emission of EVs.
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Fig. 5. Regional comparison of life cycle NOx emissions of EVs.
Fig. 6. Regional comparison of life cycle SOx emissions of EVs.
emission with EVs. There was no LNG power plant in the Hokkaido area and the share of coal power plants was high. Therefore, the NOx-emission intensity was larger than for other areas as shown in Table 5. Life-cycle NOx emission of EVs was in the range of 20 (8.6 kg/EV) to 45 (19.8 kg/EV) % of GV emissions (43.9 kg/GV).
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Fig. 7. Variations of life-cycle and charging-phase emissions by using off-peak electric power.
The regions of higher SOx emission were Okinawa, Hokkaido and Shikoku. The large SOx-emission intensity of the Shikoku area is attributable to no LNG thermal power plants in Shikoku as well as Hokkaido. The average life-cycle SOx emissions of EVs were 12.2 kg with the regional difference of 51 (6.2 kg)–312% (38.1 kg) of the mean. It was confirmed that the life-cycle emission of EVs was larger than that of GVs in some regions. This result suggests the possibility of a SOx emission increase by spreading EVs in a specific region. The replacement of a diesel vehicle (DV) by an EV is effective in mitigating SOx emissions. The regional difference of EV emissions was 11–64% of DV emissions (59.1 kg/DV). 3.4. Effect of using off-peak electric power on emission reduction It was reported that the use of off-peak electric power for charging EV batteries contributed to a constant electric power demand [10]. Assuming that a charging station utilizes electric power saved in a storage battery during the night, we examined the effect of using off-peak electric power on air pollutant emissions. Emissions from the installation of a storage battery caused the increase of each environmental load of a charging station as shown in Table 1. Fig. 7 shows that the ratios of environmental loads with the use of off-peak power to those without it during the driving phase and on the life cycle basis in the 10 regions. It was revealed that off-peak power use decreased the emission amounts during the driving phase in every region except for the Okinawa area, where a share of coal-fired power plants in the electricity generation is high during the night.
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Especially, in the Hokuriku and Kansai areas, the emissions with the use of off-peak power went down to 80% of the emissions without it. However, from the life-cycle point of view, some regions showed that the emission amount with use of off-peak power was larger than the emission without it due to the increase of emission from the installation of a charging stations. This result greatly depends on the methodology to allocate emission from building the infrastructure to an EV. We applied the method in our previous paper [6]. Improvement of the allocation method should be required to achieve a better comparison of environmental advantages between GVs and EVs.
Acknowledgements The authors would like to thank 10 electric companies in Japan for furnishing data. This work is supported by the ‘‘Distributed Autonomous Urban Energy System for Mitigating Environmental Impact’’ Project (JSPS-RFTF97P01002) under the ’’Research for the Future‘‘ Program by the Japan Society for Promotion of Science.
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