Could future electric vehicle energy storage be used for hydropeaking mitigation? An eight-country viability analysis

Resources, Conservation & Recycling 149 (2019) 760–777

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Full length article

Could future electric vehicle energy storage be used for hydropeaking mitigation? An eight-country viability analysis

T

Alfonso Romána,b, , Diego García de Jalóna, Carlos Alonsoa ⁎

a b

Department of Natural Systems and Resources, Technical University of Madrid (UPM), Ciudad Universitaria, SN, 28040 Madrid, Spain Transport Research Centre (TRANSyT), Technical University of Madrid (UPM), Ciudad Universitaria, SN, 28040, Spain

ARTICLE INFO

ABSTRACT

Keywords: Water policy hydropower river ecology renewable energy PEV V2G

Hydropower short-term response capability and operating flexibility are critical in some electricity systems, especially during peak demand periods. However, operational variability at hydro plants may originate relevant water flow fluctuations (i.e., hydropeaking) that cause severe alterations in stream ecosystem functionality. Such harmful consequences could be reduced if other energy storage were available. The increasing use of electric vehicles (EVs) has presented the application of their batteries for energy grid scale accumulation purposes. EV interaction with the grid and other renewable energy (RE) sources (primarily wind and solar) has been broadly studied. It seems, however, that EV energy storage allocation to mitigate hydropeaking impacts at regional or national scale remains unexplored. This article evaluates the possibility of using EV batteries to provide the system with additional storage capacity to support a more balanced hydropower operation. The analysis is performed for eight countries with diverse socioeconomic and technological environments. Initially, the coherency in the orders of magnitude between hydropower generation and energy storage capacity is evaluated by calculating the accumulation potential of the current passenger car parc in a hypothetical scenario in which the complete fleet is electrified and suited for vehicle-to-grid (V2G) interaction. After validating the feasibility of the proposal, a long-term analysis is performed using diverse energy, EV rates, and battery capacity forecasts gathered from reliable sources. The results indicate that a 50% PEV fleet using 300 Wh/kg batteries may provide stable storage capacity for average daily hydropower 2050 projections in China, Germany, Japan, Spain and the United States.

1. Introduction Since the initial stages of the industrial revolution, the demand for electricity has increased significantly due to the combined effects of population upsurge and improved accessibility to electric energy supply (from 71.4% in 1990 to 87.4% by 2016) (World Bank (WB), 2018). During this period, world annual electricity production boosted from 11,901 TWh to 25,082 TWh (110.8%) (International Energy Agency (IEA), 2018a). Hydropower (HP) is an important clean and renewable energy (RE) source utilized in all geographic areas. In 2016, hydroelectric plants produced 17% of the world’s electricity (International Energy Agency (IEA), 2018b). A comprehensive overview on hydropower history and present situation, its main policy issues, and the future of the industry is presented in Koch (2002) and Sternberg (2010). Hydropower’s short-term response capability and operating

⁎

adaptability are crucial for generation and demand balance operations in electricity systems (Kaygusuz, 2009). However, ecological considerations are limiting this flexibility. In addition, HP provides the system with short-term reaction units which supply power during peak demand periods, secure network voltage stability, and rapidly reestablish energy delivery in case a blackout occurs (Yüksel, 2010). From an economic perspective, hydroelectric plants are also important because this technology reduces the need to dispatch units with high marginal costs during peak periods. Thus, in markets with deregulated generation, in which production is unregulated and driven by supply and demand (Gordijn and Akkermans, 2007), electricity price fluctuation encourages hydroelectric plant managers to adapt their strategies to rapidly varying scenarios (Cushman, 1985). In consequence, turbine operation variability during generation produces intermittent water releases that originate short-term water stage and flow fluctuations, and frequent changes in the wetted areas of regulated

Corresponding author at: Department of Natural Systems and Resources, Technical University of Madrid (UPM), Ciudad Universitaria, SN, 28040 Madrid, Spain. E-mail addresses: [email protected] (A. Román), [email protected] (D. García de Jalón), [email protected] (C. Alonso).

https://doi.org/10.1016/j.resconrec.2019.04.032 Received 25 October 2018; Received in revised form 26 April 2019; Accepted 27 April 2019 Available online 25 July 2019 0921-3449/ © 2019 Elsevier B.V. All rights reserved.

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rivers (Moog, 1993). This phenomenon, known as hydropeaking, causes significant effects on river morphology, hydrology and sediment dynamics that substantially modify the natural conditions of many rivers. For instance, over 800 km (16%) of Austrian river reaches have been altered by hydropeaking (Greimel et al., 2018), causing a reduction in biomass content from 140 kg ha-1 to only 26 kg ha-1 (Schmutz et al. 2015). The biological processes required for river ecological integrity are regulated by five critical features of the flow regime: magnitude, frequency, duration, timing, and rate of change of hydrologic conditions (Poff et al., 1997). Frequent high magnitude flow variations erode and transform the natural structure and composition of river banks. Stream channel size and geometry, pool and riffle dissemination, and substrate composition and size, depend on the interactions between flow regime and local geologic configuration (Newbury and Gaboury, 1993; Harby et al., 2004). These alterations induce riffle spawning ground deterioration, pool shape variation, fine particle accumulation, deviations in water velocity-depth composition, and modifications in the riparian wetted area that reduce vegetation density and richness (Sear, 1995). Biota response to altered regimes is complex, with numerous interactions and relevant consequences still requiring further understanding. In general, hydropeaking reduces downstream species diversity, decreases overall organic mass, and favors non-autochthonous wildlife invasions (García de Jalón et al., 1994). Hydroelectric generation causes unnatural extreme flows that intensify fish energy and oxygen consumption (Puffer et al., 2015). A detailed literature review describing the effects of flow deviations from natural conditions is exposed in Poff and Zimmerman (2010) and Bejarano et al. (2017). Hydropeaking affects water flow fluctuations at multiple temporal scales (Zolezzi et al., 2009). However, most research work on fluvial hydrology evaluates flow variability considering daily, seasonal or longer time scales (Richter et al., 1996; Olden and Poff, 2003), which may not properly assess key aspects caused by flow variations on hourly basis (Zimmerman et al., 2010). In Spain, for example, the average hydro generation during February 2018 was 1,945 MW at 2 AM but tripled to 6,024 MW by 8 PM. This study identifies and evaluates a potential energy storage source capable of ameliorating the sub-daily effects of hydropeaking. If sufficient accumulation capacity were available, equivalent quantities of energy could be generated during broader periods, therefore, a reduction of the instantaneous power requirements during plant operation could be achieved. Consequently, water discharge extreme magnitudes could be lowered, and ramp rates reduced, thus mitigating the detrimental effects of hydropeaking and enabling a better simulation of the natural regime. Electromobility is expected to transform the automotive industry and modify user behavior (Pollet et al., 2012; Egbue and Long, 2012). The main advantages of electrical versus conventional propulsion systems reside in the higher efficiency of an electric motor when compared to an internal combustion engine (ICE), the regenerative braking system, and its stop-start capability that eliminates vehicle idling (Itazaki, 1999; Kirsch, 2000). These features decrease energy consumption and pollutant emissions, primarily in cities. Consequently, EVs are expected to become critical in urban displacements (International Energy Agency (IEA), 2017). Electric vehicles (EVs) can be classified as battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and fuel cell electric vehicles (FCEVs) (Chan, 2007). BEVs solely use an electric motor for propulsion, so its battery must be recharged by connecting the vehicle to the grid (G2V energy transfer) using an electric vehicle supply equipment (EVSE) outlet. Hybrid technology utilizes an internal combustion engine combined with an electric motor, and its battery is recharged

primarily during regenerative braking and coasting conditions, although a generator charges the battery if its energy reaches a minimum. A plug-in hybrid electric vehicle (PHEV) is a specific type of HEV with a higher battery capacity which may be charged from the grid, so it functions as a BEV until the battery is depleted, and then driven as an HEV until recharged. Finally, FCEVs use a fuel cell stack to produce electricity obtained from an electrochemical reaction of hydrogen and oxygen (Walters et al., 2001; Bayindir et al., 2011). Both BEVs and PHEVs (onwards plug-in electric vehicles or PEVs) must be connected to the grid for electrical charging. Concerning FCEVs, their integration in the grid is presently uncertain. EV deployment is a noteworthy challenge for the involved stakeholders, as it entails relevant innovations in generation readiness, and network capacity due to grid-to-vehicle (G2V) charging requirements, vehicle-to-grid (V2G) control, and system management (Kempton and Letendre, 1997; Mwasilu et al., 2014). Features such as user behavior (Wolinetz et al., 2018), battery capacity, EVSE availability and power, and the level of EV integration can significantly influence demand patterns if not properly managed. When standard EV charging is utilized, which begins once the vehicle is connected to the grid and lasts until the battery is fully charged, peak demand and energy prices could increase during the late afternoon, since consumers would probably recharge their vehicle after arrival from work (Hadley and Tsvetkova, 2009; Kiviluoma and Meibom, 2011). To avoid this inconvenience, a smart vehicle charging process, which controls EV load based on demand and user needs, is essential for system stability (Qian et al., 2011; Monteiro et al., 2011). The integration of electric vehicles in the Smart Grid may provide additional benefits if the EV accumulation capacity is used as an optional power source. Energy stored in EV batteries can improve power quality and level RE generation irregularities (Lund and Kempton, 2008), and be used for ancillary services due to its capacity to rapidly compensate system needs (Quinn et al., 2010). Existing research remarks that, in many countries, a theoretical vehicle parc composed exclusively by EVs would have more power capacity than the complete generation system (Kempton and Tomić, 2005; Kiviluoma and Meibom, 2011). Concerning energy, Coignard et al. (2018) conclude that if the fleet objectives in California’s zero-emissions vehicle (ZEV) mandate are reached, and PEVs are charged with controlled one-way power flow (V1G), only some vehicles with V2G capability are needed to achieve the 1.3 GW target set in the energy storage mandate. Furthermore, Forrest et al. (2016) expose that with V2G, 80% of its Renewable Portfolio Standard (RPS) could be accomplished without additional non-vehicle storage systems. Stationary energy storage (SES) systems are accumulation devices providing similar services as PEV storage, although easier to manage because they are constantly connected to the grid. Tarroja et al. (2016) evaluate how similar storage capacity allocated to either an SES or PEVs affects RE management, determining that both systems are complementary, since PEV storage favors a more efficient use of RE resources, while SES are more adequate for power balance purposes. The role of PEVs as suppliers of electricity system regulation and balancing services, and providers of energy storage capacity to enable renewable energy integration, primarily wind, has been extensively studied (Yilmaz and Krein, 2013). Borba et al. (2012) show that management of wind power using a 500,000 PHEV fleet reduces costs and increases the capacity factor by 3.7%. Demand response (DR) activities also ease system stability by incentivizing users to shift load from peak to low demand periods. Wang et al. (2011) assess the influence of PEVs on a system with wind power and DR, achieving cost reductions when smart charging and DR are both implemented. However, the possibility of using PEV batteries to smoothen hydropower generation with the

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purpose of mitigating hydropeaking impact on rivers seems to remain insufficiently investigated. This article suggests that PEVs could also be used to accumulate hydropower to favor a more gradual plant operation and decrease fluvial stress. Hydro plants may then operate more time at lower output but generating equivalent amounts of energy by functioning during periods in which no or low generation occurs (usually night time), and accumulate the energy in PEVs so these would supply it to the system when necessary (typically peak demand conditions), thus leveling generation and water discharges. In markets with a deregulated generation, such reframing of the HP production scheme could cause important economic losses to the incumbent firms because a significant quantity of energy would be produced in periods with lower electricity prices, therefore certain financial compensation is necessary, even though a more environmentally-oriented generation plan would provide additional benefits that improve the “planet” account in the triple bottom line of the generation firm (Elkington, 1998). Likewise, the present status quo based on no economic penalization for the environmental costs of hydropeaking (Harpman, 1999; García de Jalón et al., 2017) should be revised. Concerning PEV owners, these should be reimbursed for the increase in battery degradation since V2G may require biennial battery replacements at 40% depth of discharge (DOD) and annually at higher DOD levels (Bishop et al., 2013), and also obtain additional economic benefits to incentivize their participation and compensate the overall risk of low owner involvement. However, savings in mitigation measures would provide compensatory funds. Among the diverse alleviation options, compensation basins attain the best cost-benefit ratio (Person et al., 2014), despite a 30,000 m3 basin would require an investment of approximately CHF 15 M (13.2 M €) (Bieri et al., 2016). Nevertheless, a substantial level of EV deployment could significantly influence electricity markets. Aggregator agents (Bessa and Matos, 2010) could encourage EV owner integration in virtual power plants (VPP) (Raab et al., 2011) to offer similar services as those provided by hydropower. This article evaluates the viability of using PEV energy storage to ameliorate hydropeaking effects by assessing eight countries with diverse socioeconomic and technological contexts. It is important to clarify that the main purpose of this work is to perform a feasibility analysis, not to suggest a methodological assessment of how to reduce the environmental impact of hydropeaking. In this regard, our approach is overly conservative compared to a more practical method of only storing the amount above average and dispatching the quantity below average. However, we intend to verify whether the orders of magnitude are coherent or not, and therefore, prefer to stay in such safer zones. Further developments should enhance our knowledge regarding the design of more efficient storing schemes. The paper is organized as follows. In a first stage, the feasibility of the proposal is verified considering a theoretical conversion of the complete 2016 passenger car fleet into PEVs. Once the viability of the concept is corroborated, 2050 forecasts are used to foresee a possible long-term outcome based on reliable predictions. An analysis is performed to evaluate the influence of diverse EV technology deployment scenarios and battery capacity improvements.

Table 1 Input data variables and nomenclature. Demographic variable Population Electricity system variables Annual electricity generation Ratio of hydropower (HP) to total electricity generation Electric vehicle and mobility variables Total number of vehicles Ratio of battery electric vehicles (BEVs) to total passenger cars* Ratio of plug-in hybrid electric vehicles (PHEVs) to total passenger cars* Ratio of fuel cell electric vehicles (FCEVs) to total passenger cars* PEV V2G capacity ratio for each technology and country Average battery capacity for battery electric vehicles (BEVs)* Average battery capacity for plug-in electric vehicles (PHEVs)* Average battery capacity for fuel cell electric vehicles (FCEVs)* Annual vehicle kilometers traveled (VKT) Utility Factor (UF) (PHEVs) Average energy consumption for battery electric vehicles (BEVs) Average energy consumption plug-in electric vehicles (PHEVs) Average energy consumption fuel cell electric vehicles (FCEVs)

Nc Qc hc Ac bc pc fc zb,c, zp,c, zf,c B P F Kc u V W X

* The influence of these variables is assessed in the long-term scenario analysis.

Concerning vehicle data characteristics, only passenger cars are considered as potential energy accumulators, since commercial and industrial vehicles have much higher utilization rates. Within the scope of the assessment, the European passenger car (PC) class, including sport utility and “off-road” vehicles, is assumed to be equivalent to the LightDuty Vehicle (LDV) categorization used in other areas. A detailed description of the characteristics and passenger car classes according to diverse geographic areas is comprised by Yang and Bandivadekar (2017). The input variables used in the calculations are included in Table 1, being c each of the eight countries. For simplicity purposes, the equations are referred to hydropower generation, as these are analogous to those applicable to total electricity and renewable energy calculations. Initially, the average daily electricity production Ec for country c is calculated. In this study, daily energy does not differentiate between workday or weekend. Ec=Qc/d

(1)

Being d the number of days in that year. The average daily hydropower generation Hc is: Hc=hc·Ec

(2)

Next, the maximum energy storage capacity of the EV fleet, Tmax,c, is calculated as the summation of the amount of reserve available in each technology, since battery ratings differ depending on the EV class. Tmax,c=Ac·(bc·B+pc·P+fc·F)

(3)

However, at any time, only a limited proportion of the EV fleet is connected to the grid, since some vehicles are in motion, or may not be plugged-in due to user habits or charging point accessibility limitations. The PEV V2G capacity ratios zb,c, zp,c, and zf,c represent the proportion of vehicles from the fleet for each technology that could always be available for any V2G (or G2V) transaction required by the system in c. Therefore, the least energy which may usually be stocked is:

2. Methodology The information needed for each country is its population, annual electricity generation, technological mix, and additional data regarding private car volume and composition, and user travel requisites.

Tc=Ac·(zb,c·bc·B+zp,c·pc·P+zf,c·fc·F)

(4)

Presently, such energy may only be obtained from battery and plug-

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in hybrid EVs (PEVs) as strict hybrids are not connected to the grid. Concerning fuel cell technology, the influence of such technology is nowadays negligible, although it is included in the equations to be used in long-term analyses. In this section, fuel cells are not considered, since by 2017 the FCEV fleet was of 6,364 vehicles worldwide (Research and Markets, 2018). Thus, the EV storage capacity versus total daily hydropower generation, hc*, in time units, is: hc*=Tc/Hc

Electricity generation per capita in the North American countries is almost 38% higher for Canada, with HP contributing 58% of its electricity, although only 6.8% in the United States. Norway is an unusual case, as its per capita consumption is the second highest in the world, after Iceland. Furthermore, hydropower in Norway accounts for 96.4% of electricity generation. Germany and Japan have similar per capita figures, although the total energy differs due to population dissimilarity. Japan has a lower share of RE (17.1%), but primarily based on hydropower. In contrast, Germany has 31% of RE, but only 4% is hydropower. Spain shows a quite diversified and balanced portfolio, with 39.5% RE (14.5% HP). Brazil (Cohen et al., 2005) and China (He et al., 2017) represent developing countries with vast growth potential. While HP is important for China (19.2%), it is vital for Brazil (65.8%). For each country, two scenarios are assessed using 2016 data: the present situation, by calculating the energy that can be accumulated in the current EV fleet, and a hypothetical context in which all passenger cars are PEVs with V2G capability. This ideal assumption evaluates the coherency in the orders of magnitude between daily average HP generation and the energy storage that could be obtained from the PC fleet. Certainly, such approach involves eliminating all fossil-fueled cars. Legislators are currently developing plans to limit access of conventional vehicles to city centers. For instance, the EC White Paper (European Commission (EC), 2011) aims halving the number of fossil-fueled vehicles in urban areas by 2030, and to phase these out by 2050. Furthermore, some municipalities are already considering prohibiting diesel vehicle transit at a much earlier stage (Belver, 2016; Garfield, 2018). Market trends and technological developments indicate that, in the next decades, the automotive industry will experience a significant conversion in powertrain technology (International Energy Agency (IEA), 2017); European Automobile Manufacturers’ Association (ACEA), 2013), and communication systems (Papadimitratos et al., 2009). A theoretical electrification of all PCs requires additional energy for vehicle charging. Thus, daily generation figures must be revised to

(5)

And the mean battery capacity needed to accumulate the daily average hydroelectric generation per country, Bh,c=Hc/(Ac·(zb,c·bc+zp,c·pc+zf,c·fc))

(6)

3. Results and discussion 3.1. Present and theoretical scenarios This section presents a practical case to evaluate the feasibility of using V2G connectivity to accumulate hydropower generation and smoothen plant scheduling variability. The main objective is to perform an overall insight on the viability of such application, and to verify that core parameter magnitudes are coherent in a long-term realistic scenario. The study assesses eight countries with diverse socioeconomic characteristics and different energy portfolios: Brazil, Canada, China (excluding Hong Kong), Germany, Japan, Norway, Spain and the United States. Table 2 includes the total electricity generation and the population for each country in 2016, while Fig. 1 shows the electricity generation per capita and source (International Energy Agency (IEA), 2018c). Detailed information is included in Table A1 of the Appendix. Table 2 Population, electricity generation and technology mix per country data for 2016. Source: International Energy Agency (IEA). Electricity generation by fuel 2016.

Population (millions) Annual electricity generation (TWh/year)

Brazil

Canada

China

Germany

Japan

Norway

Spain

United States

208.0 578.9

36.3 667.4

1,378.2 6,217.9

82.3 649.1

127.0 1,058.0

5.2 149.6

46.5 274.8

323.3 4,322.0

Fig. 1. Electricity generation per capita and source, and passenger cars per inhabitant in 2016.

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account for the extra energy required to charge the supplementary fleet. The increment in PEV parc is: ΔAc=Ac·((bc’-bc)+(pc’-pc)+(fc’-fc)) ’

’

Table 3 Average battery storage capacity and electric vehicle energy consumption data based on 2016 vehicle specifications, calculated as average of the three most representative models.

(7)

fc’

Where bc , pc and represent the new BEV, PHEV and FCEV ratios. The increase of average daily energy caused by the incremental fleet is calculated taking into consideration the average distance traveled by each car, Kc, and its usual consumption when operating in electric mode. In Eq. 8, u represents the Utility Factor (UF), or average distance ratio traveled by a PHEV in electric mode until charge depletion, as this technology does not always function in a pure electric mode. Such parameter is relevant for demand prediction, and fuel and emission reduction forecasts, but its influence is minor in the core objective of this study, which focuses on energy accumulation capacity. Thus, the additional average daily energy needed to charge the fleet is: ΔEc=Ac·Kc·((bc’-bc)·V+(pc’-pc)·u·W+(fc’-fc)·X)/d And the required average daily electricity generation, tained adding Equations 1 and 8.

Battery electric vehicles (BEV) Plug-in hybrid vehicle (PHEV)

is ob-

Ec’= (Qc+Ac·Kc·((bc’-bc)·V+(pc’-pc)·u·W+(fc’-fc)·X))/d

(9)

It is assumed that the incremental amount of energy is produced by non-renewable sources, therefore the HP ratio must be recalculated. hc’=Hc/Ec’

(10)

The energy storage capacity of the fleet becomes, ’

Tc =Ac·(zb,c·bc’·B+zp,c·pc’·P+zf,c·fc’·F)

(11)

Thus, the EV storage capacity in time units versus daily HP is, *’

hc =Tc’/Hc

(12)

And the mean battery capacity to accumulate the daily average daily HP in c: B’h,c=Hc/(Ac·(zb,c·bc’+zp,c·pc’+zf,c·fc’))

Electric motor power

Energy consumption

kWh

kW

kWh/km

44.1

163.3

0.1625

16.3

130.7

0.2172

Battery storage capacity, electric motor power and energy consumption figures used in the calculations are obtained as the average value for the three most representative vehicles of each PEV class and based on 2016 specifications. The averaged data is shown in Table 3, with detailed information available in Table A3. As battery capacity is higher for BEVs than for PHEVs, the share of each technology has an important impact on the total accumulation capacity. In order to harmonize the comparison in the theoretical scenario, the technology mix used for every country is the overall ratio (63.7% BEVs and 36.3% PHEVs). The weighted average battery capacity for this configuration is 34 kWh. PHEV energy consumption is 33.7% higher partially due to the additional vehicle weight (18.3%) caused by the ICE. Annual vehicle kilometers traveled (VKT) values are needed to calculate PEV energy demand. For Japan and Spain, Kc is below 10,000 km/year. In contrast, the United States has highest distance (18,519), followed by China (16,800), although the figure for the Asian country is expected to gradually decrease as privately-owned cars become more accessible (Huo et al., 2012). The remaining four countries have values ranging between 10,000 (Brazil) and 15,200 km/year (Canada). Concerning PHEV Utility Factor, the 66% rate is obtained according to SAE J2841 UF and used for both scenarios and all countries (Gonder et al., 2009; Society of Automotive Engineers (SAE), 2010; Bradley and Quinn, 2010; Smart et al., 2014). Other important variables are the PEV V2G capacity ratios zb,c, zp,c and zf,c, which represent the proportion of the PEV fleet of each technology that could continuously be available for any V2G (or G2V) transaction required by the system. It is obtained using the minimum V2G availability rate (Quinn et al., 2010) expressed in equivalent hours for the complete fleet and deducting the portion of time required for charging. The remaining quantity is halved to ensure transaction feasibility in both directions, and then expressed as a ratio with maximum value 0.5. This parameter also serves as an estimation of the share of the fleet’s total accumulation capacity that can be accessed for hydropeaking stabilization purposes. The PEV V2G capacity ratio is primarily affected by charging infrastructure deployment and capacity, annual mileage, mobility patterns, and social styles. Certainly, adequate accessibility to EVSE outlets has a significant influence on PEV connectivity, as the ratio improves substantially when PEVs can also be charged at work (Liu and Wu, 2013). Throughout the study, it is assumed that the charging network supports “All-Day” operation, therefore PEVs can connect to the grid at home and during office hours. In such conditions, average vehicle V2G availability rates exceed 90% (Quinn et al., 2010; Liu and Wu, 2013), although we have used a value of 82%, which is the lowest instantaneous rate found in literature for “All-Day” connection, and equals a minimum of 19.7 hours of connectivity by the complete fleet.

(8) Ec’,

Battery capacity

(13)

The ratio of passenger cars per inhabitant for each country is shown in Fig. 1. Table A2 includes additional information regarding the number of registered passenger cars, BEVs, and PHEVs in 2016, and the average annual distance driven (VKT). These data are used to calculate the average daily energy required by the EVs in accordance with user mobility needs and the specific powertrain composition of each country. Since EV registrations are recent, no scrappage rate is applied. Concerning the PC per capita ratio, most countries have values between 0.48 (Japan) and 0.62 (Canada). The United States has the highest rate (0.77), while Brazil (0.3) and China (0.12) present the lowest indices. Current BEV and PHEV rates are very diversified, both in terms of market share and technology mix. Norway had the utmost share of PEVs in 2016 (4.3%) (with 73% BEVs), followed by China (0.4%), Japan (0.25%) and the United States (0.23%). The rate for China is influenced by its low PC per capita ratio, although it has the highest growth rate, since PEV sales in 2016 almost quadrupled those of 2014 (International Energy Agency (IEA), 2017). Germany and Canada have lower PEV shares (0.16% and 0.13% respectively), whereas PEV presence in Spain and Brazil is negligible. Accumulated BEV sales exceed those of PHEVs in all markets, although in Canada and the United States both powertrains are nearly leveled. These eight countries represent 80.2% of the 2016 PEV fleet in the world.

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Charging time is obtained using 6.6 or 3.3 kW chargers for BEVs and PHEVs respectively. The z values are the highest for Japan (39.8% for BEVs and 37.8% for PHEVs), while the lowest are for the United States (38.4% and 34.1%), with average for all countries of 39.2% and 36.1% respectively. For FCEVs, the rate would be 41% since no charging is needed. Per country rates may be found in Table A2. The revised energy data for the full PEV deployment case are included in Table A4. The increase in generation is considered to be obtained from non-renewable sources, although calculations for only-RE contribution are also done. Only average daily electricity generation is considered, so RE stochasticity is not assessed. Energy accumulation rates for average daily generation, RE and HP for this case are shown in Fig. 2, with detailed information in Table A5.

the world exceeded 3 million vehicles and increased by 56% compared with the previous year (International Energy Agency (IEA), 2018d). Obviously, much higher accumulation rates are achieved in the hypothetical case. Germany, Japan, Spain and the United States would have storage levels above the average daily RE generation. Concerning hydropower, three groups can be identified. The cited countries have the highest storage capacity (over 250%) caused by their contained HP share (below 15%), and high motorization, so high PEV penetration would provide ample capacity for their current HP. Canada and Norway would achieve low rates (28% and 9%) because they are hydro-intensive. The final group describes the situation in two relevant emerging economies. Brazil could accumulate 79% of its HP and China 64%. Although the contribution of hydropower in 2016 was of 65.8% in Brazil versus 19.2% in China, the

Fig. 2. Electric vehicle energy accumulation ratio for the average daily electricity, renewable energy and hydropower production in the theoretical full PEV deployment scenario for 2016, where additional power is non-renewable. Markers indicate share if the extra energy is all RE.

effects are partially balanced because the ratio of vehicles in Brazil almost triples the Asian. Both countries could store approximately half of their RE average daily generation, highlighting the relevance of PEV deployment in rapidly developing countries for which REs are a crucial source (International Energy Agency (IEA), 2016).

The results for the actual 2016 scenario evidence that, due to the low presence of electric vehicles, PEV storage capacity is almost insignificant, although Germany, Japan and the United States could already accumulate over 0.8% HP generation using the 2016 fleet. In addition, recent information indicates that in 2017 the total PEV fleet in

Fig. 3. Battery capacity required to accumulate the average daily electricity, renewable energy and hydropower production in the theoretical full PEV deployment scenario for 2016. 765

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addition, an inverter is installed between both components. Total PEV power capacity is computed as an aggregation of the motor outputs for all available PEV and is then compared with the hydroelectric installed capacity. Detailed results are attached in Table A6. Norway represents the least favorable situation, although the available batteries could still provide over ten times the hydropower installed capacity. However, it is important to clarify that such capability will strongly depend on network design, as the admissible power is usually dimensioned for vehicle charging purposes.

Fig. 3 illustrates the mean battery capacity needed to accumulate the usual daily generation, RE and HP in the PEV full deployment case. The red dashed line indicates the 34 kWh used in the calculations and substantiates the storage potential of PEVs, even with current capacities, if deployment rates were appropriate. The other line marks the 100 kWh milestone already available in the Tesla premium line (Tesla, Inc., 2018) to show the sensitivity to energy density. For instance, 150 kWh batteries could provide storage for daily RE in all countries except Norway, although it would be feasible to manage 39% HP and use supplementary devices, such as stationary energy storage systems (Tarroja et al., 2016) to increase capacity. PEV impact on generation differs significantly among the countries (Table 4). The moderate increase in Canada and Norway is due to their high generation without EVs. A similar value is obtained for China (6.8%), because of lower vehicle presence, since it would be similar to the Brazilian rate if PC ratios were analogous. Germany and Spain have equivalent increases, but higher for Germany in absolute terms, because its vehicle number and VKTs are disguised by higher generation. The impact for Japan (7.7%) is contained by its low VKT, 37% below the average of the others. Concerning the United States, the substantial increase is caused by the high car density and use, although partly veiled by the demand without PEVs.

3.2. Long-term scenario assessment 3.2.1. Reference scenario The previous cases illustrate the disparity which exists between the current minimal presence of PEV, and an ideal full PEV deployment. After verifying that the proposal is feasible, this section simulates diverse long-term scenarios based on socio-economic projections, and different EV rates and battery capacity predictions. A reference scenario is first assembled using 2050 forecasts obtained from reliable sources. The data gathered for population, electricity generation and power technological mix predictions for the reference case are shown in Table A7. The available energy-related information for most countries, except

Table 4 Increase in generation caused by a theoretical complete electrification of the 2016 passenger car parc.

Increase in electricity generation (%)

Brazil

Canada

China

Germany

Japan

Norway

Spain

United States

16.6%

7.9%

6.8%

14.8%

7.7%

3.4%

11.9%

16.5%

Brazil, Norway and the United States, refers to 2040, so projections to 2050 are estimated by extending the linear trend during the 2016-2040 period using per capita values. This criterion is also applied to obtain their generation mix. For the Brazilian case, only demand information could be obtained, so such quantity is assumed as an acceptable estimation of its future generation needs.

The evaluation of instantaneous power availability is also necessary to validate that EV batteries can compensate hydro generation power, thus preventing a system blackout. Power supply readiness for PEV is calculated using the rated output power of the electric motor, assuming that, if such power can be supplied to the motor, it could theoretically be delivered to the grid. Battery output should at least equal motor power output, since the accumulator is the only power supply, and, in

Fig. 4. Passenger car composition and fleet size for the 2050 reference scenario.

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Long-term vehicle predictions are shown in Fig. 4, with detailed information in Table A8. As no fleet projections could be gathered for Canada, Japan, Norway and Spain, the 2016 vehicle per capita value is applied to the 2050 population forecast. Brazil intends to almost duplicate its level of private cars and continue using ethanol as main fuel, with an expected BEV share of 9.2%, and no presence of PHEVs or FCEVs, although fossil-fueled vehicles are planned to disappear by 2045 (Empresa de Pesquisa Energética (EPE), 2016). Canada will continue having a petrol-based fleet, with 22.7% PEVs forecasted. EU predictions indicate that conventional ICEs will still dominate the market, including a 31% presence of pure hybrids, with only an expected 15% PEV penetration, and a minimal 2% FCEVs (European Commission (EC), 2016). Projections for the United States are even more reliant on fossil fuels, forecasting 11.7% PEVs and 0.3% FCEVs (U.S. Energy Information Administration (EIA), 2018a). For the Asian countries, 2050 PC mix data could not be gathered. This was foreseeable, as both countries will perform key roles in future EV technology development. However, the social and geographic peculiarities, and the environmental circumstances of these nations are different. The characteristics of Japan favor developing an infrastructure to supply hydrogen, since distances are lower, and population is concentrated. Recent agreements between the main automakers and energy companies (Schmitt, 2017a) indicate that Japan considers future cars to be powered by fuel cells (Harding, 2017; Ibusuki, 2018), and plan to achieve a 40,000 FCEV fleet by 2020. In the case of China, the expected increase in electricity consumption and motorization levels, and the deterioration of air quality in its cities (Reuters, 2016), are urging authorities to adopt drastic measures to reduce transport emissions, and are planning to ban fossil-fueled cars in a near future (BBC News, 2017; Pham, 2017). Chinese authorities want PEVs to represent at least one-fifth of car sales by 2025 and intend to reach a fleet of 5 million by 2020. For the purpose of this study, we suppose that the future PC fleet is integrally composed by FCEVs in the case of Japan, and fully PEV in China, with a mix of 75% BEVs and 25% PHEVs, coherent with the 2016 figures in International Energy Agency (IEA), 2017 and ChinaAutoWeb (2017). A complete private car electrification is also supposed for Norway based on current PEV configuration. Since some forecasts predict FCEVs (International Energy Agency (IEA), 2015; California Air Resources Board (CARB), 2018), battery capacity and consumption data are needed (Table A3). Hydrogen is assumed to be obtained by water electrolysis, so a value of 46 kWh/kg

H2 is used to calculate the energy needed for hydrogen production (United States Department of Energy (DOE), 2017a), obtaining a consumption of 0.4478 kWh/km. FCEV battery capacity is 1.3 kWh, the mean value for the two most sold models. Since FCEV deployment rates are uncertain, and V2G improbable, the study evaluates storage capacity with and without this technology. In order to facilitate result comparability, the original energy-related information in Table A7 is standardized using the 2016 vehicle consumption figures. Certain improvements may be achieved in the future concerning rolling resistance or aerodynamic drag, although these would minimally affect the results, and in such case, conclusions are upgraded. Charger capacity and UF value are also preserved in this section. VKT figures are maintained for most of the countries, although reduced for China (Huo et al., 2012), and increased by 3.2% for the United States (United States Energy Information Administration (EIA), 2018b), and by 45.2% for Spain, consistent with the mobility rates prior to the economic crisis (Instituto Nacional de Estadística (INE), 2008). Updated PEV V2G capacity ratios for these countries are shown in Table A8. The standardized information for 2050 reference scenario may be found in Table A9. Fig. 5 shows the long-term standardized electricity generation per capita and car projections, and its comparison with the 2016 data, while Fig. 6 includes the RE and HP share in the electricity mix, with markers and a dashed line indicating 2016 values. Electricity generation per capita is expected to increase in most countries, except Canada (-8%) and Norway (-2.7%). For the United States, the variation is insignificant (0.2%). The reduction in Norway is relevant in absolute terms, since its power needs include a complete electrified fleet, while the United States would still have 83.3% nonEVs. Besides, Norway has the highest population growth forecast (30%), versus 24% for Canada and 23% for the United States. Significant increases are expected in Brazil, China, Japan and Spain. Predictions for the non-OECD countries reveal their expectations to duplicate per capita generation and private vehicle ownership, although other aspects differ between both countries. A significant proportion of future electricity demand in Brazil will supply power to a 12% population increase, while long-term population in China will be similar to 2016, although its fully-electrified fleet would require 967 TWh for charging (7.9% of the standardized generation), while the level of PEVs in Brazil is low (1.2% of generation for charging). In Spain, the main effect is caused by a 23.6% increase in per capita

Fig. 5. Electricity generation per capita and passenger car per inhabitant data for the reference scenario in 2050 with standardized vehicle consumption. Numerical values are for 2050, and markers show 2016 data. 767

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Fig. 6. Ratio of renewable energy and hydropower in the electricity mix for the 2050 reference scenario with standardized EV energy. Markers indicate the actual 2016 information.

generation (without EVs), even though the population will presumably decrease 4.4%. The increase of power generation in Japan is highly influenced by 202 TWh (15.3% of generation) needed to charge the FCEVs. A moderate generation decrease is forecasted for Germany (-0.8%) because, although its per capita rate increases 3.1%, population is likely to decrease by 3.8%. Concerning RE forecasts, all countries plan to at least duplicate generation, except Canada and Norway, since these already have considerable rates, primarily based on hydropower. Norway will likely achieve a fully-renewable system by 2050. HP will continue being crucial for Brazil, Canada and Norway. Brazilian hydro production is expected to almost triple to satisfy demand. China will increase

production by 29%, but HP would not be able to deliver the amount of energy needed for progress. Germany plans to further develop fluvial resources and increase production by 24%, although HP share will still be low. Hydropower in the remaining countries is not expected to change significantly, except for a 22% decrease predicted for Spain, which is reasonable due to the decline in water levels caused by changing climate conditions (Ministerio de Agricultura, Pesca, Alimentación y Medio Ambiente (MAPAMA), 2017). PEV average accumulation rate results versus daily electricity generation, RE and HP for the 2050 reference scenario are shown in Fig. 7, with detailed information including FCEV impact available in Table A10.

Fig. 7. Ratio of electric vehicle energy accumulation capacity for average daily electricity generation, renewable energy and hydropower for the reference scenario in 2050. Results are based on current average energy density of 100 Wh/kg. Markers identify the additional capacity provided by FCEV if applicable. 768

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The results indicate that PEV forecasts would only provide acceptable accumulation capacity in countries with hydropower representing less than 15%. For Brazil, Canada and Norway, the relevance of HP conditions PEV storage capacity, although partly influenced by low mobility rates, except in the assumption for Norway, which is offset by its huge generation. In China, a complete PEV fleet would provide enough storage capacity for 63% of the average daily RE production. Fuel cell technology contribution seems unimportant due to the low FCEV battery capacity. In the case of Japan, a complete FCEV fleet would only store 9.3% of daily HP. If future hydrogen production is electricity intensive, FCEVs would represent a higher load for the system since their consumption exceeds PEV demand.

we use the 50% PEV rate projected by the EEA, and the 93% IMF prediction, with a 60%/40% BEV/PHEV ratio used in all cases (Kasten et al., 2016) for comparability purposes. It seems reasonable to assume that, as battery specifications improve, PHEVs could progressively phase out HEVs (International Energy Agency (IEA), 2014; Lutsey, 2015). Concerning battery capacity, 300 and 500 Wh/kg densities are evaluated using 100 Wh/kg (Yoo et al., 2014) as baseline for 2016. Since higher capacities increase PHEV electric range, UF values are revised to 0.9 and 0.95 respectively (Bradley and Quinn, 2010; Smart et al., 2014). The results of this analysis are in Table A11. A 50% PEV fleet with 300 Wh/kg batteries, which is a reasonable long-term outcome, could continuously accumulate more than half of average daily RE production in all countries, except Canada and Norway, penalized by their high electricity consumption, and the United States would achieve 116.6%. In the optimal scenario, with 93% PEV and 500 Wh/kg, all countries could store their full RE generation, excluding Norway (39.6%), thus confirming the relevant potential of electric vehicles as energy storage providers. Fig. A1 in the Appendix includes a graph with the RE data. Concerning hydropower, the results are shown in Fig. 8. The bars represent the range of storage ratios delimited by the least favorable 50% PEV | 300 Wh/kg case (lower values) and the optimal 93% PEV | 500 Wh/kg (upper values). Except for Norway, all countries could accumulate their complete HP daily production. For the Scandinavian country, the most favorable scenario could represent 52% HP storage. Furthermore, a 75% PEV rate with 300 Wh/kg batteries may deliver HP management capacity for all countries except Canada (67.6%) and Norway (25.4%), and enough storage for average daily RE in Brazil, China, Germany, Japan and the United States. Concerning Japan, the uncertainty of its mobility plans difficults the assessment. It seems that FCEVs will dominate the Japanese market. In such case, their low-capacity batteries would limit energy management and could be an important additional load. Nevertheless, a complete FCEV fleet could store daily HP using 13.7 kWh batteries, but this capacity is not needed, and would increase vehicle weight and price. An alternative could be deploying PEV and FCEV technologies in parallel, although it would require substantial investments and resources, and developing two infrastructures with a common objective is inefficient. Even so, in case of significant sales figures, FCEVs should be V2G capable. For countries with insufficient capacity, additional reserve could be obtained by complementing EVs with other devices installed at homes and offices.

3.2.2. EV deployment and battery capacity projections The main technological limitation for PEV commercialization is energy storage capacity (Chalk and Miller, 2006; Egbue and Long, 2012). Research and development advances and mass production forecasts are fostering battery cost reductions and increases in energy density (International Energy Agency (IEA), 2017). New technologies, like the solid-state battery (Kato et al., 2016; Schmitt, 2017b), and the use of supercapacitors (Faggioli et al., 1999) will increase energy and power density, and significantly reduce charging time. Recent projects have established clear objectives and milestones concerning battery technology advances, cost reductions and resource needs (Simon et al., 2015). The Batteries and Energy Storage subprogram of the DOE (United States Department of Energy (DOE, 2017b), plans to decrease battery production cost to a quarter of current price, and increase energy density from 100 Wh/kg to 250 Wh/kg by 2022. The European Association for Advanced Rechargeable Batteries (RECHARGE, 2013), forecasts 290 Wh/kg by 2030, and the ALISE project (ALISE, 2017) is developing new technologies to achieve densities of 500 Wh/kg. Significant advances in battery performance would encourage rapid PEV deployment. The IEA (International Energy Agency (IEA), 2011) suggests that worldwide sales should reach 50% by 2050. The European Environment Agency (EEA) evaluates two scenarios: a 2050 PEV fleet share of 50% and 80%, and compares these results with European Commission forecasts (Kasten et al., 2016; European Environment Agency (EEA), 2017). In the United States, recent studies predict sales of 7.3% in 2025, and a fleet of approximately 3% by such year (Cooper and Schefter, 2017), with at least a 65% market share by 2050, which could even reach 70-75% (Rissman, 2017). Higher penetration rates have been forecasted by the International Monetary Fund (IMF), predicting a 93% EV stock around 2040 (Cherif et al., 2017). These diverse possibilities are assessed considering two representative PEV rates and two energy densities. As 2050 fleet forecasts,

4. Conclusions This article evaluates the viability of using electric vehicles to

Fig. 8. PEV energy accumulation rate for average daily hydropower generation for PEV 50% and 93% rates and battery density analysis for 300 and 500 Wh/kg. Markers show the values for the reference scenario (without FCEV). 769

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accumulate energy and mitigate hydropeaking effects on rivers by assessing several PEV deployment and battery energy density scenarios in eight countries with diverse socioeconomic and technological environments. Numerous factors intervene and affect the energy coverage rate, but reasonable long-term forecasts indicate that hydropower-induced water flow variability could be significantly alleviated using V2G management. This possibility is especially notable in countries with low hydropower production (below 15% in 2050), such as China, Germany, the United States, Japan or Spain. If supported by adequate PEV energy storage, hydropower plants could level their production by generating equivalent amounts of energy through an increase of the unit commitment interval. Hence, lower number of turbines would be required, so plant equipment and maintenance costs could be reduced. Furthermore, the allocation of PEV energy storage may ameliorate the hydrologic alteration which originates the impact, reducing the need of measures to decrease the side effects of hydropeaking, so expenses in mitigation and compensation are lowered. Long-term forecasts for Brazil and China reveal that development plans in countries with emerging economies should also include PEV deployment initiatives to protect the natural regime of their rivers. Since a more gradual plant operation facilitates ecological flow

management and natural flow simulation, we propose policy makers, system operators, electricity markets and hydro generation firms to further assess the feasibility of storing hydropower in EV batteries, while encourage researchers to consider hydro and EV integration with the purpose of reducing HP peaking events. Further research is also needed to gather more detailed information concerning PEV user connectivity behavior and how charging infrastructure and capacity influence V2G availability, since these factors are very dependable on local conditions. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgments We thank Confederación Hidrográfica del Miño-Sil, the United States Energy Information Administration (EIA), Empresa de Pesquisa Energética (EPE) and the National Energy Board (NEB) of Canada for their kind support to our work.

Appendix A

Table A1 Population, and electricity generation and technology mix per country data for 2016.

1

Population (millions) Annual electricity generation (TWh/year) 1 Electricity generation per capita (kWh/(year x inhabitant)) Average daily electricity generation (TWh/day) Ratio of renewable energy (RE) to total electricity generation (%) Ratio of hydropower (HP) to total electricity generation (%) 2 Hydropower generation per capita (kWh/(year x inhabitant))

2

Brazil

Canada

China

Germany

Japan

Norway

Spain

United States

208.0 578.9 2,783 1.58 80.4% 65.8% 1,831

36.3 667.4 18,410 1.82 65.0% 58.0% 10,678

1,378.2 6,217.9 4,511 16.99 25.5% 19.2% 866

82.3 649.1 7,885 1.77 31.0% 4.0% 315

127.0 1,058.0 8,332 2.89 17.1% 8.2% 683

5.2 149.6 28,587 0.41 98.1% 96.4% 27,558

46.5 274.8 5,914 0.75 39.5% 14.5% 858

323.3 4,322.0 13,368 11.81 15.5% 6.8% 909

1

International Energy Agency (IEA). Statistics. Electricity generation by fuel. (Entered: October 3, 2018). https://www.iea.org/statistics/?country = WORLD& year = 2016&category = Key%20indicators&indicator=ElecGenByFuel&mode= chart&categoryBrowse=false&dataTable=ELECTRICITYANDHEAT& showDataTable = false 2 International Energy Agency (IEA). Statistics. Renewable electricity generation by source. (Entered: October 3, 2018). https://www.iea.org/statistics/ ?country = WORLD&year=2016&category = Key%20indicators&indicator = RenewGenBySource&mode = chart&categoryBrowse = false& dataTable = RENEWABLES&showDataTable = true Table A2 Vehicle registration data, annual vehicle km traveled (VKT) and availability rate for 2016.

Registered passenger cars (millions) Passenger cars per capita Registered battery electric vehicles (BEV) (thousands) Registered plug-in hybrid electric vehicles (PHEV) (thousands) Total registered BEV and PHEV (PEV) (thousands) Total registered BEV and PHEV (PEV) to total passenger car parc (%) Ratio of BEV to total BEV and PHEV (%) Annual vehicle kilometers traveled (VKT) (km/year) BEV V2G capacity ratio (%) PHEV V2G capacity ratio (%)

Brazil

Canada

China

Germany

Japan

Norway

Spain

United States

61.9 1 0.298 0.5 7 0.1 7 0.6 0.00% 77.6% 10,000 39.6% 37.3%

22.4 2 0.618 14.9 7 14.4 7 29.3 0.13% 50.9% 15,200 38.9% 35.3%

162.8 3 0.118 483.2 7 165.6 7 648.8 0.40% 74.5% 16,800 13 38.6% 34.7%

45.7 0.555 6 40.9 7 31.8 7 72.7 0.16% 56.3% 13,500 11,14 39.1% 35.9%

61.4 4 0.484 86.4 7 64.9 7 151.3 0.25% 57.1% 8,558 15 39.8% 37.8%

2.6 0.506 6 83.1 7 31.0 7 114.1 4.31% 72.9% 12,258 16 39.3% 36.4%

22.9 0.492 6 6.5 8,9 3.0 8,9 9.5 0.04% 68.2% 9,190 17 39.7% 37.6%

247.6 5 0.766 297.1 7 266.7 7 563.8 0.23% 52.7% 18,519 18 38.4% 34.1%

10

1

11,12

Departamento Nacional de Trânsito. (2018). Frota de veículos 2016 – RENAVAM. (Entered: October 4, 2018). http://www.denatran.gov.br/estatistica/261-frota2016 2 Statistics Canada. (2018). Road motor vehicle registrations, by type of vehicle. Table 23-10-0067-01. (Entered: Entered: October 4, 2018). https:// www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid = 2310006701 3 Statista. (2018). Amount of passenger cars in China from 2006 to 2016 (in millions). (Entered: October 4, 2018). https://www.statista.com/statistics/278423/ amount-of-passenger-cars-in-china/ 4 Japan Automobile Manufacturers Association (JAMA). (2018). The Motor Industry of Japan 2018. (Entered: October 4, 2018). http://www.jama.org/wp-content/ uploads/2018/07/mij2018.pdf. 5 United States Department of Transportation (DOT). Number of U.S. Aircraft, Vehicles, Vessels, and Other Conveyances. Table 1-11. (Entered: October 4, 2018). https://www.bts.gov/content/number-us-aircraft-vehicles-vessels-and-other-conveyances 770

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Eurostat. (2018). Passenger cars per 1,000 inhabitants. (Entered: October 4, 2018). http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=road_eqs_carhab& lang=en 7 International Energy Agency (IEA). (2018). Global EV Outlook 2018. (Entered: October 4, 2018). https://webstore.iea.org/global-ev-outlook-2018 8 International Energy Agency (IEA). (2016). Global EV Outlook 2016. (Entered: October 8, 2018). https://www.iea.org/publications/freepublications/publication/ Global_EV_Outlook_2016.pdf 9 Asociación Española de Fabricantes de Automóviles y Camiones (ANFAC). (2017). Annual Report 2016. http://www.anfac.com/publicaciones.action 10 Estimated. No information could be obtained from DENATRAN. 11 Organisation for Economic Co-operation and Development (OECD). (2018). Transport performance indicators: Traffic. Road traffic in thousand vehicle-km per road motor vehicle. (Entered: October 5, 2018). https://stats.oecd.org/BrandedView.aspx?oecd_bv_id=trsprt-data-en&doi=g2g5557f-en 12 Data is for 2014. 13 Huo, H., Zhang, Q., He, K., Yao, Z., and Wang, M. (2012). Vehicle-use intensity in China: Current status and future trend. Energy Policy. Vol. 43. 6-16. doi: https:// doi.org/10.1016/j.enpol.2011.09.019 14 Data is for 2015. 15 Japan Ministry of Land, Infrastructure and Transport. Road Bureau (MLIT). (2018). Statistics: Vehicular traffic volume (2016). (Entered: October 8, 2018). http:// www.mlit.go.jp/road/road_e/statistics.html 16 Statistics Norway. (2018). Road traffic volumes, by type of vehicle. Average per vehicle. Kms. (Entered: October 4, 2018). https://www.ssb.no/en/transport-ogreiseliv/statistikker/klreg/aar 17 Ministerio de Fomento. (2018). Observatorio del transporte y la logística en España. Tráfico de viajeros y mercancías por carretera (vehículos-kilómetro) por clase y tipo de vehículo. (Entered: October 5, 2018). http://observatoriotransporte.fomento.es/BDOTLE/visorBDpop.aspx?i=606 18 United States Department of Transportation (DOT). (2018). U.S. Vehicle-Miles. (Entered: October 5, 2018). https://www.bts.gov/content/us-vehicle-miles

Table A3 Average battery storage capacity and electric vehicle energy consumption data based on 2016 specifications.

Battery electric vehicles (BEV) - Nissan Leaf 1 - Tesla Model S 3 - BMW i3 4 Average (BEV) Plug-in hybrid vehicle (PHEV) - BYD Tang 6 - Mitsubishi Outlander 7 - Chevrolet Volt 8 Average (PHEV) Fuel cell electric vehicle (FCEV) - Toyota Mirai - Hyundai ix35 FCEV Average (FCEV)

Battery capacity (kWh)

Electric motor power (kW)

Energy consumption (kWh/km)

30.0 75.0 27.2 44.1

80.0 285.0 125.0 163.3

0.1742 2 0.1872 2 0.1260 5 0.1625

18.4 12.0 18.4 16.3

220.0 61.1 111.0 130.7 Battery output power 21.0 10 24.0 14 22.5

0.2300 0.2308 0.1907 2 0.2172

1.60 9 0.95 13 1.28

1

0.4581 2,11,12 0.4376 12,14 0.4478 12

Nissan Motor Company. (2018). 2016 Nissan Leaf specifications. (Entered: October 5, 2018). http://nissannews.com/media_storage/downloads/ 2016_Nissan_LEAF_Specs_FINAL.pdf 2 EPA-estimated range. 3 United States Department of Energy (DOE). Office of Transportation & Air Quality. (2018). (Entered: October 8, 2018). https://www.fueleconomy.gov/feg/Find.do?action = sbs&id = 37421 4 BMW Group. (2016). Technical specifications for the BMW i3, valid from 07/16. (Entered: October 5, 2018). https://www.press.bmwgroup.com/ global/article/detail/T0259598EN/technical-specifications-for-the-bmw-i3-94ah-valid-from-07/2016?language = en 5 EU cycle. 6 ChinaAutoweb. (2108) BYD Tang. (Entered: October 9, 2018). https://chinaautoweb.com/car-models/byd-tang/ 7 Herrero, A. (2015). Mitsubishi Outlander (2015). Información general. Km77. April 27, 2015. (Entered: October 10, 2018). https://www.km77.com/ coches/mitsubishi/outlander/2015/estandar/informacion 8 Chevrolet Pressroom. (2018). 2016 Chevrolet Volt specifications. (Entered: October 5, 2018). https://media.chevrolet.com/media/us/en/chevrolet/ vehicles/volt/2016.tab1.html 9 Voelcker, J. (2016). 2016 Toyota Mirai Review. The Car Connection. June 27, 2016. (Entered: October 9, 2018). https://www.thecarconnection.com/overview/toyota_mirai_2016 10 Toyota Myanmar. (2018). Hybrid vehicle technology file. (Entered: October 9, 2018). https://www.toyota-myanmar.com/innovation/environmenttechnology/hybrid-vehicle/technology-file 11 Toyota Pressroom. (2018). 2016 Mirai Product Information. (Entered: October 9, 2018). https://pressroom.toyota.com/releases/ 2016+toyota + mirai + fuel + cell + product.download 12 Hydrogen production is assumed to be obtained by electrolysis, using 46 kW h of energy to obtain 1 kg of H2. Source: United States Department of Energy. DOE Technical Targets for Hydrogen Production from Electrolysis. (Entered: October 8, 2018). https://www.energy.gov/eere/fuelcells/doetechnical-targets-hydrogen-production-electrolysis. 13 Automobile Catalog. (2018). 2016 Hyundai ix35 Fuel Cell (model since June 2016 for Europe) specifications & performance data review. (Entered: October 9, 2018). http://www.automobile-catalog.com/car/2016/2561420/hyundai_ix35_fuel_cell.html. 14 Hyundai Motor Company. (2018). ix35 Fuel cell. Realizing the dream of a clean environment. (Entered: October 9, 2018). http://www.hyundai. com/eu/en/Showroom/Eco/ix35-Fuel-Cell/PIP/index.html. 771

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Table A4 Electricity generation and technology mix ratios for the hypothetical complete PEV deployment scenario based on 2016 country data. Data includes both cases of additional PEV energy sourced from non-RE sources and all RE.

Annual electricity generation with full PEV deployment (TWh/year) Annual electricity required for PEV charging (TWh/year) Ratio of PEV demand versus generation without PEV (%) Electricity generation per capita with PEV charging (kWh/(year x inhabitant)) Ratio of RE to total electricity generation if additional PEV energy non-RE (%) Ratio of HP to total electricity generation if additional PEV energy non-RE (%) Ratio of RE to total electricity generation if additional PEV energy all RE (%) Ratio of HP to total electricity generation if additional PEV energy all RE (%) Average PEV battery capacity required for a complete daily HP storage (kWh)

Brazil

Canada

China

Germany

Japan

Norway

Spain

United States

675.2 96.3 16.6% 3,246 68.9% 56.4% 83.2% 68.1% 42.9

720.3 53.0 7.9% 19,870 60.2% 53.7% 67.6% 60.3% 123.4

6,641.5 425.3 6.8% 4,819 23.9% 18.0% 30.3% 22.8% 52.8

744.9 95.9 14.8% 9,048 27.0% 3.5% 39.9% 5.1% 4.0

1,139.5 81.7 7.7% 8,974 15.9% 7.6% 23.0% 11.0% 9.8

154.5 5.0 3.4% 29,509 95.0% 93.4% 98.2% 96.5% 383.7

307.4 32.7 11.9% 6,617 35.3% 13.0% 45.9% 16.9% 12.1

5,033.6 713.2 16.5% 15,569 13.3% 5.8% 27.4% 12.0% 8.6

Table A5 PEV average energy accumulation rate for average daily electricity, renewable energy and hydropower generation for current PEV rate and a hypothetical scenario with complete PEV deployment. PEV additional energy assumed to be obtained from non-renewable sources. In parenthesis, results if extra energy were all RE. Calculations based on 2016 data. 2016 actual scenario

Brazil Canada China Germany Japan Norway Spain United States

2016 hypothetical scenario

Storage capacity vs. average daily electricity generation

Storage capacity vs. average daily renewable energy (RE)

Storage capacity vs. average daily hydropower (HP)

Storage capacity vs. average daily electricity generation

Storage capacity vs. average daily renewable energy (RE)

Storage capacity vs. average daily hydropower (HP)

0.00% 0.02% 0.05% 0.05% 0.07% 0.40% 0.02% 0.06%

0.00% 0.03% 0.21% 0.16% 0.39% 0.40% 0.04% 0.36%

0.00% 0.03% 0.28% 1.26% 0.81% 0.41% 0.12% 0.81%

44.7% 14.8% 11.6% 29.4% 26.4% 8.3% 36.4% 23.0%

64.8% (53.7%) 24.6% (21.9%) 48.5% (38.2%) 108.9% (73.8%) 166.5% (114.8%) 8.7% (8.4%) 103.0% (79.2%) 173.0% (83.9%)

79.2% (65.6%) 27.5% (24.5%) 64.4% (50.8%) 843.6% (571.6%) 347.2% (239.3%) 8.9% (8.6%) 280.6% (215.7%) 394.4% (191.3%)

Table A6 Ratio of battery power output capacity versus installed hydropower capacity for the hypothetical scenario with complete PEV deployment. Calculations based on 2016 data. Hydropower capacity (MW) Brazil Canada China Germany Japan Norway Spain United States

1,2

98,015 79,323 331,110 11,258 49,905 31,626 20,354 102,485

Battery electric vehicle (BEV)

Plug-in hybrid electric vehicle (PHEV)

Total

5,390% 2,410% 4,193% 46,927% 10,495% 714% 9,579% 20,611%

2,460% 1,100% 1,914% 15,798% 4,789% 326% 4,372% 9,406%

7,850% 3,509% 6,107% 62,725% 15,284% 1,040% 13,951% 30,017%

1

International Hydropower Association (IHA). (2017). 2017 Hydropower Status Report. https://www.hydropower.org/sites/default/files/publications-docs/ 2017%20Hydropower%20Status%20 Report.pdf 2 With pumped-storage.

Table A7 Demographic prevision, and electricity generation and technology mix long-term projections per country obtained from reliable sources for the reference scenario in 2050. Brazil Population (millions) Annual electricity generation (TWh/year) Electricity generation per capita (kWh/(year x inhabitant))

Canada 1

232.7 1,605.0 6,898

*

1

4,5

China

*

Germany 1

44.9 760.7 6,7 16,924

1,364.5 12,075.6 8,850

*

1

8,9

79.2 639.8 8,074

Japan

*

Norway

1

10,11

108.8 1,116.9 10,266

1

8,9

6.8 188.0 12 27,639

Spain

*

United States

10,11

397.5 2,3 5,395.2 13 13,573

1

44.4 347.1 7,818

(continued on next page)

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Table A7 (continued)

Variation in electricity generation per capita 2016-2050 (%) Average daily electricity generation (TWh/day) Ratio of RE to total electricity generation (%) Ratio of HP to total electricity generation (%)

Brazil

Canada

148% 4.4 84.0% 58.8%

−8% 2.1 71.8% 55.8%

8,9 8,9

*

China

*

96% 33.1 34.6% 12.8%

6,7 6,7

Germany

*

2% 1.8 71.4% 10,11 5.0% 10,11

8,9 8,9

Japan

*

23% 3.1 34.2% 8,9 9.6% 8,9

*

Norway

Spain

United States

−3% 0.5 100.0% 12 75.5% 12

32% 1.0 85.9% 10,11 9.0% 10,11

2% 14.8 30.6% 14 5.5% 14

*

Energy generation and technology mix ratios for 2050 calculated as linear projection of data for 2016-2040 using per capita values. United Nations Department of Economic and Social Affairs (UN). (2017). World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. (Entered: October 10, 2018). https://esa.un.org/unpd/wpp/Publications/Files/WPP2017_KeyFindings.pdf 2 United States Energy Information Administration (EIA). (2018a, 2018b). Annual Energy Outlook 2018. Macroeconomic indicators. (Entered: October 11, 2018). https://www.eia.gov/outlooks/aeo/data/browser/#/?id = 18EO2017&cases = ref2017&sourcekey = 0 3 Population with Armed Forces Overseas value, since data without expatriate population is not included. It is assumed that the effect is offset by the foreign population residing in the United States. 4 Empresa de Pesquisa Energética (EPE). (2016). Demanda de Energia 2050. Nota técnica DEA 13/15. http://www.epe.gov.br/Estudos/Documents/DEA%20135%20Demanda%20de%20Energia%202050.pdf 5 2050 electricity demand forecast, including autoproducers, since these are connected to the grid. 6 National Energy Board. Government of Canada. (2017). Canada’s Energy Future 2017. Energy and Supply Projections to 2040. ISSN 2292-1710. http://www.nebone.gc.ca/nrg/ntgrtd/ftr/2017/pblctn-eng 7 Reference case. 8 International Energy Agency (IEA). (2015). World Energy Outlook 2015. https://www.iea.org/newsroom/news/2015/november/world-energy-outlook-2015.html 9 New policies scenario. 10 European Commission (EC). (2016). EU Reference Scenario 2016. Energy, transport and GHG emissions - Trends to 2050. https://ec.europa.eu/energy/sites/ener/ files/documents/ref2016_report_final-web.pdf 11 Reference scenario. 12 Graabak, I., and Warland, L. (2014). Deliverable 3.1 A carbon neutral power system in the Nordic region in 2050. Norstrat Project. SINTEF. Project 12 × 765. (Entered: October 11, 2018). https://www.sintef.no/globalassets/project/norstrat/d3.1—a-carbon-neutral-nordic-power-system-in-2050.pdf 13 United States Energy Information Administration (EIA). (2018a, 2018b). Annual Energy Outlook 2018. Table: Electric Power Projections by Electricity Market Module Region. (Entered: October 12, 2018). https://www.eia.gov/outlooks/aeo/data/browser/#/?id = 8-AEO2017&cases = ref2017&sourcekey = 0 14 United States Energy Information Administration (EIA). (2018a, 2018b). Annual Energy Outlook 2018. Table: Renewable Energy Generation by Fuel. (Entered: October 18, 2018). https://www.eia.gov/outlooks/aeo/data/browser/#/?id = 8-AEO2017&cases = ref2017&sourcekey = 0 1

Table A8 Passenger car parc and powertrain technology mix projections for the reference scenario in 2050. Brazil Registered passenger cars (millions) Passenger cars per capita Passenger cars per capita 2050 vs. 2016 Annual vehicle kilometers traveled (VKT) (km/year) Variation in VKT 2016-2050 (%) BEV V2G capacity ratio (%) PHEV V2G capacity ratio (%) Passenger car (PC) powertrain composition

Brazil 1

Flexfuel CNG Petrol Diesel Other non-EV Hybrid EV Plug-in hybrid (PHEV) Battery electric vehicle (BEV)

30.7% 5.3% 5.0% 49.8% 9.2%

Canada 1

129.3 0.556 86.6% 10,000 0.0% 39.6% 37.2%

6

27.8 0.618 0.0% 15,200 0.0% 38.9% 35.3%

Canada 10,11

*

China 2,3

6

511.3 0.375 217.3% 12,000 7 −28.6% 38.6% 34.7% China

77.3%

11.1% 11.6%

Germany

25.0% 75.0%

*

4

49.3 0.622 12.1% 13,500 0.0% 39.1% 35.9% Germany 4

6

Japan

*

52.6 0.484 0.0% 8,558 6 0.0% 39.8% 37.8% Japan

Norway 3.4 0.506 0.0% 12,258 0.0% 39.3% 36.4% Norway

*

6

Spain

*

21.8 0.492 0.0% 13,343 45.2% 39.7% 37.5% Spain 4

United States

8

284.2 5 0.715 −6.7% 19,113 9 3.2% 38.4% 34.0% United States 5

5.0%

5.0%

14.0% 33.0%

14.0% 33.0%

75.0% 8.3%

31.0% 9.0% 6.0% 2.0%

4.7% 1.8% 9.9% 0.3%

31.0% 9.0% 6.0% 2.0%

100%

50.0% 50.0%

Passenger car 2050 projection based on 2016 vehicle per capita rate. 1 Empresa de Pesquisa Energética (EPE). (2016). Demanda de Energia 2050. Nota técnica DEA 13/15. http://www.epe.gov.br/Estudos/Documents/DEA%20135%20Demanda%20de%20Energia%202050.pdf 2 Huo, H., and Wang, M. (2012). Modeling future vehicle sales and stock in China. Energy Policy. Vol. 43. 17-29. https://doi.org/10.1016/j.enpol.2011.09.063. http://www.sciencedirect.com/science/article/pii/S0301421511007774 3 Average between low and high scenarios. 4 European Commission (EC). (2016). EU Reference Scenario 2106. Energy, transport and GHG emissions. Trends to 2050. https://ec.europa.eu/energy/sites/ener/ files/documents/ref2016_report_final-web.pdf

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A. Román, et al. 5

United States Energy Information Administration (EIA). (2018a, 2018b). Annual Energy Outlook 2018. Table: Light-Duty Vehicle Stock by Technology Type. (Entered: October 12, 2018). https://www.eia.gov/outlooks/aeo/data/browser/#/?id = 8-AEO2017&cases = ref2017&sourcekey = 0. 6 VKT assumed to be similar to 2016. 7 Huo et al. (2012). Vehicle-use intensity in China: Current status and future trend. Energy Policy. Vol. 43. 6-16. https://doi.org/10.1016/j.enpol.2011.09.019. https://www.sciencedirect.com/science/article/pii/S0301421511007087 8 EU 2050 energy forecast used to revise VKT to values prior to the economic crisis. Instituto Nacional de Estadística (INE). (2008). Km medios recorridos al año por los vehículos para uso personal, por relación con la actividad económica de la persona de referencia y antigüedad del vehículo. (Entered: October 14, 2018). http://www.ine.es/jaxi/Datos.htm?path=/t25/p500/2008/p10/l0/&file = 10020.px 9 United States Energy Information Administration (EIA). (2018a, 2018b). Annual Energy Outlook 2018. Table: Transportation Sector Key Indicators and Delivered Energy Consumption. (Entered: October 12, 2018). https://www.eia.gov/outlooks/aeo/data/browser/#/?id = 7-AEO2018&cases = ref2018&sourcekey = 0 10 National Energy Board. Government of Canada. (2017). Canada’s Energy Future 2017. Energy and Supply Projections to 2040. ISSN 2292-1710. http://www.nebone.gc.ca/nrg/ntgrtd/ftr/2017/2017nrgftr-eng.pdf 11 2050 PEV share computed as projection of the 2040 reference scenario forecast, and PEV composition based on November 1, 2017 fleet. Source: https:// www.fleetcarma.com/electric-vehicle-sales-canada-q1-2017/

Table A9 Electricity generation and technology mix long-term projections per country standardized for the reference scenario in 2050.

Annual standardized electricity generation with EV (PEV and FCEV) (TWh/year) Annual standardized electricity generation for EV (PEV and FCEV) (TWh/year) * Average vehicle consumption from source (kWh/km) ** Average standardized vehicle consumption (kWh/km) ** Electricity generation per capita (kWh/(year x inhabitant)) Variation in electricity generation per capita 2016-2050 (%) Ratio of renewable energy (RE) to total electricity generation (%) Ratio of hydropower (HP) to total electricity generation (%)

Brazil

Canada

China

Germany

Japan

Norway

Spain

United States

1,594.3 19.3 0.2521 1 0.1625 6,852 146.2% 84.6% 59.2%

761.3 14.7 0.1762 2 0.1837 16,937 −8.0% 71.7% 55.7%

12,275.6 967.4 0.1367 3 0.1723 8,997 99.4% 34.0% 12.6%

644.2 21.0 0.1791 0.2267 8,130 3.1% 70.9% 5.0%

1,318.5 201.6 0.4478 6 0.4478 12,119 45.5% 29.0% 8.2%

189.2 6.5 0.1500 7 0.1842 27,815 −2.7% 99.4% 75.1%

348.8 9.2 0.1843 0.2267 7,856 32.8% 85.5% 8.9%

5,324.3 109.7 0.2905 8 0.1764 13,395 0.2% 31.0% 5.6%

4,5

4,5

*

With assumption that all hydrogen for FCEV fuel is obtained by electrolysis. APHEV Utility Factor of 66% is used in all calculations. Consumption values are calculated in only-electric mode. 1 Empresa de Pesquisa Energética (EPE). (2016). Demanda de Energia 2050. Nota técnica DEA 13/15. http://www.epe.gov.br/Estudos/Documents/DEA%20135%20Demanda%20de%20Energia%202050.pdf 2 National Energy Board. Government of Canada. (2017). Canada’s Energy Future 2017. Energy and Supply Projections to 2040. ISSN 2292-1710. http://www.nebone.gc.ca/nrg/ntgrtd/ftr/2017/2017nrgftr-eng.pdf 3 Electric vehicle energy consumption for China is calculated using average value suggested by The International Council on Clean Transportation (ICCT). Slowik, P., Pavlenko, N., and Lutsey, N. (2016). Assessment of next-generation electric vehicle technologies. The International Council on Clean Transportation (ICCT). White Paper. October 2016. http://www.theicct.org/sites/default/files/publications/Next%20 Gen%20 EV%20Tech_white-paper_ICCT_31102016.pdf 4 European Commission (EC). (2016). EU Reference Scenario 2106. Energy, transport and GHG emissions. Trends to 2050. https://ec.europa.eu/energy/sites/ener/ files/documents/ref2016_report_final-web.pdf 5 Calculations are performed using EU 28 reference scenario and adjusted using country PKT data proportions. 6 Hydrogen consumption is used to calculate FCEV energy demand based on production by electrolysis. 7 Liu, Z., Wu, Q., Petersen, P., et al. (2013). D2.1. Large scale deployment of electric vehicles (EVs) and heat pumps (HPs) in the Nordic Region. Technical University of Denmark (DTU). Center for Electric Power and Energy (CEE). (Available online: October 9, 2018). https://www.sintef.no/globalassets/project/norstrat/d2.1— large-scale-deployment-of-evs-and-hps-in-the-nordic-region.pdf 8 United States Energy Information Administration (EIA). (2018a, 2018b). Annual Energy Outlook 2018. Table: Transportation Fleet Car and Truck Fuel Consumption by Type and Technology (Entered: October 14, 2018). https://www.eia.gov/outlooks/aeo/data/browser/#/?id = 8-AEO2017&cases = ref2017&sourcekey = 0 **

Table A10 PEV energy accumulation rate for average daily electricity generation, RE and HP for the 2050 reference scenario. Results include with and without fuel cell electric vehicle (FCEV) V2G capability.

Brazil Canada China Germany Japan Norway Spain United States

Storage capacity vs. average daily generation

Storage capacity vs. average daily RE

Storage capacity vs. average daily HP

Without FCEV

With FCEV

Without FCEV

With FCEV

Without FCEV

With FCEV

4.8% 3.5% 21.6% 4.4% 0.0% 7.7% 3.7% 3.5%

4.8% 3.5% 21.6% 4.4% 0.8% 7.7% 3.7% 3.5%

5.6% 4.9% 63.3% 6.1% 0.0% 7.8% 4.3% 11.2%

5.6% 4.9% 63.3% 6.2% 2.6% 7.8% 4.3% 11.2%

8.0% 6.3% 171.4% 86.9% 0.0% 10.3% 40.9% 61.9%

8.0% 6.3% 171.4% 87.5% 9.3% 10.3% 41.2% 62.0%

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Table A11 PEV energy accumulation rate for average daily electricity generation, renewable energy and hydropower for PEV 50% and 93% rates and battery density analysis for 300 and 500 Wh/kg. Generation 50% 50% 93% 93%

PEV PEV PEV PEV

(300 (500 (300 (500

Wh/kg) Wh/kg) Wh/kg) Wh/kg)

Brazil

Canada

China

Germany

Japan

Norway

Spain

United States

54.0% 89.9% 95.0% 157.9%

24.4% 40.6% 43.6% 72.5%

29.5% 49.1% 52.8% 87.8%

50.2% 83.5% 87.0% 144.4%

32.3% 53.8% 58.4% 97.2%

12.9% 21.5% 23.6% 39.2%

42.4% 70.5% 74.4% 123.5%

33.8% 56.2% 58.7% 97.4%

Renewable energy

Brazil

Canada

China

Germany

Japan

Norway

Spain

United States

50% 50% 93% 93%

67.7% 112.8% 125.9% 209.8%

35.0% 58.4% 65.2% 108.6%

83.6% 139.3% 155.4% 259.1%

74.9% 124.8% 139.3% 232.2%

97.8% 163.1% 182.0% 303.3%

12.8% 21.3% 23.8% 39.6%

51.9% 86.5% 96.6% 160.9%

116.6% 194.3% 216.9% 361.4%

PEV PEV PEV PEV

(300 (500 (300 (500

Wh/kg) Wh/kg) Wh/kg) Wh/kg)

Hydropower 50% 50% 93% 93%

PEV PEV PEV PEV

(300 (500 (300 (500

Wh/kg) Wh/kg) Wh/kg) Wh/kg)

Brazil

Canada

China

Germany

Japan

Norway

Spain

United States

96.6% 161.0% 179.7% 299.5%

45.1% 75.1% 83.8% 139.7%

226.1% 376.9% 420.6% 701.0%

1,059.6% 1,766.0% 1,970.8% 3,284.7%

347.0% 578.4% 645.5% 1,075.9%

16.9% 28.2% 31.5% 52.5%

496.6% 827.6% 923.6% 1,539.3%

645.2% 1,075.3% 1,200.1% 2,000.1%

Fig. A1. PEV energy accumulation rate for average daily RE generation for PEV 50% and 93% rates and battery density analysis for 300 and 500 Wh/kg. Markers show the values for the reference scenario (without FCEV).

Appendix B. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.resconrec.2019.04.032.

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