Quantifying the Energy & Carbon Emissions Implications of a 10% Electric Vehicles Target a,b,
Aoife Foley *, Hannah Dalyb and Brian Ó Gallachóira,b a
Dept. of Civil & Environmental Engineering, School of Engineering, University College Cork, College Rd., Cork, Ireland b
Environmental Research Institute, University College Cork, Lee Rd., Cork, Ireland
*Corresponding author. Tel.: +353 87 2874092; fax: +353 21 427 6648 E-mail address:
[email protected]
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Abstract EU Directive 2009/28/EC on Renewable Energy requires each Member State to ensure 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020 (10% RES-T target). In addition to the anticipated growth in biofuels, this target is expected to be met by the increased electrification of transport coupled with a growing contribution from renewable energy to electricity generation. Energy use in transport accounted for nearly half of Ireland’s total final energy demand and about a third of energyrelated carbon dioxide emissions in 2007. Energy use in transport has grown by 6.3% per annum on average in the period 1990 – 2007. This high share and fast growth relative to other countries highlights the challenges Ireland faces in meeting ambitious renewable energy targets. The Irish Government has set a specific target for Electric Vehicles (EV) as part of its strategy to deliver the 10% RES-T target. By 2020, 10% of all vehicles in its transport fleet are to be powered by electricity. This paper quantifies the impacts on energy and carbon dioxide emissions of this 10% EV target by 2020. In order to do this an ‘EV Car Stock’ model was developed to analyse the historical and future make-up of the passenger car portion of the fleet to 2025. Three scenarios for possible take-up in EVs were examined and the associated energy and emissions impacts are quantified. These impacts are then compared to Ireland’s 10% RES-T target and greenhouse gas (GHG) emissions reduction targets for 2020. Two key findings of the study are that the 10% EV target contributes 1.7% to the 10% RES-T target by 2020 and 1.4% to the 20% reduction in Non-ETS emissions by 2020 relative to 2005.
Keywords: Carbon Emissions, Electric Vehicles, Energy, Forecasting, Internal Combustion Engines, Modelling, Passenger Car Vehicles
Introduction A number of countries including some European Union (EU) member states, Japan, South Korea, Canada, China, Israel and the United States of America (USA) have established electric vehicle (EV) targets, policies and plans. EVs are supported due to potential benefits in employment via technology research and development opportunities and the manufacture and deployment of EV infrastructure. EVs are also presented as an opportunity to integrate renewable energy sources (RES), for example EV charging by wind power, which should result in a better security of energy supply by reducing oil imports. Table 1 presents some Page 2 of 21
international targets adapted from References [1 and 2]. European policies on EVs are provided in Reference [3]. Automobile manufacturers, the electricity industry and governments have identified that EVs have the potential to reduce carbon dioxide (CO2) emissions as well as some other pollutants associated with road transport such as particulate matter (PM), carbon monoxide (CO), oxide of nitrous (NOx), nitrogen dioxide (NO2), oxides of sulphur (SOx), sulphur dioxide (SO2) and volatile organic compounds (VOC), to name just a few. Reference [4] provides a detailed review of over 40 studies carried out in the USA to examine the effects of EVs on well-to-wheel emissions. More recent studies, which examine potential greenhouse gas (GHG) emissions reductions from EVs include References [5, 6, 7, 8, 9 and 10]. Country Austria Australia Canada China Denmark France Germany Ireland Israel Japan New Zealand Spain Sweden United Kingdom United States of America
Targets 2020: 100,000 EVs deployed1 2012: first cars on road, 2018: mass deployment, 2050: up to 65% of car stock2 2018: 500,000 EVs deployed3 2011: 500000 annual production of EVs4 2020:200,000 EVs 5 2020: 2,000,000 EVs6 2020: 1,000,000 EVs deployed7 2020: 10% EV market share8 2011: 40,000 EVs, 2012: 40,000 to 100,000 EVs annually9 2020: 50% market share of next generation vehicles10 2020: 5% market share, 2040: 60% market share11 2014: 1,000,000 EVs deployed12 2020: 600,000 EVs deployed13 No target figures, but policy to support EVs14 2015: 1,000,000 PHEV stock15
1
http://www.iea-retd.org/files/RETRANS100128%20Schauer.pdf http://australia.betterplace.com/assets/pdf/Better_Place_Australia_energy_white_paper-doc.pdf http://www.evtrm.gc.ca/pdfs/E-design_09_0581_electric_vehicle_e.pdf 4 http://www.nytimes.com/2009/04/02/business/global/02electric.html 5 http://www.ens.dk/en-US/Sider/forside.aspx 6 http://www.physorg.com/news173639548.html 7 http://www.evworld.com/news.cfm?newsid=23301 8 http://www.dcenr.gov.ie/Press+Releases/2008/Government+announces+plans+for+the+electrification+of+Irish+motoring.htm 9 http://www.betterplace.com/ 10 http://www.autosavant.com/2008/08/27/japan-charges-ahead-with-electric-cars/ 11 http://www.msnbc.msn.com/id/21246592/ 12 http://uk.reuters.com/article/idUKARO04096020080730 13 http://www.powercircle.org/en/display/Projects/swedish-electric-mobility-initative.aspx 14 http://www.dft.gov.uk/pgr/scienceresearch/technology/lowcarbonelecvehicles/ 15 http://www.businessweek.com/technology/content/jun2010/tc2010063_322564.htm 2 3
Table 1 Some International EV Target Objectives In Ireland in late 2008 the Irish Government set a target that 10% of all vehicles in its transport fleet be powered by electricity by 2020 [11]. Ireland also has set National targets for renewable energy to achieve 40% electricity, 12% heat and 10% transport from Renewable Energy Sources (RES) by 2020 [12 and 13]. Energy use in transport has grown significantly, increasing 6.3% per annum average between 1990 and 2007, reflecting the country’s rapid Page 3 of 21
economic growth [14]. Notwithstanding the economic slow down, continued growth of 3.2% per annum in transport energy from 2012 to 2020 is anticipated as private passenger car ownership is lower than other EU Member States [15]. Despite the recent high growth in passenger vehicle sales in Ireland, the passenger vehicle ownership rate in 2007 was 434 per thousand population compared with 471 in the UK, 504 in France and 566 in Germany (all in 2006) [16]. This suggests continued room for growth in passenger car sales in Ireland. In fact energy use in transport accounted for 43% of Ireland’s total final energy demand and 36% of energy-related CO2 emissions in 2007 [17]. By 2020 transport energy demand is projected to reach 6,711 ktoe under a baseline scenario. In order to calculate in absolute terms the 10% RES-T target, it is necessary to exclude aviation and marine transport and to incorporate actions to reduce the transport energy demand in the National Energy Efficiency Plan [18]. The resulting transport energy projection is 5,790 ktoe, hence achieving the target requires 579 ktoe RES. It is also worth noting that the renewable contribution to electricity consumed in EVs is weighted by a factor of 2.5 in accordance with Directive 2009/28/EC Article 3.4c. In parallel with the focus on renewable energy, the EU has a complementary package of measures that directly target climate change and these are likely to results in even stronger targets for RES-T in Ireland. This high share and fast growth relative to other countries highlight the challenges Ireland faces in meeting its ambitious renewable energy targets. Initial work by the authors based on simple high-level estimates indicated that the contribution in energy terms may be lower than anticipated [19]. This early work indicates that a 10% EV penetration target would at most result in 1.5% RES-T. Separate work concludes that electrification of the passenger car fleet in Ireland could reduce primary energy consumption and CO2 emissions by about 50% for each km travelled in electric mode [20]. The overarching EU target is to reduce emissions within the EU to 20% below 1990 levels by the year 2020 [21]. This is to be achieved by extending emissions trading from 2012 to 2020 with a target emissions reduction of 21% by 2020 relative to 2005 levels. For sectors (including transport) outside of emissions trading (Non-ETS sectors) the EU target is to achieve a 10% reduction by 2020 relative to 2005. Under EU Decision 2009/409/EC on effort sharing with regard to the EU Non-ETS emissions reduction, Ireland’s target is to achieve a 20% reduction by 2020 relative to 2005. Non-ETS emissions are projected to grow by 1% above 2005 levels by 2020, even if the National renewable energy targets are met, indicating the scale of the challenge [15]. Research by the authors, which includes new-car average emissions reaching 130g CO2/km by 2015, a biofuel target comprising of 4% by volume by Page 4 of 21
July 2010 as well as EVs deployment results in an overall reduction in Non-ETS CO2 emissions of 1.4% [22].
This paper builds on previous work by the authors using a bottom-up approach, quantifying in more detail the role of the 10% EV penetration target in contributing to Ireland’s 10% RES-T target by 2020 and the 20% reduction in Non-ETS emissions by 2020 relative to 2005. The model ‘EV Car Stock’ is used to analyse three scenarios over the period up to 2025 for possible EV take-up using the historical and future make-up of the passenger car portion of the transport fleet only using a plug-to-battery approach unlike the battery-to-wheel approach generally used. These scenarios are then compared to Ireland’s quantified 10% RES-T target and GHG emissions reduction targets for 2020.
Methodology The ‘EV Car Stock’ model is a further development of an existing model called ‘Car Stock’. In this model only ICE and EVs within the passenger car fleet are considered. The ‘Car Stock’ model is presented in Reference [23]. The Business As Usual (BAU) scenario of the ‘Car Stock’ is used as the baseline dataset for the ‘EV Car Stock’ model and profiles the ICE passenger car fleet according to fuel, vintage and engine cc up to 2020. This BAU scenario assumes that: •
Only ICE technologies used with improvements based on average specific energy consumption (SEC) for each engine cubic centilitres (cc) category decreases in line with trends from 2000 to 2008,
•
EU Regulation 443-2009 (mandating new-car average emissions reaching 130g CO2/km by 2015) is met on time,
•
Average mileage for petrol and diesel cars is constant at 2008 levels,
In ‘Car Stock’ the yearly volume of ICE car sales and activity (vehicle km) [21] are based on economic projections and historical correlation of sales with gross national product (GNP) [24] in Ireland and projected oil price [25], as shown in Figure 1. The purchasing structure is determined by EU Regulation 443-2009 using a 130g/km CO2 fleet average by 2015. The retirement rates are according to fuel type, vintage and engine cc are derived from historical stock data. In the ICE fleet CO2 emissions are estimated from activity (vehicle kilometre) and Page 5 of 21
average specific emissions (g/km), using standard EU test data for ‘combined’ cycle figures for new cars from EU Directive 80/1268/EEC as amended by 2004/3/EC and an ‘on-road’ factor to correct for national driving conditions
Figure 1 GNP, petrol price and private car activity and sales, indexed on 1991 levels
Scenarios & Technology Development The methodology used in the ‘EV Car Stock’ is the consideration of three scenarios using the original BAU dataset from ‘Car Stock’ as the baseline template. The ‘EV Car Stock’ scenarios reflect a low, medium and high deployment of EVs. Figure 2 represents the low, medium and high up take of EVs in the private passenger car portion of the transport fleet in Ireland, which reflects an estimate of the current international state-of-the-art in technology development and future plans. It is estimated that 8%, 5% and 3% of the private passenger car portion of the transport fleet will be EV by 2020, under the high, medium and low scenarios respectively. Note that this does not include any potential growth of biofuel vehicles in the fleet. In the ‘Biofuels Obligation Bill’ requires all fuel sold to contain 4% of biofuel by volume from July 2010.
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EVs fall into three main groups, which includes hybrid electric vehicles (HEVs), battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). HEVs run on a liquid fuel source, which can be petrol, diesel or a biofuel and use batteries to enhance fuel efficiency. BEVs run on an electric motor powered by batteries, which are recharged by plugging in the vehicle. PHEVs use both an ICE and electric motor, like a HEV, but the electric motor can be repowered by plugging in the vehicle like a BEV. Two types of PHEVs exist, in-series and in-parallel. In the in-series PHEV the electric motor is directly coupled to the wheels and the ICE charges the batteries only when needed. In the in-parallel PHEV both the electric motor and ICE are coupled to the wheels through the transmission. HEVs are not considered in this analysis as they do not use an external source of electric power.
Figure 2 EV Sales & Stock Penetration under Low, Medium and High Scenarios
Table 2 presents the latest data available with regard to a number of original equipment manufactures (OEM) in terms of a technology roadmap [1 and 26]. The development of EVs involves two sectors, the battery manufacturers and the EV manufacturers. BMW announced in early June 2010 that it was ceasing further work on the electric mini, as it was too expensive to build and that other manufacturers were heavily subsidising their EVs and that the stage of battery development is comparable with the ICE 100 years ago [27]. BMW’s Page 7 of 21
preference is for a battery swopping programme in order that drivers are not inconvenience at charging points, such as that proposed by the Israeli Better Place company [28]. Car manufacturer BYD Auto Fiat-Chrysler Ford GM
Battery manufacturer BYD Group A123 Systems Johnston Controls-Saft LG Chem
Hyundai
LG Chem, SK Energy and SB Limotive Continental and Johnston Controls-Saft GS Yuasa Corp. AESC
Mercedes-Benz Mitsubishi Nissan REVA Renault Subaru Tata Toyota Volkswagen
Indocel Technologies AESC AESC Electrovaya Panasonic Volkswagen and Toshiba Corp.
Production Target 2015: 100,000A No date, no numbersB 5,000 per annum 2011: 10,000 & 2012: 60,000C 2018: 500,000 No date, no numbersD 2010: 5,000, 2011: 15,000 2010: 50,000, 2012: 100,000 No date, no numbers By 2010 150,000/annum 2010: 100E No date, no numbers No date, no numbers 2011: 500
A
http://blogs.edmunds.com/greencaradvisor/2010/03/byd-auto-to-offer-f3dm-plug-in-hybrid-to-chinese-individuals-starting-next-week.html http://www.autoblog.com/2010/03/22/chrysler-500ev-all-electric-fiat-500-for-u-s/ http://www.greencarreports.com/blog/1034168_2011-chevrolet-volt-talk-about-limited-production D http://green.autoblog.com/2009/09/10/officially-official-mercedes-benz-vision-s-500-plug-in-hybrid/ E http://green.autoblog.com/2007/12/26/subaru-ev-could-arrive-as-early-as-2009/ F http://green.autoblog.com/2010/03/01/volkswagen-announces-electrification-plan-500-golf-evs-in-2011/ B C
Table 2 OEM Technology Roadmap CO2 emissions and energy consumption from PHEVs and pure EVs displaced from the tailgate and ICE to the power producer are affected by the mix of generators in the portfolio, the utility factor in the case of PHEVs, the time of charging, the time to charge, the battery characteristics, the vehicle characteristics, the charger settings and the liquid fuel [29 and 30].
The vehicle characteristics are weight, frontal area, drag, engine type and transmission efficiency and the drive cycle (i.e. speed, acceleration, idle time, terrain and driving style) [20]. The battery characteristics are energy density (Whr/kg or Whr/L), the cell voltage or voltage stability of the battery, the state of charge (SOC) and the inverse of SOC, which is referred to as depth of discharge (DOD), ambient temperature, the charge depletion (when in EV mode), the charge maintenance (when in HEV mode), cycle life, internal equivalent series resistance, self discharge, recharge time and charging patterns [31 and 32]. The utility factor is the percentage of driving in all-electric mode for PHEVs and is studied in Reference [33] in detail. For the purposes of this study it is 1, as it is assumed that most urban suburban trips will be in all-electric mode. Page 8 of 21
These characteristics are grouped together as a technology development factor in the ‘EV Car Stock’ model as drivers towards improvements in reducing CO2 emissions over the 15 year time series up to 2025. The technology development factor considers the improvement of the EV portion of the fleet as technology improves over time. This technology development factor is based on an EV technology road map established by a review of the latest available published data to estimate when certain types of EVs may enter the market place. The data used in the low, medium and high scenarios are based on the ‘Car Stock’ BAU scenario as a starting point for 2010, 2015, 2020 and 2025 is tabulated in Table 3. Note for each scenario the average annual mileage for EVs is assumed to be 14,500 km (compared with 16,708 average private car mileage in 2008) and the distance commuted per day averaging 70km. This assumes that EV deployment and penetration will be predominantly in suburban and urban areas. Year
Low
Medium
High
% EV Penetration (logarithmic)
2010
0
0
0
2025
6
9
15
No. EVs
2010
0
0
0
2015
39,922
59,883
99,805
2020
85,424
128,137
213,561
2025
137,567
206,351
343,918
2010
1
1
1
2015
1
1
1
2020
0.95
0.95
0.95
2025
0.85
0.85
0.85
Technology Development Factor
Table 3 Baseline Data for ‘EV Carstock’
Energy Demand, Charging Profiles & CO2 Emissions Next the possible the overall change in energy demand and amount of additional CO2 emissions displaced from the transport sector to the electricity sector is estimated and to study the effects of the time of charging three charging profiles are investigated for peak, off-peak and wind-follow charging. The energy used to charge the EVs combines average the estimated annual miles travelled by the number of EVs in the fleet, by the electricity used in kW to charge the EVs, by the hours charging by the technology development factor.
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Internationally it is expected that apart from the battery swopping stations, already mentioned, there will be three levels of socket charging [34 and 35]. This will vary slightly from country to country depending on the voltage, frequency, transmission standards and plug standards in terms of the rating of the plug in amperes. An EV may have a higher internal electric capacity, but this will be limited by the grid connection [36]. Table 4 gives an indication of the power demand and charging options for Ireland based on the existing grid circuitry. As the alternating current (AC) electrical energy from the grid is converted to direct current (DC) in the EVs battery pack there will be power losses associated with stationary loads in the charging process such as communications controls and the battery/engine cooling system [37]. Reference [38] assumed an 88% conversion efficiency from AC to DC. Thus more power is actually required to full charge the EV. Thus for this initial case study it is assumed that charging will take place mostly at the EV owners’ home at level 1 charging using a 3.3kW charger, which includes the conversion efficiency factor over 8 hours. In addition to these plug-to-battery energy losses there are also the battery-to-wheel energy losses due to driver behaviour and terrain type (i.e. rural, urban and combined). ‘EV Car Stock’ accounts for the plug-to-battery energy losses using the 88% conversion efficiency. The battery-towheel energy losses are considered by assuming that an EV will not get the 10-25 kWh/100km as anticipated under optimum driving conditions but rather a lower range of 1025 kWh/70km under real driving conditions. Level
Type
Electrical
Level 1
Standard (Domestic) Opportunity Emergency Range Extension
230V 16A 1 or 3 phase 400V 32A 400V 32A 400V 63A
Level 2 Level 2 Level 3
Resulting Charge 100%
Time to Charge
Power
6 to 8 hours
3kW to 10kW
50% 20km 80%
30 minutes 10 minutes 30 minutes
22kW 22kW 44kW
Table 4 Charging Options & Power EVs have the potential to change load duration curves and the operation of the power system because of the increase in electrical demand. Uncontrolled EV charging may result in power losses, transformer and line overloads and a reduction in power quality (e.g. voltage, unbalance, frequency and harmonics), which may cause inconvenience to customers, especially at the distribution level [39]. This may also generate a greater amount of CO2 emissions as less efficient generators may be used. Therefore controlled (also referred to as intelligent or ‘smart’) charging, which uses excess renewable power and more efficient thermal plants is preferable to avoid increases in peak power demand. Reference [40] noted Page 10 of 21
that from the grid viewpoint there are questions with regard to the time when EV charging takes place, the number of EVs charging and the interaction between the EV technology and the grid. In order to examine the effect that time of charging has on the power system, three charging profiles are studied, peak, off-peak and wind-follow using the ‘EV Car Stock’ model outputs. This is very important as CO2 emissions levels vary from plant to plant and from country to country as no two generation portfolio mixes are the same. The CO2 emission level depends on the operation portfolio generation mix, the generator plant specifications, the age of the generators, the number of hours of operation of the generations and the fuel source, load and the time of day. A sample of CO2 emissions by EV type and fuel type in the USA and UK are presented in Reference [41, 42 and 2]. In general peaking plant emit more CO2 than mid-merit. Base load plants if dispatched efficiently have the potential to emit the least CO2, which is obviously related to the fuel type, operation and age of the plant. Base load plant can also emit more CO2 and burn more fuel if they have to cycle due to rapid rampingup and ramping-down to meet changes in demand.
It is estimated that by 2020 a grid portfolio with approximately 42% RES, which is expected to come predominantly from wind power, will produce 15.3 MtCO2 given a demand of 54 TWh in 2020, giving an electricity emission factor of 78.7 gCO2/MJ or 283.32 gCO2/kWh, falling from 161.5 gCO2/MJ or 581gCO2/kWh in 2008 [43 and 44]. In ‘EV Car Stock’ emissions levels for interim years are linearly interpolated. The dispatch order of the thermal plant in Ireland is established by the Single Electricity Market Operator (SEMO) a day head, based on the least cost merit order of the plant. Therefore it is difficult to predict the merit order in which the plant may be dispatched without using an electricity market model such as PLEXOS [45]. Reference [46] describes PLEXOS and a number of other electricity models.
Energy Demand, Charging Profiles & Avoided CO2 Emissions Figure 3 shows the energy consumed for the high, medium and low EV penetrations from 2010 to 2025. In the high scenario for 2020 the EVs consume approximately 1.1 million MWh of energy or 95 ktoe, of which 39.9 ktoe (42%) is renewable, which equates to 99.75ktoe when the 2.5 weighting is applied, in accordance with Directive 2009/28/EC. Therefore EVs under the high scenario contributes 1.72% to the 10% RES-T target. The 1.1 million MWh of energy consumed by the EVs is comparable with the 936 GWh estimated in earlier work carried out by the authors [19].
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Figure 3 EV Electricity Demand in the High, Medium & Low Scenarios from 2010 to 2025
Figure 4 presents the CO2 for the high, medium and low EV Scenarios from 2010 to 2025 without consideration to the charging profiles. Ignoring the time of charging the effect of EVs on the power system was to add 126 ktCO2 under the low scenario, 189 ktCO2 under the medium scenario and 314 ktCO2 under the high scenario in 2020 to the energy sector.
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Figure 4 Additional EV CO2 emissions in the Electricity Generating Sector in the High, Medium & Low Scenarios from 2010 to 2025
Figure 5 shows the combined results of the ‘EV Car Stock’ and ‘Car Stock’ models using the same baseline assumptions. The overall net reduction in CO2 emissions is estimated at 64 ktCO2/annum, 98 ktCO2/annum and 189 ktCO2/annum, under the low, medium and high scenario respectively in 2020. The high growth EV scenario contributes 1.4% towards to the 20% reduction in Non-ETS emissions by 2020 relative to 2005.
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Figure 5 EV Car Stock and Car Stock CO2 Emissions Comparison As part of the peak, off-peak and wind-follow analysis some of the electricity demand data under the high scenario in 2020 is examined. Figure 6 represents a potential charging profile on the 30th March 2020 for the high scenario. As can be seen from the graph the wind power available meets the EV load requirement. The wind power taken by the EV load must also be ‘made-up’ to meet regular non-EV related demand. In this instance it can be met by the base load plants as the wind-follow is at its optimum during the off-peak.
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Figure 6 EV charging profile on the 30th March 2020 Figure 7 shows a potential charging profile on the 6th April 2020 for the high scenario. As can be seen from the graph the wind power available does meet the EV load. Like Figure 5 the EV shortfall plus the regular demand can be once again met by the base load plants as the windfollow is at its optimum during the off-peak. However this may not always be the case as wind power is stochastic and the 2020 predicted wind power output in ‘EV Car Stock’ is only a possible outcome of many possible outcomes. In power systems, balance is maintained by continuously adjusting generation capacity and also by often controlling demand. Wind is inherently variable and therefore wind power is a fluctuating source of electrical energy, occasionally producing no power, very low power levels or very high power during periods of extremes [47]. It is difficult to forecast wind power accurately and usually prediction errors increase as the time horizon of the forecast increases. Therefore accurate wind power forecasting as well as the ‘smart’ controls should improve the integration of EVs using wind power.
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Figure 7 EV charging profile on the 6th April 2020
Figure 8 presents possible load duration curves in 2020 for the high scenario with peak, offpeak and wind-follow charging. It is clear that the off-peak charging has the positive effect of increasing the overall base load, whereas peak charging increases the operation of the less energy efficient more CO2 emitting peaking and mid-merit plant. Wind-follow slightly increases the operation of the peaking and mid-merit plant and slightly decreases base load plant operation.
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Figure 8 Load Duration Curve in 2020 for the High Scenario
Discussion & Conclusion ‘EV Car Stock’ examined the impact of EVs in terms of energy and CO2 emissions from 2010 to 2025 under three scenarios (i.e. low, medium and high) and under three charging regimes (i.e. peak, off-peak and wind-follow). For the high scenario in 2020, EVs account for 462 GWh of renewable electricity to transport, contributing 1.72% to the overall 10% RES-T target. With respect to GHG emissions, a total of 314.34 ktCO2 were emitted to charge the EVs compared to 504 ktCO2 avoided due to displaced petrol and diesel related emissions. The net effect is a reduction of 189.66 ktCO2 in 2020, contributing 0.5% to the 20% Non-ETS emissions reduction target. Under the high scenario, the net saving as a result of EVs is negative in 2015 (-40 ktCO2) but becomes positive in 2020 (189 ktCO2) and grows further in 2025 (457 ktCO2). This is as a result, firstly of the assumed growth in renewable electricity and consequent reduction in carbon intensity, and secondly the growth of EV charging. The high growth EV scenario contributes 1.4% towards to the 20% reduction in Non-ETS emissions by 2020 relative to 2005. Initial indications from the ‘EV Car Stock’ model that off-peak and wind-follow charging is the most effective from a power systems viewpoint in terms of plant dispatch, energy consumption and reducing CO2 emissions. Peak charging results in increased operation of peaking plant. It is also interesting to note that for this Page 17 of 21
particular potential system demand and wind power output in 2020 the off-peak charging appears to result in a slightly better operation of the base load plant than the wind-follow charging. This is very important to the power system as cycling of base load plant from ramping up and down of the plant to follow wind power and EV charging could potentially shorten the operating life cycle of base load plant, resulting in increased CO2 emissions and increased fuel consumption. Ireland is used as the case study but the approach can be readily to other countries. A number of other studies have been carried out internationally, but it is difficult to compare them as the assumptions and baseline data used varies due to the uncertainty surrounding EV technology and deployment.
EV deployment can reduce overall energy demand and CO2 emissions if compared with the BAU scenario in the ‘Car Stock’ model. However, the time of charging is critical as indicated by the results of the peak, off-peak and wind-follow load analysis. Controlled also referred to as intelligent or ‘smart’ charging, which uses excess renewable power and more efficient thermal plants is preferable to avoid increases in peak power demand. In fact accurate wind power forecasting as well as the ‘smart’ controls are necessary to integrate EVs with wind power. Usually wind power prediction errors increase as the time horizon of the forecast increases. This adds another level of uncertainty to EV integration, management and control.
Other factors such as the physical characteristics of the car, the mix of generators in the portfolio, the utility factor in the case of PHEVs, the time to charge, the battery characteristics, the charger settings and the liquid fuel of the car also play a major role in the CO2 emissions and energy consumption of EVs. Plug-to-battery energy losses during charging and battery-to-wheel energy losses during driving are also important determinants when estimating overall EV energy consumption and CO2 emissions. It is worth interest to note that technology development roadmap as indicated by the OEM’s is slower than the targets set by government policy targets and very recent comments by BMW in relation to the stage of development of the battery indicate some serious technology questions. This uncertainty and standardisation in charging infrastructure is perhaps one of the weaknesses of the current international government EV policies. However, this has been recognised and various working groups and steering committees have been formed.
Finally, the authors intend to develop ‘EV Car Stock’ further, building scenarios to allow improved incorporation of PHEVs, varied mileage patterns and further precision on which Page 18 of 21
cars are replaced by EVs, as more information n these issues becomes available. In addition, are building a PLEXOS model, which better increases understanding of how EVs will operate within Ireland’s electricity market.
Acknowledgements The authors wish to thank the Environmental Protection Agency (EPA) for funding this research under the EPA Climate Change Research Programme (CCRP). Thanks to Dr Paul Leahy, SFI Stokes Lecturer in Wind Energy Engineering for his comments and his inputs.
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