WP1. Guideline report for WP2&3 outputs

Transcription

1 Guideline report for WP2&3 outputs Date: 23 October, 2013 Contributors : D.Lorne, F.Bouvart, S.Vinot, B.Chèze, A.Kanudia 1

2 Table of Contents Introduction Initial Pan-European Times PET model Architecture of the PET model The PET model creation Initial techno-economic assumptions on transport sector Overview of data exchange for model calibration Common reference framework from existing EMF28 scenario E01 for A-LCA (WP3) Calibration and integration of new techno-economic data for the transport sector in PET Emission factors and technological efficiencies in PET36 (WP4) and GABI for A- LCA (WP3) Iterations between PET36 and GABI tools results Integration of WP2 outputs Iterations between WP3 et WP4 required for C-LCA Summary of all WP interactions (to be completed) Appendices Appendix Appendix Appendix Appendix Appendix Appendix Appendix Appendix

3 Table and Figure Index Table 1 - Bioenergy across sectors in PET model... 8 Table 2 - Blending limits for gasoline based fuels considered in the PET model Table 3 - Blending limits for diesel based fuel considered in the PET model Table 4 - Blending limits for jetfuel considered in the PET model Table 5 - Passenger vehicle technologies considered in the PET model Table 6 Electric mobility options initially available in PET36 model Table 7 Main characteristics of the selected vehicle technologies Table 8 Efficiency of the selected vehicle technologies by type of use Table 9 30 zones of the PET36 model version Table 10 Annual distance travelled in km by each vehicle technology by country implemented in the PET model Table 11 Costs for the 7 vehicle technologies in PET36 model Figure 1 High level reference energy system of the PET country single models ( 2010)... 5 Figure 2 - Representation of biomass, waste and residues for biofuels and biogas... 8 Figure 3 - Passenger car mobility demand scenarios available in the PET model Figure 4 - Passenger public transport mobility demand scenarios available in the PET model Figure 5 - Air and navigation energy demands scenarios available in the PET model Figure 6 - Freight mobility demands scenarios available in the PET model Figure 7 Data exchange workflow Figure 8 Technologies of the PET36 model concerned by data sharing with GABI Figure 9 - Prospective model (PET36) and LCA model (GaBi) positioning for the C-LCA task Figure 10 Spatial supply of dedicated bioenergy crops in Europe in 2030 in percent of a region (source: Refuel project) Figure 11 Spatial cost distribution for grassy and woody dedicated crops in Europe in /GJ (Source: Refuel project) Figure 12 - Summary of the total annual biomass resource supply potential by country (source: Refuel project) Figure 13 - EMF28 E01scenario - Energy carrier share for electricity generation in Europe for each Member State (VEDAVIZ extraction); for the meaning of the colors refer to Figure Figure 14 - EMF28 E01scenario - Energy carrier share for electricity generation in Europe for EU30 (VEDAVIZ extraction) Figure 15 Total EU27 road passenger mobility demand for the three networks urban, nonurban and motorway from TREMOVE database

4 Introduction The aims of are the development of a concept to (better) integrate/represent the European road passenger transport sector in the PET36 model (task 1.1) and the actual implementation of this concept into the PET36 model (task 1.2). In this first deliverable, we define links and consistency needs between the PET36 model and the tools/approaches used in the other WP and we precise the adaptation required for the PET model in order to realize scenario analysis of private costs in WP4, C-LCA analysis in WP3 and external cost analysis in WP3. The first chapter presents the PET36 model, its features and a global description of each sector with a focus on the technological content and the main constraints in the transport sector. The second chapter aims to adjust the PET model and the LCA GaBi tool with consistent data in order to have a suitable representation of electric vehicle pathways. The last chapter gives a comprehension of the tools positioning and implementation needed for multicriteria scenario analysis (private cost, C-LCA and external cost analysis). 1. Initial Pan-European Times PET model The Pan European Times (PET) Model is a multi-regional partial equilibrium model of Europe built with MARKAL/TIMES, the technical economic model of IEA-ETSAP. The PET36 model represents the energy system of 36 European regions and its possible long term evolution and was developed following a series of European Commission funded projects (NEEDS, RES2020, REACCESS, REALISEGRID, COMET, Irish-TIMES ). The model was developed and is maintained by the KanLo team. The actual system encompasses all the steps from primary resources in place to the supply of the energy services demanded by energy consumers, through the chain of processes which transform, transport, distribute and convert energy into services. 1.1 Architecture of the PET model PET36 is an optimisation techno-economic TIMES model covering EU27 countries + Norway, Iceland, Switzerland and the Balkan countries. The Balkans were added during the REALISEGRID project, which was mainly an electricity sector study. The non-electric parts for the six additional regions were not really tested and further, recent developments have been done only for the 30 countries. Thus, the EU27+3 version (i.e. without the Balkan countries) will be used in this project. In this 30 multi-region model, country energy systems are linked through trade of the main energy forms and most of its national energy systems were validated by national teams. The model runs from 2005 to 2050 with 5 years interval. The 30 European regions are detailed in Table 9 appendix 1. For each region, the model describes and models the following sectors: the region s supply sector (fuel mining, primary and secondary production, exogenous import and export), its power generation sector (including also the combined heat and power production and the heat production by district heating plants), and its demand sectors (residential, commercial, agricultural, transport, industrial). 4

5 Figure 1 High level reference energy system of the PET country single models ( 2010) Each element in the network is characterized by several input parameters. Technologies are described by means of technical data (e.g., capacity, efficiency), environmental emission coefficients (e.g., CO2, SOx, NOx), and economic values (e.g., capital cost, date of commercialization). Possible future developments of the energy system are modeled to be driven by the following exogenous inputs/determinants of the model: - reference demands for energy services (e.g. commercial lighting, residential space heating, air conditioning, mobility and many others), - the supply curves of the resources (e.g., amount available at each price level), - along with environmental or other constraints (e.g. Clean Air Act requirements, UNFCCC protocols). Solving the model means finding for each time period the optimum Reference Energy System by selecting the set of technologies and fuels that maximize the total surplus, which in the simplest case is equivalent to minimize the total system cost over the entire planning horizon (i.e. the optimal energy-technology pathways). Thus, the model determines the optimal mix of technologies and fuels at each period, the associated emissions, mining and trading activities and the equilibrium level of the demand. More details on 5

6 1.2 The PET model creation In its current state, PET originates from two main modeling efforts. The following description is taken from the Res2020 project with additional description on biofuels production and biofuel blending with fossil fuels made by IFP Energies nouvelles. The first version of PET model in NEEDS NEEDS 1 is a project funded by the 6th Framework Program and in its framework a model for EU-27, Iceland Norway and Switzerland was developed, using the TIMES model generator. In this model the energy systems of each one of the thirty countries are modeled separately in detail. The Pan European Model is then synthesized by allowing trade of energy commodities among the countries. The level of analysis per sector of economic activity in each country, in the NEEDS-Pan European model, is rather detailed ( Draft common structure of the National country models Deliverable D1.4, NEEDS project, August 2005). On the energy demand side the residential, commercial, agricultural, industrial, and transport sectors are analyzed as described below. Residential sector - The energy service demands that are being considered in the residential sector are very detailed. These are Space heating, Space Cooling, Water heating, Cooking, Lighting, Refrigeration, Cloth washing, Cloth drying, Dish Washing, Other electric uses (equipment) and Other energy uses (non-electric). Furthermore three building categories are used for the demands for space heating, space cooling and water heating, namely multi apartment building, single house in urban areas and single house in rural areas. Commercial sector - The energy service demands considered in the commercial sector are quite similar to the residential sector and include Space heating, Space Cooling, Water heating, Cooking, Refrigeration, Lighting, Public Lighting, Other electric uses (equipment), Other energy uses (non-electric). Furthermore the energy service demands for space heating, space cooling and water heating are divided into two building categories, namely small and large commercial buildings. Agriculture - Agriculture is not analyzed in detail, but is represented as a single energy service demand satisfied by a single technology that consumes a mixture of fuels. Transportation - The transportation sector is distinguished into road and rail transport of passengers and freight, domestic and international navigation as well as domestic and international aviation. Passengers road transport is further divided into Short and Long distance car transport, urban busses, intercity busses and motorcycles. Passenger s rail transport is further divided into Urban Metro transport and intercity train transport. Freight transport is divided into road transport by trucks and intercity rail transport. The aviation and navigation are split to domestic and international, without further analysis of alternative technologies

7 Industry - The industrial sector is analyzed in detail following an initial division into energy intensive industries and other industries. The energy intensive industries are: Iron and Steel, Aluminum, Copper, Ammonia, Chlorine, Cement, Lime, Glass, and Paper. For each one of these industrial branches a detailed description of the production processes is being used in the model. The industrial branches of other non-ferrous metals, other chemicals, other nonmetallic minerals, and the remaining industries are not modeled in detail on a process basis but they are represented using the same generic structure with the energy uses of steam, process heat, machine drive, electrochemical processes and other processes. On the energy supply side, the electricity and heat production are analyzed in detail, the refineries are modeled using a generic refinery structure and the mining and extraction of primary energy resources are modeled using a cost supply curve. Electricity and/or heat production - The electricity production sector is divided into public power plants and CHP plants, and auto production electricity power plants and CHP plants in the industrial and commercial sector. Nuclear power plants are modeled separately as well as discrete heating installations. The high, medium and low voltage grids are included in the model, with different types of technologies being able to produce at different voltage, modeling distributed generation in this way. There are also two separated heat grids for high temperature and low temperature heat. Primary resources - The mining of each primary energy resource is modeled using a supply curve with three cost steps. Biomass is modeled, but not in detail regarding the production processes. Emissions - Emissions are also calculated in the model. Those particularly include Carbon Dioxide (CO 2 ). Enhancements of PET in RES2020 for transport sector In the framework of the RES2020 project, the NEEDS-TIMES Pan-European model has been enhanced in the representation of Renewable Energy Sources, including wind, distributed combined heat and power generation, and biofuels. Much attention was paid to the improvement of biofuel pathways in PET and further enhancements were made in the representation of biomass availability. 7

8 Table 1 - Bioenergy across sectors in PET model Regarding biofuels, most of the enhancements within RES2020 were made on the supply side, for instance on the differentiation of crop types and waste and residues sources to be used for the production of biofuels. Figure 2 gives an overview of the chains for biofuels and biogas production. Figure 2 - Representation of biomass, waste and residues for biofuels and biogas The basic enhancements were: 8

9 - Differentiation of potentials of energy crops with different costs, taking into account land-use competition between different crops; - Rape oil as an intermediate product that also can be imported or traded; - Ethanol production from sugar as well as from starch crops. One of the most important issues regarding bioenergy is the available potential, especially taking in mind sustainability issues. 1.3 Initial techno-economic assumptions on transport sector For the present study, specific assumptions concerning the transport sector need to be made regarding energy supply, demand and technologies. In the following, detailed initial assumptions are provided regarding biomass and oil supply curves, mobility demand and conversion processes (biofuels, fuel blending, and vehicles) for the model construction. Biomass resources availability and cost Biomass supply scenarios in the PET model are built from the work made by the Refuel Project ( funded by the European Commission under the Intelligent Energy Europe program. A part of the project aims to estimate the European domestic biomass resource potential for bioenergy and related costs. From this work a baseline scenario has been considered in the PET model. This scenario considers five dedicated crop groups: wood, grass, oil, starch and sugar; the scenario also considers three co-product biomass groups: agricultural residues, forest residues and wood waste (Figure 2). This baseline essentially reflects effects of ongoing trends in food consumption patterns on the one hand and technological progress in food production on the other hand, and it assumes a continuation of current self-reliance levels in Europe s aggregate food and feed commodities. Competing land use requirements for Europe s food and livestock sector as well as land use conversion from agriculture to other uses, in particular built-up and associated land areas, will determine future availability of land for energy crop production. Future food and feed area requirements are the result of developments in food demand combined with changes in production intensity and trade of agricultural products. Moreover, areas of high nature conservation value are excluded from the potential biofuel crop area. From these land availability and yield increases scenarios, a spatial illustration of the techno-economical biomass potential can be shown (Figure 10 - Appendix 2). A bottom up cost analysis is executed considering 13 dedicated bioenergy crops. Total costs are calculated by summarizing the respective cost of capital goods, land, labor, and fertilizer (Figure 11 - Appendix 3). Input data are obtained from a broad literature study and European (online) databases. The total annual European biomass resource supply potential is estimated to amount to 16.6 EJ/year ( or a variation of ± 51.5% around the average) (Appendix 4 Figure 12). The variation is significant and strongly steered by the choice of dedicated bioenergy crops by the model and the degree to which residues can be exploited specifically for fuel use. The distribution by country specific supply potential is roughly following the country surfaces, although some significant differences exist with respect to the degree to which arable land can be allocated to dedicated bioenergy production. 9

10 Mobility demand Investments and use of transport technologies for passengers and freight are driven by demands for mobility. Those are split by transport mode and travel type. Figure 3 and Figure 4 present the mobility demand scenarios at the European level for passengers respectively by car and public transport, split into short and long distance. Aviation and navigation mobility are not explicitly represented. Rather, energy demands for air transport and maritime and fluvial navigation in both cases including passengers and freight are exogenously determined to feed the model (Figure 5). These mobility demands are driven by macroeconomic assumptions such as GDP, GDP per household or population. Figure 3 - Passenger car mobility demand scenarios available in the PET model 4.E+06 4.E+06 3.E+06 3.E+06 Gpkm 2.E+06 2.E+06 1.E+06 5.E+05 0.E Road - Car Long distance Road - Car Short distance 10

11 Figure 4 - Passenger public transport mobility demand scenarios available in the PET model 5.E+05 4.E+05 4.E+05 3.E+05 Gpkm 3.E+05 2.E+05 2.E+05 1.E+05 5.E+04 0.E Road - Bus Intercity Road - Bus Urban Road - Two wheels Rail - Light Rail - Heavy Figure 5 - Air and navigation energy demands scenarios available in the PET model PJ Aviation Navigation Mobility demands for goods are provided for road and rail transport (Figure 6). 11

12 Figure 6 - Freight mobility demands scenarios available in the PET model 3.E+06 3.E+06 2.E+06 Gtkm 2.E+06 1.E+06 5.E+05 0.E Freight - Road Freight - Rail Biofuel technologies Biofuel technologies considered in the model are a selection of substitutes for gasoline, for diesel, natural gas and for jetfuel. At the base year and before 2020, we only consider 1 st generation biofuels like FAME 2 and ethanol from sugar or starch. FAME is produced from oilseed crops (rapeseed, sunflower seed, soybean) which are crushed to extract vegetable oil. Vegetable oil is treated by transesterification with methanol to obtain a FAME or biodiesel. Ethanol is locally produced the fermentation of sugar contains in sugar crops (sugarbeet) or in cereals (wheat, barley, rye, corn) stored as starch. In 2015, a new biodiesel technology comes in the market, Hydrotreated Vegetable Oil or HVO. This technology uses same oily resources as FAME. The product is a synthetic diesel obtained by catalytic hydrogenation of vegetable oils. In 2020, second generation biofuels come in the market. Second generation biofuel are produced from lignocellulosic biomass (grassy crops, woody crops or biomass by-products and waste). The two main technologies in development in Europe are cellulosic ethanol to be directly blended in gasoline based engine (like the current first generation ethanol); and BtL, a high quality synthetic diesel (FT diesel 3 ) to be blended, at potentially high rates, in common diesel engine. Among BtL products, a synthetic biojetfuel is also coproduced to be blended in kerosene. 2 FAME: Fatty Acid Methyl Ester 3 FT Diesel : synthetic diesel produced by the Fischer Tropsch synthesis. 12

13 Other biofuel technologies are also considered in the model, like biomethanol, biodme 4, and biomethane, in development for dedicated vehicle fleets, due to their specific physical properties. Biomethane for transport can be produced by 3 various technologies: anaerobic organic waste digestion, wood or black liquor gasification, sewage sludge decomposition. All these forms of biomethane could potentially be included in the gas pool for NGV vehicles according to optimization results. Biofuels and conventional fuels blending assumptions Between biofuel production processes and fuel usages in vehicles, blending processes are described in the model. Blending processes consider current fuel specifications for years before 2010 and assumptions on potential blending limits which could be standardized in the future. Upper and lower bound limit types are used to express these technical constraints in order the let freedom to the model to choose the best blends endogenously. The three next tables summarize the main biofuel blending limits assumptions for each fuel pumps available for vehicles fleet. Table 2 - Blending limits for gasoline based fuels considered in the PET model E5 E10 E85 E5 E10 E LimType 2010 LimType 3-5% ethanol Min-Max 3-5% ethanol Min-Max 15% ETBE Max 15% ETBE Max % ethanol Min-Max 22% ETBE Max 85% ethanol Min-Max 85% ethanol Min-Max 22% ETBE Max 22% ETBE Max 2015 LimType LimType 3-5% ethanol Min-Max 3-5% ethanol Min-Max 15% ETBE Max 15% ETBE Max 10% ethanol Min-Max 10% ethanol Min-Max 22% ETBE 22% ETBE Max 85% ethanol Min-Max 85% ethanol Min-Max 22% ETBE Max 22% ETBE Max - E5 is a fuel adapted to all current gasoline engines cars composed of minimum 95% vol. of regular gasoline and maximum 5% vol. of pure ethanol or 15% vol. of ETBE. This type of fuel is available from the reference year - E10 is a fuel adapted to almost all gasoline engine cars composed of 90% vol. of regular gasoline and 10% vol. of pure ethanol or 22% vol. of ETBE. This type of fuel is available from 2010 onwards - E85 is a fuel adapted for specific flexfuel vehicles which can use randomly regular gasoline, E5 fuel, E10 fuel E85 fuel at any rates. E85 is composed of 85% vol. of pure ethanol (or a maximum of 22% vol. ETBE) and 15% vol. of regular gasoline. This type of fuel is available from BioDME: Dimethyl Ether from lignocellulosic biomass 13

14 Table 3 - Blending limits for diesel based fuel considered in the PET model 2007 LimType 2010 LimType 2015 LimType LimType Diesel 2-7% Min-Max 2-7% Min-Max 2-7% FAME Min-Max 2-7% FAME Min-Max FAME FAME 50% HVO Max 50% HVO Max B30 30% FAME Min-Max 30% FAME Min-Max 50% XtL 30% FAME 50% HVO 50% XtL Max Min-Max Max Max 50% XtL 30% FAME 50% HVO 50% XtL Max Min-Max Max Max - Diesel is the conventional diesel fuel for current diesel engine cars. It can be substituted by maximum 7% vol. of FAME. From 2015, HVO and synthetic FT diesel (XtL= CtL + GtL, and BtL from 2020) can be introduced until a maximum of 50% vol. - B30 is a fuel adapted for specific captive fleet composed of 30% vol. of FAME and minimum 70% vol. of regular diesel fuel. From 2015 HVO and synthetic FT diesel (XtL= CtL + GtL, and BtL from 2020) can be introduced until a maximum of 50% vol. Table 4 - Blending limits for jetfuel considered in the PET model LimType 2015 LimType LimType Jet fuel 0 50% XtL Max 100% XtL Max 100% XtL Max 100% HVO Max 100% HVO Max The jetfuel for aviation can be substituted by maximum 50% vol. of synthetic kerosene like XtL from 2010; the specification would allow a 100% substitution rate by Vehicle technologies These different kinds of liquid fuels can be used in a selection of passenger and duty vehicles technologies. Each vehicle technology is characterized by an average car model. At the reference year (2005) the passenger vehicle fleet is composed of conventional diesel car, conventional gasoline car, LPG car and NGV. By 2006, biodiesel car, one new diesel based technology comes in the market as a captive fleet, and two new gasoline based technologies, full hybrid gasoline and ethanol flex fuel vehicles. Full hybrid diesel comes by 2010, with a set of new vehicle technologies like DME car, methanol car, hydrogen car, electric car. The table below summarizes the list of passenger car technologies, fuel types they can use. Table 5 - Passenger vehicle technologies considered in the PET model Vehicle technology Types of fuel Start Conventional Diesel Diesel 2005, 2006 Biodiesel car B DME car DME, BioDME

15 Full Hybrid Diesel Diesel 2010 Conventional gasoline Regular gasoline E5 (E10 by 2010) 2005, 2006 NGV Natural gas, Biogas 2005, 2006 LPG car LPG 2005, 2006 Full Hybrid gasoline Regular gasoline E5 (E10 by 2010) 2006 FlexFuel ethanol car Regular gasoline, E5, (E10 by 2010) E Methanol car Methanol, Biomethanol 2010 Hydrogen car - internal combustion H2, BioH Hydrogen car - fuel cell H2, BioH Electric car Electricity 2010 NB: The contribution of Flex fuel, Methanol, DME and Hydrogen car technologies in the project scenarios will be discussed during first general assemblies. Moreover, some other parameters like maximum annual kilometers, occupancy rate, lifetime, maximum rate of short distance mobility, fixed operation and maintenance costs and investment costs are detailed for each technology and for each European country. They will be detailed in the next chapter on new techno-economic data implemented in PET in the SceleTRA framework. Regarding passenger public transport, seven technologies of urban buses and of intercity buses are described. Among them we find diesel, gasoline, biodiesel, natural gas, methanol, DME and hydrogen bus technologies. The blending possibilities in these engines are the same as for passenger vehicles. Regarding duty trucks, we can find the same technologies as for buses plus gasoline and diesel full hybrid trucks. Electric mobility pathways in PET model PET covers a comprehensive set of competing technologies for providing electric mobility. Overall, transport electrification is to be understood across transported "elements" (passengers or freight) and transport type (private, public including buses, rail). In terms of energy vectors, both BEVs and FCEVs constitute electrification pathways in a larger sense, they are characterized by null tailpipe emissions. The electric mobility options described in PET36 are presented in the table below. 15

16 Table 6 Electric mobility options initially available in PET36 model Direct Indirect Rail Road Road Passengers Private Public Electric trains - Electric cars; - Plug-in hybrid cars X Gaseous H2 Fuel Cell Electric cars Gaseous H2 Fuel Cell Electric urban and intercity buses Freight X Gaseous H2 Fuel Cell Electric trucks In the project, only private electric mobility will be analysed, including battery electric vehicles. Fuel Cell Electric Vehicle (FCEV) will not be considered in the project at 2030 horizon. This neglects a set of potential electrification options, which would include modal shifts (e.g. policies such as urban tolls that would encourage users to switch from passenger cars to public transport). Such an analysis falls beyond the scope of the project, since mobility demands are exogenous; incorporating modal shifts would require more detailed transport modelling. Thus, the trans-modal effect of some of the policies to be tested will be neglected. However, impact of higher share of public transport could be explored by constructing an appropriate demand scenario. 16

17 2. Overview of data exchange for model calibration In the ScelecTRA project,, WP2, WP3 and WP4 are closely connected. Some technical data will have to be shared and analysis results will have to be implemented in models. In this chapter, we try to make an inventory of these links in order to prepare the modeling works. The next chapter will try to anticipate potential links between models, the need for iteration, data exchanges between modeling results of WP2, WP3 and WP4, in order to carry out the various stages of the analysis. Figure 7 Data exchange workflow This flowchart shows the links between the PET represented by a rectangle shape and the other WP. Here we can see that existing PET runs from another project will be used in WP3 as a reference framework for energetic context of the Attributional Life Cycle Analysis (A- LCA) carried out with GABI LCA software. Technical data have to be shared between the PET36 and the GABI databases in order to ensure consistency between A-LCA and C-LCA as much as possible. These environmental and techno-economic data for this calibration stage are here described in this chapter. From a calibrated operational model, Policy scenarios defined in WP2 are implemented in scenario files of the model to realise the first private cost analysis (WP4). Then, some environmental impacts of these scenarios will be assessed with a Consequential Life Cycle Analysis (C-LCA) approach (WP3). For the C-LCA using PET36, A-LCA outputs and 17

18 additional data extracted from GABI database will be used to complete emission factors already available in the PET model. This also concerns the implementation of emission factors for processes not yet covered by the model (infrastructure for vehicle construction and end of life, for example). In the last stage of the WP4, an External Cost Analysis of the selected scenarios will be conducted. Several iterations between GABI and PET36 will probably be necessary to identify, collect and then include all environmental data (emission factors) required to cover all processes involved in the considered systems; including processes which are not represented within the boundaries of the model. These steps will be detailed in the next chapter. 2.1 Common reference framework from existing EMF28 scenario E01 for A-LCA (WP3) Attributional LCA aims to assess environmental impacts (Global Warming Potential for instance) of each passenger transport pathway considered in the study. These assessments are made for each vehicle technology; they are related to a specific functional unit (results expressed per kilometer driven) and are independent of WP2 scenarios analysed in WP4. Nevertheless, A-LCA will be carried out for a reference year (2010) and in a future timeframe (2030). As a result, A-LCA needs to assume a relevant European energy framework for this future horizon, that shall be consistent with the one considered in C-LCA later on (technology mixes, efficiencies etc.). In particular, A-LCA will need to consider at EU27 level: - The primary fuel balance (oil, gas, coal, biomass) The emission factors for the supply (without combustion) of primary fuels mainly depend on their chemical composition but also on the fuel extraction process and on the distance of transport to the conversion location. The origin (local or imported) of each primary fuel may vary over time and needs to be defined for 2030 horizon. - The average electric mix Unlike conventional vehicles (gasoline and diesel), energy/fuel production steps contribute significantly to overall A-LCA results of electric vehicles (no tailpipe emissions). Therefore, those results are very sensitive to the type of technologies used to provide electricity available on the network. In an energetic transition context the European average electric mix may evolve in different ways. These need to be defined for 2030 horizon. - The liquid fuel composition consumed by each vehicle technology For all vehicle technologies which consume liquid fuels, pollutant tailpipe emissions depend on the fuel composition. The share of fossil based fuel and the share of biofuel in each fuel supplied in a fuel station significantly varied in recent years and will still evolve at 2030 horizon. The type of biofuel blended (which product, from which biomass resource, from which zone of production) also needs to be defined to use representative emission factors associated to this fuel share. All these assumptions for 2030 horizon need to be consistent with each other. To this end, we chose to use the result of an existent European energy reference scenario from a previous exercise with the PET36 model, i.e. the EMF28 scenarios. EMF28 is a project of the Energy Modeling Forum, an international forum for sharing and facilitating discussions on energy 18

19 policy and global climate issues among experts, coordinated by the Stanford University ( The EMF 28 project is called: The Effects of Technology Choices on EU Climate Policy. The EMF 28 study is a companion study to the global and US model comparison studies and it focuses on the effect of technology choices and technology availability on climate policy. The EMF 28 study will focus on the European policy context in particular the EU 2020 and 2050 climate targets. In this project, the PET36 model is used to analyze 11 scenarios (E00 to E10): - E00: No policy baseline (no policy, also with no 2020 target) - E01 to E05: including the 2020 target and 40% GHG reduction by E06 to E10: including the 2020 target and 80% GHG reduction by 2050 E01, E04 - E06, E09: CCS on E01, E02 E06, E07: nuclear, ENR, E.efficiency = REF E03 E08: low nuclear energy E04 - E09: high efficiency and ENR E05 E10: low nuclear, high efficiency and ENR The E01 scenario (including the 2020 targets and 40% GHG reduction by 2050 CCS on Nuclear energy ref Energy efficiency ref Renewable energies ref) has been chosen as a reference energy system for the A-LCA work. Main results of this scenario are illustrated on An illustration of the energy carrier share for electricity generation in Europe is available in Appendix Calibration and integration of new techno-economic data for the transport sector in PET36 The model calibration consists in making the numerical assumptions of the model consistent for the year 2010 with respect to the reference year (2005), to have a realistic representation of the transport sector for these past periods, in terms of technology mix in the fleet, fuel mix, energy product flows between different countries, etc. Model calibration implies having the stock and usage of various technologies, in each region of the model, consistent with data in year Year 2010 values are computed by the model, but the important ones can be guided by data, when it is available. Moreover, some technical and economic data have to be shared concerning vehicle technologies selected to be assessed in the project. Each of these technologies will be analysed in the A-LCA and technical data integrated in the GABI modelling to establish the inventory of emissions and resource consumption (WP3). These technologies and their techno-economic data are also integrated in the PET36 model for WP4 works. Selection of 7 vehicle technologies and their techno-economic data The table below resume main characteristics of the 7 vehicle technologies proposed by IFPEN and considered in A-LCA work (WP3) and for scenario modelling (WP4). 19

20 Table 7 Main characteristics of the selected vehicle technologies Gasoline vehicle Diesel vehicle CNG vehicle Gasoline HEV Plug-in gasoline HEV Electric vehicle Small urban electric vehicle Weight(kg) ICE power (kw) Electric motor power (kw) Type of fuel injection Type of batteries Battery capacity (kwh) Fuel tanks Post-treatment Particulate filter Gasoline Direct injection Gasoline 3 way catalyst yes Diesel High pressure injection Diesel DOC + DPF + SCR yes CNG injection Gasoline+ CNG 3 way catalyst no Gasoline Direct injection Li-ion 3 Gasoline 3 way catalyst yes Gasoline Direct injection Li-ion 10 Gasoline 3 way catalyst yes Li-ion 20 no Li-ion 20 no In the PET36 model, each vehicle technology is characterized by input commodities corresponding to fuels able to be consumed with a specific efficiency (Mkms/PJ) and by output commodities corresponding to mobility demand in passenger.km. A specific activity factor precises the vehicle occupancy rate (person per car per km) for each output (mobility demand) and an availability factor precises the annual kilometres of each technology. The activity of vehicle technologies is expressed in traffic unit (vehicle.km). With a mobility structure by usage (urban, road, highway), each technology efficiency has to be defined by usage. The table below detailed fuel efficiency for each vehicle technology by usage from IFPEN simulations (Table 8). The other parameters for the determination of the mobility demand, like occupancy rate and annual kilometres, have also to be defined by technology. For some countries initial annual kilometres of the PET model have been adapted from data obtained in the Odysse database. They are detailed in appendix 7. These parameters are considered as constant from 2010 to the end of the horizon but they are significantly different from one technology to another. Some investment cost, annual fixed operating and maintenance costs and lifetime data for the 7 technologies are derived from IFPEN expertise (detailed in Appendix 8). The small urban electric vehicle is a new technology in PET 36 model, added from IFPEN techno-economical data. 20

21 Table 8 Efficiency of the selected vehicle technologies by type of use Interaction with the electricity sector The deployment of electric mobility pathways will naturally interact with the electricity sector by: - increasing the demand for electricity; - providing direct or indirect (in the form of other energy vectors such as hydrogen) storage options. The electricity sector is part of the Reference Energy System of PET36, comprising national mix (including investments) and electricity supply and demand balance described at the infraannual level: seasonal (fall, winter, spring and summer) and daily (day, night, peak). In the upstream part of the chain, direct or indirect electrification pathways provide options differentiated by costs, series of efficiencies but also storage capabilities. Seen as storage devices 5, large scale deployment of electric vehicles may induce changes in the electricity sector that affect the relevance of technological options in both transport and electricity sectors. Thus, dedicated transport policies in favour of electric transport may have benefits or drawbacks in other parts of the energy system. Charging station for electric vehicle technologies Currently PET model has one process of EV charging station for all EV technologies. To be more realistic and to better consider balance between electricity demand and electricity availability at various timeslice levels, two types of EV charging station can be taken into account: private and public charging stations. Private stations are mainly used at home during 5 We can for example consider the potential synergy between night storing electric cars and night wind electricity production (favorable wind regimes) in night low demand periods. 21

22 the night; public stations are mainly used at work or on the road during the day. These two types of charging stations can be differentiated by their investment cost and the minimum number of station need by EV. The European Commission has issued a guideline to define the number of public electric chargers in the Directive of the European parliament and of the Council on the deployment of alternative fuels infrastructure issued in This is for now only a proposal but it helps define a frame for the development on the electric infrastructure. The formula which is used gives the minimum number of chargers to be installed as a function of the total car stock and the electric car stock: IFPEN has already used this formula in previous studies and it appears that in order to be implemented in the model, this formula can be roughly simplified to: 2 electric chargers for 3 electrified vehicles (electric vehicles and plug-in hybrid vehicles combined) or 0.67 electric charger/electrified vehicle As for the fast charging stations, this must be considered as an emergency facility with a light infrastructure only to make the drivers confident enough to drive their electric vehicle. The European Commission also stated that this kind of infrastructure can be reduced to 10% of the traditional gasoline and diesel infrastructure. This translates to 1 fast charging station for electrified vehicles. 2.3 Emission factors and technological efficiencies in PET36 (WP4) and GABI for A- LCA (WP3) Efficiencies and emission factors are the two main categories of data to be shared between PET36 and GABI for each process of each vehicle pathway. Main common technologies implied in horizontal steps of each vehicle pathway are illustrated in the figure below. 22

23 Figure 8 Technologies of the PET36 model concerned by data sharing with GABI For the first stage of model calibration in, data exchanges are restricted to: (i) Efficiencies and GHG emissions (CO 2, CH 4, N 2 O) regarding processes that are listed below. Some other pollutant emissions will be considered in C-CLA and external cost analysis and will be discussed for model implementation in the next chapter. (ii) Processes concerned by the fuel life cycle steps (primary energy and fuel extraction, transformation, transport, fuel combustion/end use). Additional data concerned by vertical steps about vehicle life cycle for example (fabrication, maintenance, disposal), will be later processed in PET36 during C-LCA work in WP3 and discussed in the next chapter. We should probably exclude emissions associated with infrastructure (power plant construction & disposal) from now on based on lessons learned from NEEDS project. Primary energy resources extraction and transport In the model boundary, fuel life cycle starts with primary energy resource extraction in their country of origin. Concerning mining resources produced in Europe (oil, gas, coal, etc), emission factors are reported per unit of energy consumed for the extraction processes. Oil and gas processes of intra-eu transport by pipeline are also detailed with associated emissions. For biomass resources, emissions are reported per unit of energy consumed in the global agricultural sector (diesel, gas, fuel oil, coal, wood, etc). Only emissions associated with energy inputs are therefore included (neither emissions at field such as nitrous oxides or CO 2 emissions related to land use change nor fertilizer production are taken into account). Moreover, in the C-LCA task, we will determine, according to PET36 outputs, if emission factors for primary resources processes of transport (foreign transport for extra-eu imported resources and local transport for intra-eu trade) will have to be detailed for all primary 23

24 resources. These emission factors are directly linked to the distance travelled and the efficiency of the transport mode (road, rail, maritime). These assumptions could be available in LCI databases. Liquid an gazeous fuel production and distribution In fuel production plants for transport (refinery and biofuel plants) emissions are accounted per unit of energy utility consumed by the process. Each energy source is linked to the final fuel product by an efficiency rate. Fuel distribution processes are not described. Electricity production and distribution As in previous processes, emissions associated with electricity production are reported at power plant step and accounted per unit of energy resource consumed by the process. No emission is accounted for renewable electricity production like solar, wind, hydro, geothermal, etc. The list of technologies and respective technical data to consider in GABI for A-LCA has to be made consistent with those of PET36. End use of fuels Fuel combustion CO 2 emission factors (EFs) are computed at the fuel blending step since those EFs are directly related to fuel composition and properties (carbon content). Vehicle technologies consume a fuel at the pump station whose composition is endogenous and defined at the blending step. 24

25 3. Iterations between PET36 and GABI tools results This chapter aims to anticipate potential future links between models during next stages of the project (scenarios construction, scenarios private cost analysis, C-LCA, external cost assessment), the need for iterations, data exchanges between modeling results of each WP. An overall procedure and a description of logical links between tools are proposed to guide future analysis (Figures 10). This framework will be updated along the project to better reflect and clarify actual data exchanges that could likely differ from the vision we have so far (new data requirements for instance). From a calibrated operational model, Policy scenarios defined in WP2 can be implemented in scenario files of the model to realise the first private cost analysis (WP4). Then, some environmental impacts of these scenarios will be assessed with a Consequential Life Cycle Analysis (C-LCA) approach (WP3). For the C-LCA using PET36, A-LCA outputs and additional data extracted from GABI database will be used to complete existing emission factors and implement emission factors of non-covered processes of the model (infrastructure for vehicle construction and end of life, for example). In the last stage of the WP4, up-to-date external cost factors will be used for the External Cost Analysis of the selected scenarios. Several iterations between GABI and PET36 will probably be necessary to identify, collect and then include all environmental data (emission factors) required to cover all processes involved in the considered systems; including processes which are not represented within the boundaries of the model. 3.1 Integration of WP2 outputs WP2 examines the impact of European governments incentive/taxation policies in automobile sector by: - analysing the impact of existing environmental regulations and fiscal legislations (car, fuel tax, subsidy, levy, scrappage scheme, city fee, tolls) on the size/composition of the vehicle stock in various European countries; - defining different prospective scenarios of policy measures aiming at inciting passengers to adopt low-carbon vehicles. Hence, this Work Package addresses the question of the effectiveness of the influence of fiscal legislations on the carbon performance of the new car fleet. It attempts to determine which variables are the most important drivers of vehicle carbon intensity in the EU. By using econometric tools, it will quantify the influence of these variables (Task 2.1). Based on these results, it will then be possible to select different prospective scenarios of policy measures aiming at inciting passengers to adopt low-carbon vehicles (Task 2.2). Results of the econometric analysis (especially elasticities) will be organised in scenarios (task 2.2) and implemented within PET36 model (WP4), to account for the heterogeneity of fiscal practices in European countries. A review of potentially impacting policies (e.g. related to air quality) will be conducted in order to identify the most relevant ones. The policy measure selection from WP2 will be then translated in terms of modeling parameters and economic values (definition of relevant scenarios for CO2 prices, tax, subsidy, mandate, etc.) to define policy scenarios that will be included in the energy system PET36 model. These policy scenarios will be analysed in WP4 to determine the effort required to reach announced 25

26 targets, and ultimately to determine appropriate economic instruments in order to facilitate the penetration of low-carbon vehicles in the technology mix. 3.2 Iterations between WP3 et WP4 required for C-LCA Consequential LCA (C-LCA) aims at assessing environmental impacts resulting from a change in a given life cycle or system. It is usually used to assess decisions (such as investment in a new technology or implementation of new specific policy) - most often in a prospective context - and is basically based on the comparison of 2 situations: one including the change (decision) and another corresponding to a no action scenario. In the project, this approach will be taken to assess some environmental consequences associated with new policies defined in WP2. To this end, PET36 will be used to compute in a same run technology mixes and environmental flows (emissions and resource consumptions) for each considered policy scenario and for a baseline scenario which includes no specific policy measures regarding e-mobility ( no policy scenario ). C-LCA results will then consist, for each policy scenario, in the difference of environmental impacts between this scenario and the baseline (no policy). The list of environmental flows and impacts that will be assessed in C-LCA work still needs to be set. C-LCIA results will at least consist in GWP assessment. Furthermore, C-LCI results will include GHG emissions, energy resource consumption and pollutant emissions. The choice of pollutant emissions that will be studied (NOx, SOx, Particulate Matter / PM, Heavy metals?) will be made according to the requirements for external cost analysis and to data availability. Alignment of boundaries for the economic assessment (private cost analysis) and environmental assessment (C-LCA) of the scenarios C-LCA assessments will employ the PET model to compute emissions of the selected policy scenarios according to the scheme shown in Figure 9. It implies to feed the model with emission factors corresponding to material & energy flows (at the process or commodity level). At first, C-LCA and PET model process boundaries have to be clarified to identify what should be added to PET model to ensure a life cycle perspective. 26

27 Figure 9 - Prospective model (PET36) and LCA model (GaBi) positioning for the C-LCA task Since C-LCA analysis is based on the comparison between policy scenario and baseline scenario ( no policy scenario ), environmental data considered in the calculation shall hence cover all processes that differ between these 2 scenarios: new processes such as those directly related to e-mobility deployment (electric vehicles production and operation for instance) and other processes whose activity differs due to direct effects or indirect substitution effects (e.g. less internal combustion engine car operation). One option could consist in feeding exhaustively the PET model with environmental data for all processes and all commodities and even to extend its coverage to include new relevant processes that were not initially covered. However, this would imply a tremendous data collection, processing and implementation effort that is partially unnecessary (for what remains unchanged or barely different between the 2 compared scenarios). To avoid this, a procedure is proposed to restrict environmental data collection. First, A-LCA results will be used to identify processes directly involved in e-mobility deployment that contribute significantly to environmental impacts of electrified vehicles pathways. Indeed, for each pathway, A-LCI and A-LCIA 6 results will provide information on respective contribution of each step / process in the overall environmental impacts (so-called 6 Attributional Life Cycle Inventory (A-LCI), Attributional Life Cycle Impact Assessment (A-LCIA). 27

28 hot-spot analysis). This information will help in the selection of relevant processes that first shall be included in C-LCA. Then, other relevant processes to include in C-LCA analysis will be identified from PET results. This will be done by comparing PET outputs for policy scenario to PET results for the baseline ( no policy scenario ), and selecting processes whose activity (or amounts for commodities) has significantly differed between the 2 scenarios. Once relevant processes are identified through A-LCA and orientating C-LCA, environmental data are extracted from GABI database (LCI data) for these processes as much as possible, to support the subsequent C-LCA assessments. For those processes that are already covered by the PET model, environmental data will be integrated into that model and considered during the optimisation runs. For those processes that are not covered by the PET model (e.g. processes taking place outside the PET model region), environmental data will be calculated separately and added after the optimisation runs (Process Add, combining the two types of C-LCI results in Figure 9). In case CO 2 emissions (i.e. a parameter with an impact on the optimisation results) are altered in the PET model towards the initial model run, it does not only need to be adjusted but also to be run anew (cf. upper green box in Figure 9) Iterations are envisaged (diamond shape in the diagrams All vs. no Policy with EFs? in Figure 9) to ensure a complete identification of (further) relevant processes, since adding new data in PET can affect the optimization and change PET outputs regarding technology mixes (for instance, CO 2 emissions can be taken into account in the objective function via constraints such as carbon tax system or CO 2 emission cap). Since the identification of relevant processes for C-LCA work requires PET outputs, it is impossible at this stage to draw up an exhaustive list of the environmental data that will be necessary. However, it is likely that the following data will have to be collected: - New emission factors regarding selected pollutant emissions (NOx, SOx, Particulate Matter / PM?) even for processes already covered by PET. For vehicle techs, pollutant tailpipe emissions can depend on both fuel composition and vehicle tech. In project, pollutant tailpipe emissions will be aligned with European emission standards. They are expressed in g/km and can be implemented at vehicle techs level. This makes sense since car manufacturers have to comply with these standards but have none interest in reaching lower emissions levels. Assumptions regarding the possible evolutions of EURO standards will be made to define future targets that are not defined for now (EURO6, EURO7 and EURO8). - Emission factors for material and energy commodities production and transport that are imported (produced outside the PET core regions): imported primary energy resources (oil, gas, biomass, etc.), imported fuel products (diesel, biofuels, etc.) - Emission factors for material and energy commodities transport and consumption that are exported (consumed outside the PET36 regions): gasoline for instance. - Emission factors for material and energy commodities that are not consumed in the energy sector described in the model: for example, biofuel by-products (ethanol DDGS and oilseed meal used in animal feed sector, digestate from biogas production used as agricultural fertilizers, naphtha coproduced with FT biosynthetic diesel mainly used in petrochemical sector). 28

29 This implies to apply the so-called substitution approach to deal with coproducts. In this approach, it is necessary to define their end of life (recycling, landfilling, combustion, etc.) to determine potential emissions to account. There are other approaches (allocation i.e. use of prorata) and we will have to define the more adapted approach. - Emission factors for production and transport and processing material needed for some new infrastructure and equipment manufacture, maintenance and disposal: it could mainly concern material for vehicle and station construction, if their relative parts are significant. Other sectors could be considered if massive investments in a specific process are observed in the scenario results, but considering NEEDS projects recommendations, infrastructure related emissions prove to be non-significant on overall emissions of a sector. Once both relevant processes identification and emission factors collection are done, consequential life cycle inventory (C-LCI) results can be computed, to carry out the C-LCA analysis. The total C-LCI results will be computed as the sum of C-LCI results provided by PET (for relevant processes covered by PET) and C-LCI results computed apart (corresponding to relevant processes not covered by PET) on the basis of i/emission factors for each relevant processes (extracted from GaBi database) and ii/ "activities" of each relevant processes which is part of PET results. Assessment of emissions corresponding to vehicle life cycle (manufacturing, maintenance & End of Life/EoL) can illustrate these calculations since processes involved in vehicle manufacture or EoL are not described in detail in PET: emissions would be then calculated using i/ GaBi A-LCA results (LCI results per vehicle for each existing & future technology: diesel car, gasoline car, PHEV, EV etc.) and ii/ PET results on fleet composition (passenger cars) for each considered policy scenario and the baseline ( no policy scenario ). The double-counting issue For processes of infrastructure building, like vehicle construction, not detailed in the PET model but included in the industry sector demand, it is necessary to established material composition implied in these processes to avoid double counting energy and material consumption in the industry sector. Indeed, - the energy / material consumption linked to the life cycle of these infrastructures are already taken into account in the energy demand / consumption of the sectors producing the material and/or the technologies provided they take place in the PET modeling region. - the country investing is not always the country producing the technology : difficult to associate energy/material consumption correctly. The integration of pollutant emissions related to infrastructures implied by the introduction of EV techs must be made either at the transport sector level or at the industry sector level, but not at both. To determine if emissions related to infrastructure building, maintenance and disposal have to be accounted for, we will use data provided by PE Int. on vehicle life cycles and we compare the additional material & energy demand (for steel, for aluminum etc.) with the overall demand of corresponding sector (industrial sector). 29

30 - If this additional demand is very small (maximum threshold to be defined), then we know that there is a double-counting issue but this does not impact the optimization results of PET. - If this additional demand is significant (minimum threshold to be defined), then double-counting can affect PET results: we can overcome this by modifying the corresponding demand (material & energy demand of the industrial sector) or by subtracting corresponding emissions. To determine these corresponding emissions, an option would be to adjust GaBi results on vehicle Life Cycle to limit the scope of GaBi results to processes & emissions that are not covered by PET. 4. Summary of all WP interactions (to be completed) IFPEN KANLO Technology cost survey (vehicles, electric infrastructure) IFPEN KANLO IFPEN KANLO Vehicle configuration details for the A-LCA Vehicle fuel consumption results from simulation for 2010 and 2030 Pollutant emission hypothesis for 2030 New mobility structure data from TREMOVE database (urban, road, motorway) by technology (efficiency and occupation rate by usage) Emissions per material from GaBi to be included in the PET36 model PECEE KANLO Material composition of each vehicle technology WP2 IFPEN KANLO Econometric analysis of Public policies : WP2 outputs IFSTTAR KANLO Most efficient public policy tools to promote E-mobility WP3 IFPEN PECEE Vehicle configuration details for the A-LCA Vehicle fuel consumption results from simulation for 2010 and 2030 Pollutant emission hypothesis for 2030 First two PET runs (BAU and Electrification scenario) with specific focus on : -European electricity mix in Emission intensities and factors on the production of gasoline, diesel, natural gas -Fleet composition in Fuel composition at pump station in 2030 KANLO PECEE KANLO IFPEN PET36 results to be used in the C-LCA Energy consumptions & emission not covered by PET36 to be used for the C-LCA PECEE IFPEN IFPEN EIFER C-LCA results to be used in the externality assessment WP4 PECEE KANLO A-LCA results to be incorporated in the PET36 model EIFER KANLO Externality factors to be included in PET36 model 30

31 5. Appendices Appendix 1 Table 9 30 zones of the PET36 model version EU Member States AT Austria, New-Zealand, Oceania FI Finland MT Malta BE Belgium FR France NL Netherlands BG Bulgaina GR Greece PL Poland CY Cyprus HU Hungary PT Portugal CZ Czech Rep. IE Ireland RO Romania DE Germany IT Italy SE Sweden DK Denmark LT Lithuania SI Slovenia EE Estonia LU Luxembourg SK Slovakia ES Spain LV Latvia UK United Kingdom Non EU Member States CH Switzerland IS Iceland NO Norway Non included Balkan Region Countries AL Albania HR Croatia ME Montenegro BH Bosnia & Herzegovina MK FYRO-Macedonia RS Serbia 31

32 Appendix 2 Figure 10 Spatial supply of dedicated bioenergy crops in Europe in 2030 in percent of a region (source: Refuel project) 32

33 Appendix 3 Figure 11 Spatial cost distribution for grassy and woody dedicated crops in Europe in /GJ (Source: Refuel project) 33

34 Appendix 4 Figure 12 - Summary of the total annual biomass resource supply potential by country (source: Refuel project) 34

35 Appendix 5 Figure 13 - EMF28 E01scenario - Energy carrier share for electricity generation in Europe for each Member State (VEDAVIZ extraction); for the meaning of the colors refer to Figure 14 Figure 14 - EMF28 E01scenario - Energy carrier share for electricity generation in Europe for EU30 (VEDAVIZ extraction) 35