Pan European TIMES Model General Description

Transcription

1 Energy Systems Modelling Addressing Energy Security and Climate Change Least Cost Optimisation Modelling of the 22 Energy and Environmental targets in EU27 Dr George Giannakidis Centre for Renewable Energy Sources and Saving, Greece November 15 th 21, University College Cork, Ireland Pan European IMES Model General Description he Pan European imes (PE) Model is a multi-regional technical economic optimisation model built with IMES. he PE has been originally developed in the NEEDS project (FP6 Integrated Project on New Energy Externalities Developments for Sustainability In the framework of 22 it has been extended to deal with renewabled in more detail and to include renewable policy options. 1

2 Pan European IMES Model Geographic Coverage of the Model Pan European IMES Model Highlights 5 demand sectors (AGR, RSD, COM, IND and RA) Supply sector description (fuel mining, primary and secondary production, import and export) Power generation sector description (autoproducer and CHP included) Electricity Multi-grid model (high, medium and low voltage grid) Electricity/Biomass and Biofuels trade feature between the European countries Country specific differences for characterisation of the conversion and end-use technologies Country specific seasonal availability factors for wind Renewable potential (onshore wind, offshore wind, geothermal, biomass, biogas, hydro (small, large), pump storage) Land use constraints for biomass production CO2 Emissions 2

3 Pan European IMES Model (Cont.) Reference Energy System: Residential Pan European IMES Model (Cont.) Reference Energy System: Pulp and Paper 3

4 Pan European IMES Model (Cont.) Endogenous trade of Electricity Endogenous electricity import/export between the EU countries. Existing Lines with Capacities are in the model. Extention of lines possible (with associated cost). Exogenous import/export of Electricity from ROW Added the exogenous import/export with the ROW for: - Bulgaria - Spain - Finland - Greece - Hungary - Lithuania - Latvia - Slovenia - Poland - Slovakia Pan European IMES Model (Cont.) Electricity Grids 4

5 Pan European IMES Model Biofuels and Biomass he basic enhancements are: 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 the available potential of bioenergy, taking in mind sustainability issues. he main sources of data for bioenergy are a number of studies contacted by ECN. Pan European IMES Model Biomass and Biofules rade Biofuels - Biodiesel, Bioethanol, Methanol, F-diesel, Ethanol and DME Biomass he trade is based on: land trading (countries with physical borders), and sea trading (countries with harbors) So every country that has a harbor can trade with every other country that has a harbor. For each trade there is a associated cost 5

6 Oil crops Rape oil Starch crops Sugar crops Wood & grass BioDiesel F-Diesel Ethanol Methanol DME Biogas Pan European IMES Model Reference Energy System: Biofuels and biogas production Harvesting oil crops Harvesting starch crops Harvesting sugar crops Pressing Oil crops Biodiesel prod. Ethanol prod. Ethanol prod. Biogas prod. Harvesting grassy crops Harvesting woody crops F-diesel prod. Ethanol prod. Agric. residues Forest residues Methanol prod. DME prod. Wood waste Biogas prod. Overview of the data sources Wind Availability factor UCE: Monthly statistics for wind production since 25 EWEA: Wind capacities by end of year BE CZ DE ES FR GR HU I NL PL P SK DK_W Spring Summer Fall Winter 6

7 Data sources Bioenergy Potential he technology characterization, the estimation of potentials for biofuels on the level of individual technologies, and the renewable heating/cooling is based on the BRED study (Biomass strategies for greenhouse gas emission reduction) and ECN s RANS model (REFUEL project - he data on bioenergy potentials and costs originate from the European IEE project REFUEL. In 22 the potentials from the REFUEL Baseline scenario are used, which describes most likely developments under current policy settings. Baseline assumes a continuation of current self-reliance levels in Europe s aggregate food and feed commodities. Data sources Land availability 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. All these data were adopted from the REFUEL project ( 7

8 Costs ( /GJ biomass ) Potential (EJ biomass ) Data sources Potentials bio-energy crops EU Crop potential in Grassy crops Oil crops Starch crops Sugar crops Woody crops A BE BG CH CY CZ DE DK EE EL ES FI FR HU IE I L LU LV NL NO PL P RO SE SI SK UK Country Source: Refuel Project, Data sources Cost supply curve woody crops EU Cost supply curve Woody crops Potential (EJ biomass ) Source: Refuel Project, 8

9 Data sources Other Renewable Energy Sources data he sources used for the technology characterization and corresponding potential are: Data for Hydropower is an EURELECRIC forecast which can be found in: EURELECRIC (26): Statistics and prospects for the European electricity sector, EURPROG 26 Wind data is an EWEA forecast (with good policies implemented) from the RADEWIND project. he reference document is Wind Power capacity data collection, April 27, Data sources Other Renewable Energy Sources data he sources used for the technology characterisation and corresponding potential are: Data for the potential of Geothermal, PV, Biogas and Ocean power (Wave and idal technologies) come from the OP forecast. he reference document OP - Potential and cost for renewable electricity in Europe, EEG, ISI, LEI, Vienna, February 26, can be found at Data for the potential of Concentrated Solar Power come from the EREC/Greenpeace scenario with good policy implemented. 9

10 Data sources By potential in this model we mean the Upper bound of installed capacity per year. he model will decide if the installation of a technology will happen based on the least cost of the energy system. Policies applied he existing policies per country were included in the model (i.e countries that use one policy instrument continue to use it in the time horizon of the model). he three existing basic mechanisms were used: Feedin tarrifs, Quota, Subsidies (tax or initial investment subsidies), per country as described in the Reference Document on Policy and Potential 1

11 RE - argets 5% 4% 3% 2% 1% % SE LV FI A P EE RO DK SI L FR BG ES EU27 PL GR SK CZ DE I HU Share in 25 Increase to arget in 22 IE CY NL BE UK LU M RE argets - rajectory 5% 4% 3% 2% 1% % Share in 25 Share Share Share Share Share 22 SE LV FI A P EE RO DK SI L FR BG ES EU27 PL GR SK CZ DE I HU IE CY NL BE UK LU M 11

12 Emission reduction targets for non-es sectors in 22 2 [%] BU RO LV L PL SK EE HU CZ M SL P HE CY ES I DE FR UK BE NL A FI SE IE DK LU Relative to 25, as specified in the Energy and Climate package PanEuropean IMES Four Scenarios were analyzed in the project: 1. Reference - 2. Reference 3. Statistical ransfers

13 PanEuropean IMES Scenario Basic Assumptions: he same background assumptions as the Baseline Scenario in the: European energy and transport: rends to 23 Update 27 as published by DGREN. But for the prices of conventional fuels the forecast of World Energy Outlook 28 (published by IEA in November 28) are used. his is the scenario where the existing policies only are implemented, and their effectiveness is examined. PanEuropean IMES Scenario Basic Assumptions: Include the existing policies in 13

14 PanEuropean IMES Scenario Reference ( Ref) his is the scenario where the target for renewables for each Member State is imposed, together with the target of GHG emission reduction of 2% by 22. he results of this scenario show the way of achieving the targets with the least cost over EU27 given the constraints of potential per technology. PanEuropean IMES Scenario Statistical ransfers () he results of this scenario show the way of achieving the targets with the least cost over EU27 and with the use of the statistical transfer mechanism that is foreseen in the Directive. his is done on an economic optimisation basis. 14

15 PanEuropean IMES Scenario 3 In this scenario the reduction of GHG emission by 3% in 22 is analysed, together with the existing renewables target. he results present the least cost way of achieving these targets over EU Scenario Results on the EU27 level 15

16 [PJ] 9 otal Primary Energy Supply % 3% Electricity import Coal Gas Nuclear Oil Renewable 3% [Wh] 4 Net Electricity Production per Fuel % 3% Coal Gas Oil Nuclear Renewables 3% 16

17 [Wh] 14 Net Electricity Production per source % 3% Hydro Geothermal Solar Wind Biogas Biomass Ocean 3% Nuclear 24.97% Wind 16.92% Oil.6% Gas 22.42% Other 35.96% Solar 1.4% Geothermal.32% Biogas.14% Biomass 6.69% Coal 16.6% Hydro 1.82% Ocean.3% Share of Electricity Production in the total Net Electricity Production -Scenario Reference in 22 17

18 Nuclear 25.38% Wind 17.23% Oil.6% Gas 22.38% Other 38.26% Solar 1.39% Geothermal.32% Biogas.12% Biomass 6.75% Coal 13.37% Hydro 11.16% Ocean 1.3% Share Electricity Production Scenario 3 in 22 In the Electricity production sector it is Wind and Hydro that dominate the scene. Bioenergy is preferred to be used in the final energy because of the way the target of Renewables is imposed 18

19 [PJ] 3 Consumption for Heat Production % 3% Coal Gas Oil Electricity Renewables 3% [PJ] 5 45 Consumption for Heat Production % 3% Electricity Geothermal Solar Bioenergy 3% 19

20 Consumption for Heat Production in 22 Oil 13.2% Electricity 4.41% Bioenergy 14.55% Gas 5.56% Other 19.4% Coal 12.97% Solar 1.97% Electricity 2.47% Geothermal.4% Bioenergy plays an important role in heat production, and is mainly used in: Industrial CHP District Heating High efficiency boilers in residential and tertiary sector. he largest share of bioenergy comes from woody and grassy crops, agricultural waste and forestry residues. 2

21 [PJ] 2 ransport Fuels % 3% Fossil Non-Fossil 3% [PJ] 25 Non fossil ransport Fuels % 3% Electricity Hydrogen Biofuels 3% 21

22 3% 3% 3% 3% 3% 3% [PJ] Use per sector Agriculture Residential Commercial ransport Industry Central heat & power [Mton CO2] CO2 emissions per sector Agriculture Residential Commercial Industry - non-es ransformation - non ES ransport Industry - ES ransformation - ES Electricity 22

23 [PJ] 6 Final Energy Consumption Ref. rade 3 Ref. rade Renewables Non-renewables Ref. rade 3 [PJ] 6 Final Energy Consumption Non Renewable fuels % 3% Electricity Heat Coal Gas Oil Synthetic 3% 23

24 Final Energy Consumption Conventional fuels A reduction of the use of conventional fuels compared to the scenario, is due to the combined effect of energy savings and use of. here is: a reduction of the use of N. Gas by 9%-11% a reduction of Coal by 11%-18% a reduction of Oil products by 1%-12% he energy savings are consistent with the targets of the ESD directive. [PJ] Final Energy Consumption Renewable Energy 3% 3% Electricity - Heat - Bioenergy Other 3% 24

25 Final Energy Consumption Renewable Energy Sources In the policy scenarios there is 28%-35% more bioenergy 4-6% more solar for hot water 2-27% more geothermal 3% more district heating from renewables (mainly from bioenergy) in the Final Energy Consumption compared to the scenario Sensitivity Analysis to check the robustness of the results: he availability of all biomass types was reduced to respectively 5%, 25% and 1% of the original assumptions. he potential of wind energy was varied, with two runs in which it was set at 75% and 125%, respectively. he international price of oil was increased to the high price scenario of the DoE AEO29 he useful energy demand was varied in a scenario without elastic demands. 25

26 [Wh] 14 Net Electricity Generation in the Biomass availability sensitivity runs % 25% 1% 5% 25% 1% 5% Hydro Geothermal Solar Wind Biogas Biomass Ocean 25% 1% [PJ] 6 Input for Heat production in the Biomass availability sensitivity runs % 25% 1% 5% 25% 1% 5% Electricity Geothermal Solar Bioenergy 25% 1% 26

27 [PJ] 25 Non Fossil consumption in ransport in the Biomass availability sensitivity runs % 25% 1% 5% 25% 1% 5% Electricity Hydrogen Biofuels 25% 1% [Wh] 14 Net Electricity Generation in the Wind availability sensitivity runs High Low Wind Wind High Low Wind Wind High Low Wind Wind Hydro Geothermal Solar Wind Biogas Biomass Ocean 27

28 [PJ] 3 Non Fossil consumption in ransport in the High Oil price run High Oil Prices High Oil Prices High Oil Prices Electricity Hydrogen Biofuels Conclusions he target for can be met by: ~36% Electricity production from (Largest share Wind and Hydro) ~2% Heat production from (Largest share bioenergy) ~1% Renewable in transport (largest share of which is biofuels) 28

29 hank you.. More Information: he project was co-funded by the Intelligent Energy Europe Programme. he sole responsibility of the content lies with the authors, it does not necessarily reflect the opinion of the European Communities. he European Commission is not responsible for any use that may be made of the information contained herein. 29