Loading in 2 Seconds...
Loading in 2 Seconds...
Experiences with energy-related GHG projections for SloveniaStane Merše M.Sc.firstname.lastname@example.org UNFCCC Workshop on emission projection, Bonn. 6-8 September 2004
RES Industry - main drivers for projected GHG emissions PRODUCT FINAL ENERGY USEFUL ENERGY VALUE ADDED Energy intensity [TJ/1,TJ/kt] Low temp. heat ind. Value added Electricity 1-35kV Electricity 110 kV [1, kt] Product Low temp. heat Electricity ind. Efficiency Process heat District heat Hydro en. Electricity Fuels TR Fuels FE Steam Efficiency S,I Boilers Market share Aluminum electrolysis Industrial CHP S,I Ind. Electr. distribution Industrial hydro PP Electr. arc furnaces steel Factor [mioSIT/1, mioSIT/kt] Ind. heat distribution BOILERS S,I Electrical motors Electricity distribution Efficiency Emis. factor HEAT demand Market valuation EMISIONS Elec. proc. & applian. Heat distribution FUELS Standard technology Space heating Steam distribution CO2,CH4, N2O, SO2, NOX S,I Thermal processes Improved technology Non energy use Market share COSTS JSI Energy Efficiency Centre
Modeling of GHG mitigation measures - INDUSTRY • Technological progress: • Current best available technologies (BAT) • IPPC requirements (BREFs,...) • Measures (on site energy efficiency improvments): • Decrease of compressed air networks leakages,... • Overall decrease of energy intensity: -0,5%/a (M&T,...) • New technologies: • CHP, VSD, ... • Fuel switching • Penetration of measures: • driven by legislation (IPPC, EU harmonization, etc.) • Cost benefit (economic signals) ? JSI Energy Efficiency Centre
Market penetration model for energy efficient technologies (spreadsheet) JSI Energy Efficiency Centre
Modeling of future changes in the modal split of transport • Overall sector modeling • main drivers - transport demand (volume): • Passenger-kilometers (pkm) • Ton-kilometers (tkm) Market shares = modal split Fuel Emissions Load factor Vehicle km pkm Efficiency Emis. factor Passenger cars Cars Standard Buses Public transport Cars Improved Passenger trains JSI Energy Efficiency Centre
Impact of voluntary agreements ACEA • Separate model of passenger car stock: • Official database data, grouped by: • fuel type (gasoline - w/w.o. catalytic conv., diesel) • Age (year of production) • future evolution: • no. of new and eliminated cars/year, • spec. fuel consumption of new cars (agreement ACEA) • yearly mileage by group Aggregated input parameters for REES model JSI Energy Efficiency Centre
Conclusions • Bottom up – technology oriented approach: • Enables consistent modeling of measures (without double-counting of savings) • Costs calculation: • macroeconomic effects, necessary founds, budget resources,... • Data intensive: • linking of statistical data and practical experiences (on site informations, international practice,etc.) • improved monitoring, new data for modeling (ETS) • Implementation uncertainty: • Gap between policy and measures implementation (forecasting of expected policy results ) JSI Energy Efficiency Centre