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Modelling Energy Demand from Transport in SA Bruno Merven Overview 1. Why Model Energy Demand in the Transport Sector? 2. Modelling Approaches and Challenges 3. Data Available in SA and Challenges 4. Some Preliminary Results of Modelling done at the ERC Why Model Energy Use Transport Sector? Energy needs of the transport sector are large (28% of TFC in 2009): Planning for Investment in Energy Infrastructure required to support the transport sector: Refineries, pipelines, etc.., – have long lead-times – involve large sunk investments, – impacts society and environment, – supply disruptions are expensive to the economy Account for energy use in the transport-energy system: to identify “leaks” or inefficiencies Optimize the operation of the transport-energy system Account for emissions from the transport system 3 Agriculture 2% 2009 EB Other 5% Commerce 8% Residential 17% Transport 28% Industry 40% ERC Modelling the Transport Sector: The Challenge 4 Source: S. Armenia et al. ERC Different Modelling Approaches Basis: – Empirical vs theoretical (Top-Down vs Bottom-up) – Supply driven vs Demand driven – Engineering focus vs Economics focus Degrees of Freedom: Accounting vs Optimization vs Simulation Scope: – Short-term vs Long-term horizon – Supply vs Demand vs Integrated Handling of Uncertainty: Deterministic vs Stochastic In combination: Hybrid models 5 ERC Modelling the SA Energy-Transport System using a “Supply – Bottom-up Approach” Under different Assumptions around: – – – – Socio-economic Development, Policy, Fuel Price, Technology Evolution Account for mode options/“choice” (mode-switching) – Passenger: need to track passenger-km – Freight: need to track ton-km Account for technology/fuel options/“choice” Account for the evolution of existing car parc 6 ERC Modelling the SA System “Supply – Bottom-up Approach”: Calibration and Data Challenges 7 We have We want NAAMSA Vehicle Sales Fuel Consumption Enatis veh. pop Vehicle km Natmap/SOL P-km, t-km Detailed Vehicle Parc SAPIA/EB Fuel sales P-km, T-km/ Mode-share ERC Modelling the SA System “Supply – Bottom-up Approach”: Calibration and Data Challenges Scrapping Factor NAAMSA Vehicle Sales Vehicle Parc Model Vehicle Mileage/Decay Vehicle km Occupancy 8 Enatis Check P-km, T-km/ Mode-share Fuel Consumption Natmap/SOL Check SAPIA/EB Check Fuel Economy/ Improvement ERC Some Calibration Results: Vehicle sales and Scrapping Curves 9 ERC Some Calibration Results: Car Parc Cars Minibus Light Duty Vehicles 10 ERC Some Calibration Results: Vehicle sales and Scrapping Curves Years 11 Diesel Vehicles ERC Some Calibration Results: Fuel Sales (litres) Gasoline Diesel 12 ERC Modelling the SA System “Supply – Bottom-up Approach”: Projecting Energy Demand Ideally we’d like to capture all the interactions and life-cycle implications of all options but that’s tricky… At this stage Projection is done in 2 steps: 1. Using projected socio-economic drivers, project demand for mobility by different modes and transport classes 2. Given projected demand for mobility for each mode, establish mix of technologies to meet this demand, based on techno-economic criteria 13 ERC Motorisation is highly correlated to GDP/Capita. Often modelled by a Gompertz Curve (see Kenworthy & Townsend,) Motorisation (vehicles/1000 pop.) Modelling the SA System “Supply – Bottom-up Approach”: Step 1: Motorisation Model Saturation Occurs when Net Transition to Income Group High stabilises at a low rate GDP/Capita ($) We have similar approach using Household Data as follows: Fraction Pass. Car Owners Fraction No Car 14 Income Group Low Income Group Medium Income Group High ERC Modelling the SA System “Supply – Bottom-up Approach”: Projecting Energy Demand: Step 1 Household Income proj. Priv. Vehicle ownership Occupancy proj. Transport /Energy Policy Taxes and Subsidies Relative cost of modes Annual Mileage proj. Mode Share /pkm proj. Relative speed of modes Relative cost of techs proj. 15 Fuel price proj. Vehicle km by mode ERC Modelling the SA System “Supply – Bottom-up Approach”: Projecting Energy Demand: Step 1 16.00 500.0 450.0 12.00 400.0 10.00 8.00 6.00 4.00 2.00 0.00 2010 2020 Low Income (0 - 19,200) 2030 Middle Income (19,201 - 76,800) 2040 2050 Metro Rail 300.0 BRT 250.0 Minibus 200.0 Bus 150.0 Car Priv.Veh. 100.0 SUV Priv.Veh. 50.0 High Income (76,801 - ) 0.0 2010 2020 2030 2040 2050 2000.0 18.00 16.00 1800.0 14.00 1600.0 12.00 10.00 1400.0 8.00 Freight (b tonkm) Million Priv. Vehicles Gautrain 350.0 Billion Pkm Household population 14.00 6.00 4.00 2.00 0.00 2010 2020 Low Income (0 - 19,200) 2030 Middle Income (19,201 - 76,800) 2040 2050 Rail Exports 1200.0 Rail Other 1000.0 Rail Corridor HCV 800.0 MCV 600.0 LCV High Income (76,801 - ) 400.0 200.0 16 0.0 2010 2020 2030 2040 2050 ERC Modelling the SA System “Supply – Bottom-up Approach”: Projecting Energy Demand: Step 2 Vehicle km by mode Transport /Energy Policy Taxes and Subsidies Relative cost of techs proj. Vehicle Parc Model Fuel price proj. Oil Price Scenarios Supply Mix 17 Vehicle sales proj. Vehicle km by tech Annual Mileage proj. Fuel sales proj. Emission proj. ERC Modelling the SA System “Supply – Bottom-up Approach”: Projecting Energy Demand: Step 2 1800.0 Energy for Transport (PJ) 1600.0 1400.0 Electricity 1200.0 HFO 1000.0 Diesel 800.0 Kerosene 600.0 Av.Gasoline 400.0 Gasoline 200.0 0.0 2006 2010 2020 2030 2040 2500 2000 2000 Power Generation Transport 1500 Residential Industry 1000 18 Commerce Agriculture 500 0 2010 2020 2030 2040 2050 Refinery Output (PJ) Sectoral Fuel Consumption (PJ) 2500 2050 CTL 1500 GTL Crude Refineries 1000 Imports 500 0 2010 2020 2030 2040 2050 ERC