Modelling Energy Demand from T ti SA 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 y 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 i i Optimize the operation of the transport-energy system Account for emissions from the transport system Other 5% Agriculture 2% Commerce 8% Residential 17% 2009 EB Industry 40% Transport 28% 3 ERC
Modelling the Transport Sector: The Challenge 4 ERC Source: S. Armenia et al.
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 We have NAAMSA Vehicle Sales We want Fuel Consumption Enatis veh. pop Vehicle km Natmap/SOL P-km, t-km Detailed Vehicle Parc SAPIA/EB P-km, T-km/ Fuel sales Mode-share 7 ERC
Modelling the SA System Supply Bottom-up Approach : Calibration and Data Challenges Scrapping Factor NAAMSA Vehicle Sales Vehicle Parc Model Vehicle Mileage/Decay g y 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 ti 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
Modelling the SA System Supply Bottom-up Approach : Step 1: Motorisation Model Motorisation is highly correlated to GDP/Capita. Often modelled by a Gompertz Curve (see Kenworthy & Townsend,) Motoris sation (vehicle es/1000 pop.) 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 No Car Fraction Pass. Car Owners 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. proj Priv. Vehicle ownership Occupancy proj 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 Household pop pulation 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 000 0.00 2010 2020 2030 2040 2050 Low Income (0 19,200) Middle Income (19,201 76,800) High Income (76,801 ) Billion Pkm 500.0 450.0 400.0 350.0 300.0 250.0 200.0 150.0 100.00 50.0 0.0 2010 2020 2030 2040 2050 Gautrain Metro Rail BRT Minibus Bus Car Priv.Veh. SUV Priv.Veh. Priv. Vehicles Million 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 2010 2020 2030 2040 2050 Low Income (0 19,200) Middle Income (19,201 76,800) High Income (76,801 ) Freight (b tonkm m) 2000.0 1800.0 1600.0 1400.0 1200.0 1000.0 800.0 600.0 400.0 200.0 Rail Exports Rail Other Rail Corridor HCV MCV LCV 16 2010 2020 2030 2040 2050 ERC 0.0
Modelling the SA System Supply Bottom-up Approach : Projecting Energy Demand: Step 2 Vehicle km by mode Transport p /Energy Policy Taxes and Subsidies Relative cost of techs proj. proj Vehicle Parc Model Fuel price proj. Oil Price S Scenarios i Supply Mix 17 Vehicle sales proj 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 1600.0 Energy for Trans sport (PJ) 1400.0 1200.0 1000.00 800.0 600.0 400.0 200.0 Electricity HFO Diesel Kerosene Av.Gasoline Gasoline 0.0 2006 2010 2020 2030 2040 2050 2500 2500 Sectoral Fuel Consum mption (PJ) 2000 1500 1000 500 Power Generation Transport Residential Industry Commerce Agriculture Refinery Output t (PJ) 2000 1500 1000 500 CTL GTL Crude Refineries Imports 0 2010 2020 2030 2040 2050 18 2010 2020 2030 2040 2050 ERC 0