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Modelling Urban Transports in a City Energy System Model
This document discusses modelling urban transport systems in a city energy model using TIMES. It begins by outlining the characteristics of urban transport including high frequency, low speeds, and short distances. It then discusses learning from existing transport models to better represent factors like trip purposes and commodity groups that influence demand. The document proposes modelling passenger and freight transport in Malmo using TIMES, disaggregating demand based on mode, location, and trip purpose. It generates illustrative scenarios with different climate and air quality targets to explore policy impacts and tradeoffs between emission reductions. In the end, it emphasizes making underlying assumptions transparent through a city interface and optimizing for both CO2 and air quality.
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Modelling Urban Transports in a City Energy System Model
- 1. Modelling urban transportsin a city energy system model – Applying TIMES on Malmö Jonas Forsberg & Anna Krook Riekkola Division of Energy Science Department of Engineering Sciences and Mathematics Luleå University of Technology (LTU)
- 2. A climate policyframework & a climate and clean air strategy for Sweden 1. A long-term climate goal: By 2045 - at the latest – Sweden will have no net emissions of greenhouse gases. 2. Intermediate targets only for emissions outside the EU Emissions Trading System (known as the non-trading sector/NETS). NETS targets for year 2030 and 2040. Transport sector targets for year 2030 (70% reduction). 3. A clean air strategy with a focus on reducing air pollutants (NOX, SO2, VOC, NH4 and particles) and thereby improved air quality.
- 3. Modelling Sustainable and ResourceEfficient Cities AIM: Support smart city level integration of policies and measures towards a low carbon energy system including mobility services Part delivery: A generic TIMES-City model • EU ERA-NET project, 2016-2018 • Partners in Austria, Portugal and Sweden TIMES-Sweden AIM: To improve transport sector representation within and around TIMES • Identifying how the Swedish transport sector could be sufficiently described in an energy system optimisation model with the overall aim to improve the analysis of the transition to a Net-Zero GHG in Sweden. • Consider both recourse, technical, economic, environmental and behavioural (e.g. choice of transport mode) factors • NOT necessary include everything in TIMES, i.e. consider developing complementary methods/models PhD PROJECT
- 4. Modelling urban transportsin a city energy system model – Applying TIMES on Malmö What is the system CHARACTERISTICS? What can we LEARN from Transport models? Which DEMAND should DRIVE the model? An illustrative results
- 5. SYSTEM CHARACTERISTICS &MANAGEMENT Urban transport characteristics: – High frequency of movements – Low average speeds – Short distances Urban built environment creates lock-in patterns; affects energy-use for decades Mobility is key to the functioning of all cities Needs of local policy-makers: – Explore and analyse different long-term targets and policies, to… – …identify cost-efficient actions for improving overall system efficiency
- 6. SYSTEM CHARACTERISTICS &GOALS Cities account for - 2/3 of global final energy use - 75% of GHG emissions + Air pollution are a pressing problem in many cities; affects health, natural and built environments Urban transportation is a major source of local and global emissions - > 20-40% of urban GHGs - Leading local contributor to e.g. NOX, PM, CO EU level initiatives targeting city-level - Urban Mobility Package, SUMP - Covenant of Mayors, SECAP - Air Quality Directive (2008/50/EC) Complex policy landscape - Mobility of people and goods - Energy system management - Health and environment
- 7. AIM & APPROACH Aim:Investigate the impact on cost- efficient low-carbon options for urban transportation when also considering ambitious air quality targets Philosophy: Mathematical models powerful tools for ’mental experiments’ on complex systems development over longer time perspectives Based on the TIMES framework Differ between activities ‘in control’ by the municipality and activities not in control by the municipality. Transportation, Residential & Commercial buildings, Industry, Agriculture, Electricity & DH and Energy supply.
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- 9. SEA AIR What isthe Transport System? ROAD RAIL Passenger Freight Infrastructure: capacity, land-use, costs, etc. Modes Technology options Energy policy ’Behavior’ Transport policy Climate policy Fuels/energy carriers: feedstock, ’footprint’, supply infrastructure, etc. Air quality policy
- 10. WHAT CAN WELEARN FROM OTHER MODELS TRANSPORT MODELS Modal split important determining factor for both energy-use and emissions Trip purposes and commodity group characteristics important factors for frequency and mode choice of transportation Typical base-year calibration: - Travel surveys passenger transportation disaggregated by trip purpose - Goods flow surveys freight transportation disaggregated by commodity groups ESOM Disaggregating transport demand input to ESOMs can improve – Representation of different choices/ behaviour – Understanding of mode shift potentials – The ability to test effects of specific mode shift measures (targeting e.g. all commuting car-trips) Drawbacks? Further data and under- standing of the transportation system is needed
- 11. TRANSPORT SECTORIN TIMES-CITY Passenger & Freight All conventional modes and technologies, with addition of: – ’No physical travel’ (e-meetings etc) – Walking – Bicycle (conventional + electric) – Light electric vehicles (pass, freight) – Taxi – Car-pools – Public transport city ferry Conventional and emerging drivetrain and fuel options Demand disaggregated by: 1) Mode 2) City organisation/Other 3) Intra-city/Long-distance Mode shares exogenously determined, but… Input demands derived from ’scenario generator’ using trip purpose and commodity groups indirect representation of behaviour/choices
- 12. Generating Data Input Bus.Intercity.DST.Base-year. Bus.Intercity.GAS.Base-year. Bus.Urban.DST.Base-year. Bus.Urban.GAS.Base-year. Freight(tkm) • Construction mtrl. • Manufactured goods • Mining products • Products of forestry • Products of agri. • Etc. Passenger (pkm) • Commuting trips • Business trips • Shopping trips • Personal business trips • Leisure trips • Etc. Transport models Travel surveys Bus (urban/intercity) Car (urban/long-dist.) Bicycle (electric/conv.) Truck (light/heavy) Train (pass./freight) Aviation (domest./intl.) Etc. etc. Car.DST.Base-year. Car.ELC.Base-year. Car.ETH.Base-year. Car.GAS.Base-year. Truck.Heavy.DST.Base-year. Truck.Heavy.HEV.DST.Base-year. … TIMES Vehicle Technology database Goods-flow surveys … … … TIMES input Demand projection External models and surveys City Interface
- 13. Transport demand Passenger • Officialstatistics on vehicles: • Driving range • Base-year demand derived from travel survey (2014) • All trips by city residents within and to/from Malmö • Work, education, business, shopping, personal business, leisure, other • By mode and distance • Future demand driven by population growth (SCB) Freight • Official statistics on vehicles: • Driving range • No quantifiable base-year data Alternative approach: • National ’Material footprint’ approach determine freight demand (ton of goods per capita and GDP) * Malmö population • Intra-city freight: 100% by road • Long-distance freight: mode shares, distance by commodity groups based on national statistics • Future demand driven by population growth (SCB) and GDP/capita (OECD)
- 14. Mode share assumption Generate Demand projections Sub-sector Technology Base-year REF_A: 2050 REF_B: 2050 Passenger – IntraCity Walking Bicycle Bicycle (electric) Bus Car 9% 32% <0.5% 15% 44% 9% 32% <0.5% 15% 44% 9% 33% 11% 24% 22% Passenger – LongDistance Bus Car Train Train (high-speed) Aviation 12% 46% 26% 2% 14% 12% 46% 26% 2% 14% 24% 24% 36% 9% 7% Freight – IntraCity Bicycle (electric) Light electric vehicle (LEV) Truck, light Truck, heavy 0% 0% 10% 90% 0% 0% 10% 90% 5% 5% 5% 85% Freight – LongDistance Truck, light Truck, heavy Train Navigation 0% 63% 26% 11% 0% 63% 26% 11% 0% 63% 26% 11%
- 15. Mode share assumption Demand projections 0 100 200 300 400 500 600 700 800 B-Y A B 2015 2050 Intracity travel demand (Mpkm) Car Bus Walking Bicycle E-Bicycle 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 B-Y A B 2015 2050 Long-distance travel demand (Mpkm) Car Bus Train Train (h-s) Aviation 0 50 100 150 200 250 B-Y A B 2015 2050 Intracity freight transport demand (Mtkm) Light truck Heavy truck LEV E-bicycle 0 500 1 000 1 500 2 000 2 500 3 000 3 500 B-Y A B 2015 2050 Long-dist. freight transport demand (Mtkm) Heavy truck Light truck Navigation Train
- 16. MODELLING LOW-EMISSION SCENARIOS CO2 Explore cost-efficient low-carbon pathways Mitigation targets based on Swedish national policy: -70% CO2 in 2030 -95% CO2 in 2050 Model generated CO2 emission level for 2015 used as baseline Air quality Explore cost-efficient low-pollution pathways (NOX, PM, CO) Mitigation targets based on own assumptions: Model generated emission levels for 2015 used as baseline
- 17. ILLUSTRATING SCENARIOS No TargetClimate Target Air quality Target Climate & Air quality Target
- 18. ILLUSTRATING RESULTS No TargetClimate Target Air quality Target Climate & Air quality Target CO2 PMN COX NOX -100% -80% -60% -40% -20% 0% No Target CO2 PMN COX NOX -100% -80% -60% -40% -20% 0% Climate Target
- 19. ILLUSTRATING RESULTS No TargetClimate Target Air quality Target Climate & Air quality Target CO2 PMN COX NOX -100% -80% -60% -40% -20% 0% CO2 PMN COX NOX -100% -80% -60% -40% -20% 0% CO2 PMN COX NOX -100% -80% -60% -40% -20% 0% No Target Air quality Target (NOX) Climate Target
- 20. ILLUSTRATING RESULTS No TargetClimate Target Air quality Target Climate & Air quality Target CO2 PMN COX NOX -100% -80% -60% -40% -20% 0% CO2 PMN COX NOX -100% -80% -60% -40% -20% 0% CO2 PMN COX NOX -100% -80% -60% -40% -20% 0% CO2 PMN COX NOX -100% -80% -60% -40% -20% 0% No Target Air quality Target (NOX) Climate Target Climate & NOX Target
- 21. FINAL REMARKS When developinga city model: Important to consider what the municipality can impact directly, indirect or not at all. By adding a City-interface (generates the demand inputs) The underlying assumptions behind becomes ‘visible’, and easier communicated with the city/municipality There are not necessary co-benefits between CO2 and Air quality target important to also optimize for both!