1 / 28

PACT WP5 - Modelling Example of possible evolutions in the POLES model

PACT WP5 - Modelling Example of possible evolutions in the POLES model. Mid term meeting Bruxelles – 17/05/2010 Philippe MENANTEAU, Paolo AVNER. WP 5 Modelling. Partners : LEPII, Enerdata

lotus
Download Presentation

PACT WP5 - Modelling Example of possible evolutions in the POLES model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. PACT WP5 - Modelling Example of possible evolutions in the POLES model Mid term meeting Bruxelles – 17/05/2010 Philippe MENANTEAU, Paolo AVNER

  2. WP 5 Modelling • Partners : LEPII, Enerdata • Objective : Development of existing modelling tools in order to be able to reflect key drivers towards PC society as identified in other WPs • Task 5.1 up grading of VLEEM (NRD) • Improvement of data bases (urban mobility, urbanisation) – adaptation of the model structure (different urban schemes) • Task 5.2 up grading of POLES (LEPII) • Illustrate new adaptation margins, provide more realistic techno economic evolution profiles under high emission reduction scenarios • Task 5.3 linkage POLES – VLEEM (NRD) • Construction of a formal interface between VLEEM results and demand functions of POLES

  3. Content • Objectives of WP5, the POLES model and its limits • Urban form and transport modelling • Residential modelling: ways forward

  4. Resources Technologies Technologies Emission Constraint Emissions Emissions Cons, Prod Cons, Prod POP GDP The POLES model year-by-year recursive simulation process 57 Regions

  5. Present evolutions in the POLES model • Situation : • Global energetic model ; large regions ; not spatialised • The urban dimension is not taken into account : consumption paths (buildings, transport), or infrastructures (energy networks, transport …) • Aim : • Simulate the impact on consumption of different urban dynamics (rapid urbanisation, megalopolis, densification vs sprawl, slow/accelerated retrofitting, new urban vehicles, building-transport integration, …) • Project : • Introduce the urban dimension and the density variable for more realistic energy consumption / emissions trajectories, noticeably in high constraint scenarios

  6. Content • Objectives of WP5, the POLES model and its limits • Urban form and transport modelling • Residential modelling: ways forward

  7. Urban pattern is very different from one region to another • Introduction of the distinction urban / non urban in the model: • A simple but insufficient answer • All cities are not equivalent from a demand/supply of transport perspective • Cf american cities / asian cities Importance of the density variable

  8. Urban density has some connection with energy demand : ex of transport infrastructure

  9. Relation between mobility VP (PKM) and urban density Total mobility regressions Private mobility as a function of density Source : Millenium database (UITP)

  10. Establish a relationship between mobility demand and density • An inverse relationship exists between energy consumption for transport and density • Car equipment rate rises as density diminishes • Travelled Pkm rise as density diminishes • Density favours access to and use of public transport • Of course, detailed analysis show that transport and density have a more complex relationship (activity mix, urban forms beyond simple density, etc) • However possibility of using density as a proxy in a global energetic model (not a transport model, not a spatialised model) • Potentialities : • Simulation of the impact on mobility demand of urban sprawl (BAU)

  11. Density Central POP Peri POP Distance Modelling logic Disaggregation of total population in 3 classes in order to account for their specific mobility demands URB POP Total POP RUR POP Transport Supply

  12. Modal shares : relation between density and mobility modes Total pkm per capita (all modes) Modal shares Total = 100% Slow modes Cars Public transport Non – urban Périphery Centre Density

  13. Introducing Urban centre and urban periphery in the POLES model Asia Europe U.S.A • Density gradients • A tool to introduce the urban sprawl dimension

  14. Urban forms: Method for representation and distribution criteria Classical function for describing urban form (Clark, 1951) D(u) = D0eγu+ ε Ex: Toulouse 100 hab/ha 100 hab/ha

  15. Urban population : splitting among center / periphery

  16. Modelling: Distributing PKM databases Econometric relationship Total Data Calculations PKM/cap par zone Urban periphery Extra-Urban Urban center Urban centre: CAR Urban periphery: Public transport Extra Urban : CAR Urban centre: Public transport Urban periphery: CAR Extra Urban: Public transport

  17. Content • Objectives of WP5, the POLES model and its limits • Urban form and transport modelling • Urban form and buildings

  18. What are the main relations between energy demand in buildings and urban density ? • Energy consumption is higher in individual houses (per m²) compared to collective buildings (compactness / contiguity effect) • High density generates heat island effects and higher demand for AC • Living surface is reduced in high density areas (land price) • Higher density of energy demand is more favorable to network energy of energy sources • Lower density (ind. Housing) facilitates access to dispersed energy sources

  19. 1. Agglomeration positively influences energy demand for buildings • Heat energy demand for buildings decreases with density • the agglomeration effect allows a decrease in heat losses (ie energy demand for heating / cooling) with increasing compactness, heigth. Detached Heat losses may be decreased by 30 % when a building goes from 1 to 3 levels Appartment

  20. 1. Bis. Agglomeration positively influences energy demand for buildings • GHG emissions per m² are influenced by climate zone but also by building types (single, multi family, high rise) Nemry et al., 2010 : Life cycle impacts (construction, use and demolition) of building types (new buildings in blank) -

  21. 1. Ter. Agglomeration positively influences energy demand for buildings • ETHEL study confirms the negative relation between density and energy demand (CO2 emissions as a proxy) for buildings • But situation may be different in 2030 • Limited difference between urban centre and periphery • Influence of density is much more important for transport than heating

  22. 2. Heat island effect and air conditioning • Demand for AC is expected to increase with climate change • It is not yet but it will be integrated in the POLES model. • Question : is there a relation between demand for AC and density? • Yes : needs for AC appear first in dense cities (heat island effect) • No : heat island effect has also reverse effects on heat demand • Conclusion : Heat island effect generates additional AC demand but the net effect (reduced heat demand also) is not that important

  23. 3. Other determinants than compactness may be important : living surface • Area per inhabitant may be the most influent factor on energy demand for buildings (much more than agglomeration effect) • Gap in surface per cap between individual and collective housing is significant and increasing (France ?) • Surf / cap is decreasing with increasing size of urban area (id)

  24. 4. What about district heating ? • Density of demand • higher energy density allows network supply with efficient or renewable energy production (ie combined heat and power – clean biomass combustion – “waste combustion” etc) • What if very low energy buildings ? • High rise and compactness produce higher energy density but ambitious thermal regulations goes in the opposite sense (reduction of energy demand / m²) • What is the economic future of district heating in a city with a high share of very low consumption buildings ? • Conclusion : no introduction of DH in POLES • a lot of work • Is it relevant to assume a development of DH ? Needs further detailed analysis.

  25. 5. Access to dispersed renewable energy sources

  26. 5. bis. Access to dispersed renewable energy sources • Individual housing (ie low density) facilitates the access to dispersed renewable sources • POLES Status : Decentralised PV • Solar energy potential is estimated as a function of new and refurbished buildings (share of m²) • But no distinction is made between the types of buildings according to heigth (ie add m² in high rise building or ind. house is equiv) • POLES evolution : • Estimation of access to solar resource according to effective roof surface • ie. according to type of buildings (IND / COLL) or density

  27. 6. Dense vs sprawled cities : no clear model of sustainable city Density Dense cities: less heating (surf / cap effect) ! Sprawled cities: more area for dispersed renewable solutions ? Energy demand Renewable Energy production

  28. Conclusion : work perspective • Introduction of urban density in the model • done • Transport : mobility and modal share as a function of density • Calibration in progress • Buildings : energy needs and density ? • Identification of key factors (done to be validated) • Identification of data sources (share IH/CB, heigth, … to be done)

More Related