PACT - 3 rd technical meeting POLES development

1. PACT - 3rd technical meetingPOLES development Philippe MENANTEAU, Silvana MIMA LEPII – EDDEN Grenoble 11 / 04 / 2011

2. Outline of key points • Introduction • Spatialisation of the model • Impact of density in the transport sector • Impact of density on residential and service heat demand • Development of district heating modelisation • Improvements in modelisation of distributed energy sources (PV)

3. Preliminary comments • POLES is a global energy model adapted to simulate energy / climate policies, hypothesis of accelerated technical progress or limited resources, … but it is not spatialized (infra national level). • The urban dimension was not taken into account : which impact on consumption paths (buildings, transport), or infrastructures (energy networks, transport …). • Our objective was to introduce the urban dimension in the model and a density variable in order to be able to differentiate urban areas from sparse settlements and to distinguish between different urban forms

4. Preliminary comments (follow.) • This presentation will not go into details of the modifications which have been introduced in the model • The aim is to illustrate how the model behave following the introduction of the density variable • The model is not fully calibrated yet • So the results presented are clearly preliminary and will be reconsidered when the specific parameters corresponding to the 3 PACT scenarios will be introduced

5. Relation between urban density and energy consumption in transport

6. Urban density is a key factor for energy consumption in transport sector

7. Relation between density and urban form Low density Average d. High density High proportion of individualhousing High proportion of multi family / highrise buildings

8. Dense vs sprawled cities : no clear model of sustainable city Density Dense cities: Lower car mobility Less heating (surf / cap effect) ! Network energy supply Sprawled cities: Higher dispersed renewable resources per cap Energy demand Renewable Energy production

9. Outline of key points • Introduction • Spatialisation of the model • Impact of density in the transport sector • Impact of density on residential and service heat demand • Development of district heating modelisation • Improvements in modelisation of distributed energy sources (PV)

10. Introducing density and urban form in the POLES model : density gradients • 3 different steps • To estimate urban / non-urban population • To distinguish urban center / periphery (density frontier) • To introduce specific urban forms according to different regions (density gradients) • Classical function for describing urban form (Clark, 1951) • D(u) = D0*e(γu + ε) • It allows to allocate pop in two urban zones of different densities : • Urban center > 75 hab / km² • Urban periph < 75 hab / km² Asia Europe center / periphery U.S.A Bertaud, 2001

11. Urban population : splitting among center / periphery

12. Spatialization of the model Urban cores Suburbs (1st ring) In POLES Do it fast Urban center - UCEN Urban periphery - UPER II I Do it alone Do it together IV III Medium to small towns (compact cities) Sparse development (periurban, diffuse cities) Extra - Urban sparse settlements - EXTU Do it slow

13. Evolution of population according to scenarios • Reference scenario : pop growth in periphery • Scenario S1 : almostsimilar to REF (lowergrowth in UP) • Scenario S2 : high pop growth in urbancenters • Scenario S3 : break in pop decrease in extra-urban areas

14. Evolution of the population (abs.) according to the scenarios Population • UCEN large increase in S2, lowerincrease in S1 • UPER limiteddifferentiation, smalldecrease in S2, S3 compared to REF • EXTU decrease of population in EXTU isstopped in S3

15. Evolution of the population (share) according to scenarios - • Scenario S1 : share of pop in different zones is similar to REF • Scenario S2 : share of pop is increasing in UCEN • Scenario S3 : the share of pop stops decreasing in EXTU Population

16. Density and population • DENSn = DENSn-1 * [ POPn / POPn-1]^El • Elasticity of density • Hypothesis of continuing sprawl in periphery for the reference scenario

17. Evolution of density according to scenarios - • REF: density is slightly decreasing in UPER • S1 : similar to S1 but density is stabilised in UPER • S2 : density increases in UCEN • S3 : limited impact on density (stabilised in UCEN) Density

18. Outline of key points • Introduction • Spatialisation of the model • Impact of density in the transport sector • Impact of density on residential and service heat demand • Development of district heating modelisation • Improvements in modelisation of distributed energy sources (PV)

19. Transport : relation between density and mobility modes • Different mobility behaviour : • Lower car mobility as density increases (ie urban centers) • Higher public transport use as density increases Total pkm per capita (all modes) Modal shares Total = 100% Slow modes Cars Public transport Non – urban Périphery Centre Density

20. Modeling mobility in POLES model • Total mobility = Private mobility + Public mobility • Private mobility = fc (income effect + price effect + density) • Public mobility = fc (income effect (infrastructure) + price effect (opposite to price effect for private mob) + density (idem) + subsitution effect) Price effect : Short termelasticity Long termelasticity

21. Car mobility according to urban areas Car mobility per capita (Pkm) • Urban Center : • Individual car mobilityislower in UCEN compared to UPER • Individual car mobilityisdecreasing in scenario S2, as a result of densityincrease (compared to REF) • UrbanPeriphery : • Limited evolution of density in S2 compared to REF ; does not lead to significant impact on individualmobility

22. Public transport mobility according to urban areas Public transport per capita (Pkm) • Urban Center : • As a result of increaseddensity in urban center, passenger – km by public transport isincreasing in S2 scenario • UrbanPeriphery : • The highersensitivity of public transport mobility to densityleads to a lowerdecrease of public transport use in periphery

23. Public transport according to urban areas (France) Share of public transport • Share of Public Transport : • The evolution of PT marketshareis positive in S2, but the differenceis not thatsignificantat the global level • Public Transport in Urban Center : • The impact of higherdensityis more significant in urban center with a clearincrease of PT share

24. Public transport according to urban areas (Spain) Share of public transport • Share of Public Transport : • The evolution of PT marketshareis positive in S2, but the differenceis not thatsignificantat the global lever • Public Transport in Urban Center : • The impact of higherdensityismuchhigher in urban center with a clearincrease of PT share

25. New technologies : the Light Urban Vehicle • Light UrbanVehicle : • Limited mobility : urban center and periphery • Lowercosts • Lowerconsumption, loweremissions • Twodifferent technologies • Thermal engine • Electric engine

26. Development of new technologies : the Light Urban Vehicle • Light Urban Vehicle : • Limited autonomy restricts usage to urban center and periphery • Lower consumption, lower costs • Two different technologies • Thermal engine • Electric engine

27. Development of new technologies : Electric / hybrid vehicles Marketshare of car technologies • Reference : • No LUV available – development of hybrid cars • Scenario S2 • No LUV – technicalbreakthrough on hybridengines and electricstorage • Scenario S1 • Idem S2 plus light urbanelectricvehicle

28. Development of new technologies : Electric / hybrid vehicles • Scenario S1 • Multi Fonction Vehicle : conventional / hybrid • Light UrbanVehicle : conventional / electric

29. Outline of key points • Introduction • Spatialisation of the model • Impact of density in the transport sector • Impact of density on residential and service heat demand • Development of district heating modelisation • Improvements in modelisation of distributed energy sources (PV)

30. Modeling energy consumption in residential sector • Energy consumption per m² is supposed to be similar in different urban zones (ie in collective buildings and individual houses) • But surface per capita is supposed to be different between indiv./coll. dwellings and as a consequence in different urban zones • The surface per capita has been adjusted according to the share of collective / individual housing in the different urban areas

31. Evolution of energy consumption in the residential sector • Main parameters are similar in the 3 scenarios (no specific climate / energy policy at this stage) • Total final energy consumption for heating in residential sector integrates numerous factors including, unit surface, unit consumption, population, etc • Energy consumption for heating differs in the 3 scenarios : lower consumption in S2 (densification) and higher consumption in S3 (more dispersed housing)

32. Outline of key points • Introduction • Spatialisation of the model • Impact of density in the transport sector • Impact of density on residential and service heat demand • Development of district heating modelisation • Improvements in modelisation of distributed energy sources (PV)

33. Simulation of district heating supply • No representation of district heating in the previous version of the model : • Introduction of a simplified representation DH in residential and service sectors • Modulation of DH development to take into account the density of heat demand

34. District heating in residential sector Heatdemand in RES sectorfrom district heating • The development potential of DH is a function of total heat demand and urban density • Higher density in urban center compared to periphery leads to higher contribution in absolute terms • The densification in urban center in S2 scenario is favourable to the development of DH (higher population – higher density)

35. District heating in service sector Heatdemand in SER sectorfrom district heating • Behaviour is similar for the service sector in urban center – positive reaction to increase of density • Behaviour is more complex in periphery • Very limited evolution of density (decrease in REF) • Increase of population (higher in REF) and increase in heat demand for SER

36. Outline of key points • Introduction • Spatialisation of the model • Impact of density in the transport sector • Impact of density on residential and service heat demand • Development of district heating modelisation • Improvements in modelisation of distributed energy sources (PV)

37. Simulation of distributed energy demand (PV) • Individual housing (ie low density) facilitates the access to dispersed renewable sources • Decentralised PV in POLES • Potential is estimated as a function of new and refurbished buildings (share of m²) • No distinction is made between the type of buildings • Introduction of urban zones • Estimation of potential according to effective roof surface • ie. according to type of buildings (IND / COLL) or density • PV potential per dwelling adjusted according to the share of collective / individual housing in the different urban areas • Highest potential per dwelling in EXTU • Lowest potential per dwelling in UCEN • Influence of potential • Absolute ceiling • Differentiated dynamics in UCEN, UPER and EXTU

38. Simulation of distributed energy demand (PV) : access to potential • Differentiated access to solar resource according to density (ie urban areas) • Still limited (but increasing) gaps in 2050 • Per capita access to solar resource is higher in EXTU and lower in UCEN

39. Simulation of distributed energy demand (PV) : scenarios • Differentiated dynamics according to scenarios (public policies) • No / limited incentives in REF – higher in S2 and S3

40. Simulation of distributed energy demand (PV) : PV production • Total distributed PV production increases much faster in S1, S2 scenario compared to REF • Growth is more important in S3 (highest decentralised potential) • Possible ceiling effect on decentralised potential (S3 – ITA)

41. Conclusion • Significant modifications introduced in the model in order to be able to simulate different evolutions of urban form • Modifications concern transport, heat consumption in R&T, district heating and dispersed energy prod (PV) • Preliminary results show satisfying behaviour of the model • Next step is the introduction of scenarios variable not directly linked to urban form (carbon value, consumpt behaviour technology, etc.)