Electric mobility in view of green growth

Transcription

1 Electric mobility in view of green growth A synthetic information system on HPC for the global car population Sarah Wolf, Global Climate Forum with Steffen Fürst, Andreas Geiges, Jette von Postel International Conference on Synthetic Populations Lucca,

2 Motivation, on the one hand h+p:// World Economic Forum, 2016 Google FT, transition in the auto industry... transition to Green Growth? 2

3 Green Growth Environment many actors expectations incentives Economy climate policy & investment impulse re-coordinate expectations investment -> technical progress -> growth -> expectations -> investment ->... 3

4 Cars 4

5 Reverse the trend? 5

6 Motivation, on the other hand pilot studies Health Habits, Green Growth, Global Urbanization GSS: stakeholder dialogues and data-driven simulation modelling of complex systems for decision support CoeGSS: enhance GSS modelling through High Performance Computing and Data Analytics (HPC, HPDA) enable use of higher resolution data sets allow models to grow in complexity / towards global scales facilitate deeper analysis of larger sets of output data from model simulation runs 6

7 Methods consider non-deterministic dynamics, open future take a systemic perspective complex system, multiscale, feedbacks, path-dependency,... draw on the framework of extended evolution random mutations of genes & natural selection (fitness) + regulatory networks: sequence and intensity of activation of genes + niches: environment, shaped in turn by species evolution of technologies: regulatory networks & niches restrain the space of possibilities for system evolution agent-based modelling on HPC micro level description of the system on the computer to observe macro level in simulations 7

8 The car centered global system consumers local environment: e.g., charging infrastructure, regulation (cities, states) local interaction: e.g., congestion, accidents social influence, in networks with hubs, clusters, assortativity, community and hierarchical structures employment in car industry and component suppliers manufacturers operating in a global market example Volkswagen sales 2016: Europe 41%, US 6%, China 39% regulation ( regulatory networks ) example China: - plans for manufacturers EV shares 8% in 2018, 12% in megacities: quota policy to control car ownership growth: Beijing lottery, Shanghai auction, Guanzhou hybrid of these two 8

9 The car centered global system and its environment regulation ( niches ) laws and regulation for admission insurance standards (safety, emissions) => inertia of the system open system geopolitical environment: petroleum supply, climate policy,... oil industry as a niche for internal combustion engine cars prices shape the space of possibilities e.g., petroleum, carbon emissions, batteries 9

10 The model begin on the demand side spatially explicit data: population per grid cell, cars per 1000 people per country; data for , scenarios for

11 Scenarios: example total numbers of cars as estimated according to a model by Dargay, Gately, and Sommer (2007) based on GDP per capita, population density and urbanisation country specific saturation level input data (estimates) from IMF, WB, UN

12 The model begin on the demand side spatially explicit data: population per grid cell, cars per 1000 people per country; data for , scenarios for green (EV for now) and brown cars diffusion model, dynamics based on innovation & imitation - innovation happens stochastically, higher income leads to higher probability - imitiation: EVs as an indicator of EV-friendly infrastructure deterministic and stochastic version of the model working towards synthetic population of households, later also firms optimizing agents, interaction networks, environment 12

13 Implementation model implementation in Pandora HPC framework for agent-based models developed at Barcelona Supercomputing Center advantages: twin interface Python (rapid prototyping) or C++ (parallelization), reads raster files, output supports GIS tools (maps), hides MPI layer from modeller disadvantages: spatial partitioning problematic for our global maps and for agent networks beyond spatial proximity CoeGSS working on a graph-based ABM framework for HPC interested? talk with us! 13

14 Output: global maps 14

15 Example: green cars share Share of BEV cars in % 10% 15% 20-27% 15

16 Evolution of new BEV car numbers: aggregate calibration New BEV cars bought in year 3e+05 Nr. of cars 2e+05 Source simulation study 1e+05 0e Year 16

17 Large differences between countries market shares 2016: Norway 29%, Netherlands 6%, China 1% Source: ICCT blog 17

18 Battery Electic Vehicles (BEV) sales Policy parameter calibration in progress China: 2 US: 1 Italy: 0.8 Japan: 0.5 Norway: 20 match parameter with policies/ prices allow for decreasing numbers value country China data China simulated Italy data Italy simulated Japan data Japan simulated Norway data Norway simulated United States data United States simulated step 18

19 (Interactive) map 19

20 THANK YOU! Questions? 20