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1 The European Commission s science and knowledge service Joint Research Centre Optimal allocation of electric vehicle charging infrastructure in cities and regions Andreea Maria Julea, Harald Scholz, Christian Thiel Institute for Energy and Transport (IET) Rotterdam 16/06/2016 1
2 Key scientific activities IET Renewable energy Sustainable and safe nuclear energy Energy security, System and markets Energy technologies modelling and assessment Electro-mobility & alternative fuels Hydrogen and fuel cells Sustainable transport Energy efficiency 2
3 Electro-mobility Vehicle tests Deployment scenarios, Socio-economic modelling Batteries, components, new materials JRC Ispra, IT JRC Petten, NL ICT Vehicle-to-Grid Smart Grids 3
4 European Interoperability Centre for Electric Vehicles and Smart Grids Interoperability tests between electric vehicles & recharging devices at JRC: Since January 2014, 21 different EVs and PHEVs were tested against 72 AC-charging devices (24 AC-Type-1, 23 AC-Type-2, 25 AC-multi-type public columns) and DC fast charge The project triggered improvements in measurement devices, EVs and rechargers Most tests featured unique temperature coverage from -30 C to +40 C 4 Joint Research Centre
5 European Interoperability Centre for Electric Vehicles and Smart Grids 5
6 Approach - Optimal geospatial allocation of EV charging infrastructure Urban planning spatial analysis approach Use of GIS open source software tools (QGIS, GRASS etc) The required input data may vary, depending on the: purpose needs examined area (private company or municipality, existing infrastructure or not, city or national level etc) 6
7 Urban versus highway placement Conditions and required input differ Urban setting: points of interest, population density, parking availability, electrical power grid Highway (inter-city): (expected) real driving range Placement in urban settings a more difficult problem 7
8 Methodology for City level Collect and edit geospatial data (layers of polygons, lines, points) Multiple Criteria Evaluation analysis Create a grid of 100x100m cell Create buffer zones of each layer Spatial join between each layer and the grid Vector layers => Raster layers Assign weighting factors on each layer Use Map Algebra on raster maps to allocate best area Result: Land Allocation Map 8
9 Input data for City level Residential statistics (-> population density) Parking places and lots Electrical power grid Public transport stations Public access buildings (hospitals, museums, theatres, cinema, universities etc) Shopping/food areas (stores, malls, restaurants, bars etc) 9
10 Test case: City of Bolzano Assign weighting factors on each layer Use Map Algebra on raster maps to allocate best area Raster_power_grid * (Raster_file1 * Factor1 + Raster_file2 * Factor2 ) Result: Land Allocation Map 10
11 Test case: City of Bolzano Restaurants 10% Weighting factors of all layers Stores 10% Malls 80% Shopping/ Food areas 20% Population density 20% Parking places 20% Theatersmuseumscinemassightseeing 10% Universities 10% Health centers (hospitals, laboratories etc) 80% Public access buildings 20% Public transport stations 20% 11
12 Test case: City of Bolzano Land Allocation Map Red: areas with high score for allocation of charging points : areas with medium score for allocation of charging points Blue: areas with low score for allocation of charging points Normal chargers can be placed on the Red and Yellow areas. Fast chargers can be placed at: the gas stations, transport stations and parking places with limited time included inside the Red areas. 12
13 Regional/national level placement of charging stations Requirement: no electric vehicle remain without charge on the road JRC study with conventional and electric vehicles Maximum distance between charging stations 60km Input data Road network (highway versus main rural roads) Service areas 13
14 Highway network: Test case Province of Alto Adige Directions are independent Placement on already existing gas stations 14
15 Rural network: Test case Province of Alto Adige Maximum range 30km: charging stations serve both directions 15
16 Conclusions Geospatial analysis of EV charging infrastructure allocation within a city and a region Identification of high-potential areas in the city Placement on highway, rural-road networks Open source GIS tools Pilot studies: city of Bolzano, province of Alto Adige Versatile and easy to use Critical issue: Data availability Methodology to support implementation of Directive on the Deployment of Alternative Fuels Infrastructure Request from the Cypriot ministry of Transport, Communications and Works 16 16
17 Next steps Application of the methodology: to other cities to investigate the effect of different layers or weighting factors on the final allocation map using the results in studies that analyze the inter-connection of highway corridors across countries Refining of the methodology: integrating information on existing charging stations and their usage using layers of information extracted from satellite imagery (buildings, parking places, roads, etc) 17
18 Thank you for your attention JRC Report available at 18
19 Stay in touch EU Science Hub: ec.europa.eu/jrc Facebook: EU Science Hub Joint Research Centre LinkedIn: Joint Research Centre YouTube: EU Science Hub 19
20 supplement 20
21 Road network Not used for city level: it would demand highly detailed road maps the charging points maybe placed in parking lots and not on the street is important in the study of a regional or national level where the charging points have to be placed along the highways or main roads to facilitate in the crossing of the region or the country respectively. 21
22 Test case: City of Bolzano The municipality and the electricity network company managed to gather and provide us (through EURAC) all the required input data The extent of the area was decided based on the provided residential statistics layer (given in number of persons per polygon). Total population:
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