Locating Electric vehicle charging stations in Los angeles
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David Peterson UP206a – GIS (Estrada) December 6, 2010 Source: Ecotality
description
Source: Ecotality. David Peterson UP206a – GIS (Estrada) December 6, 2010. Locating Electric vehicle charging stations in Los angeles. Mayor wants LA to be #1 city for EVs EVSE can influence adoption rates Where will public investment in EVSEs generate highest benefit?. introduction. - PowerPoint PPT Presentation
Transcript of Locating Electric vehicle charging stations in Los angeles
David PetersonUP206a – GIS (Estrada)December 6, 2010
Source: Ecotality
Mayor wants LA to be #1 city for EVs EVSE can influence adoption rates Where will public investment in
EVSEs generate highest benefit?
EVs require a completely new infrastructure to connect to electricity grid
Single-family residential: not an issue
Focus on Problem Areas: Multi-family residential Employment Centers Commercial Centers
Goal: Anticipate concentrations of EV ownership
Use 2008 Hybrid ownership data as proxy for EV ownership
Origins: Multifamily residential problem Destinations: Making sure they can
charge at destination
Absolute number of vehicles? Percent capture of total vehicles in LA? Percent of total vehicles within zip
code?
Rank Zip CodePct of Zip
CodePct of LA
OwnershipNumber of
Vehicles1 90017 20.85% 0.05% 284 2 90001 9.52% 0.00% 2 3 90402 8.73% 0.38% 921 4 90272 6.76% 0.71% 1,344 5 90290 6.42% 0.21% 371 6 90022 5.64% 0.17% 264 7 90035 5.38% 0.71% 1,074 8 90501 5.33% 1.69% 2,537 9 90056 5.15% 1.08% 1,558
10 90291 4.86% 0.88% 1,202
LA Hybrid Penetration - Percent of Local Zip Code
Zip Code 90501:•#1 for vehicles: 2,537•#8 In terms of Percent of Local Zip Code
Zip Code 90001:•#2 for Penetration of Local Zip Code•Only has 2 vehicles!
What’s the best measurement?
Rank Zip CodePct of Zip
CodePct of LA
OwnershipNumber of
Vehicles1 90501 5.33% 6.18% 2,537 2 90056 5.15% 3.79% 1,558 3 90272 6.76% 3.27% 1,344 4 90291 4.86% 2.93% 1,202 5 90071 2.70% 2.85% 1,170 6 90063 3.31% 2.75% 1,128 7 90035 5.38% 2.61% 1,074 8 91604 4.06% 2.45% 1,008 9 90064 2.95% 2.45% 1,006
10 90402 8.73% 2.24% 921
LA Hybrid Penetration - Number of Vehicles
41,079 hybrids in LA 12,948 in the top 10 (32%)
Rank Zip CodePct of Zip
CodePct of LA
OwnershipNumber of
Vehicles1 90501 5.33% 6.18% 2,537 2 90056 5.15% 3.79% 1,558 3 90272 6.76% 3.27% 1,344 4 90291 4.86% 2.93% 1,202 5 90071 2.70% 2.85% 1,170 6 90063 3.31% 2.75% 1,128 7 90035 5.38% 2.61% 1,074 8 91604 4.06% 2.45% 1,008 9 90064 2.95% 2.45% 1,006
10 90402 8.73% 2.24% 921
LA Hybrid Penetration - Number of Vehicles
In top 10, what is percent housing type?
Data Problem: don’t know housing type by hybrid vehicle ownership.
Assumption: Hybrid owners reflect zip code housing type distribution
Rank Zip CodeNumber of
Hybrid Vehicles
Single Family
Multi-Family
Other
1 90501 2,537 57% 41% 3%2 90056 1,558 67% 33% 0%3 90272 1,344 82% 15% 4%4 90291 1,202 42% 58% 0%5 90071 1,170 0% 0% 0%6 90063 1,128 78% 22% 1%7 90035 1,074 27% 73% 0%8 91604 1,008 48% 52% 0%9 90064 1,006 58% 42% 0%10 90402 921 66% 34% 0%
Housing Stock Compositon by Zip Code
90071: no housing, but ranks 5th (1,070) by total hybrids – must be government/business/etc.
Multifamily charging is an issue: Mix of SFR and MFR
Range 15% to 73% Mean: 41%
Index that combines vehicle ownership and multifamily housing Greater weight on more vehicles (1-4) Greater weight on more MFR (1-4)
Combine to create Investment Prioritization Index Hotspot Analysis Index=[vehicles_weighted]+
[MFR_weighted]
Rank Zip CodeNumber of Hybrid Vehicles
Single Family
Multi-Family
Other
1 90501 2,537 57% 41% 3%2 90291 1,202 42% 58% 0%3 91604 1,008 48% 52% 0%4 90056 1,558 67% 33% 0%5 90035 1,074 27% 73% 0%6 90064 1,006 58% 42% 0%7 90063 1,128 78% 22% 1%8 90272 1,344 82% 15% 4%9 90402 921 66% 34% 0%10 90071 1,170 0% 0% 0%
EVSE Investemnt Prioritization Index Ranking
Data Problem: don’t know exactly where hybrid owners commute
Assumption: use zip code trip distribution Methodology:
Weight % allocation of trips by actual number of hybrid vehicles in origin zip codes
Aggregate for a complete picture of destinations
Is destination charging a real concern? Average Commute range: 40 miles (r/t) Battery Range: 80-100 Miles Not a real concern given current travel
behavior, but people might travel differently with EVs
Destination Zip Code
One-Way Commute Trip
Percent of Total Trips
90501 2,523 32.3%90245 492 6.3%90045 397 5.1%90502 345 4.4%90248 314 4.0%90247 267 3.4%90717 251 3.2%90744 181 2.3%90017 147 1.9%90710 142 1.8%
Top Ten Commute Destinations for 90501
Rank Zip Code Trips Pct of Total1 90025 1,561 3.8%2 90024 1,278 3.1%3 90501 1,184 2.9%4 90012 1,161 2.8%5 90045 933 2.3%6 90017 820 2.0%7 90015 788 1.9%8 91367 779 1.9%9 90245 758 1.8%10 90210 749 1.8%
Top 10 Hybrid Destination Trips
Use Index to Allocate Funds to Origin Zip Codes that will benefit the most.
Know the top 10 destinations for these origins. Not imperative to invest in public charging
given vehicle range Need to monitor/track travel behavior Providing EVSEs at these stations could induce
greater adoption (but is it best use of public funds?
Appendix A: Models Appendix B: Original Map Layer Appendix C: Metadata Appendix D: Map with 7 Layers Appendix E: Skills
Model for Rasterizing Layers for index/hotspot analysis inputs
Model for 50-mile buffer
Model for Clipping Buffer to Land Contours
Slide 8 Inset Map Geoprocessing: clipped California zip code
files Slide 10
tables Slides 11/12:
Attribute sub-set selection based on number of hybrid vehicles
Slides 13/15 Tables
Slide 17: Sub-set selection; Pie charts
Slides 19/20/21 Rasterization of data layers using a model Creation of Index for Hotspot Analysis using
a model Use of Spatial Analyst
Slide 22: Table
Slide 25: Used model to create distance buffer from top
10 zip code centroids Slide 26:
Use of Network Analyst to generate database file and OD Cost matrix
Slide 27: Table
Slide 28/29: Attribute sub-set selection
Slide 30: Table
Slides 33-35: Models
Slide 36 Original Map Layer
Slide 37: Creation of Metadata
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