The Need for a Data-Driven Strategy to Deploy Federal Funding for EV Charging by Karen Giltman
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Karen Glitman, Senior Director at Center for Sustainable Energy (CSE) gave this presentation at the Forth Charged Up: What Transportation Agencies Need To Know About Charging webinar on February 8, 2022.
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The Need for a Data-Driven Strategy to Deploy Federal Funding for EV Charging by Karen Giltman
- 1. The Need for a Data-Driven Strategy to Deploy Federal Funding for EV Charging Karen Glitman, Senior Director February 8, 2022
- 2. Our vision is a future with sustainable, equitable and resilient transportation, buildings and communities. DECARBONIZE. One simple mission — ®
- 3. • How much charging is needed? • Where should it go? • By when? 3 $7.5B is Headed to States
- 4. 3 Steps to Deploying EV Charging 4 1. PLAN 2. DO 3. CHECK
- 5. PLAN Forecast EV Demand Caret® uses behavioral economics, diffusion of innovation science and data from around the world to translate existing or proposed EV incentives into expected annual growth of EVs in a state or region. 5 Caret forecasts include: • New EV sales each year in your area • Number/types of charging required • Greenhouse gas reductions • Impacts on program spend rate • Affordability of new and used EVs based on household income • Savings for consumers and society • kWh used • Jobs created
- 6. PLAN Optimize EV Charger Siting Now that you know the EV charging you need to deploy over time, Caret’s interactive geospatial mapping software creates a customized installation plan. 6 Prioritize siting based on 60 configurable factors including: • Traffic volumes and patterns • Location of existing EV drivers/charging sites • Level of utility infrastructure • Proximity to disadvantaged communities/multifamily housing • Proximity to commercial areas/tourist sites • Land use constraints • Custom priorities based on stakeholder engagement
- 7. Estimates of EV Charging Vary Based on Assumptions 7
- 8. CHECK Aggregate and Analyze Data Caret® EVI Data Warehouse will help states make smarter decisions about the numbers and types of chargers needed and where they should go. Inform decision-making based on real data from EV charging sessions. 8 Data analysis will include: • Total charging sites based on type of chargers • Total sessions and drivers • Heat map of downtime events • Emissions reductions • Average energy consumed per session
- 9. Optimize siting of 2000 EV chargers in San Diego County 9
- 10. Optimize EV Charger Siting – example weighting 10
- 11. Optimize EV Charger Siting 11 Hexagon cell width = 0.6 miles (area of 1/3 square mile)
- 12. Optimize EV Charger Siting 12 Hexagon cell width = 0.6 miles (area of 1/3 square mile) Highest-ranked Hexagon for Charger Placement in San Diego County
- 13. Optimize EV Charger Siting 13 Hexagon cell width = 0.6 miles (area of 1/3 square mile)
- 14. 2. Optimize EV Charger Siting 14 Hexagon cell width = 0.6 miles (area of 1/3 square mile)
- 15. 3 Steps to Deploying EV Charging 15 1. PLAN 2. DO 3. CHECK
Editor's Notes
- 25 year old mission driven nonprofit with a single word mission: Decarbonize We do this through the administration of incentive programs including 6 statewide EV incentives programs, EV Infrastructure, solar on multifamily affordable housing and energy storage programs. CSE has designed and administered more than $1b worth of incentives to EVs, touching nearly 30% of all EVs in the US and through more than $200 million in EV Infrastructure. We combined data and experience to create the climate tech platform Caret to help states, cities and utilities accelerate the EV future. CSE is an independent nonprofit with no ties to any vendor or technology. We have no members or donors and are nationally recognized as a trusted, objective source of EV program design and administration.
- Data-driven decision-making: Advances effective, equitable EV charger placement Aligns placement to meet demand and stakeholder goals Complies with IIJA requirements
- 3 connected steps to answering the question: Focus of this talk on Plan and Check
- Updated Jan. 11, 2021
- Updated Jan. 11, 2021
- I’ll show an example based on an active project CSE has to optimize 2000 chargers in San Diego County
- In this example, we’ve set up Caret-EVI to create a site suitability score based on factors that emphasize access to chargers for LMI groups (such as location of apartment units, low median income) Users can define their own suitability matrix based on their goals. The software uses Geographic Information Science and multi-criteria decision analysis to define what a “suitable” site looks like and calculates a suitability score for each grid cell. You can see the weighting each of the selected criteria received in this example in the donut chart
- San Diego County is overlaid with almost 10,000 hexagonal cells Each hexagon is about 0.6 miles across, with an area of about 1/3 of a square mile Brighter (yellow) colors denote high suitability score, while darker (bluer) colors denote low suitability scores. Some parts of the county (particularly in the east) are not overlaid with hexagons because those areas are undeveloped (e.g., they do not contain roads, so are not suitable for installing EV chargers!)
- We’ve zoomed in on the top-ranked hexagon with the highest suitability score based on the factors we selected This is where you would want to start installing chargers first.
- Removing the hexagon overlay allows us to examine the details of this highly suitable area We do not recommend a specific siting within a hexagon in order to allow for flexibility if particular sites are not available or appropriate for installing chargers. Anywhere within this hexagon is good! In addition to proximity of large roads and highways…. Or available ROW
- Drilling in further … we also see that there are a number of features of this hexagon that make is optimum for siting chargers, such as a metro station with a large parking lot a Home Depot a small mall with multiple restaurants and shops nearby multi-unit dwellings and so on…