Santa Clara County Electric Vehicle Charging Demand Siting Analysis
Santa Clara County 
This map displays the results of the Electric Vehicle (EV) Charging Infrastructure Siting Analysis conducted for Santa Clara County’s Driving to Net Zero (DNZ) Project. The analysis is a data-driven exercise that looks at key EV ownership indicators and regional travel patterns to identify areas where there will likely be demand for EV charging infrastructure. It ranks each transportation analysis zone (TAZ) in the Santa Clara County based on its likelihood to have high demand for charging on a scale from 1 (low) to 6 (high).
Residential displays expected demand for residential EV charging. These are the locations where likely EV adopters live.
Workplace displays expected demand for workplace charging. These are the areas where likely EV owners travel for work.
Opportunity displays expected demand for opportunity charging—areas where likely PEV owners will shop, dine, and visit.
This map also includes the locations of existing charging infrastructure. The existing charger data was sourced from the Department of Energy’s
Alternative Fuels Data Center Station Locator
and is representative of chargers installed in the region as of February 2018.
Combined with the charging demand layer (EVSE Estimates), this allows jurisdictions
to identify gaps in the regional charging network. DNZ partner Cities also provided jurisdiction-specific land use data which can be added
in the Layers dropdown.
Both the workplace and opportunity charging demand results are inextricably linked to the residential demand results, as that is the “home base” for all vehicle trips analyzed. The first iteration of this map included the results of one scenario – what we consider our “reference” approach, as it reflects what has been shown to be key indicators of EV ownership. However, as EV become more affordable and access to charging increases, income may be less heavily weighted. The County also wanted to provide an analysis of where charging demand will likely occur in multi-family residential buildings and in disadvantaged communities. Therefore, the following scenarios were developed for jurisdictions to compare and use as needed depending on priorities.
| Scenario | Description | Weighting of Key EV Ownership Variables | |||
|---|---|---|---|---|---|
| Income | Hybrid Ownership | Home Ownership | Dwelling Type | ||
| Focusing on Single Family (SF) residential charging demand | |||||
| Scenario 1 Likely EV buyers in SF homes | Reference case, focusing on socioeconomic factors that favor EV ownership | High | Med | Low | Low |
| Scenario 2 Home owners | Focus on SF home owners as potential EV buyers, recognizes that there is still demand for EVs where home charging may be convenient i.e., in areas of high home ownership and high SF homes | Med | - | High | High |
| Scenario 3 Hybrid owners | Increased focus on hybrid ownership as proxy for environmental awareness as the key driver for EV interest | Med | High | - | - |
| Scenario 4 Workplace charging as a solution | Areas to target for workplace charging that could alleviate challenges of home charging for potential EV buyers (as determined by high income and high rates of renters and hybrid ownership) | Med | Med | Low | Low |
| Scenario 5 Education & outreach | Areas to target for education and outreach based on likely EV buyer characteristics, after removing hybrid ownership as a metric | Med | - | Med | Med |
| Focusing on Multi-Family (MF) residential charging demand | |||||
| Scenario 6 Likely EV Buyers in MF Units | Reference case, focusing on socioeconomic factors that favor EV ownership in MF units | High | Med | Low | Med |
| Scenario 7 Low Income in MF Units | Targets low income population in multi-family units | Low | Low | Low | Med |
| Scenario 8 High Income in MF Units | Targets high income populations in multi-family units | High | - | Low | Low |
The purpose of the charging demand analysis maps is to understand where EV drivers will likely live, work, and visit within Santa Clara County. It is best to consider the results of the analysis as a useful guide to coordinating and prioritizing investments in charging infrastructure at a high level. The results can be used to identify areas where the deployment of chargers will likely be the most cost effective, as chargers located in an area where EV drivers are most likely to travel will be more heavily utilized. It is important to note that the results of the siting analysis are not a deterministic approach that excludes certain areas from charging.
For a more detailed explanation of the analysis methodology, please see Section 4.2 of the County’s EVSE Toolkit for Local Governments. Survey findings of early EV adopters identify the following key indicators for EV ownership: income, hybrid vehicle ownership, homeownership, and dwelling type. These indicators were used to develop a scoring methodology that estimates the likelihood of EV adoption in a given census block group. Demographic data on income, home ownership, and dwelling type for each census block group in Santa Clara County was sourced from the American Community Survey (census data). Vehicle registration data procured from IHS Markit was used establish hybrid vehicle ownership rates by census block group. The number of trips between each transportation analysis zone was provided by Santa Clara Valley Transportation Authority.
The work upon which this publication is based was funded in whole or in part through a grant awarded by the California Strategic Growth Council.
Santa Clara County would like to acknowledge the cities of Cupertino, Morgan Hill, Mountain View, Palo Alto, San Jose, and Sunnyvale for their
contributions and support as partners in the Driving to Net Zero Project.
The statements and conclusions of this report are those of the County of Santa Clara and ICF and not necessarily those of the California
Strategic Growth Council or of the California Department of Conservation, or its employees. The California Strategic Growth Council and the
California Department of Conservation make no warranties, express or implied, and assume no liability for the information contained in the succeeding text.
ICF estimates that by 2025, there will need to be 50,200 (Low) to 78,400 (High) L2 charge ports deployed within Santa Clara County at workplaces,
multi-family housing, and public charging locations to support projected regional EV growth. These estimates are based on the siting score results from the Workplace
and Opporrunity Scenario 1 results, which can be selected from the result dropdowns.
This mapping layer shows these projections distributed by TAZ. Point representation is randomly placed within each TAZ, and do not represent actual locations. These estimates are not meant to be definitive. Ultimately, more data and improved understanding of consumer behavior
will help the DNZ stakeholder community make more robust decisions regarding the quantity of charging required.
Please see the DNZ Local Government EV Charging Station Siting Toolkit and Reference Guide for additional details.