Development of Smart Charging Infrastructure Planning Tool (SCRIPT)
Developing a tool and user interface for deployment of charging infrastructure considering grid needs, disadvantaged communities, solar generation, and economics.
Board of Trustees of the Leland Stanford Junior University (SLAC National Accelerator Laboratory)
Recipient
Menlo Park, CA
Recipient Location
13th
Senate District
24th
Assembly District
$1,500,000
Amount Spent
Completed
Project Status
Project Result
The project team completed development of the SCRIPT and achieved the tool's goals of providing predictive smart-charging, flexible load forecasts and interoperability that allows stakeholders the ability to perform analysis on efficient and effective PEV charging infrastructure planning. The project team used multiple scenarios for analysis that targeted a forecasted PEV adoption volume for 2025 and 2030, percentage of charging infrastructure across multiple segments (i.e. residential, workplace, and public), and the distribution projected across the state. The project team completed cost-benefit analysis from a societal, ratepayer, and PEV owner perspective. The flexible PEV load forecast will help utilities anticipate and plan for infrastructure investments to ensure high reliability while minimizing electricity costs. The forecasts will also provide load information at the state and county level identifying locations to install renewable generation with the appropriate capacity.
The Issue
Plug-In Electric Vehicle (PEV) growth has the potential of adding stress to the grid. There are also tradeoffs between maximizing the use of PEV charging infrastructure and building more infrastructure to facilitate flexibility and market growth. For example, a congested charging station has limited flexibility for demand management through smart charging, while a more expansive charging infrastructure network allows PEVs to stay plugged in longer than the minimum charging time without affecting other drivers' travel plans and also providing additional flexibility for smart charging.
Project Innovation
This project developed a tool that enables predictive smart charging of PEVs and performs cost-benefit analysis for investment in charging infrastructure from the point of view of different stakeholders. The tool will allow stakeholders to generate multiple future PEV adoption scenarios, evaluate the potential of smart charging in changing the overall load profile to reduce grid congestion and maximize solar utilization, understand the effects that investments in different charging segments has in the PEV load, and perform an assessment of the cost and benefits to the region, PEV owners and ratepayers. The goals of the project included: developing a predictive smart charging framework for PEVs that considers future travel plans of drivers and various power system conditions; performing a cost-benefit analysis for investment in charging infrastructure; and integrating the above elements to allow stakeholders to make decisions pertaining to new investments in charging infrastructure.
Project Goals
Project Benefits
There are significant benefits from workplace smart charging that may be shared with the vehicle owners. By exercising the flexibility of the vehicle charging in the workplace, commercial customers can save from energy and demand charge costs. Smart charging can decrease energy charges by up to 1.5% of the overall utility bill and decrease demand charges by up to 24.7%. Further benefits can be achieved from adding PV as a distributed energy resource at a site, but even without that addition there are significant benefits to both the EV owner and workplace where the EV is being charged. The project team is conducting a cost-benefit analysis that will inform the final report.
Affordability
The predictive smart charging system allows PEV owners to charge during the periods of the day when energy is lowest cost. SCRIPT also allows for intelligent management of the existing charging and grid infrastructure, which reduces maintenance costs and future equipment costs.
Reliability
SCRIPT's predictive smart charging algorithms will allow PEVs to intelligently charge during periods of renewable energy overgeneration, which reduces the amount of energy flowing back into the distribution grid.
Key Project Members
Gustavo Vianna Cezar
Siobhan Powell
Subrecipients
The Regents of the University of California, Santa Barbara
Energy & Environmental Economics, Inc.
Match Partners
The Regents of the University of California, Santa Barbara
ChargePoint, Inc.
