Transportation is one of the key contributors to greenhouse gas emissions and is recognized as one of the main factors contributing to climate change. California is at the forefront of setting aggressive transportation clean energy goals that will accelerate electric vehicle adoption. For California to reach these goals, understanding the impacts that increased adoption of electric vehicles will have on the electrical system, and the infrastructure required to support it, is paramount to ensuring a smooth transition. This project developed the Smart Charging Infrastructure Planning Tool (SCRIPT), which is an open-source, scalable, software tool for scenario generation based in real charging data from California. The tool provides an interface for users to change multiple inputs such as aggregation level, number of electric vehicles in the state, electric vehicle battery capacity, charging location (that is, residential, workplace, and public), type of charging control, type of day (weekday or weekend), and daily charging frequency. From these user inputs, the tool generates charging requirement forecasts for millions of electric vehicles, predicts how different charging locations will be affected, shows how electric vehicle load can be reshaped by optimizing vehicle charging, and assesses costs and benefits. SCRIPT uses a novel method, based in machine learning, to model the impact on aggregate load profiles of optimizing vehicle charging for a particular rate schedule.
This report uses workplace charging to demonstrate the method. The report analyzed seven different scenarios targeting California’s 2030 electric vehicle goals. The scenarios differ primarily based on the number of vehicles and how much each charging location contributes to the overall load. Across the scenarios analyzed, all resulted in positive net benefits to the state, the county, electric vehicle owners, and ratepayers. Although this report focuses on seven unique scenarios, many other scenarios can be analyzed by changing user inputs.
