Gschwendtner, ChristineWallace, Addison2026-05-0420262026-05-012026Wallace, Addison. 2026. Grid-Friendly EV Charging: A Study of Incentive-Driven Versus Rate-Based EV Load Management Strategies. Masters Thesis, Harvard University Division of Continuing Education.32672082https://dash.harvard.edu/handle/1/42736444The rapid increase of residential electric vehicle (EV) adoption presents a critical challenge for electricity systems: without effective load management, widespread uncoordinated charging risks exacerbating peak demand, straining distribution infrastructure, and undermining the integration of variable renewable energy. Electric utilities have deployed two primary strategies to shift EV charging to off-peak hours — tariff-based load management, encompassing time-of-use (TOU) and dynamic electricity rates, and incentivized managed charging programs that pair financial compensation with automated EV charging control. No prior study has directly compared the real-world effectiveness of these two strategies using large-scale empirical data, leaving a critical gap in the evidence base for utility program design and load management policy. This study addressed that gap, with the aim of identifying the most effective approach to residential EV load management and informing both utility program design and public utility commission policy. Real-world charging data from 46,822 EV drivers and 6.87 million residential charging sessions across the United States, spanning January 2023 through June 2025, were provided by ev.energy, a smart charging platform that administers utility-operated managed charging programs. Three analytical stages were employed: descriptive analysis characterizing off-peak charging ratios across program types and driver profiles; k-means cluster analysis identifying six distinct behavioral driver profiles; and gradient boosting regression with SHapley Additive exPlanations (SHAP)-based feature importance quantifying the relative contributions of charging behaviors, tariff structures, and program incentive designs to the off-peak charging ratio. The central finding was that charging behavior and managed charging programmatic intervention were most important in determining off-peak alignment, and that tariff-based price signals alone were a substantially weaker driver of off-peak charging than either. The structural conditions of a driver's charging context, particularly whether they charge primarily at home and how long their vehicles are plugged in, emerged as the dominant predictors of off-peak ratios across all model specifications. Drivers who charge primarily at home benefit from long plug-in durations and overnight availability that are structurally well-aligned with the off-peak windows that tariffs and programs are designed around. Among programmatic factors, smart charging programs offering both upfront enrollment payments and ongoing static participation incentives consistently outperformed all other program designs, while unmanaged performance-based programs were associated with negative off-peak outcomes, suggesting that automated scheduling is a necessary rather than optional program component. Tariff type variables exhibited minimal independent predictive importance after controlling for behavioral and programmatic factors, challenging the prevailing assumption that time-varying price signals alone are sufficient to drive large-scale residential load shifting. These findings point toward an EV program design paradigm centered on automated load management, long-dwell-time charging infrastructure investment, and guaranteed financial compensation, rather than tariff redesign alone, as the most effective path to shifting residential EV charging to off-peak hours at scale.application/pdfenClimate changeElectric vehicle chargingElectric vehiclesEnergyLoad ManagementSustainabilitySustainabilityEnergyTransportationThesis or Dissertation2026-05-040009-0004-1900-1350