Individual Cost Impacts of Optimized EV Charging and V2G under Dynamic Pricing and Data-Driven Mobility Patterns

Abstract

This study quantifies the economic potential for individual electric vehicle (EV) owners to optimize their charging and participate in Vehicle-to-Grid (V2G) under dynamic electricity pricing and data-driven mobility patterns. Using empirically derived vehicle usage profiles from the mobiTopp model and high-resolution day-ahead price data, charging decisions are modeled from a user-optimal perspective. Three strategies are compared: uncoordinated charging, price-optimized charging, and V2G-optimized charging. Results show that price-based optimization reduces total charging costs by up to 23.5%, while V2G further decreases average costs by over 90%, enabling negative net costs for a significant share of users. However, benefits vary considerably across vehicles. Additionally, price-driven charging leads to temporal concentration of demand and pronounced load peaks. These findings highlight both the economic potential as well as the emerging system-level challenges.