The scope of this article is to assess the economic viability of bidirectional charging strategies for scheduled electric bus fleets, focusing on their potential to reduce operational costs under real-world market conditions. This article presents a comprehensive and production-ready optimization framework for vehicle-to-grid integration, addressing day-ahead pricing, battery degradation, and grid constraints through a mixed-integer linear programming formulation. Unlike existing approaches that often rely on aggregated vehicle behavior, our framework preserves the full topology of the charging station, maintaining vehicle-level granularity. This enables precise evaluation of both marginal costs and savings, enhancing the model’s practical relevance and deployment potential. Results from real-world fleet schedules and Italian market data show that both controlled charging and vehicle-to-grid achieve substantially lower costs than uncontrolled charging, with savings exceeding 35% in current scenarios. However, the cost gap between vehicle-to-grid and controlled charging remains narrow. These findings indicate that as battery pack prices continue to decline and energy markets evolve, vehicle-to-grid will become increasingly favorable for scheduled fleets.
MILP Framework for V2G Optimization With Battery Degradation and Price Arbitrage in Scheduled Fleets
Martini, Daniele;Longo, Michela;
2025-01-01
Abstract
The scope of this article is to assess the economic viability of bidirectional charging strategies for scheduled electric bus fleets, focusing on their potential to reduce operational costs under real-world market conditions. This article presents a comprehensive and production-ready optimization framework for vehicle-to-grid integration, addressing day-ahead pricing, battery degradation, and grid constraints through a mixed-integer linear programming formulation. Unlike existing approaches that often rely on aggregated vehicle behavior, our framework preserves the full topology of the charging station, maintaining vehicle-level granularity. This enables precise evaluation of both marginal costs and savings, enhancing the model’s practical relevance and deployment potential. Results from real-world fleet schedules and Italian market data show that both controlled charging and vehicle-to-grid achieve substantially lower costs than uncontrolled charging, with savings exceeding 35% in current scenarios. However, the cost gap between vehicle-to-grid and controlled charging remains narrow. These findings indicate that as battery pack prices continue to decline and energy markets evolve, vehicle-to-grid will become increasingly favorable for scheduled fleets.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


