Impact of Lead Time on Aggregate EV Flexibility for Congestion Management Services

TL;DR

The paper analyzes how lead time and flexibility-window duration affect the aggregate electrical flexibility of EV fleets for congestion management, comparing redispatch and capacity-limitation products under three BAU dispatch strategies and two charging modes (V2G and unidirectional). Using a data-driven model with real 2023 Dutch charging transactions, it shows that shorter lead times reduce tradable flexibility and that V2G substantially enhances flexibility, albeit with cost implications depending on BAU scheduling. The study provides quantitative insights into how lead time, window length, and charging strategy interact to shape the magnitude of tradable flexibility and associated costs, informing market design for congestion-management services. The findings underscore the practical value of bidirectional charging in enabling reliable, scalable congestion responses in power networks.

Abstract

Increased electrification of energy end-usage can lead to network congestion during periods of high consumption. Flexibility of loads, such as aggregate smart charging of Electric Vehicles (EVs), is increasingly leveraged to manage grid congestion through various market-based mechanisms. Under such an arrangement, this paper quantifies the effect of lead time on the aggregate flexibility of EV fleets. Simulations using real-world charging transactions spanning over different categories of charging stations are performed for two flexibility products (redispatch and capacity limitations) when offered along with different business-as-usual (BAU) schedules. Results show that the variation of tradable flexibility depends mainly on the BAU schedules, the duration of the requested flexibility, and its start time. Further, the implication of these flexibility products on the average energy costs and emissions is also studied for different cases. Simulations show that bidirectional (V2G) charging outperforms unidirectional smart charging in all cases.

Figures (6)

• Figure 1: Schematic showcasing various market interactions alongside market timeline for capacity limitation products.
• Figure 2: Relationship between daily peak power usage and the hour of the day across all days in the year 2023. Each point represents the peak power recorded at a specific hour for a particular BAU schedule and CS category.
• Figure 3: Distribution of flexibility (redispatch and capacity limitation) for unoptimized BAU schedule shown for different start of flexibility request windows of 1 hour. For the purpose of illustration, only residential charging stations are shown.
• Figure 4: Impact of different lead times compared across different CS categories and BAU schedules. The difference between the average flexibility magnitude for the two lead times has been plotted using the secondary axis. Flexible windows with different start times and a duration of 1 hour are chosen.
• Figure 5: Effect of different lead times on the flexibility for redispatch with V2G. For the purpose of this illustration, only the case with cost minimized BAU schedule is shown for different categories of CS for a flexibility window of one hour.