[论文解读] Dynamic Menu-Based Pricing for Electric Vehicle Charging with Vehicle-to-Grid Integration

Mozhdeh Hematiboroujeni, Pierre Le Bodic+2 more|arXiv (Cornell University)|Mar 5, 2026

Electric Vehicles and Infrastructure被引用 0

一句话总结

本论文提出一种动态滚动时域菜单定价机制，联合处理实时充电、V2G 与 feeder 约束，通过对双层问题的 MILP 改写求解，在24小时停车场案例中进行评估。

ABSTRACT

The number of electric vehicles is rapidly increasing worldwide. This growth brings significant environmental benefits but also introduces new challenges: uncoordinated charging can place stress on the grid, particularly during peak hours. Beyond these challenges lies the opportunity for electric vehicles to feed energy back to the grid (V2G), which helps balance supply and demand and supports renewable energy. However, current pricing schemes such as time-of-use tariffs provide little incentive for discharging. To study incentive design in a realistic context, we focus on a parking lot operator who manages multiple EV chargers. We propose a menu-based pricing mechanism in which each EV declares its energy requirement and parking duration; given the retail real-time electricity prices, the operator offers a menu of options that trade off the allowed level of discharging and the associated price. We formulate this interaction as a bilevel optimization problem and reformulate it into a single-level model. Results show that, relative to a no-V2G baseline, the proposed mechanism increases operator profit by 30% and reduces EV payments by 17%. Compared to widely used tariff baselines, it improves operator profit by 22-29 percent, lowers EV payments by 9-18 percent, and increases V2G contribution by 87-235 percent. Overall, the results show that the proposed dynamic menu-based pricing framework provides a practical, computationally efficient, and economically advantageous approach for real-time EV charging and V2G integration.

研究动机与目标

• Motivate coordinated EV charging and discharging to reduce grid stress at parking lots with V2G.
• Design a rolling-horizon, per-EV menu pricing mechanism that adapts to real-time wholesale prices and system state.
• Ensure feasibility with feeder capacity and charger limits while preserving previously committed schedules.
• Demonstrate computational tractability and economic benefits through a realistic case study using 12 months of wholesale price data.

提出的方法

• Formulate a bilevel optimization where the operator sets a menu of discharge options and prices for an arriving EV.
• Compute per-option schedules by solving a real-time optimization that includes feeder constraints, SoC dynamics, and wholesale prices.
• Represent the EV’s choice as a lower-level problem maximizing its utility, and reformulate the bilevel problem as a single MILP using KKT conditions with indicator constraints.
• Use a rolling-horizon algorithm that updates the menu on each arrival while preserving prior commitments.

Figure 1 : Overview of the proposed menu-based pricing framework

实验结果

研究问题

• RQ1 Can a dynamic, per-EV menu pricing scheme improve operator profit while increasing V2G participation under real-time price conditions?
• RQ2 Does incorporating feeder constraints and bidirectional charging in a rolling-horizon menu improve system feasibility and economic outcomes compared with baseline tariffs?
• RQ3 Is the MILP reformulation (via KKT indicator constraints) computationally tractable for realistic day-long horizons?
• RQ4 How do system parameters (grid capacity, number of EVs, menu granularity) affect profits, EV payments, and V2G contribution?

主要发现

• The dynamic menu-based pricing mechanism increases parking lot operator profit by 27.01% on average in the case study.
• EV payments are reduced by 14.69% on average compared with baselines.
• V2G contribution rises by 161.5% on average with the proposed mechanism.
• The rolling-horizon MILP reformulation solves full-day instances with 100 EVs in under 40 seconds and 250 EVs in under 200 seconds.
• Pricing and schedules can be generated in well under one second per arriving EV, enabling real-time operation.

Figure 2 : Price profile of AEMO for Monday of each month.