三边出行-能源市场设计：作为多周期随机指派博弈
Three-sided mobility-energy market design as a multiperiod stochastic assignment game
- New York University Tandon School of Engineering（纽约大学坦顿工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对出行与充电需求时间脱节问题，提出监管者主导的双层指派博弈模型，结合PURC框架与时间分解求解，揭示充电容量阈值、利润凹形轨迹及动态定价策略，为车队与充电设施协调提供依据。
AI中文摘要：
随着出行服务提供商（MSP）和能源提供商（EP）扩展电动汽车生态系统，需要建立模型来理解它们在三边市场中的相互作用。现有框架往往忽视了出行需求与充电需求之间的时间相互依赖关系。我们通过提出一个由市场监管者监督的指派博弈形式的双层问题来弥补这一空白。上层优化服务定价以最大化平台盈利能力。下层使用可扩展的、基于链路的扰动效用路径选择（PURC）框架对多利益相关者均衡进行建模。评估时间框架被划分为离散区间，通过经验仿射函数捕捉出行需求与充电需求之间的时间滞后。我们通过将下层按时间顺序分解为相互作用的出行服务和充电子网络来求解该模型。在扩展的Nguyen-Dupuis网络上的数值实验揭示了几个关键见解。首先，存在一个关键的充电容量阈值；低于该阈值运行将迫使可部署车队规模大幅缩减，并造成局部出行荒漠。其次，对内生运营成本进行建模揭示了凹形利润轨迹，表明总利润在特定车队规模（恰在市场饱和之前）达到最大化。第三，最优动态定价在一个狭窄范围内运行，其中高峰定价作为稳定的收入驱动因素，而非高峰定价则作为高度敏感的操作缓冲。这些发现为协调车队规模和充电基础设施部署提供了可操作的策略。
英文摘要：
As mobility service providers (MSPs) and energy providers (EPs) expand electric vehicle ecosystems, models are needed to understand their interactions within a three-sided market. Existing frameworks often overlook the temporal interdependencies between mobility and charging demands. We address this gap by proposing a bilevel problem as an assignment game overseen by a market regulator. The upper level optimizes service pricing to maximize platform profitability. The lower level models a multi-stakeholder equilibrium using a scalable, link-based Perturbed Utility Route Choice (PURC) framework. The evaluation time frame is divided into discrete intervals, capturing the temporal lag between mobility and charging demand via an empirical affine function. We solve the model by chronologically decomposing the lower level into interacting mobility service and recharge subnetworks. Numerical experiments on the expanded Nguyen-Dupuis network reveal several key insights. First, a critical charging capacity threshold exists; operating below it forces a severe reduction in the deployable fleet and creates localized transit deserts. Second, modeling endogenous operating costs reveals a concave profit trajectory, demonstrating that total profit maximizes at a specific fleet size just before market saturation. Third, optimal dynamic pricing operates within a narrow range, where peak pricing acts as a steady revenue driver and off-peak pricing serves as a highly sensitive operational buffer. These findings provide actionable strategies for coordinating fleet sizing and charging infrastructure deployment.
