电动汽车充电选址决策中的创新扩散：通过市场动态整合需求与供给
Innovation Diffusion in EV Charging Location Decisions: Integrating Demand & Supply through Market Dynamics
AI总结：
本文提出整合创新扩散原则、连续时间流体排队模型与离散流加油选址模型的EV充电网络规划方法，通过三层网格策略优化站点布局，填补了区域可扩展供需整合模型的文献空白。
摘要：
本文提出了一种电动汽车（EV）充电网络规划的战略方法，强调在创新扩散原则背景下，通过连续时间流体排队模型和离散流加油选址模型整合需求与供给动态。我们采用基于常微分方程（ODEs）的连续时间近似方法来设计多年供给曲线，该方法与常忽视年际过渡和持续过程的传统做法形成对比。对于中期充电站选址规划（CSLP），我们在基于网格的多级网络中应用流加油选址模型（FRLM），同时考虑多路径网络和容量约束。基于网格的网络规划策略采用三层（宏观-中观-微观）方法进行全面的EV充电站布局：宏观层面覆盖整个城市，中观层面评估详细的EV路线并连接宏观与微观层面，微观层面聚焦于精准的站点布局以提升可达性和效率。我们对过度利用和利用不足场景的研究为政策制定和成本效益分析提供了宝贵见解。通过芝加哥草图网络的实例说明我们的方法，我们引入了适用于单一区域且可扩展至多个区域的整合供需模型，从而填补了现有文献的空白。我们提出的方法聚焦于EV站点布局，考虑未来需求、地理容量和场景分析的重要性，为长期EV充电网络的战略资源规划提供支持，进而助力向更可持续、更高效的交通系统转型。
英文摘要：
This paper offers a strategic approach to Electric Vehicles (EVs) charging network planning, emphasizing the integration of demand and supply dynamics via continuous-time fluid queue models and discrete flow refueling location modeling, all in the context of innovation diffusion principles. We employ a continuous-time approximation based on Ordinary Differential Equations (ODEs) to design multi-year supply curves, a method that stands in contrast to conventional practices which often overlook inter-year transitions and ongoing processes. For medium-term charging station location planning (CSLP), we apply a flow refueling location model (FRLM) within grid-based multi-level networks, considering both multiple-path networks and capacity constraints. The grid-based network planning strategy uses a three-tier (Macro-Meso-Micro) approach for thorough EV charging station placement, with the macro-level covering entire cities, the meso-level assessing detailed EV routes and bridging the macro to micro levels, and the micro-level focusing on precise station placement for accessibility and efficiency. Our investigation into overutilization and underutilization scenarios delivers valuable insights for policymaking and cost-benefit analyses. Illustrating our approach with the example of the Chicago sketch network, we introduce an integrated demand-supply model suitable for a single region and extendable to multiple regions, thereby addressing a gap in the existing literature. Our proposed methodology focuses on EV station placement, taking into account future needs, geographical capacities, and the importance of scenario analysis, which empowers strategic resource planning for EV charging networks over extended timeframes, thus aiding the transition towards a more sustainable and efficient transportation system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
