EVALUATE: Electric Vehicle Assessment and Leveraging of Unified models toward AbatemenT of Emissions, Phase I

This repository stores the data for the NCST project titled "EVALUATE: Electric Vehicle Assessment and Leveraging of Unified models toward AbatemenT of Emissions, Phase I." The abstract is as follows. This research explores electric vehicle (EV) and grid interactions with a focus on CO2 emissions for future scenarios where EVs comprise growing market shares (e.g., 10% of the overall fleet mix). A major contribution of this effort has been to develop a methodology that integrates sub-system models and datasets that have previously stood alone, namely models and data that characterize: vehicle energy consumption, travel demands, vehicle charging, and temporal emission profiles associated with electric power generation dispatch. This convergence research helps quantify the relative emissions of light duty vehicle use and charging during various times of day to enable comparison of EV modes against one another and against conventional vehicle baselines. An initial use case involving light duty commuter and recharging scenarios has been explored as a means of validating and tuning the methodology. Under certain simulated scenarios, observed marginal emissions can be as much as 20% lower in the overnight hours compared to marginal CO2 emissions experienced during an identical charging event during the daytime. This study also confirms that marginal CO2 assumptions generally yield higher CO2 impacts than identical simulations that assume weighted average emissions. This variance is broad, ranging from 22% less to 97% greater, depending on a host of case-sensitive factors. These findings suggest that it will be essential to coordinate charging schedules and consider upstream grid implications in order to reduce the environmental impacts of EVs. By quantifying technical parameters related to both the magnitude and the range of possible emissions impacts, the study’s findings can be useful for education and awareness by all EV users, and will help decision-makers consider the importance of emission rate assumptions and the temporal granularity of the tools and data. More specifically, stakeholders should be incentivized to charge when marginal emissions are lowest whenever possible. This idea also has important implications about the location, type, cost and ownership models for tomorrow’s charging infrastructure. Translating and operationalizing this type of guidance will require some combination of education, access to rigorous and clear decision-support tools, signals between stakeholders (e.g., utilities and consumers), and behavioral change.本仓库存储了NCST项目题为"EVALUATE：面向减排的电动汽车评估与统一模型利用（第一阶段）"（Electric Vehicle Assessment and Leveraging of Unified models toward Abatement of Emissions, Phase I）的相关数据。其摘要如下。 本研究聚焦电动汽车（Electric Vehicle，下文简称EV）与电网的交互作用，针对EV市场占比持续提升的未来场景（例如整体车队占比达10%）展开分析，核心关注二氧化碳（CO2）排放问题。本研究的一项重要贡献在于开发了一套整合此前独立运行的子系统模型与数据集的方法论，涵盖表征车辆能耗、出行需求、车辆充电以及与电力调度相关的时序排放特征的模型与数据。这项融合研究可量化不同时段轻型车辆使用与充电的相对排放量，从而实现不同EV模式之间以及与传统燃油车辆基准的对比。 本研究已探索了首个用例，即轻型通勤车辆充电场景，用于验证和调整该方法论。在部分模拟场景中，夜间充电的边际排放量可比日间同类充电场景的边际CO2排放量最多可低20%。本研究还证实，相较于采用加权平均排放假设的同类模拟，采用边际CO2排放假设通常会得到更高的CO2影响结果。该差异范围跨度较大，从低22%到高97%不等，具体取决于诸多场景敏感因素。 上述研究结果表明，为降低EV的环境影响，统筹充电调度并考量上游电网的影响至关重要。通过量化可能的排放影响的规模与波动范围，本研究成果可为所有EV用户的科普与认知提升提供支撑，同时可帮助决策者考量排放速率假设以及工具与数据的时序粒度的重要性。更具体而言，应鼓励利益相关方在边际排放最低的时段进行充电。该理念对于未来充电基础设施的布局、类型、成本与运营模式同样具有重要意义。将此类指导方针转化为可落地的实操方案，需要结合科普教育、获取严谨清晰的决策支持工具、搭建利益相关方（例如电力企业与消费者）之间的信号传导机制，以及推动行为模式转变。