To enable the electrification of transportation systems, it is important to understand how technologies such as grid storage, solar photovoltaic systems, and control strategies can aid the deployment of electric vehicle charging at scale. In this work, we present EV-EcoSim, a co-simulation platform that couples electric vehicle charging, battery systems, solar photovoltaic systems, grid transformers, control strategies, and power distribution systems, to perform cost quantification and analyze the impacts of electric vehicle charging on the grid. This python-based platform can run a receding horizon control scheme for real-time operation and a one-shot control scheme for planning problems, with multi-timescale dynamics for different systems to simulate realistic scenarios. We demonstrate the utility of EV-EcoSim through a case study focused on economic evaluation of battery size to reduce electricity costs while considering impacts of fast charging on the power distribution grid. We present qualitative and quantitative evaluations on the battery size in tabulated results. The tabulated results delineate the trade-offs between candidate battery sizing solutions, providing comprehensive insights for decision-making under uncertainty. Additionally, we demonstrate the implications of the battery controller model fidelity on the system costs and show that the fidelity of the battery controller can completely change decisions made when planning an electric vehicle charging site.

翻译：为实现交通系统的电气化，理解电网储能、太阳能光伏系统及控制策略等技术如何助力电动汽车大规模充电部署至关重要。本文提出EV-EcoSim协同仿真平台，该平台集成电动汽车充电、电池系统、太阳能光伏系统、电网变压器、控制策略及配电系统，用于成本量化分析及电动汽车充电对电网的影响评估。该基于Python的平台可支持实时运行中的滚动时域控制方案与规划问题的单次控制方案，通过多时间尺度动力学模型模拟不同系统的实际场景。我们通过案例研究验证EV-EcoSim的实用性，重点评估电池容量对降低用电成本的经济性，同时考虑快充对配电网的影响。在表格结果中，我们对电池容量进行定性与定量评估，清晰呈现候选电池容量方案间的权衡关系，为不确定性条件下的决策提供全面洞见。此外，我们展示了电池控制器模型保真度对系统成本的影响，证明控制器保真度可能彻底改变电动汽车充电站规划阶段的决策。