Initial Release for Cross-Regional V2G Evaluation

v1.0.0 - Initial Release: Cross-Regional V2G Benefit Evaluation Framework

This release marks the initial publication of the codebase for the research project: "A Paradigm for Cross-Regional V2G Benefit Evaluation based on Monte Carlo Simulation: Synergistic Analysis of Urban Scale, Vehicle Penetration, and User Behavior."

The framework implements a modular and scalable pipeline to quantify the peak-shaving and valley-filling potential of Electric Vehicles (EVs) across diverse metropolitan power grids.

Key Features

• Spatiotemporal Behavior Modeling: Full implementation of the Frank Copula model to capture the joint distribution of travel timing and distance, ensuring high-fidelity behavior simulation.
• Classified V2G Strategy: A differentiated scheduling engine that categorizes users into three types (Peak, Valley, and Flat periods) for optimized grid response.
• High-Concurrency Engine: A parallelized Monte Carlo simulation framework designed to handle million-level EV fleet computations efficiently.
• Synergy Analysis: Automated OLS regression tools for evaluating the interaction between grid elasticity and EV penetration.

Data Acknowledgments

This research is grounded in empirical data provided by the following open-source repositories:

• Grid Load: U.S. Energy Information Administration (EIA).
• Travel Behavior: National Household Travel Survey (NHTS) by FHWA.
• EV Specifications: Atlas EV Hub & California Energy Commission (CEC).
• Demographics: U.S. Census Bureau.

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v1.0.0 - 初次发布：跨区域 V2G 效益评估框架

本版本为研究课题 “一种基于蒙特卡洛模拟的跨区域 V2G 效益评估范式：城市规模、车辆渗透率与用户行为的协同分析” 相关代码的初次公开。

该框架实现了一套模块化且可扩展的计算流程，旨在量化评估电动汽车（EV）在不同城市电网环境下的削峰填谷潜力。

主要特性

• 时空行为建模：完整实现 Frank Copula 模型，用于捕获出行时间与距离的联合分布，确保行为模拟的高度仿真。
• 分类调度策略：差异化的调度引擎，将用户分为三类（峰区、谷区和平段）以实现优化的电网响应。
• 高并发仿真引擎：采用并行化蒙特卡洛仿真框架，支持百万级电动汽车集群的高效计算。
• 协同效应分析：提供自动化 OLS 回归工具，用于解析电网弹性禀赋与电动汽车渗透率之间的相互作用机制。

数据来源致谢

本研究基于以下开源库提供的实证数据：

• 电网负荷：美国能源信息署 (EIA)。
• 出行行为：全美家用出行调查 (NHTS)。
• EV 规格：Atlas EV Hub 与 加州能源委员会 (CEC)。
• 人口统计：美国普查局。