发表机构
University of Central Florida（中佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文系统综述了配电网运行中电动汽车充电需求的建模方法，将其分为确定性与不确定性感知两类，为DSO在DOPF中整合EVCD提供参考。
AI 中文摘要
电动汽车（EV）向交通系统的快速整合，为配电系统（PDS）引入了一类新的负荷，即电动汽车充电需求（EVCD）。EVCD的时空变异性给配电系统运营商（DSO）带来了根本性挑战，尤其是在确保网络安全可靠运行方面。在配电最优潮流（DOPF）问题中，将EVCD准确表示为参数，是实现可靠、成本高效的PDS运行的前提，但目前尚未出现普遍接受的建模框架。现有研究成果涵盖了广泛的假设、数学公式和求解范式。本文从DSO的运行视角，对在DOPF框架内表示EVCD的方法和模型进行了系统性综述。根据EVCD的表示方式，现有EVCD建模方法被分为两大类：确定性模型和不确定性感知模型。这两个主要类别又进一步细分为若干子类别。本文旨在为将EVCD整合到PDS运行与规划中的模型提供全面综述。
英文摘要
The rapid integration of electric vehicles (EVs) into the transportation system has introduced a new class of load into power distribution systems (PDS), i.e., EV charging demand (EVCD). The spatiotemporal variability of EVCD poses fundamental challenges for distribution system operators (DSOs), particularly in ensuring secure and reliable network operation. Accurately representing EVCD as a parameter within the distribution optimal power flow (DOPF) problem is a prerequisite for reliable, cost-efficient PDS operation, yet no universally accepted modeling framework has emerged. The existing contributions span a wide spectrum of assumptions, mathematical formulations, and solution paradigms. This article presents a systematic review of methods and models employed to represent EVCD within DOPF frameworks from the DSO's operational perspective. The existing EVCD modeling methods are classified into two primary categories based on EVCD representation: deterministic models and uncertainty-aware models. The two main categories are further divided into several subcategories. This paper aims to provide a comprehensive review on models for integrating EVCD into PDS operation and planning.
