Inner-outer Combined Approximation Method for Aggregate Feasible Region of Electric Vehicles

杨尚霖; 1; 张沛超; 1; 孙烨; 1; 赵建立; 2; 3

doi:10.13335/j.1000-3673.pst.2024.2206

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Inner-outer Combined Approximation Method for Aggregate Feasible Region of Electric Vehicles

更新时间：2026-03-13

- Inner-outer Combined Approximation Method for Aggregate Feasible Region of Electric Vehicles
- Vol. 50, Issue 2, Pages: 515-523(2026)
- 作者机构：
  
  电力传输与功率变换控制教育部重点实验室(上海交通大学)，上海市闵行区,200240
- 作者简介：
- 基金信息：
- DOI：10.13335/j.1000-3673.pst.2024.2206
  CLC： U469.72
- Published：2026
- 稿件说明：
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杨尚霖, 1, 张沛超, et al. Inner-outer Combined Approximation Method for Aggregate Feasible Region of Electric Vehicles[J]. 2026, 50(2): 515-523.
DOI：

杨尚霖, 1, 张沛超, et al. Inner-outer Combined Approximation Method for Aggregate Feasible Region of Electric Vehicles[J]. 2026, 50(2): 515-523. DOI： 10.13335/j.1000-3673.pst.2024.2206.

摘要

将大规模电动汽车(electric vehicle，EV)聚合起来可使其有效参与电网互动，现有聚合可行域的内近似和外近似方法难以兼顾精确性和计算效率。针对EV在网时段高度随机的特点，提出EV聚合可行域的内-外结合近似方法。首先，提出根据EV在网时段进行维度剪裁的最大内近似方法，改善内近似的保守性；然后在集群层面进行外近似，减少聚合结果的不可行域，并保持聚合可行域为虚拟电池模型。为定量评价不同聚合可行域近似方法的效果，提出可行度和保守度指标，并提出基于改进Hit-and-Run的EV可行域抽样方法，用于计算评价指标。最后，基于实际充电数据设计算例，从可行度、保守度和计算效率等方面对比了典型的聚合可行域近似方法，验证了该文方法的有效性。

Abstract

Aggregating large-scale electric vehicles (EVs) can enable their effective participation in grid interactions. However

the existing inner and outer approximation methods for the aggregate feasible region (AFR) struggle to strike a balance between accuracy and computational efficiency. To address the highly random nature of charging periods of EVs

we propose an inner-outer combined approximation method for the AFR of EVs. First

we introduce a maximum inner approximation method based on dimension trimming

utilizing charging periods

to enhance the conservativeness of the inner approximation. Then

we perform outer approximation at the cluster level to reduce the infeasible region of the aggregation result while maintaining the AFR as a virtual battery model. To quantitatively evaluate the effects of different AFR approximation methods

we propose indicators for feasibility and conservativeness

along with an improved Hit-and-Run-based sampling method for EV feasible regions to calculate the evaluation metrics. Finally

based on actual charging data

we design case studies to compare typical AFR approximation methods in terms of feasibility

conservativeness

and computational efficiency

thus validating the effectiveness of the proposed method.

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