Aggregated and Schedulable Capacity Modeling of Large-scale Electric Vehicles for Multi-scenario Auxiliary Services

WANG Yangyang; MAO Meiqin; YANG Cheng; ZHOU Kun; DU Yan; Nikos D. HATZIARGYRIOU

doi:10.7500/AEPS20230627012

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Aggregated and Schedulable Capacity Modeling of Large-scale Electric Vehicles for Multi-scenario Auxiliary Services

Special Topic on Key Technologies for Intelligent and Friendly Interaction Between Large-scale Electric Vehicles and Power Grid | 更新时间：2025-06-16

- Aggregated and Schedulable Capacity Modeling of Large-scale Electric Vehicles for Multi-scenario Auxiliary Services
- Automation of Electric Power Systems Vol. 48, Issue 7, Pages: 103-115(2024)
- 作者机构：
  
  1.教育部光伏系统工程研究中心，合肥工业大学，安徽省合肥市 230009
  2.国网安徽省电力有限公司，安徽省合肥市 230000
  3.国网安徽省电动汽车服务有限公司，安徽省合肥市 230000
  4.雅典国立理工大学，雅典 15780，希腊
- 作者简介：
- 基金信息：
  
  Anhui Province Natural Science Foundation(2108085UD02)
- DOI：10.7500/AEPS20230627012
  CLC：
- Received：27 June 2023，
  
  Revised：2023-10-24，
  
  Published Online：13 March 2024，
  
  Published：10 April 2024
- 稿件说明：
移动端阅览
王杨洋,茆美琴,杨铖等.面向多场景辅助服务的大规模电动汽车聚合可调度容量建模[J].电力系统自动化,2024,48(7):103-115.

WANG Yangyang,MAO Meiqin,YANG Cheng,et al.Aggregated and Schedulable Capacity Modeling of Large-scale Electric Vehicles for Multi-scenario Auxiliary Services[J].Automation of Electric Power Systems,2024,48(7):103-115.
王杨洋,茆美琴,杨铖等.面向多场景辅助服务的大规模电动汽车聚合可调度容量建模[J].电力系统自动化,2024,48(7):103-115. DOI： 10.7500/AEPS20230627012.

WANG Yangyang,MAO Meiqin,YANG Cheng,et al.Aggregated and Schedulable Capacity Modeling of Large-scale Electric Vehicles for Multi-scenario Auxiliary Services[J].Automation of Electric Power Systems,2024,48(7):103-115. DOI： 10.7500/AEPS20230627012.

摘要

大规模电动汽车聚合可调度容量是含电动汽车的虚拟电厂参与多层级多场景电力平衡辅助服务的重要技术指标之一。然而

现有电动汽车聚合可调度容量模型难以适应大规模电动汽车与省级电力调度中心互动场景。为此，文中从面向电力系统调峰、调频和调压多级多场景调控新视角，提出了数据驱动和机器学习相结合的双层聚类电动汽车聚合可调度容量建模方法。该方法通过构建电动汽车-充电桩广义储能单元的个体可调度容量模型，结合基于密度空间的聚类算法和改进的自组织映射深度聚类算法，有效地融合了电动汽车电量的时间分布和充电桩的空间分布特性，构建了面向调峰、调频和调压多场景调控的聚合可调度容量模型。采用了某省实际充电记录数据对提出的方法进行了验证，获得了“早间型”“中午型”“晚间型”等多种充电画像类型，实现了时空分布各异的电动汽车广义储能系统的自主聚合和省市级规模电动汽车参与电网不同辅助服务潜能的评估，并为聚合可调度容量的预测奠定了数据基础。

Abstract

The aggregation schedulable capacity (ASC) of large-scale electric vehicles (EVs) is one of the important technical indicators for virtual power plant containing EVs to participate in multi-level and multi-scenario power balance auxiliary services. However

the existing ASC model of EVs is difficult to adapt to the interactive scenario between large-scale EVs and provincial power dispatching center. Therefore

from the new perspective of multi-level and multi-scenario regulation for peak-shaving

frequency regutalion

and voltage regulation of power systems

this paper proposes a dual-layer clustering modeling method for ASC of EVs based on data-driven and machine learning. By constructing the individual schedulable capacity model of generalized EV-charging pile energy storage unit

this method combines the density space-based clustering algorithm and the improved self-organizing map deep clustering algorithm

which effectively integrates the time distribution of electric quantity of EVs and the spatial distribution characteristics of charging piles

and constructs the ASC model for multi-scenario regulation of peak shaving

frequency regulation and voltage regulation. The proposed method is verified by actual charging records in a province of China

and various charging profiles such as “morning type”

“noon type” and “evening type” are obtained. The self-aggregation of generalized energy storage system with different spatial and temporal distributions of EVs is realized

and the potential evaluation of provincial-level EVs participating in different auxiliary services of power grid is realized. The data foundation is laid for the prediction of ASC.

关键词

Keywords

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