Cloud-Edge Collaborative Scheduling Method for Virtual Power Plants Considering Consistency of Electric Vehicle Parameters

HU Anni; ZHANG Tiance; LI Gengyin; CHEN Weijia; WANG Jianxiao

doi:10.7500/AEPS20240701007

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Cloud-Edge Collaborative Scheduling Method for Virtual Power Plants Considering Consistency of Electric Vehicle Parameters

Special Session on Virtual Power Plant with Enormous Flexible Resources | 更新时间：2026-02-28

- Cloud-Edge Collaborative Scheduling Method for Virtual Power Plants Considering Consistency of Electric Vehicle Parameters
- Automation of Electric Power Systems Vol. 49, Issue 20, Pages: 113-124(2025)
- 作者机构：
  
  1.新能源电力系统全国重点实验室（华北电力大学），北京市 102206
  2.国网北京城区供电公司，北京市 100032
  3.大数据分析与应用技术国家工程实验室（北京大学），北京市 100871
- 作者简介：
- 基金信息：
  
  Gansu Provincial Science and Technology Major Special Program(22ZD6GA032);State-funded Postdoctoral Researcher Program(GZC20230787);Fundamental Research Funds for the Central Universities(2024MS007)
- DOI：10.7500/AEPS20240701007
  CLC：
- Received：01 July 2024，
  
  Revised：2025-01-17，
  
  Online First：13 March 2025，
  
  Published：25 October 2025
- 稿件说明：
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胡安妮,张天策,李庚银等.考虑电动汽车参数一致性的虚拟电厂云边协同调度方法[J].电力系统自动化,2025,49(20):113-124.

HU Anni,ZHANG Tiance,LI Gengyin,et al.Cloud-Edge Collaborative Scheduling Method for Virtual Power Plants Considering Consistency of Electric Vehicle Parameters[J].Automation of Electric Power Systems,2025,49(20):113-124.
胡安妮,张天策,李庚银等.考虑电动汽车参数一致性的虚拟电厂云边协同调度方法[J].电力系统自动化,2025,49(20):113-124. DOI： 10.7500/AEPS20240701007.

HU Anni,ZHANG Tiance,LI Gengyin,et al.Cloud-Edge Collaborative Scheduling Method for Virtual Power Plants Considering Consistency of Electric Vehicle Parameters[J].Automation of Electric Power Systems,2025,49(20):113-124. DOI： 10.7500/AEPS20240701007.

摘要

海量电动汽车的无序接入给电力系统优化调度带来了前所未有的挑战。虚拟电厂作为云端聚合边缘侧电动汽车、光伏等分布式资源参与输电网调度的解决思路，具有较大的应用潜力。然而，规模化同构电动汽车在充放电功率、效率、电池容量等参数上具有一致性，导致其响应虚拟电厂调控信号的边缘优化策略相同，虚拟电厂协同调度时易出现迭代不收敛甚至振荡问题。为此，文中构建了基于拉格朗日松弛的虚拟电厂云边协同优化调度框架与模型，揭示了规模化电动汽车参数一致性引发迭代振荡的机理，并提出一种基于扰动函数的收敛加速算法，在保持最优解不变的条件下打破参数一致性，从而实现海量电动汽车与虚拟电厂的高效协同优化。通过参数灵敏度分析，理论上证明了扰动极限的封闭形式，并基于中国深圳地区不同规模用户算例研究验证了所提方法的有效性和高效性。

Abstract

The disorderly integration of a large number of electric vehicles poses unprecedented challenges to the optimization scheduling of the power system. The virtual power plant (VPP)

as a solution that aggregates distributed resources such as electric vehicles and photovoltaics at the edge of the cloud for participation in the scheduling of the transmission network

has great application potential. However

the large-scale homogeneous electric vehicles have consistency in parameters such as charging and discharging power

efficiency

and battery capacity

resulting in the same edge optimization strategies for responding the control signals of the VPP. During the collaborative scheduling of the VPP

problems such as non-convergence of iterations and even oscillations are prone to occur. Therefore

this paper constructs a cloud-edge collaborative optimization scheduling framework and model for VPPs based on Lagrangian relaxation

reveals the mechanism of iteration oscillation caused by the consistency of parameters of large-scale electric vehicles

and proposes a convergence acceleration algorithm based on the disturbing function. By breaking the parameter consistency in the condition of maintaining the optimal solution unchanged

it achieves efficient collaborative optimization of a large number of electric vehicles and the VPP. Through parameter sensitivity analysis

the closed form of the disturbance limit is theoretically proved

and the effectiveness and efficiency of the proposed method are verified based on the case of the users with various scales in Shenzhen

China

关键词

Keywords

references

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