Rolling Horizon Optimization for Grid-connected Electric Vehicles Considering Demand Difference

Bingxu ZHANG; Gang XU

doi:10.7500/AEPS20191014006

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Rolling Horizon Optimization for Grid-connected Electric Vehicles Considering Demand Difference

Application Research | 更新时间：2025-06-16

- Rolling Horizon Optimization for Grid-connected Electric Vehicles Considering Demand Difference
- Automation of Electric Power Systems Vol. 44, Issue 13, Pages: 106-114(2020)
- 作者机构：
  
  华北电力大学电气与电子工程学院，北京市 102206
- 作者简介：
- 基金信息：
  
  Fundamental Research Funds for the Central Universities
- DOI：10.7500/AEPS20191014006
  CLC：
- Received：14 October 2019，
  
  Revised：2020-02-23，
  
  Published Online：11 May 2020，
  
  Published：10 July 2020
- 稿件说明：
移动端阅览
张丙旭,许刚.计及需求差异的电动汽车并网滚动时域优化[J].电力系统自动化,2020,44(13):106-114.

ZHANG Bingxu,XU Gang.Rolling Horizon Optimization for Grid-connected Electric Vehicles Considering Demand Difference[J].Automation of Electric Power Systems,2020,44(13):106-114.
张丙旭,许刚.计及需求差异的电动汽车并网滚动时域优化[J].电力系统自动化,2020,44(13):106-114. DOI： 10.7500/AEPS20191014006.

ZHANG Bingxu,XU Gang.Rolling Horizon Optimization for Grid-connected Electric Vehicles Considering Demand Difference[J].Automation of Electric Power Systems,2020,44(13):106-114. DOI： 10.7500/AEPS20191014006.

摘要

考虑电动汽车（EV）并网后用户的差异性需求，提出一种计及需求差异的EV并网优化控制策略。首先，根据需求差异将并网EV集细化为刚性集、可削减集及灵活集3个子集，并分别建立充放电控制模型。其次，建立用户行为模式模型，描述用户行为模式的分布特征，进而制定计划功率需求。最后，以实际功率与计划功率偏差及功率波动最小为目标，提出并网EV的滚动时域优化策略，每一轮优化将可削减集及灵活集中EV功率增量作为最优控制变量在充放电控制模型约束下求解。实验分析证明，所提优化策略保证了实际功率符合计划功率的同时兼顾了用户的差异性需求，对并网过程中随机动态因素具有一定的鲁棒性。

Abstract

An optimal control strategy for grid-connected electric vehicles (EVs) considering demand difference is proposed

which considers the different demands of EV users. Firstly

the grid-connected EVs are divided into three subsets

including rigid set

reducible set and flexible set according to the demand differences

and the charging-discharging control model is established

respectively. Then

the behavior model of EV users is constructed to describe the distribution characteristics of the user behavior

and then the planned power demand is formulated. Finally

the rolling horizon optimization strategy for grid-connected EVs is proposed with the objective of minimizing deviation between actual power and planned power and power fluctuation. The power increments of EVs in reducible set and flexible set are taken as the optimal control variables and solved under the constraints of the charging-discharging control model in every optimization round. Experimental analysis shows that the proposed optimization strategy ensures that the actual power matches the planned power while taking into account the different demands of users. Moreover

it is robust to random dynamic factors during the course of grid integration.

关键词

Keywords

references

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