A Bi-level Real-time Dispatch Strategy for Large-scale Electric Vehicles Into Smart Grid Based on an Improved Rolling Horizon Method

WANG He; 1; WANG Yao; 1; BIAN Jing; 1; LI Guoqing; 1; MENG Xinxin; 2

doi:10.13335/j.1000-3673.pst.2024.1646

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A Bi-level Real-time Dispatch Strategy for Large-scale Electric Vehicles Into Smart Grid Based on an Improved Rolling Horizon Method

更新时间：2026-03-13

- A Bi-level Real-time Dispatch Strategy for Large-scale Electric Vehicles Into Smart Grid Based on an Improved Rolling Horizon Method
- Vol. 49, Issue 11, Pages: 4659-4669(2025)
- 作者机构：
  
  现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林市,吉林省,132000
- 作者简介：
- 基金信息：
- DOI：10.13335/j.1000-3673.pst.2024.1646
  CLC： U491.8;TM73
- Published：2025
- 稿件说明：
移动端阅览
WANG He, 1, WANG Yao, et al. A Bi-level Real-time Dispatch Strategy for Large-scale Electric Vehicles Into Smart Grid Based on an Improved Rolling Horizon Method[J]. 2025, 49(11): 4659-4669.
DOI：

WANG He, 1, WANG Yao, et al. A Bi-level Real-time Dispatch Strategy for Large-scale Electric Vehicles Into Smart Grid Based on an Improved Rolling Horizon Method[J]. 2025, 49(11): 4659-4669. DOI： 10.13335/j.1000-3673.pst.2024.1646.

摘要

针对实时电价需求响应机制在电动汽车(electric vehicle，EV)灵活调节特性利用上的不足，该文提出了一种双层实时滚动优化策略。上层为实时定价策略，首先根据实时电网负荷数据与EV充放电行为特征，提出了一种弹性控制系数驱动的电价与EV负荷响应关系计算方法；其次，结合实时电网能量调节需求与EV负荷需求，构建了以电网综合收益最大为目标的实时定价模型。下层为EV充放电负荷优化策略，根据不同EV的灵活性特征，分别从滚动时域和优化顺序两方面进行动态调整，提出了一种自适应可变时域滚动优化法。仿真结果表明，相较于其他电价引导方案，所提优化策略能够使负荷峰谷差与标准差都有显著降低。

Abstract

In response to the limitations of real-time electricity pricing demand response mechanisms in fully utilizing the flexible adjustment characteristics of electric vehicles (EVs)

this paper proposes a bi-level real-time rolling optimization strategy. In the upper-level real-time pricing strategy

a method is first introduced to calculate the relationship between electricity prices and EV load response

driven by an elasticity control coefficient

based on real-time grid load data and EVs' charging and discharging behavior characteristics. Secondly

a real-time pricing model is developed to maximize the grid's comprehensive benefits

considering the grid's real-time energy regulation needs and the load demand of EVs. In the lower-level EV charging and discharging load optimization strategy

dynamic adjustments are made in both the rolling time horizon and optimization sequence according to the flexibility characteristics of different EVs

and an adaptive variable horizon rolling optimization method is proposed. The simulation results show that compared with other electricity price guidance schemes

the optimization strategy proposed in this paper can significantly reduce the peak valley difference and standard deviation of load.

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