Ride-Hailing Electric Vehicle Dispatching for Resilience Reserve Enhancement: An Interactive Deep Reinforcement Learning Approach

Ran Tao; Dongmei Zhao; Haoxiang Wang; Yinghui Wang; Xuan Xia

doi:10.17775/CSEEJPES.2022.01760

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Ride-Hailing Electric Vehicle Dispatching for Resilience Reserve Enhancement: An Interactive Deep Reinforcement Learning Approach

Regular Papers | 更新时间：2026-03-25

- Ride-Hailing Electric Vehicle Dispatching for Resilience Reserve Enhancement: An Interactive Deep Reinforcement Learning Approach
- CSEE Journal of Power and Energy Systems Vol. 12, Issue 1, Pages: 448-465(2026)
- 作者机构：
  
  1. School of Electrical and Electronic Engineering, North China Electric Power University,Beijing,China
  2. State Grid Shaoxing Power Supply Company,Shaoxing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.01760
  CLC：
- Published：2026
- 稿件说明：
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Ran Tao, Dongmei Zhao, Haoxiang Wang, et al. Ride-Hailing Electric Vehicle Dispatching for Resilience Reserve Enhancement: An Interactive Deep Reinforcement Learning Approach[J]. CSEE Journal of Power and Energy Systems, 2026, 12(1): 448-465.
DOI：

Ran Tao, Dongmei Zhao, Haoxiang Wang, et al. Ride-Hailing Electric Vehicle Dispatching for Resilience Reserve Enhancement: An Interactive Deep Reinforcement Learning Approach[J]. CSEE Journal of Power and Energy Systems, 2026, 12(1): 448-465. DOI： 10.17775/CSEEJPES.2022.01760.

摘要

Abstract

Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience. However

they have not been well investigated because their charging and order-serving are affected or managed by the power grid dispatching center and the ride-hailing platform. Effective pre-strategies can improve the prevention ability for high-impact and low-probability (HILP) events and provide the foundation for measures in the response and restoration stages. First

this paper proposes a resilience reserve to expand the existing research on power system resilience. Secondly

this paper puts forward an interactive method of deep reinforcement learning

which considers the interests of both the power grid dispatching center and the ride-hailing platform. It improves the resilience reserve by achieving the order dispatch

orderly charging management of ride-hailing electric vehicles

and the pricing strategy of charging stations. Finally

this paper uses a practical example covering about 107.32 km<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2 in the center of Chengdu to verify that the proposed method improves the resilience reserve of the power system without obviously damaging the interests of the ride-hailing platform.

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references

5. The White House. ( 2013 ). Presidential policy directive - critical infrastructure security and resilience . [Online ] . Available: https://www.whitehouse.gov/the-press-office/2013/02/12/presidential-policy-directive-critical-infrastructure-security-and-resil

27. The White House. ( 2013 ). Presidential policy directive - critical infrastructure security and resilience . [Online ] . Available: https://www.whitehouse.gov/the-press-office/2013/02/12/presidential-policy-directive-critical-infrastructure-security-and-resil

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