A Heterogeneous Network for Electric Vehicle Charging Station Communication

Zhou, Jiaxing; Zhao, Yi; Li, Yuejia; Kong, Jingkai; Yang, Chang Mo; Tian, Zhiqin

doi:10.1109/icpre52634.2021.9635355

Cited by 1 publication

(1 citation statement)

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“…As an important part of the electricity market, the role of EVA participating in electricity ancillary services (Yang et al, 2017;Yuan et al, 2021) is neglected. Meanwhile, there are few works in the literature (Wang and Cui, 2020;Zhou et al, 2021) that consider the behavioral characteristics of different cars such as taxis, buses, and private cars. Most of them are about path planning or pricing issues.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimal scheduling of charging stations based on deep reinforcement learning

Cui

Lin²,

Zhang³

et al. 2023

Front. Energy Res.

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With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%.

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