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September 1, 2025Journal of Physics Conference SeriesOpen Access

Research on optimal configuration of urban fast charging stations based on deep reinforcement learning algorithm

Liang Qiu Jingjing Huang Yangzi Che Diyu Chen

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Key Points

Proposed method improves operational efficiency in microgrid groups, leveraging deep reinforcement learning for optimization.
Economic optimization scheduling was achieved through a two-stage robust optimization model, with significant improvements noted.
Methodologically, a scheduling model using deep reinforcement learning was developed to address uncertainties in microgrid sources.
Findings support better collaboration in energy management among microgrid entities, benefiting overall renewable energy utilization.

Implication

Analysis explores economic optimization scheduling of microgrids with renewable energy, indicating enhanced efficiency.

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Cite This Study

Qiu et al. (2025) studied this question.

synapsesocial.com/papers/68d473ad31b076d99fa6c4f2 https://doi.org/10.1088/1742-6596/3110/1/012029

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