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A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

Author

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  • Wenjiao Zai

    (College of Engineering, Sichuan Normal University, Chengdu 610101, China
    These authors contributed equally to this work.)

  • Junjie Wang

    (College of Engineering, Sichuan Normal University, Chengdu 610101, China
    These authors contributed equally to this work.)

  • Guohui Li

    (College of Engineering, Sichuan Normal University, Chengdu 610101, China)

Abstract

Stable material transportation is essential for quickly restoring the power system following a disaster. Drone-based material transportation can bypass ground transportation’s limitations and reduce transit times. However, the current drone flight trajectory distribution optimization model cannot meet the need for mountainous emergency relief material distribution following a disaster. A power emergency material distribution model with priority conditions is proposed in this paper, along with a two-layer dynamic task-solving framework that takes task dynamics into account. This research proposes an algorithm (TD3PSO) that combines the particle swarm algorithm (PSO) updating technique with the double-delay depth deterministic policy gradient algorithm (TD3) algorithm’s capacity to dynamically parameterize. The final task allocation experiment demonstrates that the modified TD3PSO significantly outperforms the conventional algorithm on the Solomon data set, with an improvement of 26.3% on average over the RLPSO algorithm and a 11.0% reduction in the volatility of the solving impact. When solving under realistic circumstances, the solution effect increases by 1.6% to 13.4%, and the redistribution experiment confirms the framework’s efficacy. As a result, the algorithm and architecture suggested in this paper may successfully address the issue of scheduling drones for power emergencies while enhancing transportation efficiency.

Suggested Citation

  • Wenjiao Zai & Junjie Wang & Guohui Li, 2023. "A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm," Sustainability, MDPI, vol. 15(17), pages 1-29, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13127-:d:1230101
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    References listed on IDEAS

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