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Multi-Agent DDPG-Based Multi-Device Charging Scheduling for IIoT Smart Grids.

Haiyong Zeng1,2, Yuanyan Huang1, Kaijie Zhan1

  • 1Guangxi Key Laboratory of Braininspired Computing and Intelligent Chips, School of Electronic and Information Engineering/School of Integrated Circuits, Guangxi Normal University, Guilin 541001, China.

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Summary
This summary is machine-generated.

This study introduces a novel Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for electric vehicle (EV) charging scheduling. The new method significantly reduces EV charging costs and enhances smart grid stability.

Keywords:
cost optimization managementdeep reinforcement learningelectric vehicle charging schedulingindustrial internet of thingssmart gridssmart sensors

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Smart Grid Technology

Background:

  • Widespread adoption of electric vehicles (EVs) and Industrial Internet of Things (IIoT) smart grids necessitates coordinated charging strategies.
  • Traditional algorithms face scalability challenges in multi-device environments and limitations in continuous control scenarios.

Purpose of the Study:

  • To propose a dynamic charging scheduling algorithm for EVs using Multi-Agent Deep Deterministic Policy Gradient (MADDPG).
  • To optimize charging and discharging strategies for multiple EVs in continuous action spaces, reducing costs and balancing grid load.

Main Methods:

  • Developed a MADDPG-based algorithm integrating real-time electricity prices, battery status, and sensor data.
  • Employed coordinated multi-agent learning for dynamic optimization of EV charging and discharging.
  • Utilized Vehicle-to-Grid (V2G) technology for adaptation to price fluctuations and user demand.

Main Results:

  • Achieved a 41.12% reduction in charging costs over a 30-day evaluation period compared to baseline methods.
  • Demonstrated effective adaptation to electricity price fluctuations and user demand variations.
  • Showcased enhanced grid stability through optimized charging time allocation.

Conclusions:

  • The proposed MADDPG algorithm offers a scalable and effective solution for dynamic EV charging scheduling in IIoT smart grids.
  • Coordinated multi-agent learning with V2G technology significantly improves economic efficiency and grid stability.
  • This approach addresses limitations of traditional methods in managing complex, continuous control scenarios.