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

As electric vehicles (EVs) gain widespread adoption in industrial environments supported by Industrial Internet of Things (IIoT) smart grids technology, coordinated charging of multiple EVs has become vital for maintaining grid stability. In response to the scalability challenges faced by traditiona...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 17; S. 5226
Hauptverfasser: Zeng, Haiyong, Huang, Yuanyan, Zhan, Kaijie, Yu, Zichao, Zhu, Hongyan, Li, Fangyan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 22.08.2025
Schlagworte:
Algorithms
Communication
cost optimization management
Data exchange
Decision making
deep reinforcement learning
electric vehicle charging scheduling
Electric vehicles
Electricity
Energy consumption
Energy management
Industrial Internet of Things
Infrastructure
Integrated approach
Optimization
Prices and rates
Scheduling
Sensors
Smart grid technology
smart grids
smart sensors
Systems stability
Time management
electric vehicle charging scheduling
smart grids
deep reinforcement learning
industrial internet of things
cost optimization management
smart sensors
ISSN:1424-8220, 1424-8220
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Zusammenfassung:As electric vehicles (EVs) gain widespread adoption in industrial environments supported by Industrial Internet of Things (IIoT) smart grids technology, coordinated charging of multiple EVs has become vital for maintaining grid stability. In response to the scalability challenges faced by traditional algorithms in multi-device environments and the limitations of discrete action spaces in continuous control scenarios, this paper proposes a dynamic charging scheduling algorithm for EVs based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). The algorithm combines real-time electricity prices, battery status monitoring, and distributed sensor data to dynamically optimize charging and discharging strategies of multiple EVs in continuous action spaces. The goal is to reduce charging costs and balance grid load through coordinated multi-agent learning. Experimental results show that, compared with baseline methods, the proposed MADDPG algorithm achieves a 41.12% cost reduction over a 30-day evaluation period. Additionally, it effectively adapts to price fluctuations and user demand changes through Vehicle-to-Grid technology, optimizing charging time allocation and enhancing grid stability.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25175226