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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Vydáno v:	Sensors (Basel, Switzerland) Ročník 25; číslo 17; s. 5226
Hlavní autoři:	Zeng, Haiyong, Huang, Yuanyan, Zhan, Kaijie, Yu, Zichao, Zhu, Hongyan, Li, Fangyan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 22.08.2025
Témata:	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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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Zeng, Haiyong Huang, Yuanyan Zhu, Hongyan Zhan, Kaijie Yu, Zichao Li, Fangyan
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Cites_doi	10.3390/en10111880 10.1016/j.ijepes.2022.108603 10.1109/TCYB.2020.2975134 10.1109/TSG.2021.3058996 10.1109/VTC2022-Fall57202.2022.10012762 10.1016/j.est.2024.111159 10.1109/TSG.2017.2789333 10.1109/TSG.2019.2930299 10.3390/smartcities7060142 10.1109/GLOBECOM54140.2023.10437665 10.1109/TII.2019.2950809 10.1016/j.rser.2019.109618 10.1109/ACCESS.2018.2835309 10.1007/s40313-021-00741-w 10.1109/TII.2022.3152218 10.1109/TSG.2018.2879572 10.1016/j.engappai.2023.106320 10.1109/JPROC.2020.3046112 10.1109/TNSE.2023.3321048 10.1109/LWC.2024.3376774 10.1109/ICIoT48696.2020.9089471 10.1109/JIOT.2020.3015204 10.1016/j.apenergy.2022.120111 10.1109/ICTEM60690.2024.10631931 10.1109/MVT.2022.3168502 10.1109/TTE.2024.3443691 10.1016/j.tre.2024.103698 10.1049/gtd2.13047 10.1109/TSG.2022.3186931
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SubjectTerms	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
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Title	Multi-Agent DDPG-Based Multi-Device Charging Scheduling for IIoT Smart Grids
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