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20222025
most citedEnergy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park

92 citations · 184 across the 9 of their papers we have counts for

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5 papers · 1 filter

eess.SY2025

Resilient Event-Triggered Control of Vehicle Platoon Under DoS Attacks and Parameter Uncertainty

Qiaoni Han, Jianguo Ma, Zhiqiang Zuo +3

This paper investigates the problem of dynamic event-triggered platoon control for intelligent vehicles (IVs) under denial of service (DoS) attacks and parameter uncertainty. DoS a…

eess.SY2024

Modeling, Prediction and Risk Management of Distribution System Voltages with Non-Gaussian Probability Distributions

Yuanhai Gao, Xiaoyuan Xu, Zheng Yan +3

High renewable energy penetration into power distribution systems causes a substantial risk of exceeding voltage security limits, which needs to be accurately assessed and properly…

eess.SY2023

Joint Trading and Scheduling among Coupled Carbon-Electricity-Heat-Gas Industrial Clusters

Dafeng Zhu, Bo Yang, Yu Wu +4

This paper presents a carbon-energy coupling management framework for an industrial park, where the carbon flow model accompanying multi-energy flows is adopted to track and suppre…

eess.SY2022

Stochastic Gradient-based Fast Distributed Multi-Energy Management for an Industrial Park with Temporally-Coupled Constraints

Dafeng Zhu, Bo Yang, Chengbin Ma +4

Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to m…

eess.SY202292 cited

Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park

Dafeng Zhu, Bo Yang, Yuxiang Liu +3

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling…