Explainable multi-agent deep reinforcement learning for real-time demand response towards sustainable manufacturing

Yun, Lingxiang; Wang, Di; Li, Lin

doi:10.1016/j.apenergy.2023.121324

Title: Explainable multi-agent deep reinforcement learning for real-time demand response towards sustainable manufacturing

Journal Article · 30 September 2023 · Applied Energy

DOI: https://doi.org/10.1016/j.apenergy.2023.121324 · OSTI ID:1986566

Yun, Lingxiang; Wang, Di; Li, Lin

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Sponsoring Organization:: USDOE

Grant/Contract Number:: EE0009714

OSTI ID:: 1986566

Journal Information:: Applied Energy, Journal Name: Applied Energy Journal Issue: C Vol. 347; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (20)

A simulation study for residential electricity user behavior under dynamic variable pricing with demand charge Khadgi, Prajwal; Bai, Lihui IISE Transactions, Vol. 50, Issue 8 https://doi.org/10.1080/24725854.2018.1440671	journal	May 2018
Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries Ma, Shuaiyin; Zhang, Yingfeng; Liu, Yang Journal of Cleaner Production, Vol. 274 https://doi.org/10.1016/j.jclepro.2020.123155	journal	November 2020
Real-Time Residential Demand Response Li, Hepeng; Wan, Zhiqiang; He, Haibo IEEE Transactions on Smart Grid, Vol. 11, Issue 5 https://doi.org/10.1109/TSG.2020.2978061	journal	September 2020
Time-of-use based electricity cost of manufacturing systems: Modeling and monotonicity analysis Wang, Yong; Li, Lin International Journal of Production Economics, Vol. 156 https://doi.org/10.1016/j.ijpe.2014.06.015	journal	October 2014
Simultaneous task and energy planning using deep reinforcement learning Wang, Di; Hu, Mengqi; Weir, Jeffery D. Information Sciences, Vol. 607 https://doi.org/10.1016/j.ins.2022.06.015	journal	August 2022
Optimal sizing and planning of onsite generation system for manufacturing in Critical Peaking Pricing demand response program Zhang, Yunchao; Islam, Md Monirul; Sun, Zeyi International Journal of Production Economics, Vol. 206 https://doi.org/10.1016/j.ijpe.2018.10.011	journal	December 2018
A New Evaluation Metric for Demand Response-Driven Real-Time Price Prediction Towards Sustainable Manufacturing Yun, Lingxiang; Li, Lin Journal of Manufacturing Science and Engineering, Vol. 145, Issue 2 https://doi.org/10.1115/1.4055604	journal	October 2022
Data-driven real-time price-based demand response for industrial facilities energy management Lu, Renzhi; Bai, Ruichang; Huang, Yuan Applied Energy, Vol. 283 https://doi.org/10.1016/j.apenergy.2020.116291	journal	February 2021
An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management Lork, Clement; Li, Wen-Tai; Qin, Yan Applied Energy, Vol. 276 https://doi.org/10.1016/j.apenergy.2020.115426	journal	October 2020
CPS-enabled and knowledge-aided demand response strategy for sustainable manufacturing Yun, Lingxiang; Ma, Shuaiyin; Li, Lin Advanced Engineering Informatics, Vol. 52 https://doi.org/10.1016/j.aei.2022.101534	journal	April 2022
Demand Response Management for Profit Maximizing Energy Loads in Real-Time Electricity Market Wang, Shuoyao; Bi, Suzhi; Zhang, Ying-Jun Angela IEEE Transactions on Power Systems, Vol. 33, Issue 6 https://doi.org/10.1109/TPWRS.2018.2827401	journal	November 2018
A review of the current automotive manufacturing practice from an energy perspective Giampieri, A.; Ling-Chin, J.; Ma, Z. Applied Energy, Vol. 261 https://doi.org/10.1016/j.apenergy.2019.114074	journal	March 2020
Reinforcement learning for demand response: A review of algorithms and modeling techniques Vázquez-Canteli, José R.; Nagy, Zoltán Applied Energy, Vol. 235 https://doi.org/10.1016/j.apenergy.2018.11.002	journal	February 2019
Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment Yun, Lingxiang; Li, Lin; Ma, Shuaiyin Applied Energy, Vol. 310 https://doi.org/10.1016/j.apenergy.2022.118550	journal	March 2022
Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach Golpîra, Hêriş Energy, Vol. 209 https://doi.org/10.1016/j.energy.2020.118385	journal	October 2020
Deep Reinforcement Learning for Smart Home Energy Management Yu, Liang; Xie, Weiwei; Xie, Di IEEE Internet of Things Journal, Vol. 7, Issue 4 https://doi.org/10.1109/JIOT.2019.2957289	journal	April 2020
Networked Multi-Agent Reinforcement Learning in Continuous Spaces Zhang, Kaiqing; Yang, Zhuoran; Basar, Tamer 2018 IEEE Conference on Decision and Control (CDC) https://doi.org/10.1109/CDC.2018.8619581	conference	December 2018
A survey of industrial applications of Demand Response Shoreh, Maryam H.; Siano, Pierluigi; Shafie-khah, Miadreza Electric Power Systems Research, Vol. 141 https://doi.org/10.1016/j.epsr.2016.07.008	journal	December 2016
Optimal Demand Response Using Device-Based Reinforcement Learning Wen, Zheng; O'Neill, Daniel; Maei, Hamid IEEE Transactions on Smart Grid, Vol. 6, Issue 5 https://doi.org/10.1109/TSG.2015.2396993	journal	September 2015
Vehicle-to-manufacturing (V2M) system: A novel approach to improve energy demand flexibility for demand response towards sustainable manufacturing Yun, Lingxiang; Xiao, Minkun; Li, Lin Applied Energy, Vol. 323 https://doi.org/10.1016/j.apenergy.2022.119552	journal	October 2022

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