Deep Reinforcement Learning for Smart Grid Operations: Algorithms, Applications, and Prospects-Reference-Cited by-同舟云学术

Deep Reinforcement Learning for Smart Grid Operations: Algorithms, Applications, and Prospects

Published:2023-09 Issue:9 Volume:111 Page:1055-1096
ISSN:0018-9219
Container-title:Proceedings of the IEEE
language:
Short-container-title:Proc. IEEE

Author:

Li Yuanzheng¹^ORCID,Yu Chaofan²^ORCID,Shahidehpour Mohammad³^ORCID,Yang Tao⁴^ORCID,Zeng Zhigang¹^ORCID,Chai Tianyou⁴

Affiliation:

1. School of Artificial Intelligence and Automation, Autonomous Intelligent Unmanned System Engineering Research Center, Key Laboratory of Image Processing and Intelligence Control, Ministry of Education of China, and the Hubei Key Laboratory of Brain-Inspired Intelligent Systems and the Belt and Road Joint Laboratory on Measurement and Control Technology, Huazhong University of Science and Technology, Wuhan, China

2. China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, China

3. Robert W. Galvin Center for Electricity Innovation, Illinois Institute of Technology, Chicago, IL, USA

4. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Key Project of National Natural Science Foundation of China

Smart Grid Joint Key Project of National Natural Science Foundation of China and the State Grid Corporation of China

Major Program of National Natural Science Foundation of China

2020 Science and Technology Major Project of Liaoning Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/5/10251702/10241311.pdf?arnumber=10241311

Reference268 articles.

1. Implementing action mask in proximal policy optimization (PPO) algorithm

2. A Scalable Privacy-Preserving Multi-Agent Deep Reinforcement Learning Approach for Large-Scale Peer-to-Peer Transactive Energy Trading

3. PPO dash: Improving generalization in deep reinforcement learning;booth;arXiv 1907 06704,2019

4. Surrogate model enabled deep reinforcement learning for hybrid energy community operation

5. Addressing function approximation error in actor-critic methods;fujimoto;Proc Int Conf Mach Learn,2018

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