Two-Step Diffusion Policy Deep Reinforcement Learning Method for Low-Carbon Multi-Energy Microgrid Energy Management-Reference-Cited by-同舟云学术

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Two-Step Diffusion Policy Deep Reinforcement Learning Method for Low-Carbon Multi-Energy Microgrid Energy Management

Published:2024-09 Issue:5 Volume:15 Page:4576-4588
ISSN:1949-3053
Container-title:IEEE Transactions on Smart Grid
language:
Short-container-title:IEEE Trans. Smart Grid

Author:

Zhang Yiwen¹,Mei Zhen¹,Wu Xiaoqian¹,Jiang Huaiguang¹^ORCID,Zhang Jun²,Gao Wenzhong³^ORCID

Affiliation:

1. School of Future Technology, South China University of Technology, Guangzhou, China

2. School of Electrical Engineering and Automation, Wuhan University, Wuhan, China

3. Department of Electrical and Computer Engineering, University of Denver, Denver, CO, USA

Funder

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5165411/10643567/10529228.pdf?arnumber=10529228

Reference46 articles.

1. Towards future infrastructures for sustainable multi-energy systems: A review

2. Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

3. Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule

4. Robust Optimal Power Management System for a Hybrid AC/DC Micro-Grid

5. Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option

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1. Interior-point policy optimization based multi-agent deep reinforcement learning method for secure home energy management under various uncertainties;Applied Energy;2024-12

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