Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach-Reference-Cited by-同舟云学术

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Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach

Published:2024-12 Issue: Volume:375 Page:123923
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Xu Xuesong,Xu Kai^ORCID,Zeng Ziyang^ORCID,Tang Jiale^ORCID,He Yuanxing^ORCID,Shi Guangze,Zhang Tao^ORCID

Publisher

Elsevier BV

Reference60 articles.

1. Renewable energy research and technologies through responsible research and innovation looking glass: Reflexions, theoretical approaches and contemporary discourses;Carbajo;Appl Energy,2018

2. Assessing the impact of the carbon market on the improvement of China’s energy and carbon emission performance;Zhou;Energy,2022

3. Data-driven planning of distributed energy resources amidst socio-technical complexities;Jain;Nat Energy,2017

4. Configuration optimization and selection of a photovoltaic-gas integrated energy system considering renewable energy penetration in power grid;Chen;Energy Convers Manage,2022

5. Distributed bus voltage regulation and economic dispatch for multi-bus AC microgrids;Han;Int J Electr Power Energy Syst,2024

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