Multi-agent deep reinforcement learning based distributed control architecture for interconnected multi-energy microgrid energy management and optimization-Reference-Cited by-同舟云学术

Multi-agent deep reinforcement learning based distributed control architecture for interconnected multi-energy microgrid energy management and optimization

Published:2023-02 Issue: Volume:277 Page:116647
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Zhang Bin,Hu Weihao,M.Y.M. Ghias Amer,Xu Xiao,Chen Zhe

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference51 articles.

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

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

3. Towards a new renewable power system using energy storage: An economic and social analysis;Sanchez;Energ Conver Manage,2022

4. Optimal investment planning of bankable multi-carrier microgrid networks;Azimian;Appl Energy,2022

5. Multi-energy microgrids with ecotourism purpose: The impact of the power market and the connection line;Markov;Energ Conver Manage,2019

Cited by 40 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Intelligent optimal sitting and sizing of distributed resources in presence of renewable energy and fuel cells based on reliability and emission factors;Energy Reports;2024-12

2. A comprehensive review on distributed energy cooperative control and optimization method for energy interconnection system;Electric Power Systems Research;2024-12

3. A transferable perception-guided EMS for series hybrid electric unmanned tracked vehicles;Energy;2024-10

4. A novel dynamic batching energy management strategy for multi-microgrid networks with enhanced privacy;Energy and Buildings;2024-09

5. An overview: Attention mechanisms in multi-agent reinforcement learning;Neurocomputing;2024-09