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Energy Optimization Management of Multi-microgrid using Deep Reinforcement Learning
12/12 checkable references clean · checked 2026-07-22
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 12 checked references that resolve
resolves10.1016/j.apenergy.2019.01.145
Optimal energy management strategies for energy Internet via deep reinforcement learning approach
resolves10.1109/TPWRS.2020.2999890
A Multi-Agent Deep Reinforcement Learning Method for Cooperative Load Frequency Control of a Multi-Area Power System
resolves10.1109/TPWRS.2018.2794541
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
resolves10.1109/JIOT.2019.2941498
Reinforcement Learning-Based Microgrid Energy Trading With a Reduced Power Plant Schedule
resolves10.1016/j.ijepes.2020.106134
A hierarchical energy management system for islanded multi-microgrid clusters considering frequency security constraints
resolves10.1109/TSG.2018.2806973
Bi-Level Two-Stage Robust Optimal Scheduling for AC/DC Hybrid Multi-Microgrids
resolves10.1109/TSG.2019.2930299
Intelligent Multi-Microgrid Energy Management Based on Deep Neural Network and Model-Free Reinforcement Learning
resolves10.1049/joe.2017.0753
Coordinated economic dispatch and cost allocation of cooperative multi‐microgrids
resolves10.1016/j.apenergy.2020.114579
Decentralized optimal operation model for cooperative microgrids considering renewable energy uncertainties
resolves10.1109/TPWRS.2018.2823641
Reinforcement Learning Approach for Optimal Distributed Energy Management in a Microgrid
resolves10.1109/ISGT-Asia.2016.7796409
An innovative real-time price based distributed optimal energy management of multi-microgrids in a smart distribution system
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