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Multi-Rate Safe Degradation-Aware Reinforcement Learning for Coordinated Energy Management of Renewable Microgrids with Hybrid Storage and EV Fleets

https://doi.org/10.2139/ssrn.7466287
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Outgoing claims: the papers this work cites, builds on, or argues with.

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GUIDELINES FOR LIMITING EXPOSURE TO TIME-VARYING ELECTRIC AND MAGNETIC FIELDS (1 Hz TO 100 kHz)
inferredcites →https://doi.org/10.1109/jas.2023.123657openalex-snapshot
Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems
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Planar Wireless Charging Technology for Portable Electronic Products and Qi
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Synchronverters: Inverters That Mimic Synchronous Generators
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Hierarchical Control of Droop-Controlled AC and DC Microgrids—A General Approach Toward Standardization
inferredcites →https://doi.org/10.1109/tsg.2014.2359004openalex-snapshot
Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach
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A V2G Capacity-Maximized Dispatch Control Strategy via Nested Consensus Control Learning for Grid Frequency Regulation
inferredcites →https://doi.org/10.1109/tvt.2026.3651625openalex-snapshot
A DoS Attack-Aware Adaptive Switching Reinforcement Learning Control for Resilient V2G-Based Grid Frequency Regulation
inferredcites →https://doi.org/10.1149/1945-7111/abdde1openalex-snapshot
Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning
inferredcites →https://doi.org/10.3390/electronics14163303openalex-snapshot
Enhanced Distributed Coordinated Control Strategy for DC Microgrid Hybrid Energy Storage Systems Using Adaptive Event Triggering
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