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Found 8 papers, 0 papers with code
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs).
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids.
Analyzing Distribution Transformer Degradation with Increased Power Electronic Loads
To have a good understanding of current standing challenges, a knowledge of the generation and load mix as well as the current harmonic estimations are essential for designing transformers and evaluating their performance.
Designing a Transactive Electric Vehicle Agent with Customer's Participation Preference
A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure.
Optimization-Based Resiliency Verification in Microgrids via Maximal Adversarial Set Characterization
An inner-approximation of the set of adversarial events which can be mitigated by the available flexibility is constructed using an optimization based approach.
Harmonic Modeling, Data Generation, and Analysis of Power Electronics-Interfaced Residential Loads
Integration of electronics-based residential appliances and distributed energy resources in homes is expected to rise with grid decarbonization.
Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters
In a purely inverter-based microgrid, both grid-forming (GFM) and grid-following (GFL) inverters will have a crucial role to play in frequency/voltage regulation and maintaining power sharing through their grid support capabilities.
Predicting Hydroxyl Mediated Nucleophilic Degradation and Molecular Stability of RNA Sequences through the Application of Deep Learning Methods
This paper proposes and evaluates three deep learning models (Long Short Term Memory, Gated Recurrent Unit, and Graph Convolutional Networks) as methods to predict the reactivity and risk of degradation of mRNA sequences.
