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Found 19 papers, 2 papers with code
Impact of Flexible and Bidirectional Charging in Medium- and Heavy-Duty Trucks on California's Decarbonization Pathway
In particular, we examine the potential cost savings of electrical generation infrastructure by enabling flexible charging and bidirectional charging for these trucks.
On the Selection of Intermediate Length Representative Periods for Capacity Expansion
To this end, we propose a novel method for selecting representative periods of any length.
Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learning
The recent advances in graph neural networks (GNN) enable it to enhance the B&B algorithm in modern MIP solvers by learning to dive and branch.
Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models
The global deployment of the phasor measurement units (PMUs) enables real-time monitoring of the power system, which has stimulated considerable research into machine learning-based models for event detection and classification.
Optimizing Deep Decarbonization Pathways in California with Power System Planning Using Surrogate Level-based Lagrangian Relaxation
With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning.
Toward Efficient Transportation Electrification of Heavy-Duty Trucks: Joint Scheduling of Truck Routing and Charging
The timely transportation of goods to customers is an essential component of economic activities.
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events
This paper introduces pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data.
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems
Unit commitment (UC) is a fundamental problem in the day-ahead electricity market, and it is critical to solve UC problems efficiently.
A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment
To facilitate the development of reinforcement learning (RL) based power distribution system Volt-VAR control (VVC), this paper introduces a suite of open-source datasets for RL-based VVC algorithm research that is sample efficient, safe, and robust.
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events -- Part I: Overview and Results
We have trained this model on thousands of actual events and created a dataset denoted pmuBAGE (the Benchmarking Assortment of Generated PMU Events).
Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration
In this paper, we propose a novel centralized allocation and decentralized execution (CADE) reinforcement learning (RL) framework to maximize the charging station's profit.
Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis
We leverage the proposed algorithmic virtual bid trading strategy to evaluate both the profitability of the virtual bid portfolio and the efficiency of U. S. wholesale electricity markets.
Estimate Three-Phase Distribution Line Parameters With Physics-Informed Graphical Learning Method
In this paper, we develop a physics-informed graphical learning algorithm to estimate network parameters of three-phase power distribution systems.
Power System Event Identification based on Deep Neural Network with Information Loading
Online power system event identification and classification is crucial to enhancing the reliability of transmission systems.
Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks
Volt-VAR control (VVC) is a critical application in active distribution network management system to reduce network losses and improve voltage profile.
On the Maximum Mutual Information Capacity of Neural Architectures
We derive the closed-form expression of the maximum mutual information - the maximum value of $I(X;Z)$ obtainable via training - for a broad family of neural network architectures.
Improving Supervised Phase Identification Through the Theory of Information Losses
This paper considers the problem of Phase Identification in power distribution systems.
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses
We use this framework to prove that two methods, Facility Location Selection and Transductive Experimental Design, reduce these losses.
Information Losses in Neural Classifiers from Sampling
It then bounds this expected total variation as a function of the size of randomly sampled datasets in a fairly general setting, and without bringing in any additional dependence on model complexity.
