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Found 39 papers, 9 papers with code
PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power Systems
The electricity sector is undergoing substantial transformations due to the rising electrification of demand, enhanced integration of renewable energy resources, and the emergence of new technologies.
Unsupervised Congestion Status Identification Using LMP Data
The congestion part of LMPs is spanned by certain row vectors of the power transfer distribution factor (PTDF) matrix, and the subspace attributes of an LMP vector uniquely are found to reflect the instantaneous congestion status of all the transmission lines.
An Econometric Analysis of Large Flexible Cryptocurrency-mining Consumers in Electricity Markets
As the scale of these firms is likely to surge in future years, the developed electricity consumption model can be used to generate public, synthetic datasets to understand the overall impact on the power grid.
Accelerating Chance-constrained SCED via Scenario Compression
This paper studies some compression methods to accelerate the scenario-based chance-constrained security-constrained economic dispatch (SCED) problem.
The Transmission Value of Energy Storage and Fundamental Limitations
This study addresses the transmission value of energy storage in electric grids.
Foundation Models for the Electric Power Grid
Foundation models (FMs) currently dominate news headlines.
Predicting DC-Link Capacitor Current Ripple in AC-DC Rectifier Circuits Using Fine-Tuned Large Language Models
Foundational Large Language Models (LLMs) such as GPT-3. 5-turbo allow users to refine the model based on newer information, known as ``fine-tuning''.
A Review of Safe Reinforcement Learning Methods for Modern Power Systems
Due to the availability of more comprehensive measurement data in modern power systems, there has been significant interest in developing and applying reinforcement learning (RL) methods for operation and control.
The Role of Electric Grid Research in Addressing Climate Change
Addressing the urgency of climate change necessitates a coordinated and inclusive effort from all relevant stakeholders.
Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector
Large Language Models (LLMs) as chatbots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks.
Meta-Learning-Based Adaptive Stability Certificates for Dynamical Systems
This paper addresses the problem of Neural Network (NN) based adaptive stability certification in a dynamical system.
Electromagnetic Transient Model of Cryptocurrency Mining Loads for Low-Voltage Ride Through Assessment in Transmission Grids
In this paper, we developed an Electromagnetic Transient (EMT) model tailored for large cryptocurrency mining loads to understand the cross-interaction of these loads with the electric grid.
Predictive Reliability Assessment of Distribution Grids with Residential Distributed Energy Resources
Employing a scenario-based approach, we showcase the application of our probabilistic method using a Monte Carlo Simulation process to assess average system reliability indices and their variations at the user level.
Voltage Constrained Heavy Duty Vehicle Electrification: Formulation and Case Study
The electrification of heavy-duty vehicles (HDEVs) is a rapidly emerging avenue for decarbonization of energy and transportation sectors.
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems
Hosting capacity analysis (HCA) examines the amount of DERs that can be safely integrated into the grid and is a challenging task in full generality because there are many possible integration of DERs in foresight.
Physical and Economic Viability of Cryptocurrency Mining for Provision of Frequency Regulation: A Real-World Texas Case Study
Demand flexibility plays a pivotal role in modern power systems with high penetration of variable energy resources.
Optimization of Cryptocurrency Miners' Participation in Ancillary Service Markets
One of the most significant bottlenecks for the scalable deployment of such computation is its energy demand.
Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality
The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation.
Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion
Robotic ultrasound (US) imaging aims at overcoming some of the limitations of free-hand US examinations, e. g. difficulty in guaranteeing intra- and inter-operator repeatability.
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems
One standard approach to estimate the stability region of a general nonlinear system is to first find a Lyapunov function for the system and characterize its region of attraction as the stability region.
Modeling and Analysis of Utilizing Cryptocurrency Mining for Demand Flexibility in Electric Energy Systems: A Synthetic Texas Grid Case Study
We perform our simulations using a synthetic 2000 bus ERCOT grid model, along with added cryptocurrency mining loads on top of the real-world demand profiles in Texas.
On an Information and Control Architecture for Future Electric Energy Systems
This paper presents considerations towards an information and control architecture for future electric energy systems driven by massive changes resulting from the societal goals of decarbonization and electrification.
How Much Demand Flexibility Could Have Spared Texas from the 2021 Outage?
The February 2021 Texas winter power outage has led to hundreds of deaths and billions of dollars in economic losses, largely due to the generation failure and record-breaking electric demand.
Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI
This article presents a use-inspired perspective of the opportunities and challenges in a massively digitized power grid.
The Impact of Heavy-Duty Vehicle Electrification on Large Power Grids: a Synthetic Texas Case Study
In fact, we find that as little as 11% of heavy duty vehicles in Texas charging simultaneously can lead to significant voltage violations on the transmission network that compromise grid reliability.
OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation
This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms.
Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems
Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation.
A Preliminary Study on the Role of Energy Storage and Load Rationing in Mitigating the Impact of the 2021 Texas Power Outage
It also conducts a preliminary study on using energy storage and load rationing to mitigate rotating blackout's adverse impact on the grid.
A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change.
PyProD: A Machine Learning-Friendly Platform for Protection Analytics in Distribution Systems
This paper introduces PyProD, a Python-based machine learning (ML)-compatible test-bed for evaluating the efficacy of protection schemes in electric distribution grids.
An Efficient Network Solver for Dynamic Simulation of Power Systems Based on Hierarchical Inverse Computation and Modification
In power system dynamic simulation, up to 90% of the computational time is devoted to solve the network equations, i. e., a set of linear equations.
Two-stage Robust Energy Storage Planning with Probabilistic Guarantees: A Data-driven Approach
Furthermore, the column-and-constraint generation algorithm is used to solve the two-stage robust planning problem and tighten theoretical guarantees.
Transient Stability Assessment of Networked Microgrids Using Neural Lyapunov Methods
Case studies suggest that the proposed method can address networked microgrids with heterogeneous interface dynamics, and in comparison with conventional methods that are based on quadratic Lyapunov functions, can characterize the security regions with much less conservativeness.
Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power
In this paper, we introduce a new framework to address the problem of voltage regulation in unbalanced distribution grids with deep photovoltaic penetration.
A Smart Meter Data-driven Distribution Utility Rate Model for Networks with Prosumers
Distribution grids across the world are undergoing profound changes due to advances in energy technologies.
Tracking and Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector
The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U. S. becoming the epicenter of COVID-19 cases and deaths in late March.
Computers and Society
Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids
This paper introduces the concept of Deep Reinforcement Learning based architecture for protective relay design in power distribution systems with many distributed energy resources (DERs).
Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments
To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domain-invariant feature vector.
Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach
This paper concerns with the production of synthetic phasor measurement unit (PMU) data for research and education purposes.
