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Found 28 papers, 3 papers with code
Contributions of Individual Generators to Nodal Carbon Emissions
Each generator can have distinct carbon emission rates.
Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies
This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies.
Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs
In this article, the optimal sample complexity of learning the underlying interactions or dependencies of a Linear Dynamical System (LDS) over a Directed Acyclic Graph (DAG) is studied.
Graph-Structured Kernel Design for Power Flow Learning using Gaussian Processes
This paper presents a physics-inspired graph-structured kernel designed for power flow learning using Gaussian Process (GP).
Optimal Power System Topology Control Under Uncertain Wildfire Risk
Wildfires pose a significant threat to the safe and reliable operation of electric power systems.
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy.
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes
The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty.
Can locational disparity of prosumer energy optimization due to inverter rules be limited?
To mitigate issues related to the growth of variable smart loads and distributed generation, distribution system operators (DSO) now make it binding for prosumers with inverters to operate under pre-set rules.
Learning Distribution Grid Topologies: A Tutorial
Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, or actively, upon actuating grid resources and measuring the feeder's voltage response.
Markovian Decentralized Ensemble Control for Demand Response
With the advancement in smart grid and smart energy devices, demand response becomes one of the most economic and feasible solutions to ease the load stress of the power grids during peak hours.
DNN-based Policies for Stochastic AC OPF
Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty.
Efficient and passive learning of networked dynamical systems driven by non-white exogenous inputs
We present a regularized non-casual consistent estimator for this problem and analyze its sample complexity over two regimes: (a) where the interval $T$ consists of $n$ i. i. d.
PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels
We propose a novel Physics-Preserved Graph Network (PPGN) architecture to accurately locate faults at the node level with limited observability and labeled training data.
Privacy-Aware Load Ensemble Control: A Linearly-Solvable MDP Approach
Demand response (DR) programs engage distributed demand-side resources, e. g., controllable residential and commercial loads, in providing ancillary services for electric power systems.
Estimating Linear Dynamical Networks of Cyclostationary Processes
The majority of prior work in consistent topology estimation relies on dynamical systems excited by temporally uncorrelated processes.
Physics-Informed Learning for High Impedance Faults Detection
High impedance faults (HIFs) in distribution grids may cause wildfires and threaten human lives.
A Hierarchical Approach to Multi-Energy Demand Response: From Electricity to Multi-Energy Applications
Due to proliferation of energy efficiency measures and availability of the renewable energy resources, traditional energy infrastructure systems (electricity, heat, gas) can no longer be operated in a centralized manner under the assumption that consumer behavior is inflexible, i. e. cannot be adjusted in return for an adequate incentive.
Tractable learning in under-excited power grids
This paper discusses statistical structure estimation in power grids in the "under-excited" regime, where a subset of internal nodes do not have external injection.
Data-Driven Learning and Load Ensemble Control
Demand response (DR) programs aim to engage distributed small-scale flexible loads, such as thermostatically controllable loads (TCLs), to provide various grid support services.
Real-time Anomaly Detection and Classification in Streaming PMU Data
Ensuring secure and reliable operations of the power grid is a primary concern of system operators.
Learning a Generator Model from Terminal Bus Data
In this work we investigate approaches to reconstruct generator models from measurements available at the generator terminal bus using machine learning (ML) techniques.
Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging.
Physics Informed Topology Learning in Networks of Linear Dynamical Systems
Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines.
Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids
Distribution grid is the medium and low voltage part of a large power system.
Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations.
Learning the Exact Topology of Undirected Consensus Networks
In this article, we present a method to learn the interaction topology of a network of agents undergoing linear consensus updates in a non invasive manner.
Topology Estimation in Bulk Power Grids: Guarantees on Exact Recovery
For grids that include cycles of length three, we provide sufficient conditions that ensure existence of algorithms for exact reconstruction.
Exact Topology Reconstruction of Radial Dynamical Systems with Applications to Distribution System of the Power Grid
In this article we present a method to reconstruct the interconnectedness of dynamically related stochastic processes, where the interactions are bi-directional and the underlying topology is a tree.
