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Found 26 papers, 4 papers with code
Variational Quantum Eigensolver with Constraints (VQEC): Solving Constrained Optimization Problems via VQE
Variational quantum approaches have shown great promise in finding near-optimal solutions to computationally challenging tasks.
Data-driven Forced Oscillation Localization using Inferred Impulse Responses
Poorly damped oscillations pose threats to the stability and reliability of interconnected power systems.
A Chance-Constrained Optimal Design of Volt/VAR Control Rules for Distributed Energy Resources
Deciding setpoints for distributed energy resources (DERs) via local control rules rather than centralized optimization offers significant autonomy.
Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks
According to non-incremental control rules, such as the one mandated by the IEEE Standard 1547, the reactive power setpoint of each DER is computed as a piecewise-linear curve of the local voltage.
Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning
This task of optimal rule design (ORD) is challenging as Volt/VAR rules introduce nonlinear dynamics, and lurk trade-offs between stability and steady-state voltage profiles.
Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources
The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control rules among other options.
Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study
Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids.
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.
Learning Neural Networks under Input-Output Specifications
In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors.
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes
Adjusting inverter injection setpoints for distributed energy resources can be an effective grid control mechanism.
Data-Driven Modeling of Aggregate Flexibility under Uncertain and Non-Convex Load Models
The feasible set of the aggregator is then approximated by an ellipsoid upon training a convex quadratic classifier using the labeled dataset.
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.
Inferring power system dynamics from synchrophasor data using Gaussian processes
Synchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injection at all buses.
Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach
The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions.
A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data
Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids.
Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks
To shift the computational burden from real-time to offline in delay-critical power systems applications, recent works entertain the idea of using a deep neural network (DNN) to predict the solutions of the AC optimal power flow (AC-OPF) once presented load demands.
Ripple-Type Control for Enhancing Resilience of Networked Physical Systems
Distributed control agents have been advocated as an effective means for improving the resiliency of our physical infrastructures under unexpected events.
Kernel-Based Learning for Smart Inverter Control
Leveraging a linearized grid model and given anticipated data scenarios, inverter rules are jointly designed at the feeder level to minimize a convex combination of voltage deviations and ohmic losses via a linearly-constrained quadratic program.
Smart Inverter Grid Probing for Learning Loads: Part I - Identifiability Analysis
Distribution grids currently lack comprehensive real-time metering.
Smart Inverter Grid Probing for Learning Loads: Part II - Probing Injection Design
Once a probing setup is deemed topologically observable by the tests of Part I, Part II provides a methodology for designing probing injections abiding by inverter and network constraints to improve load estimates.
Enhancing Observability in Distribution Grids using Smart Meter Data
On the other hand, smart meter data, including local voltage magnitudes and power injections, are communicated to the utility operator from grid buses with renewable generation and demand-response programs.
Online Censoring for Large-Scale Regressions with Application to Streaming Big Data
Linear regression is arguably the most prominent among statistical inference methods, popular both for its simplicity as well as its broad applicability.
Online Energy Price Matrix Factorization for Power Grid Topology Tracking
Grid security and open markets are two major smart grid goals.
Grid Topology Identification using Electricity Prices
The potential of recovering the topology of a grid using solely publicly available market data is explored here.
Electricity Market Forecasting via Low-Rank Multi-Kernel Learning
The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure.
Distributed Robust Power System State Estimation
Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system state estimation (PSSE).
