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Found 44 papers, 15 papers with code
Fast and Reliable $N-k$ Contingency Screening with Input-Convex Neural Networks
Power system operators must ensure that dispatch decisions remain feasible in case of grid outages or contingencies to prevent cascading failures and ensure reliable operation.
Safe Control of Grid-Interfacing Inverters with Current Magnitude Limits
In this paper, we present a safety filter approach to limit the current magnitude of inverters controlled as voltage sources.
Oscillations-Aware Frequency Security Assessment via Efficient Worst-Case Frequency Nadir Computation
Frequency security assessment following major disturbances has long been one of the central tasks in power system operations.
Controlling Grid-Connected Inverters under Time-Varying Voltage Constraints
A particular challenge in the operation of grid connected IBRs is the variations in the grid side voltage.
Optimal Control of Grid-Interfacing Inverters With Current Magnitude Limits
We use a Lyapunov stability approach to determine a stability condition for the system, guaranteeing that a class of controllers would be stabilizing if they satisfy a simple SDP condition.
Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations.
Equilibria of Fully Decentralized Learning in Networked Systems
Existing settings of decentralized learning either require players to have full information or the system to have certain special structure that may be hard to check and hinder their applicability to practical systems.
An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees
The challenge of finding good policies to approximate the second-stage decisions is that these solutions need to be feasible, which has been difficult to achieve with existing policies.
PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
To accelerate these simulations, we propose a simulator - PINNSim - that allows to take significantly larger time steps.
Efficient Reinforcement Learning Through Trajectory Generation
Off-policy and Offline RL methods have been proposed to reduce the number of interactions with the physical environment by learning control policies from historical data.
Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning
Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms.
Optimal Inverter-Based Resources Placement in Low-Inertia Power Systems
An increase in the integration of Inverter Based Resources (IBRs) to the electric grid, will lead to a corresponding decrease in the amount of connected synchronous generators, resulting in a decline in the available rotational inertia system-wide.
Cyber-Physical Attack Leveraging Subsynchronous Resonance
This paper discusses how a cyber attack could take advantage of torsional resonances in the shaft of turbo-generators to inflict severe physical damage to a power system.
Structured Neural-PI Control for Networked Systems: Stability and Steady-State Optimality Guarantees
We explicitly characterize the stability conditions and engineer neural networks that satisfy them by design.
Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach
Specifically, we use RL to learn a neural network-based control policy mapping from the integral variables of DAI to the controllable power injections which provides optimal transient frequency control, while DAI inherently ensures the frequency restoration and optimal economic dispatch.
Safe and Efficient Model Predictive Control Using Neural Networks: An Interior Point Approach
Model predictive control (MPC) provides a useful means for controlling systems with constraints, but suffers from the computational burden of repeatedly solving an optimization problem in real time.
Equilibrium-Independent Stability Analysis for Distribution Systems with Lossy Transmission Lines
Because of the intermittent nature of these resources, the stability of distribution systems under large disturbances and time-varying conditions is becoming a key issue in practical operations.
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.
State-of-Charge Aware EV Charging
Recent proliferation in electric vehicles (EVs) are posing profound impacts over the operation of electrical grids.
A Frequency Domain Approach to Predict Power System Transients
System topology and fault information are encoded by taking a multi-dimensional Fourier transform, allowing us to leverage the fact that the trajectories are sparse both in time and spatial frequencies.
Computationally Efficient Safe Reinforcement Learning for Power Systems
We propose a computationally efficient approach to safe reinforcement learning (RL) for frequency regulation in power systems with high levels of variable renewable energy resources.
Learning to Solve the AC Optimal Power Flow via a Lagrangian Approach
Using standard and modified IEEE 22-bus, 39-bus, and 118-bus networks, we show that our approach is able to obtain the globally optimal cost even when the training data is mostly comprised of suboptimal solutions.
Decentralized Safe Reinforcement Learning for Voltage Control
We explicitly engineer the structure of neural network controllers such that they satisfy the Lipschitz constraints by design.
An Iterative Approach to Improving Solution Quality for AC Optimal Power Flow Problems
In this paper, we propose a simple iterative approach to improve the quality of solutions to ACOPF problems.
Lyapunov-Regularized Reinforcement Learning for Power System Transient Stability
The learned neural Lyapunov function is then utilized as a regularization to train the neural network controller by penalizing actions that violate the Lyapunov conditions.
Multi-Agent Reinforcement Learning in Cournot Games
This is the first result (to the best of our knowledge) on the convergence property of learning algorithms with continuous action spaces that do not fall in the no-regret class.
Reinforcement Learning for Optimal Primary Frequency Control: A Lyapunov Approach
As more inverter-connected renewable resources are integrated into the grid, frequency stability may degrade because of the reduction in mechanical inertia and damping.
Using Mobility for Electrical Load Forecasting During the COVID-19 Pandemic
In this work, we focus on the problem of load forecasting.
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks
This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints.
Transfer Learning for HVAC System Fault Detection
Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity.
Fast Calculation of Probabilistic Power Flow: A Model-based Deep Learning Approach
A deep neural network (DNN) is used to approximate the power flow calculation and is trained according to the physical power flow equations to improve its learning ability.
A tractable ellipsoidal approximation for voltage regulation problems
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation.
Real-Time Prediction of the Duration of Distribution System Outages
This paper addresses the problem of predicting duration of unplanned power outages, using historical outage records to train a series of neural network predictors.
Bayesian Renewables Scenario Generation via Deep Generative Networks
We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network~(Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected deep neural networks.
Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning
In this paper, we derive a temporal arbitrage policy for storage via reinforcement learning.
An Unsupervised Deep Learning Approach for Scenario Forecasts
Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations.
Optimization and Control
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors.
Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints
We also show that this optimization problem is convex for a wide variety of probabilistic distributions.
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems
Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars.
Deceiving Google's Perspective API Built for Detecting Toxic Comments
In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples.
An Optimal Treatment Assignment Strategy to Evaluate Demand Response Effect
The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program.
Learning Temporal Dependence from Time-Series Data with Latent Variables
We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes.
Online Active Linear Regression via Thresholding
We consider the problem of online active learning to collect data for regression modeling.
A Sparse Linear Model and Significance Test for Individual Consumption Prediction
Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well.
