Artificial intelligence-based methods for renewable power system operation
Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-
scale use of RE requires accurate energy generation forecasts; optimized power dispatch …
scale use of RE requires accurate energy generation forecasts; optimized power dispatch …
End-to-end learning of user equilibrium with implicit neural networks
This paper intends to transform the transportation network equilibrium modeling paradigm
via an “end-to-end” framework that directly learns travel choice preferences and the …
via an “end-to-end” framework that directly learns travel choice preferences and the …
E2E-AT: A Unified Framework for Tackling Uncertainty in Task-Aware End-to-End Learning
Successful machine learning involves a complete pipeline of data, model, and downstream
applications. Instead of treating them separately, there has been a prominent increase of …
applications. Instead of treating them separately, there has been a prominent increase of …
Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow
In many areas of constrained optimization, representing all possible constraints that give rise
to an accurate feasible region can be difficult and computationally prohibitive for online use …
to an accurate feasible region can be difficult and computationally prohibitive for online use …
[HTML][HTML] Constraint-driven deep learning for nk security constrained optimal power flow
The transition to green energy is reshaping the energy landscape, marked by increased
integration of renewables, distributed resources, and the electrification of other energy …
integration of renewables, distributed resources, and the electrification of other energy …
A Bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling
Before autonomous systems can be deployed in safety-critical applications, we must be able
to understand and verify the safety of these systems. For cases where the risk or cost of real …
to understand and verify the safety of these systems. For cases where the risk or cost of real …
An Auto-Tuned Robust Dispatch Strategy for Virtual Power Plants to Provide Multi-Stage Real-Time Balancing Service
To fully exploit the flexible potential of distributed energy resources (DERs) in providing
balancing service to the power system, Virtual Power Plants (VPPs) act as control centers to …
balancing service to the power system, Virtual Power Plants (VPPs) act as control centers to …
Deep-learning-aided voltage-stability-enhancing stochastic distribution network reconfiguration
W Huang, C Zhao - IEEE Transactions on Power Systems, 2023 - ieeexplore.ieee.org
Power distribution networks are approaching their voltage stability boundaries due to the
severe voltage violations and the inadequate reactive power reserves caused by the …
severe voltage violations and the inadequate reactive power reserves caused by the …
Fast and Reliable 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 …
outages or contingencies to prevent cascading failures and ensure reliable operation …
RADIUM: Predicting and Repairing End-to-End Robot Failures using Gradient-Accelerated Sampling
Before autonomous systems can be deployed in safety-critical applications, we must be able
to understand and verify the safety of these systems. For cases where the risk or cost of real …
to understand and verify the safety of these systems. For cases where the risk or cost of real …