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Found 7 papers, 1 papers with code
Equitable Networked Microgrid Topology Reconfiguration for Wildfire Risk Mitigation
Increasing amount of wildfires in recent years consistently challenges the safe and reliable operations of power systems.
Solving Decision-Dependent Games by Learning from Feedback
This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the distributional dependence is unknown.
Managing Wildfire Risk and Promoting Equity through Optimal Configuration of Networked Microgrids
As climate change increases the risk of large-scale wildfires, wildfire ignitions from electric power lines are a growing concern.
Enabling Grid-Aware Market Participation of Aggregate Flexible Resources
This paper proposes efficient optimization formulations and solution approaches for the characterization of hourly as well as multi-time-step generation cost curves for a distribution system with high penetration of DERs.
Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning
In recent years, machine learning techniques utilizing large-scale datasets have achieved remarkable performance.
Learning-Accelerated ADMM for Distributed Optimal Power Flow
We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions.
Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow
In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps on extremely fast timescales (e. g., milliseconds), bypassing solving an AC OPF altogether.
