4 citations · 9 across the 5 of their papers we have counts for
10 papers · 1 filter
A Learning-boosted Quasi-Newton Method for AC Optimal Power Flow
Kyri Baker
Power grid operators typically solve large-scale, nonconvex optimal power flow (OPF) problems throughout the day to determine optimal setpoints for generators while adhering to phy…
Computationally Efficient Solutions for Large-Scale Security-Constrained Optimal Power Flow
Mohammadhafez Bazrafshan, Kyri Baker, Javad Mohammadi
In this paper, we discuss our approach and algorithmic framework for solving large-scale security constrained optimal power flow (SCOPF) problems. SCOPF is a mixed integer non-conv…
Solutions of DC OPF are Never AC Feasible
Kyri Baker
In this paper, we analyze the relationship between generation dispatch solutions produced by the DC optimal power flow (DC OPF) problem by the AC optimal power flow (AC OPF) proble…
Learning-Accelerated ADMM for Distributed Optimal Power Flow
David Biagioni, Peter Graf, Xiangyu Zhang +3
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) whe…
Learning Warm-Start Points for AC Optimal Power Flow
Kyri Baker
A large amount of data has been generated by grid operators solving AC optimal power flow (ACOPF) throughout the years, and we explore how leveraging this data can be used to help…
Directly Constraining Marginal Prices in Distribution Grids Using Demand-Side Flexibility
Shantanu Chakraborty, Kyri Baker, Milos Cvetkovic +2
Recently, the volatility associated with marginal prices has increased due to large scale integration of renewable generation. Price volatility is undesirable from a consumer persp…