Drawing independent samples from high-dimensional probability distributions represents the major computational bottleneck for modern algorithms, including powerful machine learning frameworks such as deep learning. The quest for discovering larger …
The recent emergence of novel computational devices, such as quantum computers, neuromorphic co-processors and digital annealers presents new opportunities for hardware accelerated hybrid optimization algorithms. Unfortunately, demonstrations of …
In recent years, the power systems research community has seen an explosion of novel methods for formulating the AC power flow equations. Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the …
The recent emergence of novel computational devices, such as adiabatic quantum computers, CMOS annealers, and optical parametric oscillators, present new opportunities for hybrid-optimization algorithms that are hardware accelerated by these devices. …
This work considers the task of finding an AC-Feasible operating point of a severely damaged transmission network while ensuring a minimal amount of active power loads are removed. This AC Minimal Load-Shedding (AC-MLS) task is a non-convex nonlinear …
In recent years, the power system research community has seen an explosion of novel methods for formulating and solving power network optimization problems. These emerging methods range from new power flow approximations, which go beyond the …
This work revisits the semidefine programming (SDP) relaxation of the ac power flow equations in light of recent results illustrating the benefits of bounds propagation, valid inequalities, and the convex quadratic relaxation. By integrating all of …
Convex relaxations of the power flow equations and, in particular, the semi-definite programming (SDP) and second-order cone (SOC) relaxations, have attracted significant interest in recent years. The quadratic convex (QC) relaxation is a departure …
Convexification is a fundamental technique in (mixed-integer) nonlinear optimization and many convex relaxations are parametrized by variable bounds, ie, the tighter the bounds, the stronger the relaxations. This paper studies how bound tightening …
Linear active-power-only power flow approximations are pervasive in the planning and control of power systems. However, AC power systems are governed by a system of nonlinear nonconvex power flow equations. Existing linear approximations fail to …