Learning Optimal Power Flow Value Functions with Input-Convex Neural
  Networks
Andrew Rosemberg Mathieu Tanneau Bruno Fanzeres Joaquim Garcia Pascal Van Hentenryck
The Optimal Power Flow (OPF) problem is integral to the functioning of power
systems, aiming to optimize generation dispatch while adhering to technical and
operational constraints. These constraints are far from straightforward; they
involve intricate, non-convex considerations related to Alternating Current
(AC) power flow, which are essential for the safety and practicality of
electrical grids. However, solving the OPF problem for varying conditions
within stringent time frames poses practical challenges. To address this,
operators resort to model simplifications of varying accuracy. Unfortunately,
better approximations (tight convex relaxations) are often computationally
intractable. This research explores machine learning (ML) to learn convex
approximate solutions for faster analysis in the online setting while still
allowing for coupling into other convex dependent decision problems. By trading
off a small amount of accuracy for substantial gains in speed, they enable the
efficient exploration of vast solution spaces in these complex problems.