Increasing renewable penetration in distribution grids calls for improved monitoring and
control, where power flow (PF) model is the basis for many advanced functionalities …

An essential component of smart grid applications is the ability to solve the power flow (PF)
problem in real-time. As numerical methods are too slow, the use of neural networks (NNs) …

Due to the high nonlinearity of AC optimal power flow (OPF), numerous efforts have been
made in recent decades to find efficient methods. Machine learning (ML) has proven to …

We propose a neural network architecture that emulates the behavior of a physics solver that
solves electricity differential equations to compute electricity flow in power grids (so-called" …

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 …

To better monitor and control distribution grids, the exact knowledge of system topology and
parameters is a fundamental requirement. However, topology information is usually …

The optimal power flow is an optimization problem used in power systems operational
planning to maximize economic efficiency while satisfying demand and maintaining safety …

With the increasing penetration of renewable energy, power grid operators are observing
both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and …

There has been growing interest in employing neural network (NN) to directly solve
constrained optimization problems with low run-time complexity. However, it is non-trivial to …

Modern power systems are experiencing larger fluctuations and more uncertainties caused
by increased penetration of renewable energy sources (RESs) and power electronics …