Machine learning assisted optimal power flow (OPF) aims to reduce the computational
complexity of these non-linear and non-convex constrained optimization problems by …

This paper introduces, for the first time to our knowledge, physics-informed neural networks
to accurately estimate the AC-Optimal Power Flow (AC-OPF) result and delivers rigorous …

The data-driven method with strong approximation capabilities and high computational
efficiency provides a promising tool for optimal power flow (OPF) calculation with stochastic …

Optimal power flow (OPF) is one of the most important optimization problems in the energy
industry. In its simplest form, OPF attempts to find the optimal power that the generators …

We develop a Deep Neural Network (DNN) approach, namely DeepOPF, for solving optimal
power flow (OPF) problems that are critical for daily power system operation. DeepOPF …

Due to the nonlinear and non-convex attributes of the optimization problems in power
systems such as Optimal Power Flow (OPF), traditional iterative optimization algorithms …

Deep Neural Networks approaches for the Optimal Power Flow (OPF) problem received
considerable attention recently. A key challenge of these approaches lies in ensuring the …

To cope with increasing uncertainty from renewable generation and flexible load, grid
operators need to solve alternative current optimal power flow (AC-OPF) problems more …

The AC Optimal Power Flow (AC-OPF) is a key building block in many power system
applications. It determines generator setpoints at minimal cost that meet the power demands …

Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system
efficiency and reliability in real-time electricity grid operations. We develop a new topology …