Multi-uncertainties impose enormous challenges to the optimal scheduling of power
systems, and two-stage robust optimization (TSRO) theory has been widely investigated and …

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 …

This paper studies how to train machine-learning models that directly approximate the
optimal solutions of constrained optimization problems. This is an empirical risk minimization …

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

This article presents a use-inspired perspective of the opportunities and challenges in a
massively digitized power grid. It argues that the intricate interplay of data availability …

Abstract The Security-Constrained Economic Dispatch (SCED) is a fundamental
optimization model for Transmission System Operators (TSO) to clear real-time energy …

This paper proposes a new model of scenario-based security-constrained unit commitment
(SCUC) with BESSs. By formulating such a model as a mixed-integer programming (MIP) …

To comply with electric power grid automation strategies, new cyber-security protocols and
protection are required. What we now experience is a new type of protection against new …

This paper proposes a hybrid data-driven method for fast solutions of preventive security-
constrained optimal power flow (SCOPF) of power systems. The proposed method …

This paper proposes a novel machine-learning approach for predicting AC-OPF solutions
that features a fast and scalable training. It is motivated by the significant training time …