Citation: Feliachi, A.; Iqbal, T.; Choudhry, M.; Ul Banna, H. A Multi-Layer Data-Driven Security Constrained Unit Commitment Approach with Feasibility Compliance. Energies 2022, 15, 7754. https://doi.org/10.3390/en15207754 Academic Editor: Michael C. Georgiadis Received: 20 September 2022 Accepted: 17 October 2022 Published: 20 October 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article A Multi-Layer Data-Driven Security Constrained Unit Commitment Approach with Feasibility Compliance Ali Feliachi *, Talha Iqbal , Muhammad Choudhry and Hasan Ul Banna Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26505, USA * Correspondence: alfeliachi@mail.wvu.edu Abstract: Security constrained unit commitment is an essential part of the day-ahead energy markets. The presence of discrete and continuous variables makes it a complex, mixed-integer, and time- hungry optimization problem. Grid operators solve unit commitment problems multiple times daily with only minor changes in the operating conditions. Solving a large-scale unit commitment problem requires considerable computational effort and a reasonable time. However, the solution time can be improved by exploiting the fact that the operating conditions do not change significantly in the day-ahead market clearing. Therefore, in this paper, a novel multi-layer data-driven approach is proposed, which significantly improves the solution time (90% time-reduction on average for the three studied systems). The proposed approach not only provides a near-optimal solution (<1% optimality gap) but also ensures that it is feasible for the stable operation of the system (0% infeasible predicted solutions). The efficacy of the developed algorithm is demonstrated through numerical simulations on three test systems, namely a 4-bus system and the IEEE 39-bus and 118-bus systems, and promising results are obtained. Keywords: artificial intelligence; security constrained unit commitment; predictive modeling; mixed-integer optimization; machine learning; data-driven scheduling 1. Introduction In a power system, the daily load profile exhibits significant fluctuations between off- peak and peak demand hours. If generators are scheduled according to the peak demand of a day, then several generators will operate at their lowest power levels during off-peak hours. It will increase the cost of power production, which can be avoided using efficient scheduling. Hence, it is the job of a grid operator to determine cost-efficient generation schedules, known as a Unit Commitment (UC) problem. It is a mathematical optimization problem where the goal is to coordinate the production of a set of generators to meet the anticipated energy requirements, as illustrated in Figure 1. Figure 1. Unit Commitment Problem Illustration (Colored boxes indicate the committed generators in the corresponding-colored time intervals). Energies 2022, 15, 7754. https://doi.org/10.3390/en15207754 https://www.mdpi.com/journal/energies