A Multi-Layer Data-Driven Security Constrained Unit Commitment Approach with Feasibility Compliance

Ali Feliachi, Talha Iqbal, Muhammad Choudhry and Hasan Ul Banna
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Ali Feliachi: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26505, USA
Talha Iqbal: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26505, USA
Muhammad Choudhry: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26505, USA
Hasan Ul Banna: Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26505, USA

Energies, 2022, vol. 15, issue 20, 1-19

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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