# Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations

arXiv: https://arxiv.org/abs/2208.06742 · HTML: https://mlsanity.com/paper/2208.06742/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 13 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Solving Mixed Integer Programs Using Neural Networks](https://mlsanity.com/paper/2012.13349/) (2020)
- [Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems](https://mlsanity.com/paper/1902.01697/) (2019)
- [Data-Driven Screening of Network Constraints for Unit Commitment](https://mlsanity.com/paper/1907.04694/) (2019)
- [Reduced Optimal Power Flow Using Graph Neural Network](https://mlsanity.com/paper/2206.13591/) (2022)
- [Neural Network-based Power Flow Model](https://mlsanity.com/paper/2112.08418/) (2021)
- [Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements](https://mlsanity.com/paper/2112.00926/) (2021)
- [Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model](https://mlsanity.com/paper/2112.13681/) (2021)
- [An Accelerated-Decomposition Approach for Security-Constrained Unit Commitment with Corrective Network Reconfiguration](https://mlsanity.com/paper/1912.01764/) (2019)
- [Security Constrained Unit Commitment with Corrective Transmission Switching](https://mlsanity.com/paper/2001.00597/) (2020)
- [Security-Constrained Unit Commitment Con-sidering Locational Frequency Stability in Low-Inertia Power Grids](https://mlsanity.com/paper/2110.11498/) (2021)
- [Unit Commitment using Nearest Neighbor as a Short-Term Proxy](https://mlsanity.com/paper/1611.10215/) (2016)
- [Reducing Congestion-Induced Renewable Curtailment with Corrective Network Reconfiguration in Day-Ahead Scheduling](https://mlsanity.com/paper/2002.01090/) (2020)
- [Network Reconfiguration Impact on Renewable Energy System and Energy Storage System in Day-Ahead Scheduling](https://mlsanity.com/paper/2103.13321/) (2021)
- [Cost-driven Screening of Network Constraints for the Unit Commitment Problem](https://mlsanity.com/paper/2104.05746/) (2021)
- [The Benefits of Hydrogen Energy Transmission and Conversion Systems to the Renewable Power Grids: Day-ahead Unit Commitment](https://mlsanity.com/paper/2206.14279/) (2022)

## Built on by (most cited first, 6 of 6)

- [Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments](https://mlsanity.com/paper/2211.15755/) (2022)
- [Compact Optimization Learning for AC Optimal Power Flow](https://mlsanity.com/paper/2301.08840/) (2023)
- [Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment](https://mlsanity.com/paper/2306.01570/) (2023)
- [Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting](https://mlsanity.com/paper/2310.08793/) (2023)
- [Graph neural networks for power grid operational risk assessment under evolving grid topology](https://mlsanity.com/paper/2405.07343/) (2024)
- [A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment](https://mlsanity.com/paper/2604.21891/) (2026)

## Abstract (arXiv)

Day-ahead operations involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. The optimization process is a mixed-integer linear program (MILP) also known as security-constrained unit commitment (SCUC). Independent system operators (ISOs) run SCUC daily and require state-of-the-art algorithms to speed up the process. Existing patterns in historical information can be leveraged for model reduction of SCUC, which can provide significant time savings. In this paper, machine learning (ML) based classification approaches, namely logistic regression, neural networks, random forest and K-nearest neighbor, were studied for model reduction of SCUC. The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high-quality solutions. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, 500-Bus system, and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC (SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable generation. Simulation results demonstrate a high training accuracy to identify commitment schedule while FL and post-process ensure ML predictions do not lead to infeasible solutions with minimal loss in solution quality.