# Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling

arXiv: https://arxiv.org/abs/2102.09583 · HTML: https://mlsanity.com/paper/2102.09583/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Strong mixed-integer programming formulations for trained neural networks](https://mlsanity.com/paper/1811.08359/) (2018)
- [Approximating Trajectory Constraints with Machine Learning -- Microgrid Islanding with Frequency Constraints](https://mlsanity.com/paper/2001.05775/) (2020)
- [Simultaneous Scheduling of Multiple Frequency Services in Stochastic Unit Commitment](https://mlsanity.com/paper/1809.10391/) (2018)
- [Optimization-based Islanding of Power Networks using Piecewise Linear AC Power Flow](https://mlsanity.com/paper/1310.6675/) (2013)
- [Towards Resilient Operation of Multi-Microgrids: An MISOCP-Based Frequency-Constrained Approach](https://mlsanity.com/paper/1809.00054/) (2018)
- [Towards Optimal System Scheduling with Synthetic Inertia Provision from Wind Turbines](https://mlsanity.com/paper/1911.02857/) (2019)

## Built on by (most cited first, 3 of 3)

- [Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment](https://mlsanity.com/paper/2110.11269/) (2021)
- [Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems](https://mlsanity.com/paper/2203.07505/) (2022)
- [Exploration of Artificial Intelligence-oriented Power System Dynamic Simulators](https://mlsanity.com/paper/2110.00931/) (2021)

## Abstract (arXiv)

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interests active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which significantly enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is verified on the the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.