# Learning-Augmented Power System Operations: A Unified Optimization View

arXiv: https://arxiv.org/abs/2505.05203 · HTML: https://mlsanity.com/paper/2505.05203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 57 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 26 of 26)

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- [Backpropagation of Unrolled Solvers with Folded Optimization](https://mlsanity.com/paper/2301.12047/) (2023)
- [Cost-Oriented Load Forecasting](https://mlsanity.com/paper/2107.01861/) (2021)
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- [End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting](https://mlsanity.com/paper/2304.07151/) (2023)
- [Perturbed Decision-Focused Learning for Modeling Strategic Energy Storage](https://mlsanity.com/paper/2406.17085/) (2024)
- [Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting](https://mlsanity.com/paper/2301.01832/) (2023)
- [Load Data Valuation in Multi-Energy Systems: An End-to-End Approach](https://mlsanity.com/paper/2311.09839/) (2023)
- [Predict-and-Optimize Robust Unit Commitment with Statistical Guarantees via Weight Combination](https://mlsanity.com/paper/2411.03138/) (2024)
- [Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions](https://mlsanity.com/paper/2208.13065/) (2022)
- [Control-mode as a Grid Service in Software-defined Power Grids: GFL vs GFM](https://mlsanity.com/paper/2307.15623/) (2023)
- [Managing the Uncertainty in System Dynamics Through Distributionally Robust Stability-Constrained Optimization](https://mlsanity.com/paper/2309.03798/) (2023)
- [E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning](https://mlsanity.com/paper/2312.10587/) (2023)
- [Deep Learning based Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Systems](https://mlsanity.com/paper/2208.08028/) (2022)
- [Stability Constrained Optimization in High IBR-Penetrated Power Systems-Part I: Constraint Development and Unification](https://mlsanity.com/paper/2307.12151/) (2023)
- [Coordinated Planning for Stability Enhancement in High IBR-Penetrated Systems](https://mlsanity.com/paper/2404.14012/) (2024)
- [On the Incorporation of Stability Constraints into Sequential Operational Scheduling](https://mlsanity.com/paper/2411.11652/) (2024)

## Abstract (arXiv)

With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic efficiency, security, and robustness. Machine learning (ML) has emerged as a powerful tool for modeling complex system dynamics and uncertainty. However, standalone ML pipelines, including model selection, training, and validation, are often designed separately from the downstream optimization problems they influence, which can lead to suboptimal system-level decisions. To address this gap, this paper proposes Learning-Augmented Power System Operations (LAPSO), a unified optimization-centered framework that treats ML as an explicit component of power-system operational decision-making. First, LAPSO provides generalized mathematical template covering both decision-independent predictors that parameterize downstream optimization and decision-dependent learned surrogates that enter optimization as auxiliary constraints. Second, it designs ML pipelines using optimization-aware criteria, including solution-quality, computational tractability, constraint satisfaction, and economic performance. We instantiate LAPSO on both stability-constrained optimization (SCO) and objective-based forecasting (OBF), and show how the framework provides actionable guidance for selecting learned components. We further extend the framework to a hybrid forecast--operation--control chain and use it to organize heterogeneous uncertainty sources. Finally, we release an open-source Python package, textttlapso, for modularly augmenting existing power-system optimization models with ML components. Code and datasets are available at: https://github.com/xuwkk/lapsoexp.