LAPSO: A Unified Optimization View for Learning-Augmented Power System Operations

• URL: http://arxiv.org/abs/2505.05203v1
• Date: Thu, 08 May 2025 13:00:24 GMT
• Title: LAPSO: A Unified Optimization View for Learning-Augmented Power System Operations
• Authors: Wangkun Xu, Zhongda Chu, Fei Teng,
• Abstract summary: This paper proposes a holistic framework of Learning-Augmented Power System Operations (LAPSO)<n>LAPSO is centered on the operation stage and aims to break the boundary between temporally siloed power system tasks.<n>A dedicated Python package-lapso is introduced to automatically augment existing power system optimization models with learnable components.
• Score: 3.754570687412345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the high penetration of renewables, traditional model-based power system operation is challenged to deliver economic, stable, and robust decisions. Machine learning has emerged as a powerful modeling tool for capturing complex dynamics to address these challenges. However, its separate design often lacks systematic integration with existing methods. To fill the gap, this paper proposes a holistic framework of Learning-Augmented Power System Operations (LAPSO, pronounced as Lap-So). Adopting a native optimization perspective, LAPSO is centered on the operation stage and aims to break the boundary between temporally siloed power system tasks, such as forecast, operation and control, while unifying the objectives of machine learning and model-based optimizations at both training and inference stages. Systematic analysis and simulations demonstrate the effectiveness of applying LAPSO in designing new integrated algorithms, such as stability-constrained optimization (SCO) and objective-based forecasting (OBF), while enabling end-to-end tracing of different sources of uncertainties. In addition, a dedicated Python package-lapso is introduced to automatically augment existing power system optimization models with learnable components. All code and data are available at https://github.com/xuwkk/lapso_exp.

Related papers

• Model-free Optical Processors using In Situ Reinforcement Learning with Proximal Policy Optimization [18.41925837760181]
  We introduce a model-free reinforcement learning approach utilizing Proximal Policy Optimization for the in situ training of diffractive optical processors.<n>We experimentally validated our method across a range of in situ learning tasks, including targeted energy focusing through a random diffuser, holographic image generation, aberration correction, and optical image classification.
  arXiv  Detail & Related papers  (2025-07-08T01:39:36Z)
• DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
  This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks.<n>We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge.<n>Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
  arXiv  Detail & Related papers  (2025-02-18T02:37:26Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow [20.078717680640214]
  Security-Constrained Optimal Power Flow (SCOPF) plays a crucial role in power grid stability but becomes increasingly complex as systems grow. This paper introduces PDL-SCOPF, a self-supervised end-to-end primal-dual learning framework for producing near-optimal solutions to large-scale SCOPF problems.
  arXiv  Detail & Related papers  (2023-11-29T20:36:35Z)
• Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach [10.47302625959368]
  We present a groundbreaking paradigm integrating Mobile Edge Computing with foundation models, specifically designed to enhance local task performance on user equipment (UE) Central to our approach is the innovative Emulator-Adapter architecture, segmenting the foundation model into two cohesive modules. We introduce an advanced resource allocation mechanism that is fine-tuned to the needs of the Emulator-Adapter structure in decentralized settings.
  arXiv  Detail & Related papers  (2023-10-26T15:47:51Z)
• OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud [10.97507717758812]
  We propose OptScaler, a collaborative autoscaling framework that integrates proactive and reactive modules through an optimization module.<n> Numerical results have demonstrated the superiority of our workload prediction model and the collaborative framework.
  arXiv  Detail & Related papers  (2023-10-26T04:38:48Z)
• Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach [1.370633147306388]
  We propose the use of meta-learning to generate an initial surrogate model based on data collected from performance optimization tasks. The effectiveness of our proposed DKN-BO approach for speeding up control system performance optimization is demonstrated.
  arXiv  Detail & Related papers  (2022-10-31T18:25:47Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration [17.365135977882215]
  This study fills important voids by introducing innovative features to the policy algorithm. We show system level performances including convergence of the approximate value function, (sub)optimality of the solution, and stability of the system.
  arXiv  Detail & Related papers  (2020-06-16T09:09:48Z)
• Information Theoretic Model Predictive Q-Learning [64.74041985237105]
  We present a novel theoretical connection between information theoretic MPC and entropy regularized RL. We develop a Q-learning algorithm that can leverage biased models.
  arXiv  Detail & Related papers  (2019-12-31T00:29:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.