Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation

• URL: http://arxiv.org/abs/2505.08535v2
• Date: Thu, 15 May 2025 03:16:05 GMT
• Title: Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation
• Authors: Linna Xu, Yongli Zhu,
• Abstract summary: This paper presents a modified model predictive control (MPC) framework for real-time power system operation.<n>The framework incorporates a diffusion model tailored for time series generation to enhance the accuracy of the load forecasting module.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a modified model predictive control (MPC) framework for real-time power system operation. The framework incorporates a diffusion model tailored for time series generation to enhance the accuracy of the load forecasting module used in the system operation. In the absence of explicit state transition law, a model-identification procedure is leveraged to derive the system dynamics, thereby eliminating a barrier when applying MPC to a renewables-dominated power system. Case study results on an industry park system and the IEEE 30-bus system demonstrate that using the diffusion model to augment the training dataset significantly improves load-forecasting accuracy, and the inferred system dynamics are applicable to the real-time grid operation with solar and wind.

Related papers

• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• LAPSO: A Unified Optimization View for Learning-Augmented Power System Operations [3.754570687412345]
  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.
  arXiv  Detail & Related papers  (2025-05-08T13:00:24Z)
• Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs [0.0]
  This paper proposes a physics-informed convolutional autoencoder (PIConvAE) model to detect cyber anomalies in power distribution systems with unbalanced configurations and high penetration of DERs. The performance of the proposed model is evaluated on two unbalanced power distribution grids, IEEE 123-bus system and a real-world feeder in Riverside, CA.
  arXiv  Detail & Related papers  (2024-06-05T04:28:57Z)
• Deep Generative Methods for Producing Forecast Trajectories in Power Systems [0.0]
  Transport System Operators (TSOs) must conduct analyses to simulate the future functioning of power systems. These simulations are used as inputs in decision-making processes.
  arXiv  Detail & Related papers  (2023-09-26T14:43:01Z)
• End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control [45.84205238554709]
  We present a method for reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We show that the end-to-end trained models outperform those trained using system identification in (e)NMPC.
  arXiv  Detail & Related papers  (2023-08-03T10:21:53Z)
• Sequence-to-Sequence Forecasting-aided State Estimation for Power Systems [0.0]
  This paper proposes an end-to-end deep learning framework to accurately predict multi-step power system state estimations in real-time. Bidirectional gated recurrent units (BiGRUs) are incorporated into the model to achieve high prediction accuracy.
  arXiv  Detail & Related papers  (2023-05-22T16:46:37Z)
• DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks [1.3342521220589318]
  A drift magnitude threshold should be defined to design change detection methods to identify drifts. We propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models.
  arXiv  Detail & Related papers  (2023-05-15T16:26:03Z)
• A Dynamic Feedforward Control Strategy for Energy-efficient Building System Operation [59.56144813928478]
  In current control strategies and optimization algorithms, most of them rely on receiving information from real-time feedback. We propose an engineer-friendly control strategy framework that embeds dynamic prior knowledge from building system characteristics simultaneously for system control. We tested it in a case for heating system control with typical control strategies, which shows our framework owns a further energy-saving potential of 15%.
  arXiv  Detail & Related papers  (2023-01-23T09:07:07Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• System Resilience through Health Monitoring and Reconfiguration [56.448036299746285]
  We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration.
  arXiv  Detail & Related papers  (2022-08-30T20:16:17Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

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.