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

arXiv: https://arxiv.org/abs/2505.08535 · HTML: https://mlsanity.com/paper/2505.08535/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 4 of 4)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion-TS: Interpretable Diffusion for General Time Series Generation](https://mlsanity.com/paper/2403.01742/) (2024)
- [Generative Modeling and Data Augmentation for Power System Production Simulation](https://mlsanity.com/paper/2412.12146/) (2024)

## Built on by (most cited first, 1 of 1)

- [Generative Model Predictive Control in Manufacturing Processes: A Review](https://mlsanity.com/paper/2511.17865/) (2025)

## Abstract (arXiv)

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.