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

TL;DR

This paper introduces a diffusion-assisted MPC framework that improves real-time power system operation by enhancing load forecasting accuracy and system dynamics modeling, especially in renewable-dominated grids.

Contribution

It develops a novel diffusion model integrated into MPC for better load prediction and system identification without explicit state transition laws.

Findings

Enhanced load forecasting accuracy with diffusion model augmentation.

Effective system dynamics inference for renewables-rich power systems.

Improved real-time operation performance demonstrated on case studies.

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.