Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation
This work addresses real-time power system operation challenges for grid operators in renewable energy contexts, though it is incremental as it modifies existing MPC frameworks.
The paper tackles the challenge of applying model predictive control to renewables-dominated power systems by incorporating a diffusion model for load forecasting and using model identification to infer system dynamics. Results show significant improvements in load-forecasting accuracy on industry park and IEEE 30-bus systems, making the framework applicable to real-time grid operation with solar and wind.
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.