用扩散模型提升电力系统实时调度的负荷预测精度。
Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation
- 用定制扩散模型生成时间序列数据，增强负荷预测训练集。
- 在无显式状态转移方程下，推导出适用于风光为主的系统动态。
- 在工业园区和IEEE 30节点系统上验证了实时运行的有效性。
本文提出一种改进的模型预测控制（MPC）框架，用于电力系统实时运行。该框架引入针对时间序列生成设计的扩散模型，以提升运行中负荷预测模块的准确性。在缺乏明确状态转移规律的情况下，通过模型识别程序推导系统动态，克服了将MPC应用于高比例可再生能源电力系统的障碍。基于工业区系统和IEEE 30节点系统的案例研究显示，使用扩散模型扩充训练数据显著提升了负荷预测精度，所推导的系统动态适用于包含光伏与风电的实时电网运行。
原文摘要 · Abstract (English)
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.
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