Multi-time scale distributed robust optimal scheduling of microgrid based on model predictive control

Abstract

The multi-uncertainty of source and load poses significant challenges to the optimal scheduling of 'source-load-storage' integrated microgrid. However, a limitation of the traditional optimization model is its one-sidedness and use of a single time scale, which can result in suboptimal scheduling outcomes. Striking a balance between reliability and economy presents a considerable obstacle, as does coordinating the relationship between uncertainty analysis methods and varying time scales. Based on the data-driven multi-discrete scene distribution robust method, a two-stage distributed robust day-ahead optimal scheduling model of microgrid is proposed, which is solved by column and constraint generation algorithm. By combining the improved distributed robust optimization uncertainty method with a multi-time scale scheduling strategy and model predictive control theory, the accuracy of the scheduling can be enhanced. This is achieved through the gradual refinement of the scheduling time scale and reduction of the prediction period length. The day-ahead-day multi-time scale rolling optimization scheduling model is established to minimize the generation cost and adjustment cost, while also exhibiting a high degree of resilience to system uncertainties. Combined with the simulation analysis of the example, the proposed model has demonstrated advantages in incorporating new energy sources, reducing operating costs, and balancing considerations of safety and economy.

Keywords

• microgrid
• model predictive control
• multi-time scale optimal scheduling
• distributionally robust optimization
• multi-scene technology
• data-driven