A two-layer optimal scheduling method for microgrids based on adaptive stochastic model predictive control

Abstract Renewable energy is highly susceptible to weather and environmental factors, and changes dramatically in a short period, which makes it difficult for traditional fixed-period scheduling of microgrids to capture the time-series variations of source and load, exacerbating the power imbalances of systems. To address this problem, a novel two-layer rolling optimization framework for microgrids based on adaptive stochastic model predictive control is proposed in this paper. Firstly, a two-layer microgrid optimization model based on stochastic model predictive control is established, in which measurement technology plays an indispensable role in the quality of uncertain scenarios and optimal decision results of the microgrid, as well as providing impetus for the development of the microgrid. In the upper-layer prescheduling stage, the optimal control sequence under multiple uncertainties is obtained by solving a scenario-based chance constraint programming model. In the lower-layer power compensation stage, the measured scenario errors are regarded as fluctuations, and the energy storage devices are preferentially used for smoothing processing. Secondly, an adaptive period division method using Wasserstein distance-based hierarchical clustering is developed to guide intraday online scheduling. The concepts of distribution similarity and distribution loss are presented to adaptively divide the periods under uncertain conditions, so as to overcome the disturbance of uncertainty and improve the flexibility of microgrid scheduling. Finally, the simulation results show that the proposed method can flexibly deal with the uncertainty at different time scales, and achieve 53.63 kWh less compensation power and 19.12% lower operating cost than traditional fixed-period scheduling methods, and thus effectively improve the economy and reliability of microgrid operation.