A Robust Method for Optimizing Multi-Timescale Scheduling of Flexible Loads in Distribution Station Area

This paper proposes a two-stage robust optimization method that incorporates multiple time scales to mitigate power fluctuations in low-voltage distribution station areas, arising from the uncertainties of distributed photovoltaic generation and the stochastic charging behavior of electric vehicles. First, a polyhedral uncertainty set is employed to characterize the uncertainties in both generation and loads, while comprehensively considering operational costs, transformer loading rates, and other relevant performance indicators within the distribution station area. Second, by treating flexible loads—such as air conditioning systems, energy storage units, and electric vehicles—as controllable resources, a multi-timescale optimization model is developed to capture the diverse response characteristics of these loads. Finally, the original problem is decomposed into a master problem and a set of subproblem, which are solved iteratively using a column-and-constraint generation algorithm. The results demonstrate that the proposed method reduces operating costs by 6.7%, decreases the loads peak-to-valley difference of the distribution station area by 75%.