A Day-Ahead Optimization of a Distribution Network Based on the Aggregation of Distributed PV and ES Units

The increasing penetration of distributed photovoltaic (PV) and energy storage (ES) systems in power grids, while advancing the transition to clean energy and enhancing grid flexibility, poses resource dispersion, uncertainty, and scheduling challenges. Consequently, it becomes crucial to manage and optimize these resources. In this paper, we innovatively propose a distribution network day-ahead optimal scheduling model that takes into account distributed resource aggregation and uncertainty. Firstly, distributed PV aggregation (PVA) is performed using the Minkowski summation method, and distributed ES aggregation (ESA) is performed using the polytope inner approximation method. Then, in order to deal with the uncertainty, the supply–demand balance of flexibility is modeled using kernel density estimation (KDE). Finally, the aggregation model and the flexibility supply–demand balance model are incorporated into the distribution network day-ahead optimization. The simulation study of the IEEE 69-node distribution system shows that the aggregate feasible region (AFR) is improved by about 90% and the active flexibility is improved by about 10% compared to box inner approximate aggregation methods, demonstrating their effectiveness in managing operational uncertainties and optimizing the utilization of distributed energy resources in day-ahead scheduling.