Optimized peak-valley differential scheduling strategy for distribution networks considering EV and 5G base station energy storage

The large-scale integration of distributed photovoltaic (PV) systems has led to the gradual transformation of the local distribution network's net load curve from a traditional dual-peak shape into a "duck curve", thereby increasing the peak-shaving pressure on the power grid. To address this issue, this paper proposes a hierarchical coordinated scheduling strategy that fully exploits the flexibility of load-side electric vehicles (EVs) and 5G base station energy storage. The strategy establishes a three-tier architecture of "distribution network dispatch center - load aggregator - distributed resources". It utilizes Markov chains to simulate the cross-regional travel behavior of EVs to assess their dispatchable potential and develops detailed models for 5G base stations and distributed energy storage systems. On this basis, a peak-valley smoothing optimization model is constructed with the objective function of minimizing the peak-to-valley difference of the net load, which is solved using the CPLEX solver. Simulation results demonstrate that the proposed strategy can effectively achieve peak shaving and valley filling, enhancing the operational security of the distribution network.