Multi-Time-Scale Optimal Scheduling in Active Distribution Network with Voltage Stability Constraints

Abstract

The uncertainty associated with loads and renewable-energy sources affects active distribution networks in terms of the operation and voltage stability on different time scales. To address this problem, a multi-time-scale voltage stability constrained optimal scheduling framework is proposed, which includes a day-ahead model with a coarse-grained time resolution and an intra-day model with a fine-grained time resolution. The day-ahead economic-scheduling model maps out a scheme to operate different types of devices with the aim of minimizing the network losses. Following the scheme, the intra-day corrective-adjustment model based on model predictive control is proposed to regulate the flexible devices, such as the energy storage systems and the photovoltaic converters. In particular, the proposed optimal scheduling framework embeds a voltage stability constraint which is constructed by using a novel index, defined based on the Distflow model Jacobian. As the index at each bus is a linear function of the locally measurable power flow variables, the proposed constraint does not introduce additional computational burdens. Simulation results demonstrate the necessity and effectiveness of the proposed multi-time-scale voltage stability constrained optimal scheduling model. The results also show that the variation trend of the proposed index is consistent with that of the commonly used voltage stability index.

Robust optimization of the active distribution network involving risk assessment

“…Meanwhile, the uncertainty model is constructed based on the stochastic optimization method, improving the system reliability. In Song et al, 2021, considering voltage stability constraints, an optimized scheduling framework with multiple timescales is proposed, which achieves the network loss minimization objective and effectively copes with load and RES uncertainties. However, the temporal series coupling constraints between ESSs and DGs easily lead to the model falling into local optimization, which has not been fully solved and has vital research significance.…”