A rapid assessment method of total supply capability in urban medium voltage distribution networks

Accurate assessment of Total Supply Capacity (TSC) is crucial for the planning of urban medium-voltage distribution networks (UMVDNs). However, due to the increasing complexity of feeder interconnections led by integration of numerous circuit breakers and switching stations, existing TSC assessment methods suffer from limited accuracy and low computational efficiency. To better handle the trade-off between accuracy and computational efficiency, this paper proposes a rapid TSC assessment method based on an equivalent two-layer topological model and a linearized model of UMVDNs. First, we propose an equivalent two-layer topological model to divide the UMVDNs into a substation layer and a feeder cluster layer with the aim of reducing the dimensions for network reconfiguration. Second, the power supply constraints of each layer are analyzed separately and coupled to formulate a mixed-integer nonlinear programming model (MINLP), where the TSC of UMVDNs can be evaluated under both transformer and feeder N–1 contingencies for more accurate results. We further linearize the original MINLP problem by a mixed-integer linear programming (MILP) model, which can be efficiently solved by the branch-and-bound method. Finally, we validate our proposed rapid TSC assessment method in a real meshed UMVDN system adopted from Shaoxing City, China, which contains more than 300 tie-lines and 140 loads. The simulation results show better accuracy and computational efficiency compared to the benchmark TSC assessment methods. Moreover, we quantitatively analyze the key factors of TSC and provide suggestions for optimal capacity planning of UMVDNs. • Identify key bottlenecks limiting TSC assessment in large-scale meshed UMVDNs. • Propose an equivalent two-layer GS–FC topological model to reduce reconfiguration complexity. • Formulate a globally optimal TSC model with unrestricted switching operations. • Incorporate transformer and feeder N–1 contingencies in TSC assessment. • Validate on a real large-scale meshed UMVDN, providing TSC-driven insights for capacity planning.