Resilience-oriented siting and sizing of storage systems in active distribution networks: A worst-case-driven approach

Distribution networks are increasingly exposed to severe contingencies that can lead to major economic losses and supply interruptions, particularly under extreme events driven by climate change. Enhancing network resilience is therefore essential to ensure reliable operation under multiple component failures and potential customer isolation. This paper proposes a novel optimization framework for the optimal siting and sizing of energy storage systems in active distribution networks under line and distributed generation outages. The approach combines Benders Decomposition with a Column-and-Constraint Generation scheme. A key contribution of this work lies in the endogenous identification of worst-case scenarios, whereby critical contingencies are not predefined but are instead determined within the optimization process itself, enabling a more realistic and comprehensive assessment of system resilience while simultaneously determining optimal storage investments. The methodology is validated on 13-, 33-, and 116-bus test systems. Results show that the proposed approach identifies highly critical scenarios, with potential ENS values reaching up to 3.17 MVAh in the 33-bus system, while maintaining zero ENS after storage deployment. In addition, the method demonstrates strong computational performance, with solution times below 100 s even for the 116-bus network. These results confirm the effectiveness and scalability of the proposed framework for resilience-oriented storage planning.