Coordinated Planning of Sectionalizing Switches and Mobile Energy Storage for Distribution System Reliability
With the growing integration of distributed energy resources and the increasing operational complexity of modern distribution networks, ensuring high reliability and maintaining voltage stability during fault recovery have become critical challenges for utilities. To address these challenges, this paper proposes a novel optimization framework for the coordinated planning of sectionalizing switches, tie lines, and mobile energy storage systems (MESSs). The framework employs a bilevel optimization model to achieve coordinated asset placement and operational scheduling. Specifically, the upper-level model aims to achieve a balanced trade-off between economic investment and reliability performance by minimizing both the installation costs and the outage penalties quantified by the expected energy not supplied (EENS). To efficiently solve the upper-level multiobjective problem, an improved multiobjective gold rush optimizer is employed, generating the Pareto-optimal planning solutions. During the iterative optimization process, an enhanced fault incidence matrix–based reliability assessment method is integrated, in which the contribution of MESSs is quantified through a sparse matrix R dis derived from the lower-level operational model, enabling more accurate analytical evaluation. Complementarily, the lower-level model focuses on operational quality during fault recovery by optimizing MESS dispatch using a mixed-integer second-order cone programming formulation. This model minimizes voltage deviations at isolated load nodes, thereby ensuring stable and effective power supply restoration. Additionally, the ZIP load model is also embedded to precisely capture load reduction behavior under fault conditions, enabling a more realistic evaluation of energy curtailment. Case studies conducted on a 41-node distribution network demonstrate that the proposed bilevel optimization approach effectively reduces EENS by up to 21% and reduces the number of unqualified voltage nodes with a moderate investment increase, highlighting the importance of the proposed method.