Uncertainty-Aware Deployment of Mobile Energy Storage Systems for Distribution Grid Resilience

With the spatial flexibility exchange across the network, mobile energy storage systems (MESSs) offer promising opportunities to elevate power distribution system resilience against emergencies. Despite the remarkable growth in integration of renewable energy sources (RESs) in power distribution systems (PDSs), most recovery and restoration strategies do not unlock the full potential in such resources due to their inherent uncertainty and stochasticity. This paper develops a novel restoration mechanism in PDSs for routing and scheduling of MESSs integrated with stochastic RESs to achieve agile system response and recovery in facing the aftermath of high-impact low-probability (HILP) incidents. The proposed integrated model is presented as a non-convex non-linear stochastic optimization formulation with joint probabilistic constraints (JPCs). The problem is equivalently reformulated to a tractable mixed-integer linear programming (MILP) model that can be solved by commercial off-the-shelf solvers. Case studies on the IEEE 33-node and 123-node test systems demonstrate the effectiveness and scalability of the proposed framework in boosting the system resilience. This is achieved via effective routing and scheduling of MESSs jointly managed with dynamic network reconfiguration in presence of stochastic RESs.

Automated summary

The paper introduces a novel restoration mechanism for power distribution systems (PDSs) involving routing and scheduling of MESSs integrated with stochastic RESs to enhance system response and recovery post high-impact low-probability (HILP) incidents. This integrated model is formulated as a non-convex non-linear stochastic optimization with joint probabilistic constraints (JPCs), which can be transformed into a tractable mixed-integer linear programming (MILP) model. Case studies on IEEE 33-node and 123-node test systems confirm the effectiveness and scalability of the proposed framework in enhancing system resilience through dynamic network reconfiguration and MESS routing and scheduling in the presence of stochastic RESs.