Operator-Oriented Blackstart Restoration Planning for Active Distribution Networks via QSTS-Based Multi-Objective Co-Simulation

Blackstart restoration in active distribution networks requires switching strategies capable of balancing restoration speed, voltage recovery quality, and operational feasibility under heterogeneous distributed energy resources and unbalanced conditions. This paper presents an operator-oriented multi-objective framework for blackstart restoration planning based on MATLAB-OpenDSS quasi-static time-series (QSTS) co-simulation. Candidate plans are assessed through chronological simulations with time-indexed voltage constraints, while a feasibility-preserving repair mechanism enforces logical energization dependencies among inverter-based and dispatchable sources, reactive support devices, and load blocks. The problem jointly minimizes the integral of time-weighted absolute error (ITAE) of voltage deviations and the total restoration time, and a genetic algorithm generates Pareto-optimal plans revealing the trade-off between faster service recovery and smoother voltage stabilization. The framework is evaluated on the IEEE 123-bus feeder, in which the staged re-energization is referenced to a firm voltage source rather than to an autonomously formed island. Representative non-dominated solutions are analyzed through switching schedules and per-phase voltage trajectories at critical buses: the ITAE is reduced by 56% at the cost of a 36% increase in restoration time, and all trajectories remain within the admissible band. The framework thus provides operationally meaningful restoration alternatives for decision support under different priorities.