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

Lucas F. S. Azeredo; Jussara F. Fardin; Helder R. de O. Rocha

doi:10.18618/REP.e202640

Authors

Lucas F. S. Azeredo Instituto Federal do Espírito Santo https://orcid.org/0000-0001-8696-3139
Jussara F. Fardin Federal University of Espírito Santo https://orcid.org/0000-0003-4785-556X
Helder R. de O. Rocha Federal University of Espírito Santo https://orcid.org/0000-0001-6215-664X

DOI:

https://doi.org/10.18618/REP.e202640

Keywords:

Blackstart, distribution system restoration, multi-objective optimization, quasi-static time series (QSTS), MATLAB-OpenDSS co-simulation, ITAE

Abstract

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.

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Author Biographies

Lucas F. S. Azeredo, Instituto Federal do Espírito Santo

received the B.Sc. and M.Sc. degrees in Electrical Engineering from UFES, Vitória, Brazil, where he is pursuing the Ph.D. degree. He is a Professor at IFES, Linhares Campus, working on power system restoration and multi-objective optimization.

Jussara F. Fardin, Federal University of Espírito Santo

received the B.Sc. degree from UFES in 1978, the M.Sc. from PUC-Rio in 1983, and the Ph.D. from UNICAMP in 2001, all in Electrical Engineering. She is a Full Professor at UFES, working on energy management, microgrids, and smart grids.

Helder R. de O. Rocha, Federal University of Espírito Santo

received the B.Sc. degree in Electrical Engineering and the M.Sc. and Ph.D. degrees in Scientific Computing and Power Systems from UFF, Brazil. He is an Associate Professor at UFES and a CNPq Fellow (PQ-2), working on artificial intelligence, metaheuristics, and smart grids.

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