A Scalable and Unified Framework for Optimal Black Start Placement and Restoration Path Selection in Large-Scale Power Systems: The Hybrid ACO-A* Approach

Kayar E., CARLAK H. F.

IET Generation, Transmission and Distribution, cilt.20, sa.1, 2026 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 1
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1049/gtd2.70347
  • Dergi Adı: IET Generation, Transmission and Distribution
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, Environment Index, INSPEC, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Business Source Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
  • Anahtar Kelimeler: A* search algorithm, AC power flow validation, ant colony optimization, black start, hybrid metaheuristics, optimal path selection, power system restoration
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Power system restoration (PSR) is a time-critical and complex combinatorial optimization problem, particularly in the transmission path selection phase following a blackout. When applied to large-scale power grids, traditional metaheuristic algorithms often suffer from slow convergence and entrapment in local optima. To address these limitations, this paper proposes a novel hybrid ACO-A* algorithm, which embeds the deterministic A* search heuristic into the probabilistic transition rules of ant colony optimization (ACO). A comprehensive comparative analysis is conducted against the standard genetic algorithm (GA), standard ACO, hybrid GA-A*, Dijkstra shortest path and mixed-integer linear programming (MILP) via the IEEE 39-, 68- and 118-bus test systems. The methodology incorporates a hierarchical Dijkstra-based subsystem partitioning strategy to decompose complex networks into manageable restoration zones. All restoration paths are validated using AC power flow analysis to ensure voltage, thermal and transient stability constraints are satisfied. The simulation results demonstrate that the proposed hybrid ACO-A* significantly outperforms other methods, achieving a 100% success rate and reducing computation time by approximately 69% compared with standard ACO on the IEEE 118-bus system, demonstrating favourable scaling characteristics. The method shows potential for online decision support pending validation on utility-scale systems. Furthermore, this study extends the algorithm's application to strategic infrastructure planning, identifying optimal black-start (BS) unit locations that reduce the total system restoration impedance by 18% compared with betweenness-centrality-based approaches. Statistical validation using 20 independent runs per test case confirms result robustness. The findings demonstrate that the hybrid ACO-A* is a robust, scalable and superior solver for both operational recovery and resilience planning.