A benchmark model for power system restoration studies: Review and application

Recent widespread blackouts in Europe (e.g., the Iberian Peninsula) and worldwide (e.g., Chile) have highlighted the critical importance of robust restoration processes. Traditionally, restoration has relied on synchronous generators and predefined strategies, which have been only partially tested in practice and mainly evaluated with simulators capturing basic system dynamics. The changing grid environment raises question of how these strategies can be further improved. The increasing dominance of inverter-based resources introduces reduced inertia, new dynamic interactions, and greater uncertainty from variable renewable generation. This motivates a re-evaluation and new study of existing restoration approaches. International efforts have proposed updated restoration guidelines and explored advanced technologies such as battery energy storage systems, grid-forming inverters, and high-voltage direct current systems. Yet, despite the growing importance of managing dynamics in the delicate restoration phase, field testing remains limited. This highlights the need for a benchmark restoration model. As a first step toward such a model, this paper reviews existing restoration processes, identifies technical challenges, and develops a simulation framework including key network components typically available during restoration. The model enables scenario-based testing of both conventional and advanced strategies, supporting a better understanding of dynamic behaviour and system stability. The proposed model has been implemented for EMT and RMS simulations using both commercial tools (PowerFactory, PSCAD) and non-commercial tools under development. By providing a flexible platform, it bridges the gap between research and practice, supporting system operators, researchers, and policymakers in designing more resilient and adaptive restoration strategies for future power systems. • Restoration literature review identifying key challenges and knowledge gaps. • Systematic classification of dynamic phenomena in power system restoration. • Development of a benchmark model for power system restoration studies. • Formulation of six detailed restoration scenarios capturing critical challenges.