@article{CHEN2026, 
author = {Changming CHEN and Feixiong CHEN and Zhenguo SHAO},
title = {Experimental platform for power systems large-scale blackout restoration optimization considering multi-level scheduling collaboration},
year = {2026},
journal = {Experimental Technology and Management},
volume = {43},
number = {4},
pages = {195-204},
keywords = {power systems large-scale blackout, multi-level scheduling collaboration, blackout restoration optimization, experimental platform, case-based teaching},
url = {https://www.sciopen.com/article/10.16791/j.cnki.sjg.2026.04.024},
doi = {10.16791/j.cnki.sjg.2026.04.024},
abstract = {ObjectiveIn recent years, frequent extreme natural disasters such as typhoons have posed severe threats to the secure and stable operation of power systems. Large-scale blackouts caused by these disasters can result in serious socio-economic losses and system instability. Consequently, post-disaster power grid restoration optimization has become an important research topic with both theoretical and practical significance. However, existing studies mainly focus on model construction and algorithm design, while lacking experimental platforms that can directly support teaching and provide students with intuitive and operational learning resources. To address this gap, this study designs an experimental platform for optimizing large-scale blackout restoration in power systems considering multi-level scheduling collaboration. The platform aims to transform complex research results into practical teaching tools, enabling students to understand restoration mechanisms and develop optimization modeling and engineering application skills under disaster scenarios.MethodsThe proposed platform simulates post-typhoon grid restoration processes under a multi-level dispatch framework and includes five core functional modules: power grid restoration safety risk assessment, national-level dispatch restoration simulation, provincial-level restoration simulation, regional-level restoration simulation, and multi-level coordinated restoration optimization. The risk assessment module evaluates restoration risks of transmission lines and substations based on the spatiotemporal characteristics of typhoon wind fields, providing quantitative indicators for subsequent optimization. The regional-, provincial-, and city-level modules model restoration processes for 500 kV, 220 kV, and 110 kV grids, respectively, including black-start unit scheduling, network topology restoration, load restoration, and inter-level coupling mechanisms. The multi-level coordination module establishes a hierarchical optimization framework in which dispatch centers at different levels exchange real-time data and coordinate decision-making to improve overall restoration efficiency. The experimental design adopts a layered case-based teaching approach. Students simulate a full-scale blackout scenario, beginning with local restoration and gradually progressing to inter-level coordination. Using MATLAB as the simulation environment, they conduct model construction, optimization solving, and result analysis through interactive experimentation.ResultsExperimental applications demonstrate that the proposed platform effectively integrates disaster simulation, restoration optimization, and teaching practice. In simulated typhoon scenarios, students can visualize the influence of wind speed distribution on restoration security and efficiency, analyze dynamic interactions among different dispatch levels, and understand how information exchange improves global optimization performance. The hierarchical optimization process, in which provincial dispatch collects regional load forecasts, coordinates with national dispatch for generation restoration, and iteratively refines restoration schemes, significantly improves overall restoration speed and reliability compared with independent single-level operations. Through the designed teaching cases, students can understand the mechanisms of risk assessment, black-start strategy formulation, and multi-level coordination while developing skills in mathematical modeling, simulation analysis, and practical problem-solving. The platform therefore bridges the gap between theoretical research and hands-on education and provides a systematic approach for integrating disaster restoration optimization into engineering training.ConclusionsThis study develops an experimental platform for power systems large-scale blackout restoration optimization considering multi-level scheduling collaboration, achieving the transformation of complex scientific models into modular teaching resources. By combining optimization modeling with case-based instruction, the platform improves students' understanding of restoration optimization theory and strengthens their ability to apply these concepts in practical engineering contexts. The platform also validates the effectiveness of multi-level coordinated restoration strategies in improving restoration speed, efficiency, and system resilience after large-scale blackouts. In future work, the platform can be extended to scenarios such as AC–DC hybrid grid restoration, pre-disaster prevention, and emergency repair, thereby promoting deeper integration of research achievements with teaching innovation and talent cultivation in power system engineering.}
}