Robustness of Evolving Power Grids

Tse and co-authors take a multidisciplinary approach to address the complex issues facing the sustainable and robust management of modern power grids â" particularly the integration of renewable energy, artificial intelligence, and cyber networks â" and the resulting necessity for enhanced resilience.

This book concentrates on three aspects central to robustness. First, it presents a methodology for modeling cascading failures in power grids using complex network theory to develop cascading failure models that capture the propagation of failures triggered by initial events. Next, it proposes new evaluation metrics to accurately assess the robustness of complex power grids against major blackout incidents caused by cascading failures. Unlike traditional structure-based metrics, these metrics incorporate network characteristics, electrical variables, and learning-based methods. Last, it presents new strategies for enhancing the robustness of evolving power grids, focusing on both the system planning phase and system control/protection phase. Throughout, the book offers comprehensive and in-depth analyses to ensure that readers gain a comprehensive understanding of the topic. It also draws from the latest advances in the field of complex circuits and systems and incorporates findings from academic studies, industry reports, and real-world case studies, providing a solid foundation for the technical descriptions. The content is designed with practical applicability in mind; readers will benefit from actionable strategies directly implementable in real-world scenarios. Readers will acquire a holistic and multidisciplinary understanding of the robustness of evolving power grids and will be able to take a holistic perspective on modern power grid challenges and opportunities.

This is a vital and cutting-edge resource for research students, undergraduate students, and researchers in power engineering. Engineers and policymakers will also find valuable recommendations and methodologies to enhance robustness and mitigate risks.