Understanding the Impact of Decision Making on Robustness During Complex System Design: More Resilient Power Systems

Abstract

Robust design strategies continue to be relevant during concept-stage complex system design to minimize the impact of uncertainty in system performance due to uncontrollable external failure events. Historical system failures such as the 2003 North American blackout and the 2011 Arizona-Southern California Outages show that decision making, during a cascading failure, can significantly contribute to a failure's magnitude. In this paper, a scalable, model-based design approach is presented to optimize the quantity and location of decision-making agents in a complex system, to minimize performance loss variability after a cascading failure, regardless of where the fault originated in the system. The result is a computational model that enables designers to explore concept-stage design tradeoffs based on individual risk attitudes (RA) for system performance and performance variability, after a failure. The IEEE RTS-96 power system test case is used to evaluate this method, and the results reveal key topological locations vulnerable to cascading failures, that should not be associated with critical operations. This work illustrates the importance of considering decision making when evaluating system level tradeoffs, supporting robust design.

Resilience in an Evolving Electrical Grid

“…With enough data available, one can also use these datasets as sources of features and train modern machine learning approaches for predicting and quantifying risk [169,170]. Machine learning and artificial intelligence approaches also can provide timely recommendations to the operator in charge of remedial actions [171,172].…”

A Risk-Based Approach to Assess the Operational Resilience of Transmission Grids

“…Similarly, to the data-driven approach in this paper, other works have used large datasets and statistical or probabilistic approaches to the analysis of power system events in terms of quantifying risk [44,[84][85][86][87]. With enough data available, one can also use these datasets as a source of features and train modern machine learning approaches to predicting and quantifying risk [78,86].…”