Initial Evidence for Self-Organized Criticality in Electric Power System Blackouts

We examine correlations in a time series of electric power system blackout sizes using scaled window variance analysis and R/S statistics. The data shows some evidence of long time correlations and has Hurst exponent near 0.7. Large blackouts tend to correlate with further large blackouts after a long time interval. Similar effects are also observed in many other complex systems exhibiting self-organized criticality. We discuss this initial evidence and possible explanations for self-organized criticality in power systems blackouts. Self-organized criticality, if fully confirmed in power systems, would suggest new approaches to understanding and possibly controlling blackouts

Characteristics and Risk of Microgrid Outages from a Complex Systems Point of View

Cordova is a town of approximately 2,000 people located on the southern coast of Alaska. A power grid for a town this size, with a large seasonal fishing economy, is considered a moderate to large sized microgrid in terms of power produced. Understanding the vulnerabilities and risks of failures in such a grid is important for planning and operations. Investigating these characteristics in the context of complex system dynamics is a novel approach. The analysis of Cordova’s microgrid is a case study relevant to a large class of microgrid communities. We analyze the outage data based on size, cause characteristics and load demand on the system and find long time correlations and power laws in the failure size distributions. Finally we apply a risk metric to give a single numerical value to the risk of an outage occurring during certain time periods and under certain conditions

Does size matter?

Failures of the complex infrastructures society depends on having enormous human and economic cost that poses the question: Are there ways to optimize these systems to reduce the risks of failure? A dynamic model of one such system, the power transmission grid, is used to investigate the risk from failure as a function of the system size. It is found that there appears to be optimal sizes for such networks where the risk of failure is balanced by the benefit given by the size