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Outages and Aging Infrastructure
Recently I had a conversation with Vivian Sultan, Adjunct Professor at California State University (CSULA), College of Business & Economics and a PhD candidate in Information Systems and Technology at Claremont Graduate University (CGU). Her research focus includes energy informatics and the role of analytics. Of particular interest to Sultan is the use of geographic information system (GIS) technology as an analytics tool. An example of her research we discussed was a study that looked at the correlation between the age of utility infrastructure and power outages.
The aging infrastructure in the United States has been in the news lately. While most of the discussion has been on roads, bridges and dams it is well known that the electric grid infrastructure is also aging. It is not unusual to find transmission or distribution lines well over fifty years old or substation transformers that have long ago exceeded their expected lifespan. While weather is certainly a major factor in power outages, an expectation would be that aging infrastructure would play a distinct role. Compounding the age of the infrastructure itself is the related electromechanical systems that need to be adapted in today’s digital age for monitoring and control of the infrastructure.
Using California as a representative example, Sultan utilized GIS analytics to validate this expectation. An immediate challenge was the public availability of information related to the electric grid infrastructure and outages. While utilities internally have access to this information, what is published for public consumption is typically at a higher level of detail. The information published by utilities also varies in format and content or detail such as the specificity of the location of outages. As a result, Sultan focused on outage data available from just one of the large California Investor Owned Utilities (IOU) along with infrastructure information available from the California Energy Commission (CEC).
The information available from the California IOU was the system average interruption duration index (SAIDI) per city within their service territory. Four years of historical data was utilized. The CEC provided information on the age of power plants and also the energy consumption per city. This information was then used to calculate a weighted average of the age of the power plants in each city.
Having outage and infrastructure age data aligned at the city level, the use of GIS then came into play. The first steps were to geocode and load the outage and infrastructure data into map layers utilizing ESRI’s web ArcGIS. Statistical tools within ESRI’s ArcMap 10.3 software were then used to provide various analytics.
The first tool utilized was the Optimized Hot Spot Analysis. This tool identified two cities where there was a possible correlation between outages and the age of the infrastructure. However, a further analysis identified weather, in this case a major wind storm, as being the more likely contributor to the outages.
A second tool, Ordinary Least Squares (OLS), was then used to assess the relationship between the two variables of outages, the dependent variable, and age of the infrastructure, the explanatory variable. This tool did not show a correlation between the two variables.
While the research did not identify a correlation between outages and infrastructure age based on the data used, it did result in a GIS based model for conducting such analysis. Sultan notes that the model would be enhanced by the inclusion of more detailed information, typically available within a utility, such as outage data by circuit rather than city and more detailed information on the infrastructure age. Additional publicly available information, particularly weather data, would also enhance the model.
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