Essentially, the term “smart grid” refers to the digitizing of the electricity delivery system through the installation of devices that enable two-way communication and control. This improvement to a utility’s infrastructure is primarily focused on getting more data and being able to take more intelligent action. This data will become the most important asset that the utility will be able to use in the future to understand how their operations are working in real time and how the utility can better—and more efficiently—serve its customers.

There are two challenges that utilities must answer with the evolution of the smart grid—complexity and data volume.

Traditionally, utilities sent out operational meter readers once per month to gather data on energy use. The utility then had a monthly data point to work from to determine energy consumption patterns.

With smart meters, data is collected remotely – for example, every 15 minutes. This amplifies the data volume from one data point per meter per month to 96 data points per day. But, it also means that forecasting models can use granular, detailed data rather than aggregate data to better predict and anticipate energy use.

The complexity comes in when utilities begin to consider the use cases for all of that data volume. The types of data analytics that can be developed from a “load once, use many times” multiply beyond what’s been possible in the past.

Examples include:

• Customer Analysis.
  With much more data coming from meters at more granular intervals, utilities have the opportunity for better revenue protection. By looking deeper into smart meter data, it’s possible to determine where supply is being lost, identify theft situations and also understand customers better. The ability to analyze consumption behavior matched against payment behavior, for example, can help utilities create better payment programs to reduce bad debt.
   
• Asset Management.
  Analysis can also be done on capital intensive equipment, such as transformers, to monitor use and potential maintenance issues to avoid an outage. With foresight, new equipment can be ordered in advance of the problem and replaced before an outage occurs. Having a contextual basis for asset decision making also helps operators understand how assets are functioning and whether required maintenance should occur sooner for heavily loaded assets versus extending time between maintenance for under-utilized assets and optimizing operating expense. 
   
• Distribution Management.
  With data that provides insights to population trends, an overlay can be done with consumption patterns to better determine how much to invest in distribution for a certain community or geography. Once again, these types of insights can allow utilities to find alternative plans for building out into new sub-divisions or business parks. 

To learn more, listen to this podcast with Jill Feblowitz, Practice Director, Business Technology at IDC Energy Insights, and me as we discuss smart grid technologies and how utilities can use data to improve operations and customer service.