Smart Grid Data Management – The Start of an Outsourcing Trend?

The new volumes of data that can be collected via Smart Grid-enabled solutions and innovative technologies create opportunities and challenges for utilities and consumers in residential, commercial, and industrial categories.  New data about equipment performance, building performance, and electricity consumption holds tremendous potential to help utilities and consumers manage energy use and costs.   However, there are some common challenges to the collection, management, and intelligent use of this data.  These challenges reflect the relative newness of the solutions, evidenced in the lack of resources (vendor and utility) that have experience in these areas, and the lack of experience within existing resources in handling new data. 

Here are two areas impacted by new volumes of familiar data or entirely new data – Distribution Automation and SCADA operations; and meter data collection and management.  I’ll discuss customer information and energy use data in a future blog.    

Distribution Automation and SCADA operations.  Distribution Automation is defined in the Smart Grid Dictionary as “the range of applications and technologies, such as substation and feeder SCADA, OMS (Outage Management System), integration, and smart metering that add intelligence to improve reliability, efficiency, customer service, and asset management for utilities.” SCADA, which is shorthand for Supervisory Control and Data Acquisition, is defined as “systems used by utilities to monitor and control their generation, transmission, and distribution equipment and facilities.”   Smart Grid innovations make it much easier to remotely monitor more substation equipment and obtain data about operating conditions through IP-enabled sensors, such as low oil levels or abnormal temperatures in critical assets.  Analysis of this new data enables predictive maintenance of these assets, which means that substation operators can prevent equipment failures and extend the life of expensive equipment.  It also means that they can schedule field resources to be at the right place at the right time.  Most power failures that consumers experience occur at the distribution level of the grid, so improving maintenance schedules can result in better grid reliability metrics and fewer inconveniences for us.

Meter Data Collection and Management.  As smart meters replace monthly manual or drive-by meter reads, utilities can obtain data as often as they like, although a 15 minute interval is typical.  This means an exponential increase in meter data.  A million meters, read every 15 minutes, results in 36 billion annual meter reads.  Meter Data Management Systems (MDMS) help collect and manage this data. However, utilities have not dealt with data in these volumes before, and that creates many data management challenges – such as how to organize and define what data is most important for different uses and how to extract meaningful information from it. 

In both cases, the sheer volume of data can be overwhelming, and utilities and vendors recognize the importance of expertise and knowledge in organization and management of this data.  For instance, how should predictive maintenance alarms in a substation be prioritized?  If you receive 20 alarms at once, are the proper analytics in place and are resources appropriately trained to focus responses to the most critical equipment issues first? 

Given the resource and budget constraints that many utilities face, it is no surprise that 60% of utilities responding to a recent survey indicated that they planned to outsource some or all of their MDMS operations and/or data storage.  Perhaps a similar percentage would be happy to outsource predictive maintenance.  Outsourcing selected data management functions may be the most realistic answer to accelerating the deployment of Smart Grid solutions in the distribution grid and keeping costs and rates down for utilities and their customers.