In the movie “The Graduate”, Dustin Hoffman’s character was told that the future was in plastics.  Today, he’d be told that the future is in data analytics.  Just like many other business sectors are discovering, analytics will play an increasingly significant role in the management of Smart Grid networks.   

As mentioned in my previous blog, the rollout of Smart Grid projects supports a larger concept called the Internet of Things.  Utilities could eventually manage networks with device numbers in the millions to hundreds of millions.  These are many challenges to designing and running the best network architectures that can manage these numbers of devices.  They range from building in flexibility and scalability to absorb a dizzying array of devices that have different needs in terms of network speeds and responsiveness to successfully making necessary cultural and organizational changes within utilities to support these operations. 

In the past, utilities built application-specific networks, but utilities will be under pressure from policy-makers, constituents, or competitors (depending on the regulated nature of your electricity sector) to reduce operations costs.  To achieve that, these subnetworks will transition into combined or converged heterogeneous networks, and utilities will have to determine how to assign priorities to the vast amounts of data that are offered from different devices on these networks.  There will be interesting discussions about Quality of Service (QoS) requirements between various stakeholders, because often what is “mission-critical” to one group may not be so important to another.  For instance, the group responsible for billing will consider the meter data to measure kilowatthours to be mission-critical, but an operations manager will want last gasp signals from a meter, signaling an outage, to take priority.

The abilities to aggregate and correlate data along with business rules that allow decision-making as close to the decision point as possible will help these dynamic, flexible, and high-growth networks serve mission-critical and routine requirements as time-effectively as possible too.  Normalization of data – time-synchronized to ensure accurate reflections of activity – from different sources will be critical to delivering useful information. 

There are a number of practical benefits that advanced analytics offer to utility operators as they confront the new challenges that Smart Grids pose to them.  How much is it worth to a utility to proactively identify and address network congestion problems?  Probes and self monitoring network elements deliver high value Quality of Service (QoS) and bandwidth usage data that can help manage Smart Grid communication networks.  Analysis of network routing, reliability and performance statistics enable better management decisions.  Analytics can also determine predictive maintenance for departments focused on asset management, allowing them to schedule planned maintenance that extends the life of equipment rather than react to expensive failures of equipment and subsequent service outages.   

 As networks grow in diversity and complexity, managing Service Level Agreements (SLAs) will take on increasing importance – utilities will need to know which vendors are meeting their contractual obligations, and which ones aren’t.    Utilities not only have diverse communications networks, they have a range of vendors supplying equipment for their power and communications networks.  Many utilities lack the tools to track their negotiated agreements against actual performance.  Correlation of fact-based vendor service statistics enable utility companies to verify compliance claims to performance using historical fault records and service trends. 

The utility industry is beginning to explore the multiple, valuable uses of data analytics, and we’ll see an increase in the conferences and presentations devoted to the topic.  The best learning experiences will include careful attention to the lessons and best practices derived from other similar business sectors – such as communications service providers and retail/hospitality markets.