Data analytics solutions will be prominent tools for managing Smart Grid networks – both power and communications – at the distribution level and at the grid edge. This is one of the common conclusions based on recent interviews with two companies, Aclara and PreClarity Utilities. Whether the data being analyzed is to develop the most acceptable Demand Response (DR) programs or identifying chokepoints on a network, analytics solutions can help utilities reduce costs, optimize existing assets to postpone new asset investments, and design programs that appeal to consumer segments. Aclara builds networks to collect data for utilities, and then manages that data through software applications. Some of the data supplies back office functions like billing or engineering, but they also have a web portal for end users to view their individual consumption data. PreClarity Utilities delivers advanced data analytics solutions that are used by utilities and other companies to manage “big data” – large volumes of data from meters and other assets in a distribution network for a range of uses.
In separate interviews, Aclara, represented by Andy Zetlan, VP of Product Management, and PreClarity, represented by Bob Becklund, Co-founder, agreed that there are a couple of related challenges for utilities to intelligently incorporate analytics into operations at the distribution and consumption points in the electricity supply chain. The first has to do with composition of organizations, and the second with approaches to problem solving, and how these challenges are addressed can have profound implications for the success of analytics solutions and any other Smart Grid initiatives.
Utilities are commonly described as siloed organizations, meaning that departments like operations, engineering, marketing or regulatory relations work very independently of each other. The introduction of communications networks to transmit the data that smart meters can deliver – also known as Advanced Metering Infrastructure* (AMI) networks – creates challenges for siloed organizations since different groups have expectations and requirements, Andy Zetlan noted. For instance, the group responsible for billing needs a network that reliably delivers volumes of meter data – although it may not need to be at near real-time speeds. An operations center may need relatively small streams of data that have little tolerance for latency or delays to transmission. Building cost-effective communications networks in the distribution grid that can adequately satisfy both needs is the challenge. Utilities that can bring all groups together to document their use cases – what they need the communications network to do – are taking the first steps to reducing, if not removing, those silos.
Similarly, once the communications networks are in place, utilities also need to determine the value of data to build the proper analytics tools. Use cases are a great way of helping to define requirements. Some data may have no value, and other data may be immensely useful. Marketing will find data that helps them predict the profiles of early adopters of DR programs to be extremely valuable, but this data doesn’t need to be delivered instantaneously. On the flip side, the operations group might identify a combination of asset data with Geographic Information Systems (GIS) or spatial relationship data that helps them respond to outages faster, providing a higher prioritization for that data in terms of how it is loaded and stored for analysis.
The related challenge is in how utilities solve problems. There are some aspects of utility operations that are unique to electric, gas, or water utilities, but when it comes to communications networks and data analytics, there’s a lot to be said for learning from the experiences of other business sectors. Telecom companies have experimented with flat rate pricing to application-specific pricing, and companies like PreClarity have experience in designing analytics solutions that help regulatory and marketing groups determine the right pricing designs and programs. Putting a different spin on consumer segmentation, Bob Becklund pointed out that the cable industry is similar to utilities in that what was once a uni-directional service (entertainment rather than electricity) is now transforming into bi-directional flows of communications or electricity. That forces changes in how these companies will relate to consumers, and what they’ll need to know about them via analytics.
Both PreClarity and Aclara also voiced similar themes about the needs to normalize data – which means providing a common synchronization of data so it can be used in the correct context. Time-skewed data creates false relationships and erroneous conclusions that can undermine the value that correctly normalized data can provide to different utility users.
Two different companies, but the same messages that utilities best serve themselves and their customers by finding new ways of doing business that start with addressing siloed operations and looking to other industries for parallel applications of how data analytics can improve operations, reduce costs, and improve consumer relationships.
*you can get a definition of AMI in the Smart Grid Dictionary 3rd Edition.