Using Smart Meter Data for Greater Operational Effectiveness

by Bradley Williams, Oracle Utilities

Smart meters were just the beginning.

Energy consumption data from smart meters is providing utilities with abundant opportunities to optimize its efforts across the enterprise, both in increased customer engagement and operational effectiveness.

It was always clear that there would be increased utility efficiencies that would result from more frequent meter reads that can more granularly define customer energy consumption patterns. But as advanced metering infrastructure (AMI) and automated meter reading (AMR) projects proliferate across the continent, as well as in other countries, it is also becoming increasingly clear that this industry—at least initially—vastly underestimated the operational value of the new data.

New Data Feeds Actionable Information

It isn’t just AMI and AMR that are contributing to the new utility data influx. Automated sensors all along the grid are providing scores of new, near real-time information about how the grid is functioning. Meter data analytics has become a large part of utilities’ new road maps to continue to meet and exceed safety, efficiency and reliability goals.

Whether it is increased safety through a clearer understanding and mitigation of hidden risks to utility employees or customers and the general public, improved reliability through more effective monitoring and proactive maintenance of utility assets, reduced cost to serve and enhanced operational efficiency supported by better planning and execution, or improved customer satisfaction through targeted interactions, there are many reasons to implement AMI-fed data analytic processes.

As utilities begin to address their operations in new ways, analytics-enabled use cases are mounting. Here are a few examples:

Distribution operations. Near real-time monitoring and operations analytics are being used for such applications as overload management, outage management, conservation voltage reduction, phase balancing, the localization of nontechnical losses and new equipment deployment health. And for long-term system planning, the applications are just as expansive, including device capacity planning, device life cycle management, electric vehicle planning, connectivity model audits and the identification of stressed assets.

The value propositions for these applications are numerous, as well:

  • Increased customer satisfaction with reduced outages and momentaries;
  • Improved communications performance;
  • Enhanced network management system analytics performance;
  • The ability to defer specific capital projects;
  • More efficient monitoring of existing assets; and
  • The ability to better meet regulatory reporting requirements for efficiency and effectiveness.

Improved public safety. High-temperature meters can indicate unsafe conditions such as overheating from the socket, installation issues or manufacturing flaws. These meters need to be flagged quickly and then quickly dealt with to resolve the issue. In some cases, regulators have required the utilities within their jurisdiction to specifically monitor meter temperatures—data only recently available to them through AMI meters—and to provide daily reports of meters with temperatures greater than a specified temperature. Data analytics can, using a multitude of variables, as well as historical analysis of meter temperatures and meter failures, proactively identify meter temperature-related problems before they become critical issues. These meters are then flagged for a utility to initiate proactive field investigation and repair or replacement.

The same types of analytics also are being used to identify water leaks and gas consumption spikes, as well as those issues described for electric issues. And the benefits are easily measurable, both for regulators and the utilities and customers: reduced safety hazards within the community, reduced write-off from unaccounted for usage, reduced unbilled and unused commodity resource, and cost avoidance of damages and legal fees associated with a potential safety incident.

Build metering operations capacity. Analytics also are being used to help utilities track smart meter deployment, monitor new meter performance, track existing meter inventory, identify meter malfunctions and monitor and report on network performance.

This use of analytics to better inform the business can result in:

  • Significantly reduced break-to-fix time, as well as a significant decrease in re-bills;
  • Increased identification of zero-consumption meters, resulting in decreased truck rolls necessary to fix the same number of nonfunctioning meters;
  • Proven identification of energy theft situations, leading to the recovery of significant lost revenue;
  • A reduction in the number of field appointments required to settle high bill disputes through proactive identification of problematic meters; and
  • Reduced premature asset failure and outages by identifying overloaded transformers.

Using Analytics to Change Operations

In every case, utilities have specific drivers that are pushing their analytics projects. As more automation was introduced and processes became easier, one utility sought to better understand the underlying technology for each of its processes and systems and how it behaved (how and why it worked, as well as how and why it failed).

After deploying AMR, it brought together the metering and billing sides of its customer service organization, and this also offered it the opportunity to review its exception processing to understand the reason for backlogs and the impact of those backlogs to customers and the utility.

The utility discovered—aside from information on spreadsheets and other issues common to utilities’ consolidating data and processes to clean its data and identify one version of the truth—that it needed to establish and implement key performance indicators for its entire meter-to-bill process and to leverage its daily AMR data through analytics.

The utility was dealing with many operational issues—something common as more utilities automate and change their processes—and it had to learn how to work smarter, not harder.

Through data analytics, the utility substantially reduced back-office exceptions, considerably shaved the process time for exceptions going directly to the field (break-to-fix time), better detected and more quickly repaired faulty equipment, and significantly reduced its old work (including no-consumption review and AMR read needs review flags) from its customer information system.

There were significant qualitative results, too, including a reduction in internal information technology maintenance, as well as nonvalue added field investigations, advancement in resource planning and prioritization of all back-office work, and the flexibility to tweak algorithms for analysis as the utility’s business needs change.

The 21st-Century Utility is Evolving

Utilities are increasingly able to re-examine how they look at their information strategies, operational structures and customer engagement with a mind to optimizing expenditures, developing new efficiencies and providing greater benefits for their businesses and customers.

In so doing, the data- and analytics-rich utility is on the road to creating a more responsive, interactive and proactive enterprise with the ability to evolve as needs change.

Utility analytics are about identifying and executing on the most immediate value use cases that drive operational effectiveness for specific utility needs.

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