Advanced Meter Infrastructure Reporting and the Case for Virtual Metering

By Don Rankin

Historically, utilities are slow to adopt new technology and for good reason. Utilities have some of the most expensive infrastructure requirements of any industry. Infrastructure that has to be repaired regularly and replaced periodically. Full replacement costs for even a mid-size utility can top a billion dollars, so utilities tend toward infrastructure investments that have low risk. A new technology that fails early can result in large financial losses, loss of customer confidence and a public relations nightmare for the utility.

Uses for Internet of things (IoT) devices are rapidly developing, and utilities are naturally slow to react to it until the value is clear and the risk is low. AMI (Advanced Metering Infrastructure) is an IoT technology that is gaining widespread utility acceptance and enabling smart grid technologies. Utilities are attracted to it to reduce costs and improve customer service, but they struggle with what to do with the accumulated data. A utility with 50,000 meters read once per month is a total of 600,000 reads a year. With an AMI, assuming one read per hour, the utility will collect 438 million reads per year. This granularity of data essentially closes a feedback loop for the utility by creating a semi-real time feedback on customer usage that can be used to improve utility operation. While the amount of data is not huge by Big Data standards, it creates a challenge on how best to make use of it.

It is common that a utility will make use of “canned” reports such as zero consumption meters on active accounts or consumption on inactive accounts, since these reporting capabilities are built into the AMI system.

The basic infrastructure for an AMI system is comprised by the endpoint, network and headend. A MDMS (Meter Data Management System) is added to build meaningful report information and add functionality and capabilities into canned reports. You can think of the AMI system as the meter reader and the MDMS as the means to convert a lot of data to useful information (reports).

AMI enabling use cases have been developed for electric utilities to use AMI/MDMS data and include: Outage Management (OM), Demand Response (DR), Distributed Energy Resources (DER) analysis, and even more complex systems such as Distribution Automation (DA) and Distribution Management System (DMS). But these require more financial investment for the software / hardware and the utility must be staffed to manage these systems and make significant business process changes – increasing the risk.

Water and gas utilities have more limited pre-packaged use cases beyond the canned reports they get from an AMI or MDMS.

While the use cases for electric utilities can represent significant value, there are other reports that can be built from the AMI data to gain strategic business information, operational reports and customer usage insights. Actual Risk and ROI depend on the utilities individual business needs and situation, readiness for change, as well as other factors. Risk goes up as cost and / or complexity increase. The ROI goes up as potential benefits from the technology go up. Utilities use cases show “reporting” is low risk and high value. If the utility has already purchased a MDMS, they may already have the tools to do these reporting functions.

The problem is that these are not canned reports and the utility has to make use of MDMS tools to develop the reports they need. A virtual meter is one basic MDMS tool that opens a plethora of reporting capabilities. A virtual meter is the aggregation of multiple meters. Any number of meters are grouped to give one aggregated usage of electricity, water or gas. How meters are grouped depends upon the desired report. A virtual meter can be charted over time to identify usage trends, patterns and anomalies over the course of a day, months or years. Some examples include:

• Meters within a customer class such as Industrial, Residential, Commercial. The usage for each can be analyzed to identify trends that impact generation, production, purchase and revenue. This allows corrective action or identifies opportunity.
• Each electric meter in a feeder. Analysis of transformer load profiles, line loss or theft.
• All meters within a water pressure zone. Can provide a water usage profile for each zone that can be used to minimize pump starts, identify the cause of SCADA anomalies or identify zones with the highest water loss.
• Landscape or irrigation virtual meters allow tracking conservation program targeted users to identify trends or response to pricing signals or quantify the value of other conservation efforts such as educational programs.
• Low income housing areas to identify quantifiable and impactful low-income assistance programs.
• Wholesale or large customers. Tracking and extracting the trends of the aggregated usage of these customers only makes good sense, since they are the largest revenue drivers. The value of marketing programs or incentive programs can be measured for effectiveness and adjusted accordingly.

There are many uses of AMI data, but the virtual meter is a relatively unknown and unused powerful reporting tool. The ability to view customer trends, as a group identified by its importance to the utility, provides valuable insights about customer usage that impacts revenue or generation or purchase programs. Other sensor data that can impact customer usage of electric, water or gas can be added or overlaid on the virtual meter data for analysis and to provide predictors of usage for forecasts. Temperature is a common sensor overlay but as the IoT world grows, utilities will only be limited by their imagination.

Edited by Jose Medina

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