The Argument For “Real Time” Meter Data Management

The advent of Advanced Metering Infrastructure (“AMI”) and Automated Meter Reading (“AMR”) and the ability to remotely collect and manage customer usage data in a central data repository and Meter Data Management (MDM) system is resulting in significant improvements in efficiency for the utility industry. Through the use of available technologies, utilities can better manage the inventory of meters, better manage the process of meter reading, readily access current customer usage information, prepare accurate and timely customer bills, and allow presentment of information in ways that were not possible just a few years ago.

The direct, and most often referenced, impact of this has been reduction of basic operating costs related to reading meters and preparing customer bills. Secondary benefits include improvement in the accuracy of billing information with fewer numbers to be manually entered into the customer billing system, required estimations are more robust, and usage statistics can be automatically and electronically verified prior to being read into the customer billing system. This results in fewer customer calls to the service center and, by extension, less time having to be spent addressing those types of issues. Additionally, utilities may realize a reduction in both the time and cost of collection of utility revenues as it becomes possible to literally read a customers meter one day, prepare the customer invoice, and send out the bill the next. The effect of which is to reduce by some number of days the required amount of working capital. Further, the savings that can be realized from timely identification of customer move-in and move-out situations are potentially very significant.

Utility customers may also benefit directly from the ability to access their usage information more easily and in greater detail. This ability allows customers to become better informed about their energy use and provides greater knowledge about how changes in behavior (usage) can affect their overall cost of energy. More advanced systems may provide information about impact on the customer’s contribution to utility peak demand or the specific contribution to environmental emissions (Sox, NOx or CO2) associated with a customer’s usage pattern. All of this gives the utility customers greater knowledge and an improved sense of control over their energy usage and “environmental foot print”. Customer knowledge and control over their energy picture is certain to result in an improved level of customer satisfaction.

Utilities that invest in AMI/AMR and the MDM systems and tools required to manage the data these systems enable, may also realize an improvement in the integrity of their revenue stream through earlier and improved detection of customer fraud (a.k.a. theft) or of potential system or meter related malfunctions such as meter resets. The benefit here is to allow the utility to recover an increased portion of the revenue stream which otherwise may not be billed and therefore not collected. It is obvious that as the ability to reduce the time between data collection (meter reads) and analysis of the resultant data stream improves, the potential to measure every kWh becomes a possibility. If the data can be collected on a daily or even more frequent basis, the utility can respond sooner to begin measuring actual usage again. While this may represent only a fraction of the overall revenue stream, with most utility revenues exceeding $1,000,000,000 per year, improving on the collection of even a fraction of a percentage results in millions of dollars which will flow directly to the bottom line.

With the deployment of AMI/AMR technologies, utilities make a conscious decision to reduce the number of times a physical meter is actually seen by a utility worker. The MDM gives a robust and efficient alternative method of “seeing” anomalies and issues that may be occurring in the field at any service point or meter. In fact, the current data and volumes of incremental historical data available to the MDM give the utility a means to analyze and validate issues before rolling a truck and crew thus saving operational dollars. Identification of many issues can occur before a customer is even aware of a problem thus allowing the utility to be proactive in serving their customers.

All of the above benefits offer tremendous savings to the utility and improvement in customer satisfaction. Yet these benefits only begin to touch on the true potential associated with current generation of available AMI/AMR and MDM technology.

Currently available hardware and software are capable of collecting and analyzing data on decreasing time intervals allowing utilities to deploy tools that provide for improved diagnostics of the operation of the distribution system and more accurate forecasts and management of customer demand. The implications of this are astounding; the potential benefits can be quite dramatic.

Start with a scenario where data is collected and maintained on an hourly (or even more frequent basis) for every meter on a large utility’s system. Continue to build the database to include weather statistics (temperature, degree days, wind speed, humidity), day of the week, holiday, critical peak event day, etc. Next, overlay the size of the house (square footage), number of residents living there, some additional statistics (is there a swimming pool at this address), etc.

With this information, it becomes possible to build a very detailed picture of a specific customer’s energy usage. Supply this information directly to the customer and allow them to compare their usage to that of other “similar” customers (other households with 2,000 square feet, a swimming pool that were constructed within three years of when my house was built) and you create a basis for inviting customers to ask where they can make improvement to lower their bill or reduce their environmental impact. This type of knowledge is potentially very powerful information when placed into the hands of the customers who can ask “… why is it that other supposedly similar customers use an average of 10% less energy?”

Combine the picture of multiple customers who are connected to a particular sub-station (or gas or water main) and it becomes possible to very accurately determine what the peak (average, minimum, etc) loading on this component is likely to be. This provides an effective tool to utility planners who can more easily determine the correct size of distribution components (substations, feeders, etc) required to reliably provide service to a particular sub-division. If I know that the peak coincident demand for all of the customers on a particular distribution feeder is likely to cause me to regularly exceed the rating, I might consider installing a larger feeder so there may be fewer failures in the future.

Incorporate this information across all customers on a utility’s system and you can build a very detailed picture of the demand that may be experienced tomorrow (Thursday, July 12th) when the temperatures are expected to reach 96 degrees with 60 percent relative humidity and a 10 mph breeze out of the southeast. This very detailed information can be used to more accurately predict what the system peak is likely to be as well as when it is likely to occur. Such information can be used to insure that the energy supply function has procured sufficient capacity and energy needed to meet peak demand (expected between 5:45PM and 6:00PM) while minimizing the amount that is paid for “insurance” (i.e. I am now highly confident that I can plan for a 100 MW cushion instead of 300 MW).

Customers who have agreed to some form of interruptible rate can be provided information in advance that may tell them when they are likely to be curtailed (2:00PM to 7:00PM). This may give them options to start their work day two hours earlier and benefit from a full day’s production instead of having to decide to close the facility the next day and have everyone stay at home.

This type of information will allow system managers to more efficiently dispatch the system by starting peaking facilities closer to the expected time of actual need, thus reducing the cost and consumption of fuel associated with no load or minimum load situations. Again, these actions can be taken with greater assurance that system reliability won’t be placed in jeopardy.

The use of “real-time” data management techniques and data provided by a deployed AMI/AMR system can also assist in ascertaining the location and nature of specific outages earlier and with more accuracy than traditional outage management systems alone. Through the use of a Meter Data Management system that can communicate directly with customer end-point devices, specific queries can be initiated to determine which devices are responding and which are not (indicating a potential outage). You can more accurately and timely determine what type of failure you are being presented with by applying an “intelligent screen” to this information.

Consider a call coming in from a customer indicating their power is out. Through the AMI/AMR and MDM system, a signal can be sent to other end points in the same area (the neighbors); if all of these devices respond indicating no outage, you know to send someone to the specific address to look into a problem with the service. If some end points on a specified feeder are not responsive, you can begin the investigation just ahead of the last responsive device. If there are devices not responding on multiple feeders, you know to start at the substation.

If weather radar indicates a storm is approaching, the system may be switched to a mode where regular communications are initiated with devices in the affected geographic area. In the same manner as above, if devices stop responding you can determine the nature of the outages that may be occurring. With this advanced information the nature of the outages can be analyzed, a plan can be developed, and crews can be dispatched to respond most efficiently to the outages being experienced. As restoration begins and customers are brought back on line, the same techniques can be used verify that all customers are restored.

The benefits identified above can easily support a utility’s investment in a fully functional AMI/AMR deployment. Current data management tools combined with the latest available end point devices creates the ability to realize these benefits in the current environment. Most MDM solutions on the market today will offer a modular approach with solutions for some or all of the following areas plus more:

• central repository for the collection and storage of meter data
• validation, estimation and editing of consumption reads (including time of use and demand) and interval data; for residential mass market, C&I and agricultural customers
• graphical user interface to let users view meter readings, flags, service orders and more
• automatic service/field order generation based on configurable rules
• automated meter reading management tool to let the system find, request, and obtain the best possible read for any meter at any time for any purpose
• outage and restoration validation method that is electronically linked with other systems to ensure an outage is not the result of a service call on that meter or a situation such as locked for non-pay
• command and control message management to handle and prioritize all communications to the end point to allow on-demand power status checks, on-demand reads, remote connects/disconnects, etc
• virtual metering and aggregation modules to provide combined data for any desired group of meters, service points, or accounts – either arbitrarily assigned or grouped by transformer, bus, feeder, or substation for example
• demand response management and handling of critical peak pricing
• flexible configuration of new rates and tariffs
• auditable tracking and logging of all data events

The capability of Meter Data Management systems will continue to evolve as uses for information provided by AMI/AMR technologies are conceived and established. The power in the utility now lies in its consistent, intelligent and creative use of its data assets.