By Betsy Loeff, contributing writer
When managers at the Honduran electric utility Empresa Nacional de Energia Electricia (ENEE) flipped the switch on a new advanced metering system last May, you could practically hear the utility’s cash register start to “cha-ching.”
ENEE had already saved $1.2 million in U.S dollars by finding theft and other problems during site visits prior to deployment of some 35,000 smart meters. By October, ENEE managers had collected another $368,000 through alerts provided directly from the new metering system serving customers in San Pedro Sula, Honduras. Those alerts let utility officials know when someone was tampering with meters themselves.
But the big bucks rolled in after the utility team started crunching data provided by those new meters. “ENEE recovered about $1.2 million U.S. dollars in the first few days after deployment,” says Jacobo Da Costa, general manager of ENEE’s Northwest region.
Within seven months, the utility had recovered some $21 million in non-technical losses, which are those not included in line loss and other system inefficiencies. Ultimately, utility executives anticipate savings of $24 million during the first year of the advanced metering system’s operation.
According to Da Costa, many houses were hard-wired to steal electricity from the moment they were built. To find those illegal taps, the utility mounted smart meters on transformers and built a dedicated facility for data analysis of consumption information from both customer sites and the transformers serving them. Most of the losses were on the illegally hard-wired accounts, Da Costa says. He adds that they would not have been found without the load-balancing system ENEE personnel put in place.
A new tool
ENEE’s experience is right in line with what has happened for many utilities adopting AMI. Although the systems provide tampering alerts, it is the data, not the alerts, contributing to bottom-line results.
In fact, the alerts often are false. “Those flags were really designed to detect problems with the meter,” explains Wayne Willis, vice president of Detectent, a provider of software-based revenue protection services. And, although alerts have been repositioned as revenue-protection tools, he says they fall short in that capacity.
For instance, one common flag detects tilts in the meter, which might occur if a would-be thief removed or tampered with the device. Willis says such tilt alerts generally are mercury switches that may jiggle as a result of any strong vibration. Even the passage of heavy trucks has been known to trigger these flags, according to some revenue protection professionals.
Power-down flags have limited value, too. Contractors and utility personnel set them off, Willis says. So do temporary outages. It’s little wonder the Detectent team believes as much as 95 percent of “tamper” flags are erroneous.
However, data analysis is another game altogether. Not only does it help utilities catch energy diversion, it helps cut administrative losses, too. “About 80 percent of losses we help utilities find are not related to theft,” Willis says.
Rather, losses stem from a variety of slip-ups, including meter malfunction. At one Midwest utility, Detectent’s energy-pattern analysis uncovered a series of cases that pointed to a built-in meter glitch. After several accounts showed similar problems, utility staff went out, pulled 1,000 meters from the field and found a consistent corrosion problem inside the units. In that case, the data pointed to “a defect in the meters” themselves, Willis recalls. “The utility was losing about $1.5 million a year on those meters.”
Incorrect meter multipliers are another issue that shows up with smart analysis. Often the meter sees only a percentage of the energy flowing to the customer. For example, suppose a meter registers 1/400th of the consumption, so the meter reading should be multiplied by 400 in the billing software to determine the correct billing amount.
If the multiplier is off, a customer may be paying only 40 times the reading, instead of 400. “Believe it or not, some customers only pay one time what the meter reads,” Willis says. “Those are some of our biggest cases. We see customers who should be paying $30,000 a month, but they’re only paying $300.”
To find such losses, Detectent’s analytics compare consumption data to other pieces of information, such as consumption patterns, business type, consumption of other commodities and more.
Dectectent aims to help utilities find likely suspects to investigate, but the investigation is still up to the utilities. Data merely identifies the anomalies. It takes people to verify trouble, then collect the money that’s due.
Betsy Loeff has been freelancing for the past 14 years from her home in Golden, Colo. She has been covering utilities for almost four years as a contributor to AMRA News, the monthly publication of the Automatic Meter Reading Association.