Three DA Initiatives That Leverage AMI Investment

by Michelle McLean, Silver Spring Networks

Now that you’ve deployed or are on your way to deploying an advanced metering infrastructure (AMI) solution, how can you leverage it to meet your distribution automation (DA) needs?

It might sound far-fetched to tie together these two projects, but a robust AMI system can do more than automate meter reading and improve billing. Because each smart meter has sensing capabilities, an AMI infrastructure provides thousands—even millions—of automated sensors throughout a service area and can help boost grid reliability and efficiency. With the ability to leverage the data it collects and the network, an effective AMI solution can provide granular, precise, timely data to feed distribution automation (DA) applications and ensure you get the most from your smart metering investment. The following are three ways you can use AMI to serve DA initiatives.

1. Improving Conservation Voltage Reduction, Voltage Monitoring

Conservation voltage reduction (CVR) can be a great tool for reducing energy waste, as long as the voltage data used for the control algorithm is timely and relevant. Traditionally, utilities have relied on one of two methods for collecting voltage data:

  • Installing meters at or near the ends of feeders and polling for data at regular intervals; or
  • Monitoring voltage and current at the substation and using an electrical circuit model/load flow to predict the voltages across the circuits.

Both approaches to acquiring voltage data have limitations.

Deploying separate infrastructure to collect voltage information is costly, might not cover all parts of the distribution network and typically fails to deliver premise-level voltage data.

The second approach, using data modeling, requires a detailed circuit model that can be expensive to develop and maintain. Utilities spend millions of dollars in this effort annually, and one major storm can undo a lot of that work. For a data model to be accurate, it must be updated to reflect grid changes.

For example, ties, jumpers, conductors and other aspects of the grid are changed frequently and impact grid behavior. Unless the model is updated to reflect these changes, you won’t have accurate voltage data. Also, both approaches, using feeder meters or models, fail to incorporate the secondary drop from the transformer to the premise, where some 40 percent of the voltage drop on the circuit occurs.

Because of this lack of visibility of the endpoint voltage, the CVR systems must be run more conservatively (and therefore generate less savings) or risk voltage violations. Studies show that pockets of unsuspected low voltages exist in the grid as a result of equipment overloads, incorrect tap changer settings and equipment failures.

Typically these pockets are not identified until AMI is deployed and utilities can leverage readings from meters deployed throughout the grid to assess voltage levels more accurately.

An effective AMI solution provides actionable, timely voltage data across many premise-level points. Fed with such data, your CVR or volt/VAR optimization (VVO) applications can act on precise information. Look for an AMI-based, voltage-monitoring solution that lets you combine polled data with exception-based data. Polling is a good way to get a clear picture of grid status; however, if all meters are polled for all voltage information, the smart grid network and back-office applications will be overwhelmed by the volume of mostly irrelevant data. In addition, voltage-monitoring solutions that rely solely on polled AMI data often relay that data only when meter interval reads are collected. Such data is fine for historical analysis, but it cannot feed real-time CVR or VVO applications.

You want the ability to poll the entire network when you set up your CVR system to get a snapshot of voltage across the network. But for day-to-day operations, you must be able to poll a subset of meters—those that represent the low-voltage points in the network, for example—and dynamically change that subset as grid conditions change.

You also should be able to poll meters on demand; for example, to monitor voltage status after receiving or acting upon exception data. A good, AMI-based, voltage-monitoring solution can report voltage levels immediately—in less than a minute—that are at or near high and low limits rather than sending them at the next scheduled meter read or polling. And it won’t require you to reprogram your meters.

An effective, AMI-based solution will:

  • Deliver true, premise-level voltage data;
  • Poll reliably and quickly to ensure complete, accurate voltage data;
  • Continually monitor voltage at the premise as frequently as every five seconds;
  • Support on-demand polling for immediate visibility into network changes;
  • Enable identification of sags and swells in less than a minute;
  • Reduce the risk of voltage violations through real-time visibility and control;
  • Avoid unnecessary traffic on the smart grid network; and
  • Cut the cost of deploying voltage monitoring.

2. Improving Outage Detection, Resolution

Before AMI, utilities typically learned of outages when customers reported problems. Because fewer than 20 percent of customers ever call about an outage and those reports tend to trickle in, scoping an outage this way is time-consuming and sometimes inaccurate. Often repair crews are dispatched only to find problems are on the customers’ side of meters. In addition, because crews confirm service restoration through random checks, they can overlook nested outages and leave an area, resulting in another truck roll.

Courtesy DOE.

In contrast, a good AMI solution gives you automatic notification of outages. For example, smart meters issue last-gasp alerts when they lose power, which can help pinpoint outages quickly. A good AMI system also accurately records and sends messages on behalf of each meter when power is restored so utilities can see whether outages remain and locate them. An AMI network can generate much outage-related data, so you need a solution that filters that data and feeds only actionable information to your outage management system.

A good, AMI-based outage solution will:

  • Distinguish momentary from sustained outages;
  • Filter momentary outages;
  • Pair outage and restoration data so you can identify nested outages easily;
  • Let you ping a meter, perform an on-demand read or run other tests to determine whether a problem is on the line side or customer side; and
  • Resolve network packet-forwarding issues to ensure notifications are received in the proper order; for example, a sequence of “outage, restoration, outage” notifications could arrive out of order as “outage, outage, restoration,” which would lead you to believe power is up when it’s not.

An AMI-based, outage-detection solution that delivers only actionable data ensures you can: identify and scope outages quickly, including nested outages; avoid unnecessary truck rolls; and restore service in a timely manner.

3. Improving Situational Awareness With Next-generation Sensors

After deploying AMI, you should have a high-performance, two-way communications network. Sharing a common network for smart grid applications lets you reduce capital and operational expenses—including the cost of the network and management software—and leverage staff training and expertise. Look for providers with proven AMI and DA deployments that run on the same network, including the ability for sensors to leverage network infrastructure deployed for AMI to deliver DA data to back-office applications.

The right AMI solution gives you the opportunity to monitor grid health and dramatically improve situational awareness of devices downstream of a substation without deploying additional network devices. Look for places to add communications to existing faulted circuit indicators (FCIs), transformer monitors and other grid devices and to add monitoring capabilities to existing devices on the line. Adding communications to existing devices should improve a mesh network by increasing its coverage and redundancy.

Likewise, talk to your smart grid communications provider about next-generation FCIs and other grid sensors that have two-way communications capabilities already built in. Traditional FCIs, for example, lack communications capabilities and require that utility crews manually identify faults—a potentially time-consuming and costly task.

In contrast, next-generation networked FCIs have measurement and processing capabilities that help locate a faulted line and identify parts of the grid that might be susceptible to outages before they occur. For example, these FCIs can highlight power fluctuations that might be the result of a tree limb’s contacting a power line.

By leveraging the two-way communications and distributed intelligence of your AMI network, you can:

  • Pinpoint an outage faster;
  • Improve maintenance scheduling and reduce outages by proactively monitoring transformers and other devices and performing condition-based maintenance; and
  • Enhance vegetation management by identifying more precisely the patterns of power fluctuations that are consistent with a tree’s or other vegetation’s contacting a power line.

Leveraging AMI to Improve DA

Part of smart grid’s value is its users’ better understanding of the current grid state. Once you deploy AMI, you have real-time, two-way communications throughout the power infrastructure, which gives you the ability to collect data from intelligent endpoints such as smart meters, communicate with and control more DA devices, and deploy next-generation sensors for greater situational awareness and outage detection.

With the right AMI solution, you can implement any or all of these high-priority DA initiatives easily for greater grid efficiency and reliability.

Michelle McLean is Silver Spring Networks’ director of product marketing. She has more than 20 years of networking and market experience and is responsible for market strategy, positioning and solutions marketing. Prior to Silver Spring, she held positions at ConSentry Networks, Peribit Networks, Trapeze Networks and Pluris. She previously served as program director at the research firm META Group, providing technology and strategy direction to global 2000 enterprise clients, and was a journalist for two leading networking publications, LAN Times and LAN Magazine.

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