AMI’s Limit is the Sky

Benefits Abound Beyond Meter-to-Cash

By Wayne Pales, The Chapel Group

Electric utilities and energy providers have developed a pattern over the years when exploring potential investments in advanced metering infrastructure (AMI) technologies.

They begin by identifying AMI’s obvious benefits such as reductions in meter reading costs and nontechnical losses, improved billing accuracy and other cost-saving benefits. The conversation then moves out of the meter-to-cash realm and spreads to those within the organization who focus on customer experience. This is where utilities and energy providers explore products and services that can improve net promoter scores and other metrics that reflect customer satisfaction. This typically includes giving customers access to their consumption information via web portals and smartphones. More forward-thinking utilities will take this further and offer services such as predictive billing, bill alerts and more.

Some utilities and energy providers go even further by introducing new products based on time and demand pricing. These type products can benefit utilities because they help them avoid or defer generation, transmission and network infrastructure investments and improve operational efficiencies by increasing load factor. The underlying principle is to engage energy consumers and provide financial rewards or penalties to encourage them to change their consumption behavior.

This is where the business case often stops. There is mention of network benefits, but apart from outage management, the utility rarely includes hard numbers that can be used to justify the investment. Enormous financial benefits are often left on the table for years after smart meter installation.

There are good reasons why many of the network benefits are left out of the original business case. First, while metering departments have always been part of the utility, they were rarely considered core to grid operations. Metering departments were mostly related to the meter-to-cash process as opposed to ensuring safe, reliable and efficient energy supply. It has taken many years for utilities to buy into the fact that smart meters are an essential network device. In many utilities, this buy-in has yet to occur.

In addition, regulated utilities agree with regulators on the amount of money available for investment over a period, usually five to 10 years. Each department within that regulated utility has an approved budget and must spend that exact amount. Exceeding that amount is not an option because it impacts the tariff. Coming in under budget reduces return on capital, which means lower shareholder returns.

These departments have operated in silos for decades, and many barriers still exist due to internal financial and management structures. AMI, especially the more advanced versions that offer edge processing, can replace or at least reduce spending in many areas of the network business.

This is a challenge that requires an enterprise-wide approach. Many utilities’ existing financial and organizational structures don’t support investments in enterprise-wide infrastructure such as AMI. In addition, new financial structures can require senior managers to support investments that might weaken their position and even their long-term value to the utility.

Some utilities are beginning to understand what is possible with analytics and how they can significantly increase value when data is mashed up from multiple sources, both within and external to the organization. A simple example is transformer load management. By knowing the rating of a transformer, what meters are linked to that transformer and the consumption of those meters, a utility can see how that part of the grid performs and, from that information, much more accurately determine how much investment is needed on the network.

Given the barriers mentioned here, it’s easy to understand why a utility might omit many network benefits from its initial AMI business case. An extreme case of disregarding AMI’s value to the network businesses occurred in Australia. The Australian regulator decided that the retailer, not the network operator, will be responsible for determining AMI specifications, as well as when and where smart meters are to be implemented. Despite the regulator’s view that network businesses can negotiate with retailers for access to smart meter data, it is likely that the regulator’s decision has all but killed any hope of AMI playing a key role in optimizing the Australian grid.

Network Benefits to be Explored

Although utilities are beginning to understand how AMI provides many benefits beyond just meter-to-cash and customer experience, many still challenge the network benefits that AMI can provide. A typical comment is: “But we already have a technology for this.”

That might be true, but a utility must take a holistic, long-term view. Take smart phones for example; four or five different technologies that can do what smartphones can do are available, but it makes no sense to buy all of them when one device can do all those things and more. Utilities must see AMI as an enterprise-wide infrastructure when looking to invest in it. They must forget how things are done today and the current roadmaps and instead think of AMI as an infrastructure that replaces, or at the least supplements, grid operations.

Utilities also must consider that not all AMI technologies are the same. For example, edge processing enables a smart meter to analyze data at the edge of the network in near real time and then make decisions independent of the back-office systems. Not all AMI technologies can do this, and it is not a feature that can be added later. In the coming years, as more capability is pushed to the edge of the network, utilities will experience “buyers regret,” wishing they could get access to more granular data and make decisions closer to the edge of the network and closer to real-time.

When exploring smart metering technology, therefore, a utility’s network business should consider the following services:

Volt/VAR Optimization (VVO). In an article published in 2014 titled “Should Utilities Implement CVR & VVO?” Patterson and Dewar Engineers provides a good explanation of conservation voltage reduction (CVR) and VVO. The article explains that CVR is a utility’s attempt to reduce the voltage to customers to conserve energy without adversely impacting the performance of customers’ equipment. Utilities have been doing this for many years, but CVR has some challenges mostly due to a utility’s inability to monitor what is happening at the edge of the network at customers’ premises. VVO is a newer form of CVR used to level and maintain voltage profiles to an optimal level. The major improvement with VVO is that it incorporates near real-time voltage information that comes from the edge of the network-often from smart meters. VVO can help minimize power loss or demand without causing voltage violations. Savings of up to 3 percent of distribution losses and comparable reductions in system peak demand are possible, according to the article.

Location Specific Demand-Side Management (DSM). Introducing time- and demand-based pricing products, along with effective customer engagement programs and an enhanced customer experience, can influence consumer demand. This change in consumer demand can be used to flatten peak demand. Targeted at specific locations, and by using either behavioral or automated DSM approaches, networks can extend the life of existing assets, deferring or altogether avoiding the need for further investment.

Detect Unsafe Grid Conditions. In the article titled “The Utility’s Role in IoT: Leveraging the Power of the Active Grid,” published in POWERGRID International’s March 2016 issue, the author, Tim Wolf of Itron, explains unsafe grid conditions well. He writes: “High impedance connections (HIC) or ‘hot spots’ on the low-voltage distribution system represent a safety risk, while also causing customer voltage problems and utility energy losses. A high impedance connection is simply a poor electrical connection that can be created when splicing, tapping or connecting wires, when foliage touches a line, or when a cable or connection fails. When current is drawn through the high impedance connection, heating occurs, and the voltage drop across the connection occurs. As heating continues, the connection is further degraded, causing the HIC to worsen over time. Symptoms begin as voltage problems and can ultimately deteriorate to power outages or fires or both. Until now, there has been no practical way to identify and resolve these issues until they lead to significant voltage problems, failure or fire.”

With data from smart meters, especially those with the ability to capture and process this data near real time at the edge of the network, a utility can now address these unsafe grid conditions before they cause an incident.

Improved Outage Management. Depending on the metering technology used and the smart metering communications network’s design, a smart meter can tell a grid operator when it loses power. These notifications help operators identify an outage before a customer calls in. They also lower the cost and time associated with investigating and rectifying the fault.

The most frustrating part of an outage to a customer often is not the loss of power, but the lack of being kept informed. This frustration can be addressed by using the smart metering data to advise customers when an outage occurs, and when power is likely to resume. Again, another reason for a utility to explore grid edge processing. A smart meter with edge processing capability can perform much of the outage analysis at the edge of the network and provide more detailed information to the utility, further helping reduce cost and time spent on investigating the incident and prioritizing the workload.

Transformer Load Management: Monitoring transformer health and implementing effective maintenance practices to mitigate transformer failure is important. With AMI and advanced analytics, it is possible to diagnose or predict transformer problems and assist with prioritizing and planning for transformer fleet maintenance and replacement. Smart meters equipped with edge processing can deliver real-time transformer management and avoid overloading.

Consider electric vehicles (EVs) for example. Imagine that there are 10 houses receiving power from a single transformer and that transformer is near its rating capacity. Five of those customers purchase an EV. The first two customers bring their EVs home and plug them in to be recharged, taking the transformer to capacity. The remaining three customers return home and plug in their EVs to begin the recharge. This additional demand blows the transformer and supply is lost to all 10 customers.

Smart meters would not allow a demand level to be placed on the transformer that would take it over its safe capacity. In this example, appropriate smart meters would not allow the three remaining EVs to charge until they could see that demand on the transformer had dropped to a satisfactory level.

There are many other benefits that should be explored, including improvements to grid investment planning, avoiding back-feed incidents with line workers and distributed generation, and avoiding customer overloading incidents by matching the maximum demand of a customer with the designed capacity of the premise.

It is important for a utility to identify and realize AMI benefits beyond the realms of meter-to-cash and enhanced customer experience. The utility should forget how its network is managed today, and think about how it could operate its network if it had the capabilities made available by AMI.

Wayne Pales has worked in the energy industry for over 17 years in numerous senior management positions for various gas and electric utilities in Australia and Hong Kong, with exposure to China and India. Pales is the author of “utilidocsâ„-: building blocks to a digital utility” where all profits are donated to Solar Sisters. He is the co-founder of The Chapel Group, helping utility professionals succeed with smart metering and demand response, and is a guest lecturer at the Asian Institute of Technology

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