Part 1 of this series discussed the paradigm of potential surgical deployments of incremental new smart meter capabilities.
In Part 2 of this series, this article will explore this potential surgical deployment paradigm in the context of a specific example facing many smart grid programs today.
That is, the consideration for deploying new smart meters under an existing AMI system that incorporate a specific incremental new feature. The specific example that was examined is the potential deployment of meters with service disconnect switches.
As part of this analysis there were four potential deployment scenarios that were considered to compare business case and deployment strategies. These were:
“- Limited Surgical Deployment
“- Broader Surgical Deployment
“- Stand Alone Universal Deployment
“- Incremental Universal Deployment
In this scenario, the utility attempted to identify repeatable targeted locations for deployment of the service disconnect equipped new meters based on recent historical demonstration of the premises where this switch could have been utilized in the past. In short, the utility examined a scenario where they might deploy the new meters to locations which could have used the disconnect switch in the recent past.
This limited surgical deployment strategy presumes that locations that have demonstrated a potential for use of this new feature in the recent past are likely to need it again in the near future. In fact, data can be examined to try to establish the validity of this presumption.
The utility attempted to develop a probability correlation that examined whether there is a reliable repeatability of the need for disconnection or reconnection at specific sites; such as high turnover locations.
If this correlation holds true, this deployment scenario provides a highly likely payback based on the expected benefit achievement (in this case, avoided truck rolls to disconnect and reconnect service in the future among others). However, as it is a highly targeted strategy, this scenario also limits the achievement of overall benefits as it only installs the new feature after the premise demonstrates the need for it once first.
Thus it will consciously miss the first chance to utilize the new feature at each unique premise and the probability that the premise will realize that benefit again is based on the correlation confidence.
The advantage of this scenario is that it has the opportunity to limit costs by only deploying the new feature to those sites with high likelihood of re-use. In this case, the utility will only deploy the service disconnect equipped meters as part of a service call to a premise for disconnection or reconnection of service.
Thus, they only incur the incremental cost of the switch itself as the cost of the underlying meter and the cost of the installation were expected based on the fact that the utility would have visited the site anyway to disconnect or reconnect the service.
In this scenario, the utility may attempt to identify likely target segments based on demographic segmentation data, which have a higher likelihood of use of the new functionality than the general population.
While this scenario will broaden the likelihood of achieving the benefits by broadening the target pool, it will also expand the costs incurred and may lower the “hit rate” of benefit achievement. Thus, the business case is dependent on a strong demographic correlation rather than a historical demonstration of use. This scenario provides broader benefit achievement but is highly reliant on solid demographic correlation and may incur larger overall costs.
In this specific example, the utility attempted a broader surgical deployment strategy by simply deploying the new meters with disconnect switches to every site where a new meter replacement order was executed. In this broader deployment scenario the utility simply relies on the normal execution of change meter orders and field disconnect orders over time to systematically integrate the new feature into the population.
This version of the broader surgical deployment reduces the incremental cost of deployment as the utility would have incurred the installation cost and the basic meter cost anyway. However, while this deployment strategy minimizes the costs of deploying the new feature, this version of a surgical deployment strategy is really not totally surgical at all as it is only partially targeting a specific likely need for the specific feature based on historical or demographic correlation.
In this case, a universal deployment model examines the deployment strategy whereby all meters are replaced over an extended, planned complete replacement of all meters in the service territory.
In this “stand alone” case, the new feature is examined within a business case where the entire replacement strategy is based on the deployment of this new feature. Thus, the justification for the new feature is burdened with the deployment costs for the entire service territory population.
As was discussed in the first article in this series, this is a difficult business case to make as the incremental benefits of the singular new feature alone are unlikely to provide a sound justification for a complete meter replacement strategy.
In this specific example, the utility extended the broader surgical deployment strategy (replacing all CMOs with a new meter equipped with the disconnect switch) by adding a proactive replacement program of all meters over a specified time period.
Finally, the “incremental” universal deployment scenario modifies the above case by examining the inclusion of the new feature as part of a previously planned meter replacement strategy.
This scenario becomes applicable when the utility may be considering a full replacement program based on other justifications (technology obsolescence, increased failure rates, regulatory mandate, customer service, reliability). This examination solely determines whether the additional feature should be included in the capabilities of the new smart meters to be deployed based on the incremental cost/benefit advantage of this specific feature.
In this case, the utility examined the incremental costs and benefits of the disconnect switch feature alone under the presumption that they would be changing out all their basic smart meters for other reasons anyway.
This scenario of the business case provides a clearer examination of the cost/benefit analysis of the singular feature across the entire population. It provides the maximum benefit achievement, without the dependency of targeting correlations and it limits the costs incurred to only the incremental cost of the new feature.
These scenarios were examined in the context of the specific example whereby a utility was examining the cost/benefit opportunities of deploying smart meters equipped with disconnect switches as upgrades to their “basic” smart meters. The results provide significant insight into deployment considerations for any incremental new capability.
First, the more limited the benefits to be targeted, the more targeted the surgical deployment should become. This is especially true if the costs incurred to deploy the new feature are just as limited (and targeted) as the expected benefits and if there is a reliable correlation of previous need to future use.
The broader the benefits that are available, the broader the deployment strategy should be. In this case, if the benefits available from the new feature are broad in nature, can be predicted based on demographic correlation, and remain incremental in nature (i.e. do not require substantive system level upgrades); then the business case may prove positive.
Finally, if the new feature can be considered an incremental added feature of an already planned replacement strategy; and its benefits are broad enough (or its costs are limited enough); then a positive business case can be made.
As utilities evolve into a continuous improvement model for maintaining their smart grid technologies, their abilities to effectively examine how to model their incremental investments in their smart grid will need to improve to ensure sound, prudent future deployment strategies.
Author: Jeff Buxton is an executive consultant with Black & Veatch. He has 30 years of industry experience with a primary focus on smart grid and AMI solutions. His expertise includes strategic business planning, technology roadmapping, deployment and organization planning, and regulatory support. He holds a BS in Electric Engineering with an MBA in Marketing. Reach him at BuxtonJT@BV.com