Natalie opens her Energy Solutions mobile app to check her electricity usage. She sees a notification that shows the declining efficiency of her washing machine. Then she sees a notice from her electric utility. They are offering a one-hundred-dollar rebate on the purchase of a more energy efficient model. They are also offering financing that can be bundled with her regular bill.
Her app sources three competitive options for washing machines that are most cost-effective, given her usage patterns. She is presented with an opportunity to opt into the demand response program. It will delay the start time of the machine to ensure it runs during hours when the cost of energy is low.
The app summarizes all this information. It shows that the purchase of a new washing machine, bundled with the demand response program, will save her sixty-five dollars a year. Knowing that her washing machine will need to be replaced in the near future anyway, she takes advantage of the Energy Solutions rebate and purchases her new washing machine with the click of a button.
In the 2000s the business case for smart meters was based on reliability improvements and streamlining billing processes. It was also based on the ability to optimize the work force and the prospect of reducing network losses.
These gains would initially be realized by the utility and ultimately shared with customers. Many utilities have taken the first steps to unlock this value from their advanced metering infrastructure data. For example, Ameren Illinois estimates that smart meter data will allow them to identify over- or under-used transformers in order to more appropriately match capital expenditures to demand. This will result in capital savings, and it will also yield a reduction in operating expenses related to low voltage distribution system management. Those are real savings that add up over their twenty-year business case.1
Today, utilities are looking towards more sophisticated applications to harness additional value from cloud driven data management and machine learning. AMI data can feed maintenance and predictive tools to achieve additional distribution planning improvements, and reliability metrics and capital expenditure efficiencies. It has been predicted that there is a twenty-dollar annual value per meter that the utility and customer share.
However, the majority of utilities are still staring into the big data abyss. They are struggling to create the analytic engines required to harness the data and to put it into a format that can drive decision-making and, ultimately, value. In the future, we expect utilities to transition away from focusing on operational efficiency and other cost reductions. They will move toward realizing the benefits of offering customized services.
The biggest value of advanced metering data is its potential to compare and forecast energy usage of similar customers. This is computed using machine-learning tools and cluster algorithms to segment customers. It will allow utilities to optimize pricing, products and energy plans. It will even potentially be broken down to the appliance level.
Utilities will no longer use location or age to determine which products to offer customers. Rather, they will use individual customers’ load profiles, obtained from AMI data, to determine how to engage with a range of customers, including customers focused on green energy.
They will also use the data for the convenience-at-any-cost customer. This transition to a more sophisticated and analytically rigorous segmentation is necessary for utilities to obtain the most value of the customer-funded energy efficiency budget, projected to be fourteen billion dollars by 2025. 2 EverSource has already taken steps in this direction. It is developing customer engagement applications for both residential and commercial customers.
These applications give customers targeted insights into their energy use. It also shows rebates they are eligible for and provides tips on reducing their energy bills. The project has expected economic benefits of a hundred fifty million dollars per year.
Utilities will more effectively forecast customer energy use through more accurate understanding of energy use. This will also allow utilities to reduce the cost of procurement and hedging in energy and derivative markets. It will also open the potential to sell data to third parties such as hedge fund managers.
There are also regulatory incentives that most utilities can access, if they start to leverage their advanced metering data. Regulators are increasingly aligning program cost recovery and performance incentives. They are also aligning loss margin recovery with utilities’ eff orts to pursue energy efficiency initiatives.
Advanced metering data allows utilities to better understand their customers’ usage patterns and, therefore, allows them to be more creative and effective in using various demand response mechanisms. It also fosters more creativity in time-of-use tariff pricing.
Several states have introduced decoupling mechanisms and are including energy efficiency adders in rate of return calculations. For instance, in 2016 Minnesota adopted rules that allow investments and expenses incurred in connection with energy conservation improvements to be included in the regulated rate base. 3
This underscores the prospect that there is a large pot of value in smart meter data sitting in the cloud that utilities have yet to tap. The faster and more effectively utilities utilize this data, the sooner they can achieve additional operational efficiencies and the sooner they can make more effective capital investments. Finally, and most importantly, they can provide tailored solutions to their customers in an increasingly digital world.
1. Ameren Illinois, Advanced Metering Infrastructure Cost Benefi t Analysis, June 2012.
2. IEI Issue Brief, Electric Utility Customer- Funded Energy Effi ciency Savings, expenditures and budgets – 2014, November 2015.
3. Minnesota Statutes 2016, 216B.16, Subd. 6
This article was published in Public Utilities Fortnightly, October 2016. Copyright PUR Inc. (www.fortnightly.com). Used by permission of the publisher.