TODAY’S STUDY: The Solutions In Data Analytics
AMI-Driven Insights Report
August 2019 (Bidgely)
Advanced Metering Infrastructure is becoming ubiquitous throughout the utility industry, providing many sources of value for both utilities and ratepayers. When it comes to analytics in particular, Bidgely’s unique ability to unlock previously unavailable insights to help customers understand their consumption and take informed action has allowed the company to serve as a pioneer in the use of AMI data and analytics for customer engagement. However, the full potential benefits of analyzing this data have not yet been realized. With eight years of experience in load disaggregation and applying artificial intelligence to household consumption data, no other company is better equipped than Bidgely to help utilities unlock this value. This report is focused on the residential sector, Bidgely’s core area of expertise, though some of the analyses can also be applied to other markets. The goal of this report is to describe and provide examples as to how AMI data analysis can support utilities across core operational use cases. The executive summary highlights five of these use cases: non-wires-solutions, TOU rate optimization, EV adoption, rooftop PV analysis and demand side management. The balance of the report provides additional detail on each use case as well as anonymized, representative examples of analyses performed for utilities around the world. These examples are built from analytics provided by Bidgely’s Insights Engine.
Non-wires solutions (NWS) are becoming increasingly important for utilities. However, the tools to plan and develop them have not yet matured. Load and savings potential are typically analyzed using aggregate estimates based on periodic surveys, and therefore are neither up to date nor sufficiently geographically specific. For example, if pool ownership is 3% in aggregate across the territory, the utility might determine that pool pump load shifting will not play a valuable part in its NWS. But what if in fact the load-constrained geography ownership is actually >20%? In that case, pool pumps should be a critical component of the NWS. Further, when implementing the solution, the utility will know which customers to target with which programs. This report will dive into the types of insights and analytics that are available to support NWS planning and execution.
Time Of Use Rates
Similarly to NWS, the ultimate goal of time of use rates (TOU) is generally load shifting\ across the whole population rather than just one substation or feeder line. Therefore, the analysis is similar. The benefit of disaggregation-based analytics is the ability to analyze at a more up-to-date and granular level and identify who would benefit from TOU rates, what kinds of loads are available to shift, and, as the program goes on, what applications customers are using to shift load.
Electric vehicles present both a challenge and an opportunity for utilities. If they are integrated properly, they offer increased revenue potential. But at the same time, they threaten grid stability. Because EVs often cluster behind transformers and substations,they can have a big impact on the grid even at very low penetration and can easily cause local blackouts. However, the low penetration makes it very difficult to develop and market solutions for EV-specific issues. AMI-based EV detection and analytics unlocks the potential for utilities to target EV owners precisely,to ensure the EV adoption curve goes smoothly and benefits both utilities and customers.
While most utilities currently know which homes have rooftop PV, they generally don’t have submetering on the PV. This limits their ability to identify precisely how much energy is being fed back into the grid. Analysis of net consumption AMI data allows utilities to develop an 8760-hourper-year energy production profile for each home, thereby analyzing the effect of PV on the grid and planning PV rates without having to deploy expensive submetering.
Demand Side Management
AMI-based end-use disaggregation can improve DSM programs at every stage, including program planning and estimation, program targeting, analysis and supporting M&V. This report will dive deeper into these use cases, and provide examples as to how these ideas can be applied to specific types of programs.