Time-of-use (TOU) pricing might seem like the ultimate solution to ensure electric vehicle charging loads won’t overburden the grid. But will TOU rates guide drivers’ behavior when it’s time to...
DSM: Don’t Stop Maximizing
Useful analytics to improve program performance.
improved customer experience as a result of participating in utility programs, and lessons learned from using data in a new way. Successfully communicating how analytics are either improving demand-side programs or how they’re providing insight into customers’ energy use is vital to the continued application and justification of demand-side analytics.
While utilities might want to expand their analytical capabilities to move beyond the PoV phase and implement processes beyond Tier 2, it doesn’t necessarily mean a large financial investment is required. For example, a utility could implement the open source statistical computing and data visualization software R 2 in combination with the open source relational database MySQL. 3 Since neither R nor MySQL have licensing fees, a utility could utilize both tools to further develop analytical capabilities without creating a sunk cost in software. In practice, however, most utilities will need some form of user support (particularly if they lack the appropriate in-house expertise), and will need to consider commercially licensed statistical software.
Getting started with analytics that have an initial focus on DSM offers many benefits. This area promises opportunities for greater energy and demand savings, improved customer engagement, and reduced costs. Examples of successful targeted marketing campaigns in the DSM community are beginning to appear, as program managers are learning to identify the customers who are most likely to participate in their programs as well as those customers who have the greatest potential for savings from their programs. DSM program managers also are starting to benefit from reduced measurement and verification costs as third-party evaluators take advantage of enhanced verification approaches enabled by interval data. The up-front investment to get started is relatively low and the potential benefits are great.
1. “Smart Grid Data Analytics for Consumer Engagement.” Research Brief, 3Q, 2013. Navigant Research. See p.13. for market forecast of smart meter installed base by region.