ACEEE 2018: Flexibility and Efficiency as Time and Locational Resources

Posted
on
August 21, 2018

The ACEEE Summer Study is a wonderful time to take stock of the progress of the industry and look ahead to the next frontiers in energy efficiency. For us, themes of embedded M&V, performance-based programs, meter-based efficiency, and methodological transparency were great opportunities to contribute to the wide-ranging discussions over the course of the week.

Alongside the many informal conversations that happened over the course of the week, McGee and Carmen teamed up to deliver a paper reviewing some lessons learned from managing the CalTRACK process, while Hassan showcased a multimedia presentation on risk that was aimed at helping implementers develop better tools for delivering efficiency under a metered-performance paradigm. Encouragingly, several other papers presented findings from work where OpenEE had provided the underlying analytical tools.

More broadly, many attendees gave talks that resonated with the goals and vision that OpenEE has been pursuing over the last three years. These talks support our contention that efficiency is on the brink of a historic transformation.

Three Takeaways from ACEEE 2018 Summer Study:

1. Efficiency is on the verge of becoming a time and locational resource

Countless presentations, posters, and demonstrations addressed the time and locational impact of energy efficiency savings, suggesting that efficiency is on the verge of becoming a resource that can be acquired alongside solar and storage as part of an integrated, distributed energy use strategy. Whether through connected home devices, integrated demand response tools, or algorithmically developed optimization platforms, it is clear that energy efficiency is ready to be counted as a genuine grid resource.

2. Program administrators and implementers need regulatory guidance for Pay for Performance markets

California’s embrace of Normalized Metered Energy Consumption (NMEC) as an alternative paradigm to the tradition of deemed savings is at risk as stakeholders await guidance from Public Utilities Commission for claiming savings. This uncertainty is exacerbated by pending solicitations for third party programs to replace program administration by the utilities. Old-fashioned measure-based savings attribution is a poor fit for NMEC and even worse for pay-for-performance. The chorus of voices calling for clarity and simplicity are being augmented with concrete ideas to test as various parties try to figure out how to innovate in a climate of uncertainty. Lessons learned from variations on implementing pay for performance in other jurisdictions offer valuable insights for the future.

3. New technologies are better at shifting and shaping electricity load than they are at saving energy

The days of new approaches to deep savings may be over. Zero Net Energy (ZNE) proponents increasingly count solar and storage as the primary drivers of energy savings opportunities. At the same time, electrification of heating and transportation point to increasing electricity use in the coming years as a means of reducing carbon footprints. In both cases, innovation is now more focused on balancing the grid to integrate renewables than on technologies that result in large scale decreases in the average building’s energy use.

These trends should encourage the energy efficiency industry to reflect on its relevance to an energy sector whose primary goal is to reduce carbon emissions and a population that recognizes the value of saving energy but prefers convenience and comfort over sacrifice and higher bills. Getting attribution analysis right is helpful, but probably less important than getting electric cars in our driveways. There are big gains to be made, but only if we choose to focus our attention on the right path forward.

Learn how Open Source creates transparent weights and measures

Performance-based demand flexibility requires standardized and transparent weights and measures. Learn how open source methods and software can turn meter data into a true grid resource.

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