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Energy efficiency programs that rely on deemed savings values based on individual measures will typically specify an "Expected Useful Life" of the measure as a way to calculate a full lifecycle savings benefit. In these programs, the deemed savings is often tied to a code baseline, so the EUL becomes a way to quantify the lifetime savings attributed to the measure above the code replacement. In some cases, a Remaining Useful Life (RUL) is calculated if the measure replacement occurs before burnout.
However, an existing conditions baseline and whole-building CalTRACK savings methods call into question the usefulness of EUL for quantifying savings persistence. Should the existing conditions baseline be used to project forward over the entire life of a measure? What if a project involves a mix of measures? What if the measure being replaced has burned out? How should savings persistence be captured in these and other scenarios?
We propose that the GRID working group take up the issue of quantifying savings persistence. We expect that the concept of EUL will need to be abandoned, or deeply modified, to be able to derive a value that reflects long term savings from projects using CalTRACK methods to calculate from an existing conditions baseline. If there is sufficient interest from stakeholders, we would expect to start developing and testing alternative approaches to measuring savings persistence. We would expect to produce a transparent, replicable methodology for quantifying savings persistence that draws on core CalTRACK methods while achieving broader policy objectives.
The text was updated successfully, but these errors were encountered:
At the 7/10 meeting we talked about a range of issues of getting parameter assumptions to fit in a meter based context. In initial stages of PG&E's response to meter-based programs (in 2017), they issued a Rulebook for public comment; which included EUL and persistence - see Section 5.4 Persistence and 5.5 EUL Determination (including bundled approaches); other parameters like measure cost, and net claimable savings determination are also in this draft rulebook as a starting point for discussions. MBS Platform Rulebook v0.5_DRAFT_PG&E_2017.pdf
Pardon my ignorance. AFAIK CalTRACK methods define only a "baseline period" and "reporting period", and these methods can be used to measure savings for reporting periods covering year 1, year 2, .... or year N after an intervention. Why can't program administrators define the required persistence (and associated payments or penalties) within the contract with each aggregator, based on the proposed intervention(s)? Why should this be constrained or codified? Isn't cost effectiveness of measured savings the key goal?
Energy efficiency programs that rely on deemed savings values based on individual measures will typically specify an "Expected Useful Life" of the measure as a way to calculate a full lifecycle savings benefit. In these programs, the deemed savings is often tied to a code baseline, so the EUL becomes a way to quantify the lifetime savings attributed to the measure above the code replacement. In some cases, a Remaining Useful Life (RUL) is calculated if the measure replacement occurs before burnout.
However, an existing conditions baseline and whole-building CalTRACK savings methods call into question the usefulness of EUL for quantifying savings persistence. Should the existing conditions baseline be used to project forward over the entire life of a measure? What if a project involves a mix of measures? What if the measure being replaced has burned out? How should savings persistence be captured in these and other scenarios?
We propose that the GRID working group take up the issue of quantifying savings persistence. We expect that the concept of EUL will need to be abandoned, or deeply modified, to be able to derive a value that reflects long term savings from projects using CalTRACK methods to calculate from an existing conditions baseline. If there is sufficient interest from stakeholders, we would expect to start developing and testing alternative approaches to measuring savings persistence. We would expect to produce a transparent, replicable methodology for quantifying savings persistence that draws on core CalTRACK methods while achieving broader policy objectives.
The text was updated successfully, but these errors were encountered: