A tool for estimating the future energy use, carbon emissions, and capital and operating cost impacts of energy efficiency and demand flexibility technologies in the U.S. residential and commercial building sectors.
State/utility first cost incentives are represented at AEO ref. case levels, based on EIA modeling data that are updated with each AEO and reflect current programs. However, we need the ability to explore more aggressive scenarios of these incentives that go beyond the Reference Case forecast.
Proposed approach:
Create a database that lists assumptions about new incentives (e.g., % of total installed cost for given performance threshold; start and end year); extensions of existing incentives (through end year) and/or increases (or, for fossil equipment, removals) of existing incentives (% max ref. case incentive for start/end year) by state, building type, building vintage (new/existing), and technology, with applicability factors to map incentives that affect a portion of the state’s area.
Database could include rows for representing federal incentives (e.g., state = “all”) or in broad groupings of states (e.g., state = ”leading”; state= “usca”).
Apply the assumptions from (a) to code that applies first cost incentives (here):
For new incentives, assume new incentive is added on top of any existing incentives.
For extension, find the first year in the reference case forecast that reaches the maximum incentive level and extend it forward through the end year in (a).
For an increase, same as ii. but then increase by % specified in (a) for subsequent years.
State/utility first cost incentives are represented at AEO ref. case levels, based on EIA modeling data that are updated with each AEO and reflect current programs. However, we need the ability to explore more aggressive scenarios of these incentives that go beyond the Reference Case forecast.
Proposed approach: