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.
Retail energy rates are represented at the state-level in accordance with scenario projections (either AEO reference case or alternate grid cases for electricity). We will likely want to consider the effects of alternate rate structures (e.g., higher fixed charges to support electrification) and/or possible rate sensitivities (e.g., high gas costs with increasing electrification) on at least the state-level.
Proposed approach:
Create a database that lists assumptions about electrification-friendly rate structures (e.g., % or absolute reductions in volumetric $/kWh, total annual fixed charge) by state, customer class (res/com), fuel, and start/end year, with applicability factor to map rates that only affect a portion of the state’s area.
Database could include rows for representing rate structure sensitivities that are tested across all or a large grouping of states (e.g., state = “all”; state = ”leading”; state= “usca”).
Use data from (a) to modify energy cost application in partition_microsegment function here (e.g., reduce volumetric rate for scenario, add fixed rate for given microsegment/state)
Additional sensitivities in rate escalation are best tested via alternate versions of the state-level rate projections (e.g., “state_emissions_prices-” here) that are pulled in by the ecm_prep.
Retail energy rates are represented at the state-level in accordance with scenario projections (either AEO reference case or alternate grid cases for electricity). We will likely want to consider the effects of alternate rate structures (e.g., higher fixed charges to support electrification) and/or possible rate sensitivities (e.g., high gas costs with increasing electrification) on at least the state-level.
Proposed approach: