lanesmith / derive Goto Github PK

It would be helpful to have a representation of capacity expansion that could help determine the size of different resources that should be installed based on the available mechanisms and consumer characteristics. While we won't be able to predict future rate structures 10-15 years out, we can apply potential rate increases, which should help make the resources more attractive to deploy. On the other hand, determining a future year could also take resource degradation into account, which might lead to an initial oversizing of resources.

This type of look-ahead could potentially be introduced by simulating two years, one at the first year (i.e., present day) with contemporary prices and new resources and the second at the end of life time of the resources (i.e., about 15 years out) that has scaled prices and derated resources.

It would be helpful to add a representation of tax credits to the model to see how they impact operation decisions (e.g., in the past, some of the tax credits depended on storage charging from solar for some percentage of the time) and investment decisions. Tax credits appear to continue to be the incentive of choice, though further investigation is needed here to make sure that this model captures the impactful incentives.

As a commonly suggested demand charge component, it would be helpful to have a representation of critical peak pricing (CPP) for use in comparison studies.

Tiered rates remain a common rate structure in residential rates. As such, it would be good to have a representation of tiered rates in this tool. Tiered rates could be modeled in a linear way by assigning charges for going into the higher consumption level(s), as determined by total energy consumption for each allowable consumption block.

The model already has the simple shiftable demand (SSD) model, but as was seen in this paper, the abstraction of demand is really only useful in understanding operational decisions. In other words, it would be difficult to use the SSD model to try to understand investment decisions. As such, it would be helpful to have representations of common home electrification technologies, such as electric vehicles (EVs) and heat pumps. Both would need to include (preferably simple) models that consider temperature effects and can take inputs to consider different user interactions (e.g., vehicle availability for EVs).