Thanks Paul,
Currently, existing Diesel gensets, PV farm and WT's supply the load. Gensets are dispatched to generate a min of 500 kW for efficiency, Solar comes in as generated and WT are operated to make up the rest of the load. I wish to model additional new WT's, Solar, and batteries with dispatch strategy of renewables first (and only they charge the battery) and the Gensets making up shortfalls when WT, PV, (and Battery if competitive) don't meet load. I want to minimize the LCOE for new equipment additions (of course) and thus would benefit from having the LCOE figures being sensible.
I had tried the Residential BTM model thinking to model the gensets as grid purchased power. I have the heat rates and fuel costs for the gensets so I input the $/kWh costs at different loads as the tiered energy buy rates, but was not sure how to get (or if SAM can calculate) the hour by hour grid (genset) cost on that basis as I think it only does the tiering on a daily/ monthly basis?. (but being able to do this would be a nice feature). The other issue I have, is surplus system power being sold back to the grid. I set the sell rates very low, which has the benefit that SAM does show a difference in bill with and without system (if sell rate is zero - there is no difference between the bills) but the total system power generation is now used as the basis for the LCOE calculation when in fact I would want to be curtailing WT generation when system greater than load. Not sure how to do this.
Still, SAM does track the Grid supplied power to system and system power grid, so in the interim if have been downloading these vectors and the load and calculating in excel a truncated system production and working out the correct costs for grid (genset) supplied power in the System and No system cases, and thus the savings from the system. and whacking that back into the exported cash flow model.
Any advice you can give about a better way to set up these configurations to reduce the extracting and external manipulation would be much appreciated.