ELCC denotes the Effective Load Carrying Capacity of power generation, illustrating how reliably that capacity will be available during times when grids are strained. ELCCs are highest for nuclear, gas generation and coal; lower for batteries, hydro and offshore wind; lowest for onshore wind and solar. ELCC by power source varies, however.
ELCC by power source is tabulated in this data-file, across studies from different ISOs and other technical papers. Specifically, ELCC denotes the Effective Load Carrying Capacity of a power generation source, during times of potentially strained grids, as a percentage of its nameplate capacity.
ELCCs are calculated via complex grid modelling, looking back at the availability of power generation systems. Some studies separate ELCCs by month or by season. Or separately for ‘afternoon’ versus ‘evening’ peaks in grid demand. Some studies show how ELCCs will fall as the penetration of a particular resource rises.
EFORs, QCCs and DLOLs are similar concepts. EFOR denotes the Equivalent Forced Outage Rate; for thermal generation, ELCC ≈ 1 – EFOR. QCC is the Qualifying Capacity Contribution during capacity critical hours (CCHs), a variant of ELCC, particularly used in the Western Power Pool in Canada. DLOL is the Direct Loss of Load Rating, which is another variant of ELCC, favored across MISO, and especially focused on high-risk periods.
ELCCs matter for reserve margins and capacity auctions. Logically, the capacity payments for a power generator with 80% ELCC should be at least 4x higher than for one with a 20% ELCC.
ELCCs are highest for nuclear, different forms of gas generation and other thermal power generation. ELCCs are lower for hydro, batteries, wind and solar. This matters for modeling the total system cost of renewables.
Demand response, aka virtual power plants or load-shifting, fascinatingly, have been ascribed very high ELCCs in some recent studies and technical papers, at least similar to, and possibly even higher than for batteries.
Costs of demand response resources are 50% lower than the costs of batteries, per our case studies here. We are increasingly excited by AI enabling more demand response, and the low-voltage, networked equipment enabling greater DR deployment.