Historically, grid operators tapped into voluntary load reduction as a last resort for keeping the lights on. But now, smart grid technologies and dynamic pricing mechanisms bring vastly greater...

## Redefining PV Capacity

Effective metrics give solar its due credit.

*e.g.*, a contiguous utility’s service territory) without increasing its loss-of-load probability. This is determined by calculating the loss-of-load probability of the considered generating resource (here PV) and comparing it to an ideal equivalent resource with a constant output.

An analysis of load-duration curves consists of two metrics. One is load-duration capacity, defined as the mean relative PV output for all loads greater than the utility’s peak, minus the installed PV capacity. The other is demand-time interval matching, details of which are provided in technical reports. ^{5} Basically, the demand-time interval matching capacity over a given evaluation period represents the worst-case output of the PV system by subtracting the PV system output from the load, * i.e. *, the difference between the peak of the load duration curves with and without PV generation.

Load metrics quantifying the synergies between short term storage/load control and PV generation use solar load control capacity to answer a basic question: Given a certain amount of cumulative demand response available to a utility, how much more guaranteed load reduction is possible if PV is deployed? This also can be calculated on minimum buffer energy storage capacity, using minimum storage requirements, ^{6} rather than cumulative demand response.

Using predefined peak demand window metrics, the time-season window method calculates capacity credit across predefined hours, months, or seasons. It is cited often as the ERCOT method, named after the practice to assign capacity credit to wind generators operating in the ERCOT regional reliability council, a practice also used by the MAPP grid operator. There are several possible variations on the calculation. The ERCOT method predefines a peak demand time frame— *e.g.*, May through October and 10 a.m. to 6 p.m.—and defines capacity as the probability a minimum output is likely to occur (8 percent in the case of ERCOT).

#### Capacity Drivers

The main driver of effective capacity is the relationship between load demand and PV supply. All of the above metrics can be calculated by analyzing concurrent time series of PV generation data and load data. In addition to the supply- demand relationship, there are two contextual items that also have relevance on effective capacity.

PV grid penetration represents the amount of PV installed on a given grid, quantified as the percentage ratio between the deployed PV peak output and the considered grid’s peak. Because of its potential as a peak shaver, penetration is highly relevant to PV’s capacity—the more PV penetrates a grid, the less it can be solely targeted to serve peak demand, and hence the less effective it becomes at providing capacity *(see Figure 3) *. All the considered metrics account for the effect of penetration, except for the time-season window method, which only can provide a probability of availability in a given time window, independent of how much PV is deployed. In the three case studies cited, PV penetrations range up to 20 percent of peak loading.

Time frequency also is relevant. But it is necessary only to look at an hourly frequency for load and PV generation data, not any sub-hourly variability. This is justified