When microgrids are optimized in a smart grid, they’ll usher in a new era of utility resilience and flexibility. Get ready for dynamic microgrids.
Redefining PV Capacity
Effective metrics give solar its due credit.
because: 1) dispersed PV generation tends to eliminate short term variability; and 2) very high frequency variability is an ancillary service issue rather than a capacity issue. In addition, some of these metrics methods absorb short-term fluctuations (in particular, minimum buffer energy storage and solar load control) and would lead to identical results regardless of the considered time frequency.
Three Case Studies
Following is an analysis of the hourly PV-load relationship and extracted capacity metrics for three distinct utilities: Nevada Power (NP), Portland General (PG) and Rochester Gas and Electric (RG&E) (see Figure 4) .
NP is a metropolitan utility in an arid western state, endowed with a considerable solar resource and a large commercial air conditioning demand. NP is summer-peaking by a wide margin (with a summer-to-winter peak ratio approaching 2). RG&E serves a medium-sized industrial city in upstate New York, where cloudy conditions are frequent. It also peaks in summer, driven by daytime industrial and commercial air conditioning but much less than NP (summer-to-winter peak ratio = 1.3). Finally PG serves the city of Portland, Ore. and vicinity. It was a winter-peaking utility until recent years, but is now becoming marginally summer peaking due to increased air conditioning use and a general climatic trend to warmer summers.
Using experimental load data for each utility for year 2003, site-time coincident PV outputs were generated via simulation of satellite-derived irradiance data. 7 Stationary flat-plate PV installations were optimized for mid-afternoon production (30 degrees tilt and south-west orientation).
Upon comparing these sets of metrics, the most striking observation is that all the metrics that account for PV penetration provide comparable measures of capacity. Only the time-season window leads to different results. With no dependence on penetration, the time-season window is unreflective of any load-PV relationship. This underestimation is understandable because within an arbitrarily predefined peak time window, there are many occurrences when the load is small and when reliance on PV output is not critical. It is thus arguable that the time-season window is not an appropriate measure of PV capacity credit, no more than the capacity factor should be a measure of capacity credit.
Selecting between the other metrics is not a critical choice, because they provide comparable results. By bundling minimal control/storage with generation, both solar load control and minimum buffer energy storage metrics eliminate the notion of risk associated with a non-dispatchable resource, introducing the notion of firm power delivery (100 percent reliability). The effective load-carrying metric is a slightly more conservative measure of capacity. Demand time interval matching shows more discontinuity than the others when plotted against penetration, because it is based on one single critical data point at the top of the load duration curve, and this point may shift significantly depending on the size of PV relative to the grid it serves.
There are two opposite viewpoints regarding PV capacity credit. In general, utilities consider PV to be an intermittent, energy-only source of electricity, while the solar industry regards it as a demonstrably reliable peaking resource. Presentations and arguments for each viewpoint were exchanged at