Reducing Electricity System Variability with Solar Power

Solar power is often thought to increase the variability of electricity systems. In a recent paper (open access), Ken Caldeira and I show that adding solar to electricity systems, where solar power correlates with electricity demand, can actually reduce the variability in peak residual electricity load.

Residual Load

Residual load is electricity load (demand) minus generation from variable resources, such as wind and solar. Residual load represents the load that must be supplied by more controllable resources: firm generation (gas, nuclear, etc.), energy storage, and demand response.

Depiction of residual load (dashed line) and the peak residual load value

For a system operator, peak residual load indicates a lower bound on the quantity of firm generation, stored energy, and demand response that must be available in their system to supply all electricity loads.

As wind and solar are added to our electricity systems, system planners will likely rely on estimates of future peak residual load and how the peak values vary from year to year as crucial planning metrics.


We estimate the peak residual load and how much it varies from year to year as wind and solar generation are added to four example electricity systems. From this, we find that the variability in the peak values changes as more wind and solar are added.

The variability (spread) in the peak residual load values from year to year is depicted for systems as wind and solar generation increase. In PJM, for example, progressing vertically in the figure indicates adding solar generation. As solar generation is added, the variability decreases as indicated by the darker colors.

Interestingly, for the three modeled systems that experience their peak electricity usage in the summer months (ERCOT in Texas, PJM in the mid-Atlantic, and NYISO in New York state), adding solar statistically reduces the spread in the peak values from year to year.

These three summer peaking systems show a strong correlation between peak electricity usage and the hottest days. The hottest days are indicated by the largest “daily degree day” values in the below figure.

Thus, by adding generation that correlates with the most extreme peak load hours, electricity systems can become more predictable even if that generation is from a variable renewable resource like solar.

Reducing the spread in the peak values from year to year could possibly make system planning simpler by having more predictable peak residual load values.


We used historical electricity load data from the four studied systems: ERCOT, PJM, NYISO, and France.

Ten years of historical electricity load (demand) for the studied regions.

We used historical weather data to derive plausible wind and solar generation profiles concurrent with the load data.

We incrementally increased the contributions of wind and solar generation from zero to generation equivalent in quantity to providing 100% of annual load. For each residual load profile, we assessed the spread in the peak values from year to year.

To calculate the spread in the peak values, called the inter-annual variability (IAV), we take the mean of the 10 peak residual load values from each year of data and calculated the standard deviation of these 10 mean values.

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