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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Emerging opportunities for hydrogen production as a flexible electricity load

Wind and solar generation are powering more and more of our electricity systems. Along with their zero-carbon electricity comes their variability and uncontrollable power output.

Utilities are increasingly tackling the variable nature of wind and solar power by building energy storage to shift available power from when it can be produced by nature to when it is most needed by the grid.

There is growing interest and possibilities in tackling the variability issue not by shifting available power to meet electricity demands, but by shifting electricity demands to meet available power.

One potential candidate flexible load candidate is producing hydrogen gas by splitting water using electrolysis. Producing low-cost hydrogen with minimal carbon emissions is currently viewed as a cornerstone of an energy transition away from carbon emitting sources.

Our new paper

We recently published a paper in Advances in Applied Energy considering producing hydrogen as a flexible electricity load (demand) in future low-carbon electricity systems.

We asked how the operations of future electricity systems would change if we introduced a small, flexible hydrogen producing load. Is there essentially “free” electricity available to a business who can choose to operate only when the sun is shining and wind is blowing? How much “free” electricity will there be?

Study results

We find that in systems with substantial wind and solar power, zero cost electricity is available sometimes and low-cost power is available almost always. In fact, in modeled systems powered exclusively by wind and solar power, zero-cost, zero-carbon power was available more than 95% of the time.

One enticing thing about flexible loads is when other electricity uses are pushing the grid to its maximum extent and power costs are high, flexible loads can simply throttle back or even turn off.  This would save them considerable money and could save the grid from needing to expand generation capacity, a win-win situation.

However, if we really push the envelope with vast amounts of flexible loads like electric vehicles and by producing hydrogen, the grid’s generation capacity will eventually need to expand. After all, there is only so much zero-cost and low-cost power available in the original electricity system.

Many more interesting results and all the details can be found in the paper.

I am looking forward to continuing this line of work and further exploring the integration of hydrogen production with low-carbon electricity systems and how both can enable a low-carbon energy transition.