For Maximum Renewable Integration, Load Following Is King

July 5th, 2016 by Christopher Arcus

A previous CleanTechnica article reviewed the basics of power generation. That article is a good primer for this follow-up article delving deeper into the subject.

How do we most efficiently integrate the maximum amount of renewables into the grid? Let’s start with understanding how things work. As first-order simplification, we can examine a renewable source like solar to understand some of its characteristics. At a simple level, in a sunny area like the desert, and depending on latitude, solar will be available some eight hours of the day. Other places, it may shine through to the panels only six hours a day.

The ratio of output to maximum output is expressed as capacity factor. For well-sited utility-scale solar, this translates to capacity factors around 30%. With other techniques, like tracking, capacity factor can be higher.

But analyzing wind or solar output by capacity factor alone misses the bigger picture. If we look at daily demand variation, we see that solar matches some of the daily load variation very well.

What’s Wrong with Capacity Factor?

Looking at solar capacity factor without taking into account demand variation is like assuming demand is constant. That’s the problem with capacity factor. It doesn’t take into account load matching. Capacity factor only measures the amount of output available versus the maximum possible. The maximum possible is constant output. A constant output with no other sources could only drive a constant load.

“Looking at solar capacity factor without taking into account demand variation is like assuming demand is constant. That’s the problem with capacity factor. It doesn’t take into account load matching.”

By ignoring demand variation, we miss the essential problem. We must match demand all the time, but demand varies. We cannot match demand variation with fixed output sources like coal and nuclear alone. We must add flexible ones like gas to do that. The graph above shows that. That’s how we balanced demand variation in the past.

“We cannot match demand variation with fixed output sources like coal and nuclear alone. We must add flexible ones like gas to do that.”

That’s a source of the misunderstanding in the myth of baseload power. So called “baseload” power perpetuates the myth that there is a base “load” and that inflexible power plants are necessary to meet it. More on that later.

Inflexible power plants have a different problem. Their capacity factors are misleading because they cannot match load. A high capacity factor means being unable to match load variation. Capacity factor is a calculation as if the load never varies.

“Inflexible power plants have a different problem. Their capacity factors are misleading because they cannot match load.”

What about Wind?

Solar has a reliable daily variation, but wind is different. Wind can have a diurnal pattern near coastlines, as it does in California.

And wind can have other variations in different locations. A University of Wyoming study highlights the differences between California’s diurnal wind patterns that peak in summer months, driven by increased land–sea temperature differences.

“Analyzing this precise wind data over the course of days and over the course of a year, the UW researchers confirmed that Wyoming and California wind patterns are not only very different, but also very complementary. Based on a yearly average, California wind is strongest at night, while Wyoming wind is strongest during the day and peaks in the afternoon — coincident with the time when the sun is beginning to set while the electric load is still increasing into the evening hours.”

The study goes on to say:

“Although the benefits of geographic diversity to renewable energy have been suggested for some time, only recently have there been attempts to quantify these benefits,” says the study’s author, Jonathan Naughton, a UW professor of mechanical engineering and director of the Wind Energy Research Center. “The renewable energy quality metrics proposed in this study are a start at being able to characterize different combinations of renewable energy sources. The result of applying these metrics to energy produced from Wyoming wind and California renewables provides a quite compelling case for geographic diversity.”

… For example, the study looks at a scenario for adding incremental renewables to an existing portfolio and, when comparing Wyoming wind to more same-profile California solar, Wyoming wind would yield a 50 percent higher capacity factor; a 41 percent lower relative variability; and increase by 86 percent the amount of time in which power is producing at 25 percent or greater.

“Capacity factor alone does not fully describe a renewable resource or … the combination of renewable resources,” the report notes. “How these quality metrics would benefit California’s electrical grid are outlined in a separate companion report by Jim Detmers, former COO of the California Independent System Operator Corporation.”

“Capacity factor alone does not fully describe a renewable resource or … the combination of renewable resources.”

A Tale of a Different Metric

And there lies that tale. Capacity factor is not the most important factor in renewable integration.

Let’s take the case of solar. We can see the typical shape of the daily solar curve in the graph. A quick take on capacity factor says it’s about 30%, but what does that mean? It means that, compared to a flat power output all day long, solar is available for only those hours with its given curve. But demand is not flat all day long or over weekends, seasons, and so on. Demand has an annual pattern, with daily, weekly, and seasonal patterns. If we assume solar must have constant output, as the capacity factor metric implies, we will not realize that much more can be integrated than capacity factor suggests. That’s why load matching matters. Let’s take a look at those now.

“If we assume solar must have constant output, as the capacity factor metric implies, we will not realize that much more can be integrated than capacity factor suggests.”

How Demand Works

See how the peak demand varies over the year? The system operator must keep enough generators available for the annual peak demand, usually in July or August during hot summer air conditioning use.

Reserves to the Rescue

But that’s not all. There must be enough generation available so that unplanned generation failures can be quickly substituted with other sources. These are referred to as reserves. A look at this graph shows that, for much of the year, these reserves are much greater than the load or demand. The dashed line above the load curve represents the additional reserves available on a non-summer peak day.

The graph does not show weekly and other variations, but this animation shows demand variation all year long in great detail, but quickly.

This is one reason renewables can be integrated to high levels, as much as 40%, even without storage. Reserves must be available for the annual peak demand during summer. But most of the year, they are available to accommodate more variation than load.

But there is more.

“This is one reason renewables can be integrated to high levels, as much as 40%, even without storage. Reserves must be available for the annual peak demand during summer. But most of the year, they are available to accommodate more variation than load.”

Matching Generation to Demand

For electric power systems, the one rule is that generation must always equal load. But how do we follow this kind of load variation?

This graph shows how daily demand variation is met by various sources:

Here is an illustration, showing more clearly how inflexible generation like coal and nuclear must be kept below the daily minimum to avoid reducing output:

But how can we match variable renewables to demand?

Breaking the Storage Myth

Storage is not the only way to match variable renewables to demand. While storage has evolved rapidly, and is welcome, it’s need has been exaggerated. German experts don’t think large-scale storage will be needed for at least a decade, or until renewables reach 60%.

Other techniques such as demand management are cheaper and effective.

These two videos show some of the ways we can match renewable generation to demand without high levels of storage.

Busting the Capacity Factor Myth

The power system requires flexible generation to meet demand. Inflexible sources require flexible generation to meet demand variation like daily variation and peak demand in summer months. Reserves are necessary for generation failures and must be larger than the peak demand all year long, plus an extra amount for failures. Most of the year, these are available to accommodate other variation. Any amount of load following reduces the need for reserves or storage.

What happens if we size solar much larger than today’s amounts? Do we run into an integration percentage limit at capacity factor? Aside from using other techniques like demand management, transmission, geographic dispersal, and storage, have we accounted for renewable demand management? Not if we analyze renewables integration by capacity factor alone. What appears more obvious is that using all those techniques to match demand is a practical approach used even today. A simple approximation of demand versus solar output shows that solar integration could be higher than capacity factor because of load following. If we look at the summer demand curve and superimpose the solar output, we can see that more solar can be integrated than a fixed load implies.

But then the rest of the year, when demand is lower, solar output is reduced as well. Any load matching increases the amount of potential variable renewable integration beyond a simple capacity factor analysis because capacity factor inherently implies constant load.

“Any load matching increases the amount of potential variable renewable integration beyond a simple capacity factor analysis because capacity factor inherently implies constant load.”

One thing is clear: Any analysis of integration must take into account annual load variation and generation matching. Our exploration of the subject shows it’s too complex to accurately sort out from simple analysis and anecdotal information. That’s why NREL and other researchers run long simulations using years of wind and solar data to model generation and match them against load data to figure out renewable integration. We know how that came out.

What about Overcapacity?

Since demand reaches a peak in summer months, a variable renewable like solar sized to meet summer peak demand would have excess capacity the rest of the year. But that is what we have with today’s conventional generation. We see that, just as with today’s system, a future system of variable renewables will have excess generation, or reserves. We shouldn’t be afraid of overcapacity, since we are using it in today’s conventional generation system.

Conclusion

We don’t need to be concerned about arguments that renewable capacity factor is like falling off the edge of the world. A deeper understanding of the power system reveals what the real issues are and that the technical problems, while challenging, are not insurmountable. Simple hand waving is not the way to understand complex systems. An approach like that used by researchers at NREL is a more realistic way of understanding renewables integration. And any load matching that renewables provide reduces the need for flexible sources and other techniques to adjust to variable output and demand.











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