We cannot just swap 24/7 fossil fuel power plants for intermittent renewables. To prevent electricity shortfalls the capacity of a solar or wind plant must exceed that of the fossil fuel plant it replaces. But by how much? That’s the question that the Effective Load Carrying Capability (ELCC) metric is designed to answer. It’s not a new concept, but is now becoming very important. Mark Specht at the Union of Concerned Scientists explains the challenges and describes the factors that determine a particular plant’s ELCC. It’s a measurement of a plant’s ability to produce energy when the grid is most likely to experience electricity shortfalls. So the two most important factors are when a resource generates electricity (e.g. season, time of day), and when electricity shortages are most likely to occur (i.e. when are the demand peaks, and what resources already exist in your grid that can support those peaks). For example, in a particular grid in a particular region, a 100MW solar plant with an ELCC of 30% could make a 30MW contribution towards reliability requirements. Specht flags up that increasing the quantity of a resource affects its own ELCC. As you add more solar to your grid the ELCC of that solar goes down. To counter that, and even increase solar ELCC, a diversity of electricity-generating technologies is required.
As clean energy continues to grow rapidly across the country and clean energy technologies become our dominant source of electricity, grid operators and utilities are re-examining how to ensure grid reliability and prevent electricity shortfalls.
Back in the days before renewable energy was widespread, it wasn’t very hard to figure out if the grid could be operated reliably. Grid planners simply projected peak load levels, which typically occur during the afternoon of a hot summer day, and made sure they had enough polluting fossil-fuelled power plants sitting around and ready to turn on when needed. Now we live in a day and age where some parts of the country generate more than 30% of their electricity with variable renewables, and that number will fast approach 100% in the decades to come.
To completely transition away from fossil fuels, we’ll need to replace not only the energy from fossil-fuelled plants, but the capacity and grid reliability contributions as well. However, it’s actually pretty tricky to determine the extent to which renewable capacity ensures grid reliability.
This has grid operators across the nation asking, “To what extent can we count on renewables to ensure grid reliability by preventing electricity shortfalls? And what is the best methodology to answer that question?”
Great questions. This blog has answers. So take a seat, and I’ll connect the dots between an esoteric concept, Effective Load Carrying Capability (ELCC), and its critical role in smoothing the transition to clean energy.
A crucial concept: Effective Load Carrying Capability (ELCC)
To determine the extent to which renewables can ensure grid reliability, many grid planners have embraced a concept called effective load carrying capability, or ELCC for short. ELCC is not a new concept, but its use has skyrocketed in the past decade.
At its core, the ELCC of a generating resource is a measurement of that resource’s ability to produce energy when the grid is most likely to experience electricity shortfalls. ELCC is typically expressed as a percentage of a resource’s capacity, for example, a 100 MW solar plant that has an ELCC of 30% could make a 30 MW contribution towards reliability requirements.
It’s important to note that the reliability requirements discussed here are designed solely to prevent electricity shortfalls. However, there are other causes of power outages and other facets of grid reliability that ELCC does not really address. ELCC is all about a resource’s ability to prevent power outages due to electricity shortages, and though this type of power outage is rare, these outages can have serious political consequences and a big impact on the people who lose power.
How is ELCC calculated?
Calculating ELCC values requires probabilistic grid modelling. Without going into too much detail, the process involves running many different simulations where important variables like electricity load and renewable generation vary randomly in each simulation. There are a few different methods for performing ELCC calculations, but it usually involves determining how much “perfect capacity” would be required to “replace” a resource (or group of resources) across all those simulations.
Here, “replace” means substituting the resource in question with just enough “perfect capacity” to achieve the same level of grid reliability. “Perfect capacity” can be thought of as a mythical power plant that never has any outages, can ramp up and down instantly, and can operate 24/7/365. (The ELCC of perfect capacity is, by definition, 100%.) So if it takes 30 MW of “perfect capacity” to replace a 100 MW solar plant, the ELCC of that solar plant would be 30 MW / 100 MW = 30%.
On what factors does ELCC depend?
When ELCC calculation methodologies get applied in the real world, it quickly becomes apparent that the results are sensitive to a whole slew of factors. For instance, ELCC calculations depend on the type of renewable technology being studied, patterns in electricity usage, and the type and quantity of other resources already on the grid. But ultimately, ELCC is a measurement of a resource’s ability to prevent electricity shortages.
Therefore, there are two overarching factors affecting ELCC calculations. The first factor is the resource’s abilities, which determines when a resource generates electricity. The second factor is electricity shortages, or more specifically, when electricity shortages are most likely to occur. Because energy usage patterns and the resources already on the grid can significantly influence the likely timing of shortages, they heavily impact the outcome of ELCC calculations.
Example 1: Factors determining solar ELCC
Solar is a great example of how a resource’s abilities influence its ELCC. There are natural variations in the solar production profile over the course of year, which results in different solar ELCC values for different seasons. In fact, California regulators actually calculate monthly ELCC values for solar and wind power because their contributions towards reliability vary so much.
The basic idea is that, since many parts of the country typically encounter peak load in the summer during the late afternoon or early evening, solar is better at ensuring grid reliability during the summer when the sun shines well into the evening, but that’s not the case in the winter.
Another factor that influences solar production profiles, and in turn, solar ELCC values, is the technology type. For instance, a fixed-tilt solar farm would have a lower ELCC value than an axis-tracking solar farm (i.e. solar panels that reorient themselves to face the sun as it moves through the sky). That’s because the axis-tracking solar farm follows the sun and continues to produce electricity at higher levels into the evening hours, when that electricity is needed most.
Example 2: As renewables go up, their ELCC can go down
As I already mentioned, ELCC values depend heavily on when electricity shortages are most likely to occur, and the type and quantity of resources already on the grid have a significant impact on the likely timing of electricity shortfalls. This leads to an interesting phenomenon where the quantity of a resource affects its own ELCC. For example, holding all other variables constant, as you add more solar to the grid, the ELCC of that solar goes down.
Why does this happen? The very first solar power plant you add to the grid is a reliability rockstar, tackling daytime reliability shortfalls with ease. But as you add more and more solar plants that are all producing electricity at the same time, it reaches a point where all those solar plants are preventing daytime reliability issues so effectively that the remaining reliability challenges move into the evening hours when solar can’t help. At this point, adding more solar does very little to prevent electricity shortages.
…so diversity matters
Unfortunately, ELCC is much more complicated in the real world because you’re never “holding all other variables constant.” In reality, different types of generating resources interact with each other and create “diversity benefits” that can boost ELCC values.
For instance, adding lots of solar power can move reliability issues into the evening hours, which would increase the ELCC of wind power that produces electricity in the evening hours. But adding evening-producing wind power could push reliability issues back into the daytime, increasing the ELCC of solar power. Thus, diversity in renewables can boost ELCC values and ensure grid reliability at a level greater than the sum of the individual reliability contributions. For this reason, renewable diversity can be a critical component of ensuring grid reliability in the transition to clean electricity.
Why does ELCC matter?
In order to successfully transition away from fossil fuels and actually shut down coal and gas plants, you can’t just replace the energy from those plants, you have to replace their reliability contributions as well. Adding replacement renewable energy to the grid is easy, but it’s much more complicated to figure out the extent to which you can rely on renewable capacity to ensure grid reliability by preventing electricity shortfalls.
That’s where ELCC comes in – it’s one of the main methods that grid operators use to measure the reliability contributions of renewables. Accurate ELCC calculations can help smooth the transition to a grid powered by high levels of renewables, ensuring the lights stay on as the grid transforms.
Lastly, some of you may be wondering why I haven’t said a word about energy storage, which is poised to play a critical role in the transition to clean energy. Of course, there’s a reason for my silence: the application of ELCC to energy storage is complicated enough that it deserves its very own blog post… Coming soon!
Mark Specht is an energy analyst for the Climate & Energy program at the Union of Concerned Scientists
This article is published with permission