Moving towards a low-carbon economy will require integrating massive quantities of renewable power into the electricity grid. This transition will create many challenges as existing electricity market institutions created for conventional fossil fuel-fired power plants must adapt to renewable resources. Here we discuss one of these challenges: accounting for renewable sources of power in electricity capacity markets.
Electricity grid reliability is a crucial problem, as the crisis in Texas during February 2021 showed. Many parts of the country use administratively-created capacity markets to compensate generators for their contributions to grid reliability. Capacity markets operate separately from electric power markets, and pay resources for being available to produce power rather than for actually providing power itself. Capacity markets have become an important source of revenue for generators, and therefore the availability of such revenues can determine whether a new generation resource gets built. Yet determining the contributions of intermittent solar and wind generators to the reliability of the grid poses difficulties.
The traditional way to account for a generator’s contribution to reliability is to discount its compensation based on its unavailability. Thus, if a power plant is down for unscheduled maintenance or otherwise unavailable 5 percent of the time, then its capacity can be measured as 95 percent of its output. This measure is known as a capacity factor, and it is relatively easy to calculate with available data. The average capacity factor of a wind turbine is about 35 percent, and the average capacity factor of a solar project is about 25 percent.
But capacity factors provide only a crude measure of a generation resource’s actual contribution to reliability, because they account only for the average availability of a resource over time. This implicitly assumes (a) that contributions to reliability are equally valuable at all times included in the calculation; and (b) that each resource’s availability is independent of the availability of other resources on the grid. Neither of these assumptions holds true, especially for renewable resources. There are times at which ample power is available from other generators, and so a particular generator makes almost no incremental contribution to reliability. At other times, a single generator may be crucial to keeping the entire grid operational. Moreover, factors that affect one renewable resource, such as whether the sun is shining brightly, will tend to affect others as well.
Ideally, because the ultimate goal of a capacity market is to protect reliability, capacity markets should compensate generators for being able to produce power at the times when reliability is most threatened—for example, during periods of peak demand. A method known as Effective Load Carrying Capability (ELCC) attempts to apply this principle. ELCC evaluates how much electric power can be expected from a particular generator during periods when overall system capacity is most scarce and therefore reliability is most threatened. Unlike the calculation of a capacity factor, the method of calculating ELCC is complex and subject to controversy. And because the ELCC calculation would determine a resource’s compensation in the capacity market, a generator’s revenue likely depends heavily on its ELCC rating.
This combination—methodological complexity and high stakes—creates the potential for a regulatory quagmire. Generators of all kinds—renewable, fossil fuel, nuclear—can be expected to argue in favor of higher ELCC ratings for themselves that will lead to higher revenues in the capacity market. The institutions that ultimately decide such questions—regional transmission organizations (RTOs), the Federal Energy Regulatory Commission, and courts—seem unlikely to be in a position to properly evaluate competing ELCC calculations. Policymakers within RTOs and FERC would be thus left in the unenviable position of deciding between the oversimplified capacity factor methodology and the overwhelming complexity of ELCC.
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