Measuring the contribution of variable and supply-limited resources to grid reliability is becoming increasingly important as such resources expand their role in the electricity grid. Several large system operators have recently adopted Effective Load Carrying Capability (ELCC) as an approach to measuring these contributions. ELCC measures a resource’s contribution to reliability based on the incremental quantity of load that can be satisfied by adding the resource to the grid.
ELCC represents an important advance in calculating resource adequacy, as it better reflects the realities of how supply resources contribute to system reliability compared to previous methods. Computing ELCC, however, generally involves sophisticated and complex Monte Carlo modeling to account for the numerous factors that affect system reliability. Embedded in this modeling are many judgments that affect the results. The complexity of the modeling creates a ‘black box’ that makes the embedded judgments and their implications difficult to assess.
In this paper, we provide a simple model that avoids much of the complexity of the full Monte Carlo model while preserving the core essence of the ELCC calculation. While it does not generate precise estimates of ELCC, the model illustrates the basic factors that affect ELCC calculations and illuminates some core results. Results from the model show the following:
Different ELCC methods, and in particular whether to measure ELCC based on marginal or average values, significantly affect how much particular resources are credited for their capacity.
The marginal ELCC for solar and wind resources declines quickly as their share of total power production increases in the grid, implying that increases in these variable renewable resources will increase the benefits of complementary resources such as electricity storage facilities. The effect is especially strong for solar power, apparently because output from solar resources is inherently limited to certain hours of the day. This prevents solar from contributing to reliability at other hours when it is not available. In contrast, storage, which has no such limitation, does not have similar declines in marginal ELCC.
Using any type of ELCC averaging approach creates choices about ELCC resource classification that can have large impacts on derivate ELCC ratings.
Speaker Bios: Todd Aagaard is Professor of Law at the Charles Widger School of Law at Villanova University. He is the author of numerous law review articles in the fields of environmental law, energy law, and administrative law, as well as a casebook, Practicing Environmental Law (Foundation Press 2017). Professor Aagaard earned his J.D. and M.S. degrees from the University of Michigan. Following his graduation from Michigan, he clerked for Judge Guido Calabresi on the United States Court of Appeals for the Second Circuit and then served for eight years as a staff attorney in the Environment and Natural Resources Division of the U.S. Department of Justice.
Andrew N. Kleit is Professor of Energy and Environmental Economics and MICASU Faculty Fellow at The Pennsylvania State University. He is the author of over 80 published academic articles and six books in the areas of energy, regulation, and antitrust, including Modern Energy Market Manipulation (Emerald 2018). He has been a staff economist at several government agencies, including the Federal Trade Commission and the Federal Energy Regulatory Commission. Professor Kleit has a Ph.D. in economics from Yale University.
Professors Aagard and Kleit together have co-authored the only book focusing on U.S. electricity capacity markets, aptly entitled Electricity Capacity Markets (Cambridge University Press 2021), as well as several journal articles about capacity markets.