Researchers are often interested in whether the effects of an intervention differ conditional on individual- or group-moderator variables such as children's characteristics (e.g., gender), teacher's background (e.g., years of teaching), and school's characteristics (e.g., urbanity); that is, the researchers seek to examine for whom and under what circumstances an intervention works. Furthermore, the researchers are interested in understanding and interpreting variability in treatment effects through moderation analysis as an approach to exploring the sources of the treatment effect variability. This study develops formulas for power analyses to detect the moderator effects in designing three-level cluster randomized trials (CRTs). We develop the statistical formulas for calculating statistical power, minimum detectable effect size difference, and 95% confidence intervals for cluster or cross-level moderation, nonrandomly varying or random slopes, binary or continuous moderators, and designs with or without covariates. We demonstrate how the calculations can be used in the planning phase of three-level CRTs using the software PowerUp!-Moderator.