The purpose of this study is to propose a general framework for power analyses to detect the moderator effects in two- and three-level cluster randomized trials (CRTs). The study specifically aims to: (1) develop the statistical formulations for calculating statistical power, minimum detectable effect size (MDES) and its confidence interval to detect the moderation effects in two- and three-level CRTs, which include same and cross-level moderation, binary and continuous moderators, and covariates; and (2) operatize these formulas in the enhanced version of "PowerUp!" (Dong & Maynard 2013) to create spreadsheets for calculating power, MDES, and other information. This study covers the same and cross-level moderation in two- and three-level CRTs. For the cross-level moderation, i.e., the moderator at the level lower than the treatment level, there are two options: the fixed slope and random slope of the moderator variable. The results of two-level CRTs with a treatment variable at Level 2 and a moderator at Level 1 are presented. The standard error formulas indicate that the standard error of the moderation effect estimate is not associated with the residual variance for the intercepts, but is associated with the residual variance at Level 1. This suggests that adding more covariates in the intercept model would not reduce the standard error or improve power to detect the moderation effect, which is different from the main effect analysis. However, adding more covariates at Level 1 that can further explain Level 1 variance would reduce the standard error and increase power. A table is appended.