By "closely related samples" we mean that you have to have some data that can be used to make a reasonable estimate of the variability you expect to see between replicates in your data. If there are other methods that purport to do a power analysis without doing this then they may be just making stuff up. This is because any power calculation requires: the false positive rate, the magnitude of the change, and the variability between measurements. The first two you can set. The variability you have to measure.
By "may not provide actual scenerio" I think you must be referring to the warning labels. We put these in to appease reviewer 3, no wait, I mean we put these in to warn our users that power analysis is difficult and if the samples have higher than expected variability then your power estimates can be wildly off. For example, partially-degraded or low input clinical samples have a lot more technical variability than samples freshly out of a cell line, and if you try to predict the behavior of clinical samples from cell line data you are going to have an under powered experiment. But if your samples have similar variability to your experiment, as you would get from pilot data, the predictions work well.
But for easy experiments the variability in the biological replicates was around 30% over-dispersed from Poisson for several experiments. I did this chart for a talk last week, based on that and our methodology. It might be helpful. It shows the predicted power based on a typical well-measured gene (1000 reads total). The total number of reads is fixed. They just get divided into more replicates.
In general, I am typically skeptical about statistical power calculations - there are so many variables, it is very hard to tell how the results will turn out. I would advise just picking a method (like Scotty) to make some estimate that justifies including X samples to be able to detect genes with greater than Y fold-change. In reality, I would recommend getting as many patient samples as you can get your hands on (I prefer public data sets with at least 100 patients per cohorts), triplicates for cell line studies, and somewhere in between for animal models (I would recommend at least 6 replicates per group for mouse model studies).