You partially answered your own question. Consider the reason you're often performing a permutation test. It's usually in circumstances where you have little faith in any particular parametric distribution or for some other reason want a non-parametric solution. In that case, how does one estimate power? You could be doing the permutation test in situations where the populations have multimodal distributions, uniform distributions, any combination for your conditions that don't match, etc. All of those might have different power functions.
Keep in mind that the way power is estimated requires an assumption of some world where you know effect size and distribution of the effect. Since you have no notion of the latter you can't estimate power a priori. The best you could do is estimate power for some particular distribution you might think is close, subset of likely distributions, or taking your data as the population. But the latter leaves you know way to get to power other than post hoc.