The impact of using different scaling strategy with Clustering
Welcome to the community!
Some points which might help:

Clustering, as an unsupervised task, can not be evaluated and usually some external criteria are used to find the best clustering.
According to the point above, better to make those assumptions as direct as possible. Starting with EDA (inspecting histograms, plotting boxplots, etc.) gives you a better initial point for analysis. Trying to understand the underlying structure of data directly from decrease of WSCC seems pretty indirect to me thus more difficult. For example a simple EDA tells you if data needs scaling urgently or not. Specially iin your data, ranges are not &quot;dramatically&quot; different, but if you see histogram, you may see, for instance, a exponential feature which needs a log-transform instead of scaling.
The last but not the least, the larger the number of clusters get, the smaller the WCSS become. You see it in plots. So the question is if these three scaling show a significant difference in dropping point of WCSS, which they don't and boosts this idea that scaling is not the biggest help here. Otherwise your code is pretty right (and well-written ;) )

I hope it could help to some extent.