I tried to understand what we should do before PCA: standartization (x-m)/s or normalization (scale into [0, 1] interval). In the sklearn tutorial they use standardization and show that PCA with standardization performs better:

https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html

And I found the same in other answers, e.g this one states that PCA will pay more attention to features with higher variance, so you should make all vatiances the same:

https://datascience.stackexchange.com/questions/86448/principal-components-analysis-need-standardization-or-normalization

But this is an approach of PCA – maximize the variance of projected data. How will PCA maximize the variance if standardizised data have the same variance in all directions?