I want to analyse the Price of electricity in germany and have already bulid a dataset with 70 Variables

(Solarproduction, Windproduction, Nuclearproduction and so on all on same scale )

These Variables are all more or less correlated with the price and the dataset is about 20000 observations (hourly resolution)

**Goal:**find the variables that cluster together and those who are responsible for changes in the price variable.

I am already aware what PCA does so you do not have to explain this to me.

**Problem:**If i am making my PCA i have a few issues: 1) my 1. PCA only describes 24 % of the variance and my second and third only around 15 % which isnt much

2) When trying to find clusters and dependencies in the factor map i also end up with no nice picture,

So i dont get a "Price is high" and "Price is low" cluster in any of these PC1/PC2 PC1/PC3 PC2/PC4 and so on plots

Further i thought i can get the important variables by looking at the PC1 and PC2 contribution. But also here my best Variables only have around 5% of PC1 and PC2 and so i cant tell which variables are important and which not

So what can i do to find the variables that cluster with high prices or are the variables that are responsible for the variance in the dataset

Am i considering to much/to uncorrelatet/to few variables ???

What am i doing wrong ?

I would be so happy if someone can help me a bit out