I'll go with the cliche example - coin flipping. Note that I'm abandoning rigor and some important assumptions in this example, but that's just fine.
Let's say I have a regular coin - that is, once I flip it, it has a 50% chance of landing heads and a 50% chance of landing tails.
So if I flip it 10 times, I'd expect 5 tails and 5 heads. But I could very well get 6 heads and 4 tails. Or 7 heads and 3 tails.
But wait a second - why would I expect 5 tails and 5 heads? Maybe it's obvious - because each flip has a 50% chance of landing heads - so $50\% \times 10 = 5$. In other words the expected value of my coin flipping exercise is 5 heads (and therefore 5 tails) .
Let's make the example more interesting now. Let's flip the coin 100 times. But check it - nothing changes in terms of my expected value. I still expect half of the tosses to be heads - i.e. 50 heads.
But in reality I might not get 50 heads. Let's say I got 45 heads. Is that far from my expected value of 50? Should I be surprised by that result? Would you be? Probably not. If I told you that I got only 20 heads, then you might think something's up. Why do you think that is?
That's sort of the intuitive notion of variance. How likely is it for our results to deviate from the expected value? Some things (like coin flips) have a pretty good chance of deviating from their expected value. Other things don't.
We can put a number on this. In some instances, for mathematical convenience and interpretability, we can take the square root of this number. That's the standard deviation.
The definitions you refer to above are more technically accurate and have direct mathematical formulations, hence terms like probability weighted and random variable.
But if you understand the coin flipping example, then you'll understand the spirit of the terms.