You may indeed sum the part-worths to get a relative preference score for different product concepts. But, the real trick is if you get a score of 180 for one concept and 90 for the other, what does this mean?
Certainly we know that the first product is more preferred on average than the second. But, is it twice as preferred? Only a little bit more preferred?
Part-worth utilities (whether raw or zero-centered) are not ratio scaled, so one cannot say that a total utility of 180 is twice as good as a total utility of 90.
Because researchers often want to be able to state if one product is twice as preferred as another (as opposed to just saying one is better than the other), researchers are usually looking for analysis approaches that lead to probability (ratio) scaled outcomes.
Running market simulations based on the RFC or Logit rule for CBC data can lead to such ratio-type interpretations. But, summing the part-worth utilities (which are interval scaled) does not.