The "purchase likelihood" simulation approach is an old relic from conjoint analysis in the 1980s (full-profile ratings-based conjoint analysis, or also ACA, where the data were scaled properly for the equation below to return a least-squares fit to the respondents' stated likelihood of purchasing concepts on a 100-point scale).
The math does not consider the None alternative. It simply is:
p = e^U/(1+e^U)*100
where p is the purchase likelihood,
e is Euler's constant
U is the total utility of the product concept
(e^U may be computed in Excel as =EXP(U) )
If you want to compute the likelihood of respondents selecting a specific product vs. the None, then you should use "Share of Preference" simulation method, and specify just the product and the None alternative to capture share of choices (share of votes) across respondents.