To clarify for others that might be reading this post, the calibration step in ACBC only affects the scaling of the None utility; it does not modify the HB estimates of the attribute levels included in the experiment.
I gather you are using the simulation method for estimating WTP, based on finding the difference in price that causes the share for two products (one with enhanced feature and an otherwise identically defined product without it) to be 50/50. Because a constant utility added to alternatives in a logit simulation factors out, it wouldn't matter what other attribute levels the two products were given, as long that they are equally given to both products.
However, when a third (or additional) alternatives are specified in the competitive scenario (such as the None alternative), then the WTP will change.
In general, we think that WTP estimates (from the two-product simulation scenario approach) are exaggerated and tend to represent the theoretical maximum the market would pay if there weren't competitive offerings also providing those enhanced features or other features that would otherwise compensate.
So, to obtain more realistic estimates of WTP, a suggested approach is to try to represent as completely as possible the full set of competitive alternatives in the marketplace, which includes the ability to opt out (the None choice). So, let's assume it is believed that the market is adequately represented by 12 products specified in a competitive market simulation scenario (the highest market share options represented at their respective brands, features, and prices) along with a 13th option being the None. One of those products becomes the test product for which WTP for enhanced features will be estimated. We first simulate that test product versus the other 12 alternatives at the base price and base feature for the test product. Next, we modify that product by enhancing a given feature and simultaneously adjust the price upward until its share is driven back to the base case value. Note we are not adding a new enhanced product alternative to the simulation scenario; we are modifying the test product to improve it while holding the number of product alternatives constant.