Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling

Round 1

Reviewer 1 Report

This paper proposed a framework to compare the load shifting potential of power-system considering different charging strategies of BEV. A case study is conducted for the German situation. The results showed that vehicle user-based charging decision significantly decreases the load shifting potential of control charging comparing to the power-system-based charging decision. The paper is well-written and very interesting.  There are a couple of minor issues.

Line 137, ‘… form power demand in the transportation sector ()’ is not completed. Section 2.3, the equations are not numbered. Moreover, the notations are not given for some of the variables in the equations. in Fig. 4, the title of the x-axis is not given.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

This is a well-written paper! The authors have done a thorough job in combining different kinds of models to understand BEV representation in energy systems models--which is the most challenging part of the transportation sector representation due to its numerous moving parts. 

A few minor comments:

The authors do mention the limitations of the study especially the case where the user has to charge in his home in one study--this is actually a really important point. As more people start adopting BEVs, we may start to see people from multi-household buildings purchase the car. They may not have dedicated access to the charger, and we may see the increasing dependency on the workplace or public chargers. Similarly, as rideshare technologies or gig economies gain traction, we may see a drastic difference in the trips/travel behavior (which is again listed as limitations, but it could be elaborated in this context). Sentence 287-289 talks about the probability of finding an available charger and charging power differences--believe they are selected in Monte Carlo simulation. How are they different from each other? Is it randomly selected? Or is there a distribution to choose from or any other underlying factors? For eg. charger availability could be a function of the density of BEVs in the neighborhood, etc. Sentence 305-334: This section has really important points that illustrate the differences between CURRENT and VencoPy model outcomes. But, it is a bit hard to follow as the reader has to remember various parameters / go back to check. It would be nice to represent this as a table/figure so that the assumptions and outcomes are easy to follow and look-up. 

Author Response

Please see the attachment.

Author Response File: Author Response.docx