feat: generate charging profiles for smart charging HDV #301
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dmuldrew
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Aug 31, 2022
| hdv_daily_mat = loadmat(filepath) | ||
| hdv_daily_vmt = hdv_daily_mat["hdv_daily_vmt"] | ||
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| return hdv_daily_vmt[hdv_year-1, hdv_cat-1] |
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Probably want to check that input year is in the list mentioned in the doc strings
dmuldrew
reviewed
Aug 31, 2022
| """ | ||
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| range_vmt = data["Trip Distance"].copy() | ||
| range_vmt[data["Total Vehicle Miles"] > veh_range] = 0 |
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Check that veh_range values are as expected
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Pull Request doc
Purpose
What is the larger goal of this change?
To add the Python translation of the smart charging HDV code
What the code is doing
How is the purpose executed?
Similar to the smart charging code for LDVs and LDTs, this code iterates through the HDV vehicle data to determine the optimized charging while taking into consideration dwelling activity and charging costs.
Testing
How did you test this change (unit/functional testing, manual testing, etc.)?
Output from this code was compared against the MATLAB output.
Where to look
It's helpful to clarify where your new code lives if you moved files around or there could be confusion/
The new code can be found in the transportation_electrification folder, "PreREISE/prereise/gather/demanddata/transportation_electrification/".
What files are most important?
smart_charging_HDV.pywhich contains the main algorithmdwelling.pywhich has additional helper functions specific to the HDV codefdata_v10st.matwhich is a new dataset with HDV vehiclesUsage Example/Visuals
How the code can be used and/or images of any graphs, tables or other visuals (not always applicable).
Time estimate
How long will it take for reviewers and observers to understand this code change?
It can take a few days to go over the code change.