nrel / evi-pro-lite Goto Github PK

Software

• This script was written using python version 3.7.4. Python can be installed through downloading either:
  • The Anaconda Navigator which comes with the necessary packages preinstalled (recommended)
  • Downloading Python 3 directly
    • This will require the installation of the packages used in this script. Each of those packages can be downloaded through terminal using the pip install <package name> command
    • Packages include: pandas, numpy, matplotlib, requests, json, urllib3, requests, datetime, os

To Run

• Run this script by opening terminal and navigating to the directory the script is downloaded to

• Enter 'python' to start python shell and 'import EVIProLite_LoadPlotting'

• EVIProLite_LoadPlotting.run("<file path to scenario csv with parameters as described above>","<file path to optional temperature csv>","<Your API Key>")

  • Without your own API Key, the script will use the public key (DEMO_KEY) which is rate limited
  • Sign up for an API Key here: https://developer.nrel.gov/signup/

  You can run the script using the provided csvs as a test with: EVIProLite_LoadPlotting.run("./InputData/Scenarios_test.csv","./InputData/ShortTemps_test.csv")

Plotting

• The script will produce plots automatically as part of the run function but csvPlotting can alternatively be used to import data and plot

• Defaults to plotting the first week of data

  • Alternatively, the user can use the csvPlotting function to import and plot data from a start date and for a given number of days
    • This function allows the user to specify the number of days and the start date (in yyyy-mm-dd format) for plotting the grid load.
    • Run with EVIProLite_LoadPlotting.csvPlotting(<file path to load profile csv>, <start date>, <number of days>, <optional filename>)

• The plots will be saved to the OutputData folder

• Input csv must have values in order as follows:

  • fleet_size, mean_dvmt, temp_c, pev_type, pev_dist, class_dist, home_access_dist, home_power_dist, work_power_dist, pref_dist, res_charging, work_charging
  • View ./InputData/Scenarios_test.csv for proper formatting of this file

• Each row then represents a single run. All runs from the input csv are output in a dataframe with data for both weekend and weekday

• The temperature csv needs two columns: "date" and "temperature"

  • Each row in the temperature csv is one day and function aggregates across all included dates.
  • View ./InputData/ShortTemps_test.csv for proper formatting of this file
  • Graph is cumulative so doesn't matter how many days are included in the input

• Assume temp CSV is in celsius

Available parameter values for running the tool include:

"fleet_size" : [50000,10000,1000],
"mean_dvmt" : [45,35,25],
"temp_c" : [40,30,20,10,0,-10,-20],
"pev_type" : ['PHEV20','PHEV50','BEV100','BEV250'],
"pev_dist" : ['BEV','PHEV','EQUAL'],
"class_dist" : ['Sedan','SUV','Equal'],
"home_access_dist" : ['HA100','HA75','HA50',],
"home_power_dist" : ['MostL1','MostL2','Equal'],
"work_power_dist" : ['MostL1','MostL2','Equal'],
"pref_dist" : ['Home60','Home80','Home100'],  
"res_charging" : ['min_delay','max_delay','midnight_charge'],
"work_charging" : ['min_delay','max_delay']