import_scenario

Read a .json file that contains the configurations

Description

A .json file contains basic simulation parameters, and the filenames of EV data, and a demand (base load) or a price of electricity, both of which are written in .xlsx (MS-Excel) files.

Syntax

sc = import_scenario('my_json_file.json')
sc = import_scenario("my_json_file.json")

Input Arguments

Name	Type	Description
`jsonfile`	See Additional Explanation	The .json file name with extension .json.

Output Arguments

Name	Type	Description
`sc`	Scenario object	Validated class variable

Additional Explanation

Manual creation of a scenario may be a hard job. In order to facilitate the job, one can use Microsoft Excel program for managing EV list and their properties, for the base loads, and the electricity prices. The simulator presents a function import_scenario which read the Excel file as well as a JSON file that contains basic parameters for the simulation.

1. Components of JSON file

A text file, called a JSON file, can be located in a working directory, whose sample looks like:

{
  "Problem_type": "Lf",
  "N_c": 50,
  "N_ev": 50,
  "T": 24,
  "N_g": 1,
  "Delta": 60,
  "P_min": -200,
  "P_max":  200,
  "Baseload_file": "Baseload.xlsx",
  "EV_file": "EV_Lf.xlsx"
}

The JSON file should contain the following components.

Key	Type	Description	Note
`Problem_type`	“Lf” \| “Uf”	Type of the optimization problem to be solved: either load flattening or user-friendly problem
`N_c`	positive integer	Number of chargers
`N_ev`	positive integer	Number of EVs
`N_g`	positive integer	Number of groups (default: 1)	used for analysis
`T`	positive integer	Number of time slots. Since each time slot corresponds `Delta` minutes, the length of the scenario is `Delta` x `T` min.
`Delta`	positive integer	Time interval of a time slot (min)
`P_max`	positive integer	Maximal charging power of aggregate EV load (kW)
`P_min`	negative integer	Maximal discharging power aggregate EV load (kW)
`graph_type`	“star” \| “ring” \| “complete”	Shape of communication graph	Only for distributed optimization
`stareye`	positive integer	Node index of the center for the star graph	Only for the case of star graph
`EV_file`	excel file (.xlsx)	File name of EVs data
`Baseload_file`	excel file (.xlsx)	File name of the base load profile	Only for “Lf” case
`Price_file`	excel file (.xlsx)	File name of the electricity price profile	Only for “Uf” case

2. Excel File Constraints

Two excel files are required to complete a scenario. One contains the base load or the electricity price for the optimization horizon (e.g., 24 hours) depending on the problem type to be solved. The other file contains the data of all EVs whose charging/discharging schedules are to be computed.

Note

The column headers of the excel files must be the reserved keywords, which are listed below. It is recommended to use the sample excel files.

Base load (excel file)

Column name	Type	Description
`time_slot`	positive integer	Starting from 1, this number indicates the id of the time slot whose length is `Delta`.
`base_load`	positive integer	Predicted power load used for the load flattening problem

Electricity price (excel file)

Column name	Type	Description
`time_slot`	positive integer	Starting from 1, this number indicates the id of the time slot whose length is `Delta`.
`electricity_price`	positive integer	Electricity price of the corresponding time slot, predicted or announced ahead, for the use of the user-friendly scheduling problem

EV information (excel file)

Column	Type	Description
`Charger_ind`	positive integer	Index of charger
`Group_ind`	positive integer	Index of group to which the EV belongs
`Arr_time`	positive integer	Index of arrival time slot
`Dep_time`	positive integer	Index of departure time slot
`E_min`	nonnegative integer	Minimum required battery energy (kWh)
`E_cap`	positive integer	Battery energy capacity (kWh)
`E_ini`	nonnegative integer	Initial energy at arrival (kWh)
`E_ref`	nonnegative integer	Reference (target) energy at departure (kWh)
`U_max`	positive integer	Maximum charging power (kW)
`U_min`	nonpositive integer	Maximum discharging power (kW)

Goup_ind is introduced for the convenience of the analysis after the optimal scheduling problem is solved. By indicating the group id for individual EV, one can compute different statistics depending on the group id. For example, one can assign group 1 to all the cars made by Hyundai Motors, and compute average discharing power for the group.

Price_file: The number of rows in the electricity price data must be equal to T in the Scenario.