Define Input Data

Now that you have the correct folder structure and templates for the data, you can start to fill the templates and folders with data. First, you need to further define your energy system by following the steps below. Note that the package includes predefined technologies and networks with respective performance and cost parameters, but the advanced user is free to define new technologies or adapt the templates provided.

The geographical coordinates of your nodes in NodeLocations.csv in terms of longitude, latitude and altitude. Note: longitude and latitude should be written in decimal degrees, and altitude in metres. Climate data is loaded from this data. Additionally it is used to calculate the position of the sun for PV modelling (using pvlib). Note that the distance between nodes is not based on the provided locations.
```
# Define node locations (here an exemplary location in the Netherlands)
node_locations = pd.read_csv(input_data_path / "NodeLocations.csv", sep=";", index_col=0)
node_locations.loc["node1", "lon"] = 5.5
node_locations.loc["node1", "lat"] = 52.5
node_locations.loc["node1", "alt"] = 10
node_locations.to_csv(input_data_path / "NodeLocations.csv", sep=";")
```
The networks in Networks.JSON for each investment period, where we distinguish between new (to be installed) networks and existing networks, using the network names as in .\data\network_data (a list of these can be found here) or by:
```
adopt.show_available_networks()
```
Note that all networks are based on the Network Class. Specify the networks in the json file like this:
```
"existing": [],
"new": ["electricityOffshore"]
```
- Then, for each of the networks that you specify, an input data folder with that network name should be added in the corresponding folder (“existing” or “new”) in the network_topology folder. To these folders, you should copy the CSV files connection.csv and distance.csv. You then define the topology for all of your networks by filling in these files (as illustrated in an elaborate example here) in their respective folders. Note that the distance needs to be added manually, even if the node locations were specified previously. It is also possible to specify a maximum size for each arc individually by creating another file called size_max_arcs.csv having the same layout as connection.csv or distance.csv. Note that for new networks the size_max_arcs.csv contains an upper limit, the actual size is determined by the optimization.
```
Connection: Example - Unidirectional (offshore to onshore only)
                        onshore    offshore
    onshore             0          0
    offshore            1          0

Connection: Example - Bidirectional
                        onshore    offshore
    onshore             0          1
    offshore            1          0

Distance [km]:
                        onshore    offshore
    onshore             0          100
    offshore            100        0

Maximum size [MW]:
                        onshore    offshore
    onshore             0          20
    offshore            20         0
```
  Note: all these files are matrices with the columns and the rows being the nodes you specified in the initial topology. You should read it as “from row, to column”. Networks can only exist between nodes, so the diagonal of this matrix always consists of 0s.
- In order to read in the required data for our “electricityOffshore” network, the JSON file of that network type has to be either copied into the “network_data” folder in your input data folder or made from scratch (based on the template). You can do this manually, but if you have many different network types in your system, you can do it by running the following code (see also here). After copying the file you can also change the performance and cost parameters provided.
```
adopt.copy_network_data(input_data_path)
```
The technologies in Technologies.JSON for each investment period and each node, where we distinguish between new (to be installed) technologies and existing technologies. A list of these can be found here) or by running:
```
adopt.show_available_technologies()
```
Specify the technologies used in this model e.g. as:
```
"existing": {"WindTurbine_Onshore_1500": 2, "Photovoltaic": 2.4},
"new": ["Storage_Battery", "Photovoltaic", "Furnace_NG"]
```
Note: For wind turbines, the capacity of one turbine is specified in the name (1500 W), and the size is an integer. Here, we thus have two 1.5MW wind turbines installed (totalling to 3MW), and 2.4MW of solar PV.
Similar to the network data, we can now copy the required technology data files by running (see also here). After copying the files you can also change the performance and cost parameters provided.
adopt.copy_technology_data(input_data_path)
For the carriers, whether or not curtailment of generic production is possible in EnergybalanceOptions.JSON (0 = not possible; 1 = possible).
After the complete system topology and system characteristics are finalised, time dependent data can be loaded into the input data folder. This remaining data covers:
- Carbon costs: Prices of carbon emissions and/or subsidies for emission reductions. These are defined for each investment period and node.
- Climate data: global horizontal irradiance (ghi), direct normal irradiance (dni), diffuse horizontal irradiance (dhi), air temperature, relative humidity, inflow of water, wind speed at 10 metres. The data is defined for each investment period and node based on the geographical location.
- Carrier data: Data on demand, import/export limits/prices/emission factors, and generic production for each carrier at each node in each investment period.
All this data is time-dependent, so you need to specify data for all time steps of your model run.

All data can be simply changed directly in the csv file. For example, you can copy a demand profile from a national database (if your nodes are countries) into the correct column in the csv. Additionally, we provide a couple of functions to make defining the time dependent data more convenient:
- For climate data, you can use the API to a JRC database for onshore locations in Europe. E.g.:
```
adopt.load_climate_data_from_api(input_data_path)
```
- For all other time series, you can specify a fixed value, if the values do not change over time. E.g.:
```
adopt.fill_carrier_data(path, value_or_data=10, columns=["Demand"], carriers=["electricity"], nodes=["onshore"], investment_periods=['period1'])
```

Climate data from API

For importing climate data from the JRC PVGIS database, the method load_climate_data_from_api() can be used, passing your input data folder path. This imports the climate data for each node, accounting for the location of the nodes as specified in NodeLocations.csv. If no location is specified, it takes the default coordinates (52.5, 5.5) with an altitude of 10m.

load_climate_data_from_api(folder_path: str | pathlib.Path, dataset: str = 'JRC')

Reads in climate data for a full year from a folder containing node data and writes it to the respective file.

Reads in climate data for a full year from a folder containing node data, where each node data is stored in a subfolder and node locations are provided in a CSV file named NodeLocations.csv. The data is written to the file

Parameters:

folder_path (str) – Path to the folder containing node data and NodeLocations.csv
dataset (str) – Dataset to import from, can be JRC (only onshore)

NB: this imports all climate data, except for the hydro inflow. Hydro inflow needs to be specified for any technologies based on the technology type “OpenHydro” (see here). For this, replace the “TECHNOLOGYNAME” in the column name with the technology in your system, e.g., “PumpedHydro_Open” and load a profile for water flow into the reservoir.

Specifying a fixed value

For carrier data, you can use the fill_carrier_data() method if your value does not vary over time.

fill_carrier_data(folder_path: str | pathlib.Path, value_or_data: float | pandas.core.frame.DataFrame, columns: list = [], carriers: list = [], nodes: list = [], investment_periods: list = None)

Updates carrier data for a time series based on a provided value or DataFrame and writes it to file.

Allows you to update Demand, Import limit, Export limit, Import price, Export price, Import emission factor, Export emission factor and/or Generic production.

Parameters:

folder_path (str) – Path to the folder containing the case study data
value_or_data (float | pd.DataFrame) – A float value to be applied or a DataFrame containing the new values for the carrier data
columns (list) – Name of the columns that need to be changed
investment_periods (list) – Name of investment periods to be changed
nodes (list) – Name of the nodes that need to be changed
carriers (list) – Name of the carriers that need to be changed

Copy technology and network data

For the technologies and networks, you can copy the JSON files automatically using the copy_technology_data() and copy_network_data() methods documented below. Note: the method automatically checks which technologies and networks it has to copy from the model repository by reading in the Technology.JSON and Network.JSON files, respectively. Thus, make sure to use the naming conventions as in the JSON files in the model repository.

The technologies and networks shipped with AdOpT-NET0 can be seen as templates and the performance and cost parameters as well as technology specific options can be further modified by the user. Therefore, you can modify the json files in the input data folder of your case study after they have been copied. Also it is possible to specify technologies or networks that are not provided in AdOpT by using the models defined in the network class or the technology classes.

copy_network_data(folder_path: str | pathlib.Path, ntw_data_path: str | pathlib.Path = None)

Copies network JSON files to the network_data folder for each investment period.

This function reads the topology JSON file to determine the existing and new networks for each investment period. It then searches for the corresponding JSON files in the specified ntw_data_path folder (and its subfolders) using the network names and copies them to folder_path.

Parameters:

folder_path (str | Path) – Path to the folder containing the case study data.
ntw_data_path (str | Path) – Path to the folder containing the network data (if left

empty, standard folder is used). :return: None

copy_technology_data(folder_path: str | pathlib.Path, tec_data_path: str | pathlib.Path = None)

Copies technology JSON files to the node folder for each node and investment period.

This function reads the topology JSON file to determine the existing and new technologies at each node for each investment period. It then searches for the corresponding JSON files in the specified tec_data_path folder (and its subfolders) using the technology names and copies them to the output folder.

Parameters:

folder_path (str | Path) – Path to the folder containing the case study data.
tec_data_path (str | Path) – Path to the folder containing the technology data.