Get Started

For a more detailed description of each of the steps mentioned below, see the following pages:

• Creating Model Templates
• Defining System Topology
• Creating Input Data Templates
• Define Input Data
• Define Model Configuration
• Model construction and solving
• Check Model Diagnostics
• Manage results

In short

This workflow documentation will guide you through all the steps that are required to prepare an exemplary model. Here, we optimize an energy system with a flat electricity demand over a full year that can be served by imports from the grid, or by installing photovoltaic panels and a battery.

The modelling steps are as follows:

• Create an empty working directory for all input data. The subsequent functions will create the data structure you need for the model

• Create the templates for the system topology and the model configuration.

  import adopt_net0 as adopt
  import json
  from pathlib import Path
  
  input_data_path = "path_to_your_input_data_folder"
  adopt.create_optimization_templates(input_data_path)
  

• Define your system topology and the model configuration.

  with open(input_data_path / "Topology.json", "r") as json_file:
      topology = json.load(json_file)
  # Nodes
  topology["nodes"] = ["node1"]
  # Carriers:
  topology["carriers"] = ["electricity"]
  # Investment periods:
  topology["investment_periods"] = ["period1"]
  # Save json template
  with open(input_data_path / "Topology.json", "w") as json_file:
      json.dump(topology, json_file, indent=4)
  

• Create the folder structure and templates for the input data files.

  adopt.create_input_data_folder_template(input_data_path)
  

• Load and define input data (e.g., weather data, technology performance, demand data, etc.).

  # Define new technologies
  with open(input_data_path / "period1" / "node_data" / "node1" / "Technologies.json", "r") as json_file:
      technologies = json.load(json_file)
  technologies["new"] = ["Photovoltaic", "Storage_Battery"]
  with open(input_data_path / "period1" / "node_data" / "node1" / "Technologies.json", "w") as json_file:
      json.dump(technologies, json_file, indent=4)
  
  # Copy over technology files
  adopt.copy_technology_data(input_data_path)
  
  # Define climate data
  adopt.load_climate_data_from_api(input_data_path)
  
  # Define demand
  adopt.fill_carrier_data(input_data_path, value=0.01, columns=['Demand'], carriers=['electricity'], nodes=['node1'])
  adopt.fill_carrier_data(input_data_path, value=100, columns=['Import price'], carriers=['electricity'], nodes=['node1'])
  adopt.fill_carrier_data(input_data_path, value=1, columns=['Import limit'], carriers=['electricity'], nodes=['node1'])
  

• Define model configuration if you want to change something from the defaults. Make sure that the result folder path in reporting/save_path refers to an existing folder

• Construct and solve the model and, possibly, incorporate options to lower the complexity of the model.

  m = adopt.ModelHub()
  m.read_data(input_data_path, start_period=None, end_period=None)
  m.quick_solve()
  

• If something unexpected happens: check the model diagnostics.

• Obtain and interpret the optimization results. Therefore, you can upload the H5 file saved on the visualization platform provided: https://resultvisualization.streamlit.app/.

An elaborate example of how to set up the model accordingly can be found below. To understand what happens behind the scenes, please take a look at the Source Code Documentation.