Industrial_Heat.ftt_chi_main module

Industrial_Heat.ftt_chi_main module#

ftt_chi_main.py#

Industrial chemical sector FTT module.#

This is the main file for FTT: Industrial Heat - CHI, which models technological diffusion of industrial heat processes within the chemical sector due to simulated investor decision making. Investors compare the levelised cost of industrial heat, which leads to changes in the market shares of different technologies.

The outputs of this module include changes in final energy demand and emissions due chemical heat processes for the EU28.

Local library imports:

Support functions:

  • divide

    Bespoke element-wise divide which replaces divide-by-zeros with zeros

Functions included:

  • solve

    Main solution function for the module

  • get_lcoih

    Calculates the levelised cost of industrial heat

Industrial_Heat.ftt_chi_main.get_lcoih(data, titles, year)#

Calculate levelized costs.

The function calculates the levelised cost of industrial heat in 2019 Euros It includes intangible costs (gamma values) and together determines the investor preferences.

Parameters:

  • data (dictionary) – Data is a container that holds all cross-sectional (of time) for all variables. Variable names are keys and the values are 3D NumPy arrays.

  • titles (dictionary) – Titles is a container of all permissible dimension titles of the model.

Returns:

data – Data is a container that holds all cross-sectional (of time) data for all variables. Variable names are keys and the values are 3D NumPy arrays. The values inside the container are updated and returned to the main routine.

Return type:

dictionary

Notes

Additional notes if required.

Industrial_Heat.ftt_chi_main.solve(data, time_lag, titles, histend, year, domain)#

Main solution function for the module.

Simulates investor decision making.

Parameters:

  • data (dictionary of NumPy arrays) – Model variables for the given year of solution

  • time_lag (type) – Description

  • titles (dictionary of lists) – Dictionary containing all title classification

  • histend (dict of integers) – Final year of histrorical data by variable

  • year (int) – Curernt/active year of solution

  • specs (dictionary of NumPy arrays) – Function specifications for each region and module

Returns:

data – Model variables for the given year of solution

Return type:

dictionary of NumPy arrays