IAM_metal_optimisation

The python code "IAM_Techno_Optimisation.py" is a software tool designed, on one hand, to quantify metal supply and demand by sector through 2050, based on energy projections from various IAMs and SSP-RCP scenarios. On the other hand, this code enables the optimization of the minimum variation in the IAM technological mix necessary to limit demand exceedance beyond metal supply constraints.

1. System requirements

• Programming language: Python 3.11.5
• Packages / dependencies:
  • Pyomo 6.6.2
  • CPLEX 12.10.0.0 (solver required, must be installed separately)
  • numpy
  • pandas
• Operating system tested: Windows 11
• Non-standard hardware: none (standard desktop computer sufficient)

2. Installation guidelines

1. Clone the repository (or download it)
2. Download the contents from the 'Data' folder in the location of your "folder path" (see the parameters section)
3. Download the data of the IAM you want to use in your "folder path" (see the parameters section)
4. The code is ready to be used !

(Typicaclly, the installation should only take a few seconds)

How to download data of the IAM you want

1. You want to study the modelisation from the article

The data needed to generate the results of RCP 2.6, for various SSP, modeled by each IAM markor is available on Zenodo at the following link: — Data: 10.5281/zenodo.15319014

2. You want to study an other IAM or SSP-RCP scenario

To study an other IAM and SSP-RCP modelisation, use the data from the IIASA database : https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=20

After logging in, go to the "IAM Scenarios" section, and download data for :

• Regions : choose 5 regions
• Model/Scenarios : choose the scenarios you want to study
• Variable : At least, the GDP, the Secondary Energy (in Energy folder) and the Power Capacity Indicators Folder > Capacity > Electricity) The data are stocked in a csv file for every IAM, every SSP-RCP scenario, every region, for each variable chosen.

To use the data in the code, they have to be downloaded in a folder for each variable. The folder contains a folder for each modelisation (IAM - SSP-RCP), which is filled with 5 Excel files for each region. The folder and files name are:

• Capacity Factor IAM > FC models0_scenario > FCmodels0_scenario_region
• GDP IAM > GDP models0_scenario > GDPmodels0_scenario_region
• Power Capacity IAM > Dossier s0 models0_scenario > Cap_models0_scenario_region

To do so, you can use the python code "IAM_Download_Data.py"

3. Demonstration

To run the initial and optimized scenario of a specific IAM and SSP-RCP scenario will usually take a few minutes.

Choose parameters

The software tool is structured as a class, divided into several methods. To initialize the class, the user must provide seven parameters to the init constructor:

• folder_path: [string] path on the user's computer to the external data used in the code (finishing with /)
• result_path: [string] path on the user's computer for storing the results generated by the code (finishing with /)
• model_s0: [string] IAM that the user wishes to study
• scenario: [string] SSP-RCP scenario combination that the user wants to study, in the format “SSPx-numRCP”
• ModelisationType: [string] ; " Init " to study the metal demand of the initial energy mix ; " Opti " to perform an optimization of the initial energy mix and study the metal demand of the optimized mix
• Alpha: [int] percentage value of the percentage of refined capacity reserved for the transition sectors (storage, network, electric vehicles, power plant)
• ResLimit: [string] limit to apply when optimizing cumulative metal demand, choosing between Reserves or Resources

Only the other method Demand_Other_Sector requires a parameter.

• Growth: [string] ; "GDP" to estimate that future demand from the rest of the economy follows GDP growth, main hypothesis of the article ; "Pop" to eestimate that future metal demand from the rest of the economy remains constant and follows population growth

All the other methods do not require any parameters

Expected output

The results of the software tool are stored in a folder named “ModelisationType”, in the "result_path" chosen by the user, structured as follows:

• “Metal Demand ByYear”: a folder containing one Excel file per metal, with annual demand in tonnes divided by sector (rows) and by decades (columns).
• “Metal Demand Cumulated”: a folder containing one Excel file with cumulative demand in tonnes per metal (rows) and per sector (columns).
• “Power Capacity”: a folder containing installed capacities, with one Excel file per region, showing power in GW per energy source (rows) and per decade (columns).
• "Vehicle”: a folder containing annual vehicles sold in millions, per specific vehicle type (rows) and per year (columns).
• "SecondaryProdEV”: a folder containing the annual secondary flow of metals from the recycling of vehicles, by metal (rows) and per decade (columns).

Additionally, in a folder named “Broader Economy”, the outputs include:

• "FutureHistProd": an excel file with the annual refinery production capacity in tons by metal (rows) per decade (columns).

4. Vizualize results

The results can be vizualized on the Streamlit application at: IAM Metal Bottlenecks App