Monte Carlo analysis

Monte Carlo analysis is a statistical technique that uses random sampling and statistical methods to estimate uncertainty and variability in system performance and economic outcomes. In this framework, Monte Carlo analysis is applied specifically to economic input parameters, including technology and network capital expenditures (CAPEX) as well as import and export prices. To perform the Monte Carlo analysis, you first need to specify the number of simulations in the ConfigModel.json file. Next, you can select the sampling method in the ConfigModel.json file. The framework includes two different sampling methods:

1. Normal Distribution Sampling: Parameters are varied based on a normal distribution, using a standard deviation provided by the user in the ConfigModel.json file. For this method, you can also specify the components you want to perform the analysis on.

2. Uniform Distribution Sampling: Parameters are varied based on a uniform distribution between minimum and maximum values provided by the user in a MonteCarlo.csv file. In order to create this file, you pass the path to the input data folder through the create_montecarlo_template_csv() method in the main module (main.py) (similar to creating the model templates or the data templates. For documentation of this create_montecarlo_template_csv() function, see the source code documentation.