How to make money in Natural Gas with Python

Posted: 2021-01-25

Ka-Ching!

In one of the previous posts, we were talking about simple gas storage model as a business opportunity. That model has one serious drawback: it does not take injection and withdrawal ratchets.

An inventory ratchet means different maximum daily injection or withdrawal quantity depending on the amount of gas in storage. The picture below shows injection ratchets as a function of storage level.

Previous excel model has limited features and also Excel’s solver convergence is not great. Industry use requires a model that would closely mimic reality which means a need to use nonlinear functions in constraint definition. On the top of that, the problem is a Mixed Integer Programming which forces to use specialized solvers.

One of them with a decent quality is CBC solver from fantastic COIN-OR project. We’re going to sprinkle the model with a bit of Python and bake it in Jupyter to get this notebook.

There are some interesting results. When injection ratchet is constant at 2000 Dth per day, the storage peaks 100,000 in 2nd and 3rd month. Additionally, withdrawal strategy is trying to monetize small peak during summer demand for cooling power.

flat ratchet at 2k

Not much change when the ratchet is reduced to 1000 Dth.

ratchet 2k to 1k at 50%

However, ratchet reduction by 75% at half storage shows rather dramatic injection profile. This time storage peaks roughly in 6th month.

ratchet 2k to 500 at 50%

This model is still very basic because it skips discounting and costs. Nonetheless, ability to specify fairly complex constraints, combined with python and MIP solvers allow for very powerful scenario analysis.

There is one more draw back of the model. It does not take volatility into account, but this is a material for the next post.

Image by Kevin Schneider from Pixabay