Check out our interactive example notebooks in Google Colab to find out just how simple it is to solve multistage stochastic programming problems with QUASAR®!
What to expect from this notebook? In this example notebook, we are going to demonstrate how QUASAR can be used to price storages for natural gas. To keep matters simple, we pursue a spot-based approach which models only the spot price for gas but not term-structure dynamics. Deviations of the spot prices are captured by a one-factor model (mean reverting, Ornstein-Uhlenbeck process) that captures the random deviation of daily spot prices from the price forward curve. The decision problem entails injection in / withdrawal from storage subject to capacity restrictions.
Using QUASAR is clearly an overkill for solving such a simple stochastic-dynamic program, but it nicely demonstrates that QUASAR can easily handle multistage stochastic programs with hundreds of time stages, without having to compromise computational speed for ease-of-use. In fact, it is not difficult to imagine that the decision model can be easily adapted to account for specific storage properties, such as ratchets, or include custom organization constraints, such as time-dependent injection/withdrawal rates. Unlike other solution methods, QUASAR is also not limited to modeling a single storage, but could be used to model networks of storages including transport capacity. Moreover, the stochastic process can be exchanged against any other two-factor or multi-factor model that is in common use among energy quants.
If you are interested in seeing a more production-ready model in action, check out or solutions for storage optimization to see what is possible.
Any QUASAR® model can be transformed into a fully-fledged software solution for end users with QUASAR® Cloud. 
