The sheer size of our reservoir makes it necessary that our hydropower storage assets must be managed over the medium-term, when there is still significant uncertainty about future hydrologic inflows and power prices. Our asset management therefore clearly benefits from stochastic modeling, but only with QUASAR, we were able to solve the stochastic optimization problem in hourly time steps over a three-year planning horizon.
Dr. Andreas Eichhorn, Portfolio Management at VERBUND Trading, Vienna, Austria
The business environment of energy utilities and energy traders is changing constantly, which makes dealing with uncertainty a daily challenge. To cope with this change, flexible and user-friendly tools are required. QUASAR’s Jupyter integration combines productivity, flexibility, and usability in one tool, which makes it a pleasure to prototype and analyze models for everyday’s work tasks.
Dr. Elke Moser, Research and Analysis at Energieallianz, Vienna, Austria
Optimal gas storage valuation and futures trading under a high-dimensional price process. Optimization Online (2015)
Optimizing trading decisions for hydro storage systems using approximate dual dynamic programming. Operations Research (2013)
Directly deploy from Docker:
Repository with sample files:
Python references for PyQUASAR: docs.quantego.com/pyquasar
Javadocs for the QUASAR Java API: