Meet the Quantego team at our exhibition booth 5-047 in the innovation area (Hall 5) and get your individual demo of QUASAR® and our latest optimization solutions for energy storage and energy trading.
The QUASAR optimization software finally makes stochastic programming available for a wider audience. Check out the example in this post to see for yourself, just how easy it is. In a previous post, we have used our Python interface. In this example, we call our Java API from Scala.
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®!
Simple arbitrage model for battery storage optimization under price uncertainty. Demonstrates how to turn policy simulation output into a simple decision rule of when to buy and when to sell.
An example from operations management – invest into production capacity under uncertain demand and then control production and inventories over subsequent periods.
The QUASAR optimization software finally makes stochastic programming available for a wider audience. Check out the example in this post to see for yourself, just how easy it is with our Python interface.
Spot-based valuation of a storage for natural gas over the course of one year. Model prices as one-factor model. Learn how to approximate storage value and delta positions.
New research on how to calculate optimal day-ahead bids that account for the option to rebalance a portfolio in the EPEX SPOT continuous intraday market.
Quantego has been awarded an 130k € Industrial Fellowships Research Grant of the Fonds National de la Recherche (FNR), Luxembourg for its joint research project on "Optimal Power Trading in Illiquid Markets (OPTIM)" with the NTNU Norway.
Quantego S.à r.l. received the governmental accreditation as private research institute by the Grand Duchy of Luxembourg, in light of its fundamental and applied research on stochastic programming and math optimization.
Learn how to calculate the extrinsic value of storage when using an empirical model of term structure dynamics.
Learn how to optimize a hydropower planning in hourly time granularity under uncertainty about day-ahead electricity prices and natural inflows.
Learn how virtual power plants can optimize their bidding strategy under uncertainty about future electricity prices and wind power generation.
In this presentation, Dr. Elke Moser explains how Energieallianz (formerly e&t), an energy trading house in Vienna (Austria), used QUASAR to develop a custom-made solution for optimizing bids for the German-Austrian auction for balancing reserves.
Take a look at client presentations and learn which business insights can be gained from using QUASAR®.
Learn how making compromises in model selection affects solution quality and how to make a hydro-thermal planning robust against a mis-specified inflow process.