Modeling and managing energy risks


The continuous development of electricity markets (creation of day-ahead markets at the end of the 90s, recent introduction of intraday markets, integration of European electricity markets etc.), deregulation of the industry and the increasing development of intermittent renewable energies leads to major planning and risk management issues at different scales for energy industry players.
The objective of this course is to introduce students to these new issues in the energy industry, the approaches used, and the mathematical and statistical tools developed to face these new challenges.

Course validation: QCM + presentation of a paper to choose from "additional references"


  • Overview of the features and the main risks of the electrical energy industry
  • Economic framework and risks associated to intermittent renewable production
  • Estimating wind and solar potential; forecasting renewable resources
  • Probabilistic forecasting: mathematical tools and applications to renewable energy
  • Electricity markets and derivative products
  • Case studies in renewable energy risk management


  • Aïd, René. Electricity derivatives. Springer, 2015.
  • Mougeot, Mathilde, et al., Forecasting intra day load curves using sparse functional regression. In : Modeling and Stochastic Learning for Forecasting in High Dimensions. Springer, 2015. 161-181.
  • Gneiting, Tilmann, and Matthias Katzfuss, Probabilistic forecasting.  Annual Review of Statistics and Its Application 1 (2014): 125-151.
  • Collet, Jérôme, Olivier Féron, and Peter Tankov. « Optimal management of a wind power plant with storage capacity,'' in: Forecasting and Risk Management for Renewable Energy. Springer, 2017
  • Bensoussan, Alain, and Alexandre Brouste, Cox–Ingersoll–Ross model for wind speed modeling and forecasting. Wind Energy (2015).
  • Olivares, Daniel E., et al., Trends in microgrid control.  IEEE Transactions on smart grid 5.4 (2014): 1905-1919.
  • P. Pinson, Wind Energy: Forecasting Challenges for its Operational Management, Statistical Science, 28 (2013), pp. 564-585