The continuous development of electricity markets (creation of day-ahead markets at the end of the 1990s, recent introduction of intraday markets, integration of European electricity markets etc.), the deregulation of the industry and the increasing development of renewable energies of an intermittent nature lead to major planning and risk management problems on different scales for the players in the energy industry.
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 deal with these new challenges.
Knowledge assessment: continuous assessment in the form of an MCQ + presentation of a research paper to be chosen among the complementary references.
Functioning of electricity markets, price modelling and energy derivatives
Energy demand modelling and forecasting
Estimation and forecasting of wind and solar potential; modelling of renewable production
Producer-consumers and demand response techniques
Probabilistic Forecasting: Mathematical Framework and Application to Wind Energy
Economic Framework for Renewable Energy Production
Case Studies in Renewable Energy Risk Management
Management of microgrids and smart grids
- 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.
- Z. Tan and P. Tankov, Optimal trading policies for wind energy producer, preprint (2016).
- 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