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"
- Functioning of electricity markets, price modeling and energy derivative products
- Energy demand modeling and forecasting
- Estimation and forecasting of wind and solar potential; modeling of renewable production
- Producer-consumers and demand response techniques
- Probabilistic forecasting: mathematical framework and application to energy
- Economic framework for renewable energy production
- Case studies in renewable energy risk management
- Management of micro-grids 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.
- 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