https://hal-centralesupelec.archives-ouvertes.fr/hal-03355604Baili, HanaHanaBailiL2S - Laboratoire des signaux et systèmes - CentraleSupélec - Université Paris-Saclay - CNRS - Centre National de la Recherche ScientifiqueWind Power Forecasting and Reliability Stochastic Control in Wind Energy Conversion SystemsHAL CCSD2020[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC][MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS]Baili, Hana2021-09-27 14:26:192022-06-26 03:13:572021-09-27 14:26:19enConference papers10.1109/ISEC49495.2020.92300781The integration of renewable energy systems affects the entire power system by increasing the uncertainty. Power system operators have always to manage uncertainty in one way or another. The fundamental principle in this regard is to guarantee reliability by balancing load variations with controllable generation resources. If the production has to integrate unpredictable and non-controllable energy resources, then the power system may fail to achieve the required balance. This paper addresses the problem of uncertainty management for reliability stochastic control and optimization in energy systems with wind generation. Specifically, uncertainty management is based on an assemblage of the controller and wind power forecaster design tasks as a hybrid stochastic differential system. To begin with, the identification of the unknown model parameters is established based on the observations. A power tracking controller for some given set point reference, which is to operate in two switching regimes, is proposed afterwards. Here the derivation is founded upon stochastic analysis along with the dynamic programming principle. The complexity of a stochastic control approach resides in the resolution of Bellman's equation. A martingale approach for an alternative to the solution by dynamic programming is still a work in progress.