Portrait de Hanane Dagdougui

Hanane Dagdougui

Membre académique associé
Professeure titulaire, Polytechnique Montréal, Département de mathématiques et de génie industriel
Sujets de recherche
Apprentissage profond
Optimisation
Systèmes distribués
LinkedIn

Biographie

Hanane Dagdougui est professeure titulaire à Polytechnique Montréal et membre académique associée à Mila - Institut québécois d'intelligence artificielle. Elle a obtenu le doctorat en ingénierie des systèmes de la Faculté d'ingénierie de Gênes et des Mines Paris-Tech en France, dans le cadre d'un programme international conjoint en 2011. Avant de rejoindre Polytechnique Montréal en 2017, elle a été assistante de recherche au département d'informatique, de bio-ingénierie, de robotique et d'ingénierie des systèmes de l'Université de Gênes en 2013. De 2013 à 2016, elle a été chercheuse institutionnelle au département de génie électrique de l'ÉTS Montréal.

Ses recherches portent sur la théorie de l'optimisation distribuée et les applications de l'optimisation mathématique. Elle s'intéresse particulièrement aux applications de l'optimisation mathématique et des techniques d'apprentissage automatique aux problèmes des réseaux intelligents, des micro-réseaux et des bâtiments intelligents. Ses recherches portent également sur la modélisation technico-économique et la planification des systèmes basés sur les énergies renouvelables, la réponse à la demande et le transport électrique.

Étudiants actuels

Fathipasandideh Fathipasandideh
Doctorat - Polytechnique
Github
Arian Shah Kamrani
Doctorat - Polytechnique

Publications

A Two-Stage Optimization Framework for Electric Vehicle Fleet Day-ahead Charging Management
Arian Shah Kamrani
Hanane Dagdougui
Nowadays electric vehicles (EVs) have become one of the important means of transportation all over the world. The importance of EV owners’… (voir plus) privacy as well as smart EV fleet charging has always been one of the challenges in smart charging planning and management. Furthermore, in smart charging, the distribution system operator must also coordinate with EV aggregators to insure that the power system is operated within security limits while reducing charging costs and satisfying EV users’ energy needs. In this paper, a semi-private framework for EV owners has been introduced which solves a two-stage optimization problem for the smart control of EV charging. This framework considers charging cost reduction and peak load shaving as well as satisfying power grid constraints. At the higher stage, based on optimal power flow calculations, the proposed control signals are transferred to the lower stage in order to facilitate optimal scheduling in accordance with the mentioned goals. The obtained results based on the proposed optimal method implemented on the IEEE 33-bus network show that compared to uncontrolled charging, the cost of charging and the peak of the network are reduced by 5.31% and 4.90%, respectively. Moreover, all the constraints of the power grid are satisfied.
2023-08-13
2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE) (publié)
doi.org
Machine learning-assisted selection of adsorption-based carbon dioxide capture materials
Eslam G. Al-Sakkari
Ahmed Ragab
Terry M.Y. So
Marzieh Shokrollahi
Hanane Dagdougui
Philippe Navarri
Ali Elkamel
Mouloud Amazouz
2023-08-01
Journal of Environmental Chemical Engineering (publié)
doi.org
An Extended State Space Model for Aggregation of Large-Scale EVs Considering Fast Charging
Sina Kiani
Keyhan Sheshyekani
Hanane Dagdougui
This article presents an extended state space model for aggregation of large-scale electric vehicles (EVs) for frequency regulation and peak… (voir plus) load shaving in power systems. The proposed model systematically deals with the fast charging of EVs as an effective solution for immediate charging requirements. Furthermore, the proposed extended state space model increases the flexibility of the EV aggregator (EVA) by enabling the EVs to participate in ancillary services with both regular and fast charging/discharging rates. This will help the EVA to provide a prompt and efficient response to severe generation-consumption imbalances. A probabilistic control approach is developed which reduces the communication burden of the EVA. Furthermore, the uncertainties related to EV users' behavior are modeled in real-time. The simulations are conducted for a typical power system including a large population of EVs, a conventional generator (CG), and a wind generation system. It is shown that the proposed aggregation model can accurately describe the aggregated behavior of a large population of EVs enabling them to efficiently participate in frequency regulation and peak load shaving services. Finally, the performance of EVA is evaluated for different driving behaviors and state of charge (SOC) levels of the EV population.
2023-03-01
IEEE Transactions on Transportation Electrification (publié)
doi.org
Hierarchical Distributed Energy Management Framework for Multiple Greenhouses Considering Demand Response
Ehsan Rezaei
Hanane Dagdougui
Kianoosh Ojand
Greenhouses are a key component of modernised agriculture, aiming for producing high-quality crops and plants. Furthermore, a network of gre… (voir plus)enhouses has enormous potential as part of demand response programs. Saving energy during off-peak time, reducing power consumption and delaying the start time of subsystems during on-peak time are some strategies that can be used to limit power exchanged with the main grid. In this work, a hierarchical distributed alternating direction method of multipliers-based model predictive control framework is proposed that has two main objectives: 1) providing appropriate conditions for greenhouses' crops and plants to grow, and 2) limiting the total power exchanged with the main grid. At each time step in the framework, an aggregator coordinates the greenhouses to reach a consensus and limit the total electric power exchanged while managing shared resources, e.g., reservoir water. The proposed framework's performance is investigated through a case study.
2023-01-01
IEEE Transactions on Sustainable Energy (publié)
doi.org