Portrait of Hanane Dagdougui

Hanane Dagdougui

Associate Academic Member
Full Professor, Polytechnique Montréal, Department of Mathematical and Industrial Engineering
Research Topics
Deep Learning
Distributed Systems
Optimization
LinkedIn

Biography

Hanane Dagdougui is a Full Professor at Polytechnique Montréal and Associate Academic Member of Mila - Quebec Artificial Intelligence Institute. She received the Ph.D. in Systems Engineering from the Faculty of Engineering of Genova and the Mines Paris-Tech in France, as part of an international joint program in 2011. Prior to joining the Polytechnique Montreal in 2017, she was a research assistant at the department of Informatics, Bioengineering, Robotics and System Engineering at the University of Genoa in 2013. From 2013 to 2016, she was an institutional researcher at the department of Electrical Engineering, ÉTS Montreal.

Her research interests are in the distributed optimization theory and applications of mathematical optimization. She is particularly interested in the applications of mathematical optimization and machine learning techniques to problems of smart grids, microgrids, and smart buildings. Her research interests include also the techno-economic modeling and planning of renewable energy-based systems, demand response and electric transportation.

Current Students

Fathipasandideh Fathipasandideh
PhD - Polytechnique Montréal
Github
Arian Shah Kamrani
PhD - Polytechnique Montréal

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’… (see more) 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) (published)
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 (published)
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… (see more) 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 (published)
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… (see more)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 (published)
doi.org