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

Mohamed Charara
Maîtrise recherche - Polytechnique
Superviseur⋅e principal⋅e :
Antoine Lesage-Landry
Fathipasandideh Fathipasandideh
Doctorat - Polytechnique
Github
Giorgi Gamkrelidze
Maîtrise recherche - Polytechnique
Co-superviseur⋅e :
Pierre-Luc Bacon
Arian Shah Kamrani
Doctorat - Polytechnique

Publications

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities.
Eslam G. Al-Sakkari
Ahmed Ragab
Hanane Dagdougui
Daria Camilla Boffito
Mouloud Amazouz
2024-01-01
Science of the Total Environment (publié)
doi.org
Game Theoretical Formulation for Residential Community Microgrid via Mean Field Theory: Proof of Concept
Mohamad Aziz
Hanane Dagdougui
Issmail ElHallaoui
Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential communit… (voir plus)y MG (RCM) while allowing consumers’ assets to participate in the operation of the power system in critical peak times. RCM implementing demand response approaches are of high interest as collectively, they have a high impact on shaping the demand curve during peak time while providing a wide range of economic and technical benefits to consumers and utilities. The penetration of distributed energy resources such as battery energy storage and photovoltaic systems introduces additional flexibility to manage the community loads and increase revenue. This letter proposes a game theoretical formulation for an incentive-based residential community microgrid, where an incentive-based pricing mechanism is developed to encourage peak demand reduction and share the incentive demand curve with the residential community through the aggregator. The aggregator’s objective is to maximize the welfare of the residential community by finding the optimal community equilibrium electricity price. Each household communicates with each other and with the distributed system operator (DSO) through the aggregator and aims to minimize the local electricity cost.
2024-01-01
IEEE Control Systems Letters (publié)
doi.org
Deep Learning Model for Multi-Step Ahead Prediction of Solar Irradiance: Case of Study of Morocco
Saad Benbrahim
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
Accurate solar irradiance forecasting is crucial for managing energy generation and consumption in the rapidly evolving landscape of renewab… (voir plus)le energy. It enables renewable energy operators to make informed decisions and maximize their output. This study employs deep learning-based forecasting models to predict the Global Horizontal Irradiance (GHI) of the R&D platform situated in Ouarzazate, Morocco. A sensitivity analysis was conducted on multiple scenarios for a one day-ahead horizon. Moreover, a forecasting technique that encompasses numerous horizons, ranging from one day to three days in advance, was evaluated. The study's findings suggest that the encoder-decoder model we proposed exhibited superior performance compared to the other models tested and produced dependable predictions.
2023-11-22
2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (publié)
doi.org
Towards an Effective Electrical Market Design: Identifying and Defining Key Criteria for Decision-Making
Souhaila Chiguer
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
In our changing energy landscape, electricity is taking a major role in achieving decarbonization goals. Electricity can be a clean and effi… (voir plus)cient source of energy, and it is well-suited to help countries meet their climate goals. However, the electrical market is complex and constantly evolving, and it is important to carefully choose the design elements of the market to ensure that it is meeting its objectives. In this context, evaluating an electrical market's effectiveness requires a multifaceted approach that takes into account a range of elements, from environmental impact to economic viability. This paper provides an overview of several evaluation methods for different objectives to finally select the key criteria to consider in assisting decision-makers, regulators, and stakeholders in developing an electricity market that is not only effective but also reliable and sustainable.
2023-11-22
2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (publié)
doi.org
Predicting Solar PV Output Based on Hybrid Deep Learning and Physical
Models: Case Study of Morocco
Samira Abousaid
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelaziz Berrado
Hicham Bouzekri
2023-10-13
Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) (publié)
doi.org
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