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

Mohamed Charara
Master's Research - Polytechnique Montréal
Principal supervisor :
Antoine Lesage-Landry
Fathipasandideh Fathipasandideh
PhD - Polytechnique Montréal
Github
Arian Shah Kamrani
PhD - Polytechnique Montréal

Publications

ADMM-Based Hierarchical Single-Loop Framework for EV Charging Scheduling Considering Power Flow Constraints
Sina Kiani
Keyhan Sheshyekani
Hanane Dagdougui
This article presents a three-layer hierarchical distributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed… (see more) hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.
2024-03-01
IEEE Transactions on Transportation Electrification (published)
doi.org
An Analytic Hierarchy Process based approach for assessing the performance of photovoltaic solar power plants
Meryam Chafiq
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
2024-01-01
IFAC-PapersOnLine (published)
doi.org
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 (published)
doi.org
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 (published)
doi.org
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 (published)
doi.org
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 (published)
doi.org
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 (published)
doi.org
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 C. Boffito
Mouloud Amazouz
2024-01-01
Science of the Total Environment (published)
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… (see more)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 (published)
doi.org
The Bifurcation Method: White-Box Observation Perturbation Attacks on Reinforcement Learning Agents on a Cyber Physical System
KIERNAN BRODA-MILIAN
Hanane Dagdougui
Ranwa Al Mallah
2024-01-01
2024 International Conference on Machine Learning and Cybernetics (ICMLC) (published)
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… (see more)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) (published)
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… (see more)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) (published)
doi.org