DOCTORAL RESEARCHERS
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Mathieu Besançon
Ph.D. student in engineering mathematics 514 340-6053 x3926 mathieu.besancon@polymtl.ca |
The integration of renewable energy generation into the power grid requires a higher flexibility on the demand side and motivated the creation of demand response programs. My research focuses on leader-follower frameworks to develop these programs in a robust and scalable way, sharing the benefits between users and grid suppliers.
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Christian Bingane
Ph.D. candidate in engineering mathematics 514-340-6053 x3925 christian.bingane@polymtl.ca |
My research interests are: optimal power flow in power network and conic optimization. I am concerned with using linear programming, second-order cone programming or semidefinite programming to provide guaranteed global optimal solution to the optimal power flow problem for a large-scale electrical grid.
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My research is concerned, firstly, with bi-level programming theory to design an optimal involvement strategy of electric vehicles to the Smart grid market. Secondly, I am interested about the message-passing distributed optimization algorithms to make near-optimal real-time decision to co-optimize the operation of Transmission and Distribution power grids profiting from the storage technologies.
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Michael David de Souza Dutra
Ph.D candidate in engineering mathematics 514 340-6053 x3644 michaeldavidsd@gmail.com |
Following the strong trend of smart homes, home automation can be seen more and more in modern homes and apartments. We can remotely control appliances and obtain consumer data. But instead of just controlling and visualizing data, could we produce money in this context? I believe so. Developing a scheduler to relate residential energy production to the residential power consumption of the various residential appliances without loss of comfort and optimizing budgeting is one of my PhD's goals.
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My project aims to propose a mixed integer nonlinear optimization approach that takes into account both the standard constraints in maintenance planning for hydropower plants and the nonlinear aspects of the power output function, often linearized in the literature.
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Neda Etebari
Ph.D. student in engineering mathematics 514-340-6053 x3643 Neda.etebarialamdari@polymtl.ca |
Since demand forecasting plays a key role in any revenue management system, robust and accurate predictions are of great importance to the companies. My research focuses on improvement of booking prediction accuracy in railways industry through stable and generalizable machine learning and optimization algorithms. The provided methods can easily be extended to other transportation or hospitality industries.
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Vinicius Neves Motta
Ph.D. student in industrial engineering 514 340-6053 x3926 vinicius.neves-motta@polymtl.ca |
My research interests are stochastic and robust optimization, and mathematical modelling of power grids considering demand response (DR). Currently, I'm working on the problem of optimal management demand contracts of the grid operator and generation contracts, through demand response, of companies considering the topology of the transmission network.
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Mariana Faria Pires Gama Rocha
Ph.D. candidate in industrial engineering 514 340-6053 x3926 mariana.rocha@polymtl.ca |
My research interests are: optimization and mathematical modeling of power systems equipment operation, especially electrical and thermal energy storage devices and renewable related resources, for its optimal integration into electrical and smart grids giving careful consideration to power flow (load flow) methods, short circuit analysis, voltage stability, security analysis, energy price and other market information.
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My research project consists in solving conic relaxations of Optimal Power Flow problems for large-scale power transmission networks. These problems being originally formulated into complex variables, my research also focuses on polynomial optimization with complex variables and the possible extension of methods to the complex case.
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Mathieu Tanneau
Ph.D. candidate in engineering mathematics 514-340-5121 x3388 mathieu.tanneau@polymtl.ca |
As the penetration of renewable generation soars, so does the need for a more flexible and decentralized grid. This need can be met by leveraging the potential of Distributed Energy Resources (DERs) including - but not limited to - energy storage, smart appliances, and electric vehicles. My goal is to overcome scalability and heterogeneity challenges raised by DERs. I do so by developing efficient aggregation algorithms which exploit decomposition and parallelization techniques.
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