Publications of Antoine Dubois
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See detailComputing Necessary Conditions for Near-Optimality in Capacity Expansion Planning Problems
Dubois, Antoine ULiege; Ernst, Damien ULiege

E-print/Working paper (2021)

In power systems, large-scale optimisation problems are extensively used to plan for capacity expansion at the supranational level. However, their cost-optimal solutions are often not exploitable by ... [more ▼]

In power systems, large-scale optimisation problems are extensively used to plan for capacity expansion at the supranational level. However, their cost-optimal solutions are often not exploitable by decision-makers who are preferably looking for features of solutions that can accommodate their different requirements. This paper proposes a generic framework for addressing this problem. It is based on the concept of the epsilon-optimal feasible space of a given optimisation problem and the identification of necessary conditions over this space. This framework has been developed in a generic case, and an approach for solving this problem is subsequently described for a specific case where conditions are constrained sums of variables. The approach is tested on a case study about transmission expansion planning of the European electricity network to determine necessary conditions on the minimal investments in transmission, storage and generation capacity. [less ▲]

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See detailSiting Renewable Power Generation Assets with Combinatorial Optimisation
Berger, Mathias ULiege; Radu, David-Constantin ULiege; Dubois, Antoine ULiege et al

in Optimization Letters (2021)

This paper studies the problem of siting renewable power generation assets using large amounts of climatological data while accounting for their spatiotemporal complementarity. The problem is cast as a ... [more ▼]

This paper studies the problem of siting renewable power generation assets using large amounts of climatological data while accounting for their spatiotemporal complementarity. The problem is cast as a combinatorial optimisation problem selecting a pre-specified number of sites so as to minimise the number of simultaneous low electricity production events that they experience relative to a pre-specified reference production level. It is shown that the resulting model is closely related to submodular optimisation and can be interpreted as generalising the well-known maximum coverage problem. Both deterministic and randomised algorithms are discussed, including greedy, local search and relaxation-based heuristics as well as combinations of these algorithms. The usefulness of the model and methods is illustrated by a realistic case study inspired by the problem of siting onshore wind power plants in Europe, resulting in instances featuring over ten thousand candidate locations and ten years of hourly-sampled meteorological data. The proposed solution methods are benchmarked against a state-of-the-art mixed-integer programming solver and several algorithms are found to consistently produce better solutions at a fraction of the computational cost. The physical nature of solutions provided by the model is also investigated, and all deployment patterns are found to be unable to supply a constant share of the electricity demand at all times. Finally, a cross-validation analysis shows that, except for an edge case, the model can successfully and reliably identify deployment patterns that perform well on previously unseen climatological data from historical data spanning a small number of weather years. [less ▲]

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See detailModel Reduction in Capacity Expansion Planning Problems via Renewable Generation Site Selection
Radu, David-Constantin ULiege; Dubois, Antoine ULiege; Berger, Mathias ULiege et al

in Proceedings of the 2021 IEEE Madrid PowerTech (2021, June)

The accurate representation of variable renewable generation (RES, e.g., wind, solar PV) assets in capacity expansion planning (CEP) studies is paramount to capture spatial and temporal correlations that ... [more ▼]

The accurate representation of variable renewable generation (RES, e.g., wind, solar PV) assets in capacity expansion planning (CEP) studies is paramount to capture spatial and temporal correlations that may exist between sites and impact both power system design and operation. However, it typically has a high computational cost. This paper proposes a method to reduce the spatial dimension of CEP problems while preserving an accurate representation of renewable energy sources. A two-stage approach is proposed to this end. In the first stage, relevant sites are identified via a screening routine that discards the locations with little impact on system design. In the second stage, the subset of relevant RES sites previously identified is used in a CEP problem to determine the optimal configuration of the power system. The proposed method is tested on a realistic EU case study and its performance is benchmarked against a CEP set-up in which the entire set of candidate RES sites is available. The method shows great promise, with the screening stage consistently identifying 90% of the optimal RES sites while discarding up to 54% of the total number of candidate locations. This leads to a peak memory reduction of up to 41% and solver runtime gains between 31% and 46%, depending on the weather year considered. [less ▲]

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See detailAssessing the Impact of Offshore Wind Siting Strategies on the Design of the European Power System
Radu, David-Constantin ULiege; Berger, Mathias ULiege; Dubois, Antoine ULiege et al

in Applied Energy (2021), 305

This paper provides a detailed account of the impact of different offshore wind siting strategies on the design of the European power system. To this end, a two-stage method is proposed. In the first ... [more ▼]

This paper provides a detailed account of the impact of different offshore wind siting strategies on the design of the European power system. To this end, a two-stage method is proposed. In the first stage, a highly-granular siting problem identifies a suitable set of sites where offshore wind plants could be deployed according to a pre-specified criterion. Two siting schemes are analysed and compared within a realistic case study. These schemes essentially select a pre-specified number of sites so as to maximize their aggregate power output and their spatiotemporal complementarity, respectively. In addition, two variants of these siting schemes are provided, wherein the number of sites to be selected is specified on a country-by-country basis rather than Europe-wide. In the second stage, the subset of previously-identified sites is passed to a capacity expansion planning framework that sizes the power generation, transmission and storage assets that should be deployed and operated in order to satisfy pre-specified electricity demand levels at minimum cost. Results show that the complementarity-based siting criterion leads to system designs which are up to 5% cheaper than the ones relying on the power output-based scheme when offshore wind plants are deployed with no consideration for country-based deployment targets. On the contrary, the power output-based scheme leads to system designs which are consistently 2% cheaper than the ones leveraging the complementarity-based siting strategy when such constraints are enforced. The robustness of the reported results is supported by a sensitivity analysis on offshore wind capital expenditure and inter-annual weather variability, respectively. [less ▲]

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See detailStudying migrant assimilation through Facebook interests
Dubois, Antoine ULiege; Zagheni, Emilio; Garimella, Kiran et al

in Social Informatics, 10th International Conference, SocInfo 2018: St. Petersburg, Russia, September 25-28, 2018: Proceedings, Part II (2018, September 25)

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See detailIntegrating facial detection and recognition algorithms into real-life applications
Van Lishout, François ULiege; Dubois, Antoine ULiege; Wang, Menglan Linda ULiege et al

Conference (2018, June 27)

Performances of facial detection and recognition algorithms on publicly available datasets do not always reflect their true effectiveness in practical real-life applications. Parameters such as distance ... [more ▼]

Performances of facial detection and recognition algorithms on publicly available datasets do not always reflect their true effectiveness in practical real-life applications. Parameters such as distance to camera, blur or pose, which vary across datasets, have an important impact on performances. Furthermore, computing speed may also be a key factor for applications requiring real-time decisions. In our department, we work on an application localizing any registered user present in the building in real-time (we also provide an application allowing users to manage their privacy), based only on a few pictures automatically taken during the registration process. In this work, we first compare four open-source facial detection algorithms on the WIDER FACE dataset and on an independent one constructed in our department with volunteers, containing images having a large variation in terms of size, pose, illumination and level of blur. We show that Single Stage Headless Face Detector (SSH) leads to way better precision- recall performances, but is about twice slower than the second best method Faster R-CNN. Second, we compare three open-source facial recognition algorithms on the MegaFace dataset and on our above mentioned one. The latter shows to be much more challenging for all methods, suggesting that publications comparing methods on the former may display performances that cannot be achieved in real-life contexts. We show that InsightFace leads to slightly better precision-recall performances than Dlib, but is about three time slower than the latter. [less ▲]

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See detailAn App-based Algorithmic Approach for Harvesting Local and Renewable Energy Using Electric Vehicles
Dubois, Antoine ULiege; Wehenkel, Antoine ULiege; Fonteneau, Raphaël ULiege et al

in Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART 2017) (2017, February)

The emergence of electric vehicles (EVs), combined with the rise of renewable energy production capacities, will strongly impact the way electricity is produced, distributed and consumed in the very near ... [more ▼]

The emergence of electric vehicles (EVs), combined with the rise of renewable energy production capacities, will strongly impact the way electricity is produced, distributed and consumed in the very near future. This position paper focuses on the problem of optimizing charging strategies for a fleet of EVs in the context where a significant amount of electricity is generated by (distributed) renewable energy. It exposes how a mobile application may offer an efficient solution for addressing this problem. This app can play two main roles. Firstly, it would incite and help people to play a more active role in the energy sector by allowing photovoltaic (PV) panel owners to sell their electrical production directly to consumers, here the EVs’ agents. Secondly, it would help distribution system operators (DSOs) or transmission system operators (TSOs) to modulate more efficiently the load by allowing them to influence EV charging behaviour in real time. Finally, the present paper advocates for the introduction of a two-sided market-type model between EV drivers and electricity producers. [less ▲]

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