References of "Gemine, Quentin"
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See detailRelaxations for multi-period optimal power flow problems with discrete decision variables
Gemine, Quentin ULg; Ernst, Damien ULg; Louveaux, Quentin ULg et al

in Proceedings of the 18th Power Systems Computation Conference (PSCC'14) (2014, August)

We consider a class of optimal power flow (OPF) applications where some loads offer a modulation service in exchange for an activation fee. These applications can be modeled as multi-period formulations ... [more ▼]

We consider a class of optimal power flow (OPF) applications where some loads offer a modulation service in exchange for an activation fee. These applications can be modeled as multi-period formulations of the OPF with discrete variables that define mixed-integer non-convex mathematical programs. We propose two types of relaxations to tackle these problems. One is based on a Lagrangian relaxation and the other is based on a network flow relaxation. Both relaxations are tested on several benchmarks and, although they provide a comparable dual bound, it appears that the constraints in the solutions derived from the network flow relaxation are significantly less violated. [less ▲]

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See detailGestion active d’un réseau de distribution d’électricité : formulation du problème et benchmark
Gemine, Quentin ULg; Ernst, Damien ULg; Cornélusse, Bertrand ULg

in Proceedings des 9èmes Journées Francophones de Planification, Décision et Apprentissage (2014, May)

Afin d’opérer un réseau de distribution d’électricité de manière fiable et efficace, c’est-à-dire de respecter les contraintes physiques tout en évitant des coûts de renforcement prohibitifs, il devient ... [more ▼]

Afin d’opérer un réseau de distribution d’électricité de manière fiable et efficace, c’est-à-dire de respecter les contraintes physiques tout en évitant des coûts de renforcement prohibitifs, il devient nécessaire de recourir à des stratégies de gestion active du réseau. Ces stratégies, rendues nécessaires notamment par l’essor de la production distribuée, reposent sur des politiques de contrôle à court-terme du niveau de puissance des dispositifs producteurs ou consommateurs d’électricité. Alors qu’une solution simple consisterait à moduler à la baisse la production des générateurs, il paraît néan- moins plus intéressant de déplacer la consommation aux moments adéquats afin d’exploiter au mieux les sources d’énergie renouvelables sur lesquelles reposent généralement ces générateurs. Un tel moyen de contrôle introduit néanmoins un couplage temporel au problème, menant à un problème d’optimisation non-linéaire, séquentiel sous incertitude et à variables mixtes. Afin de favoriser la recherche dans ce domaine très complexe, nous proposons une formalisation générique du problème de ges- tion active d’un réseau de distribution moyenne tension (MT). Plus spécifiquement, cette formalisa- tion se présente sous la forme d’un processus de décision markovien. Dans cette article, nous pré- sentons également une spécification de ce modèle décisionnel à un réseau de 75 noeuds et pour un ensemble de services de modulation donnés. L’instance de test qui en résulte est disponible à l’adresse http://www.montefiore.ulg.ac.be/~anm/ et a pour objectif de mesurer et de comparer les performances des techniques de résolution qui seront développées. [less ▲]

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See detailActive network management: planning under uncertainty for exploiting load modulation
Gemine, Quentin ULg; Karangelos, Efthymios ULg; Ernst, Damien ULg et al

in Proceedings of the 2013 IREP Symposium - Bulk Power Systems Dynamics and Control - IX (2013)

This paper addresses the problem faced by a distribution system operator (DSO) when planning the operation of a network in the short-term. The problem is formulated in the context of high penetration of ... [more ▼]

This paper addresses the problem faced by a distribution system operator (DSO) when planning the operation of a network in the short-term. The problem is formulated in the context of high penetration of renewable energy sources (RES) and distributed generation (DG), and when flexible demand is available. The problem is expressed as a sequential decision-making problem under uncertainty, where, in the first stage, the DSO has to decide whether or not to reserve the availability of flexible demand, and, in the subsequent stages, can curtail the generation and modulate the available flexible loads. We analyze the relevance of this formulation on a small test system, discuss the assumptions made, compare our approach to related work, and indicate further research directions. [less ▲]

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See detailImitative learning for designing intelligent agents for video games
Gemine, Quentin ULg

Master's dissertation (2012)

Over the past decades, video games have become increasingly popular and complex. Virtual worlds have gone a long way since the first arcades and so have the artificial intelligence (AI) techniques used to ... [more ▼]

Over the past decades, video games have become increasingly popular and complex. Virtual worlds have gone a long way since the first arcades and so have the artificial intelligence (AI) techniques used to control agents in these growing environments. Tasks such as world exploration, constrained pathfinding or team tactics and coordination just to name a few are now default requirements for contemporary video games. However, despite its recent advances, video game AI still lacks the ability to learn. In this work, we attempt to break the barrier between video game AI and machine learning and propose a generic method allowing real-time strategy (RTS) agents to learn production strategies from a set of recorded games using supervised learning. We test this imitative learning approach on the popular RTS title StarCraft II and successfully teach a Terran agent facing a Protoss opponent new production strategies. [less ▲]

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See detailImitative Learning for Real-Time Strategy Games
Gemine, Quentin ULg; Safadi, Firas ULg; Fonteneau, Raphaël ULg et al

in Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games (2012)

Over the past decades, video games have become increasingly popular and complex. Virtual worlds have gone a long way since the first arcades and so have the artificial intelligence (AI) techniques used to ... [more ▼]

Over the past decades, video games have become increasingly popular and complex. Virtual worlds have gone a long way since the first arcades and so have the artificial intelligence (AI) techniques used to control agents in these growing environments. Tasks such as world exploration, con- strained pathfinding or team tactics and coordination just to name a few are now default requirements for contemporary video games. However, despite its recent advances, video game AI still lacks the ability to learn. In this paper, we attempt to break the barrier between video game AI and machine learning and propose a generic method allowing real-time strategy (RTS) agents to learn production strategies from a set of recorded games using supervised learning. We test this imitative learning approach on the popular RTS title StarCraft II® and successfully teach a Terran agent facing a Protoss opponent new production strategies. [less ▲]

Detailed reference viewed: 81 (37 ULg)