References of "Ernst, Damien"
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See detailDistributed Model-free Control of Photovoltaic Units for Mitigating Overvoltages in Low-Voltage Networks
Aristidou, Petros ULg; Olivier, Frédéric ULg; Hervas, Maria Emilia et al

in Proc. of CIRED 2014 workshop (in press)

In this paper, a distributed model-free control scheme to mitigate overvoltage problems caused by high photovoltaic generation in low-voltage feeders is proposed. The distributed controllers are ... [more ▼]

In this paper, a distributed model-free control scheme to mitigate overvoltage problems caused by high photovoltaic generation in low-voltage feeders is proposed. The distributed controllers are implemented on the photovoltaic inverters and modulate the active and reactive power injected into the network. In particular, they direct photovoltaic units first to consume reactive power and, if necessary, curtail active power generation to reduce high voltages in the feeder. [less ▲]

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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) (in press)

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 detailOptimized look-ahead tree policies: a bridge between look-ahead tree policies and direct policy search
Jung, Tobias ULg; Wehenkel, Louis ULg; Ernst, Damien ULg et al

in International Journal of Adaptive Control and Signal Processing (2014), 28(3-5), 255-289

Direct policy search (DPS) and look-ahead tree (LT) policies are two popular techniques for solving difficult sequential decision-making problems. They both are simple to implement, widely applicable ... [more ▼]

Direct policy search (DPS) and look-ahead tree (LT) policies are two popular techniques for solving difficult sequential decision-making problems. They both are simple to implement, widely applicable without making strong assumptions on the structure of the problem, and capable of producing high performance control policies. However, computationally both of them are, each in their own way, very expensive. DPS can require huge offline resources (effort required to obtain the policy) to first select an appropriate space of parameterized policies that works well for the targeted problem, and then to determine the best values of the parameters via global optimization. LT policies do not require any offline resources; however, they typically require huge online resources (effort required to calculate the best decision at each step) in order to grow trees of sufficient depth. In this paper, we propose optimized look-ahead trees (OLT), a model-based policy learning scheme that lies at the intersection of DPS and LT. In OLT, the control policy is represented indirectly through an algorithm that at each decision step develops, as in LT using a model of the dynamics, a small look-ahead tree until a prespecified online budget is exhausted. Unlike LT, the development of the tree is not driven by a generic heuristic; rather, the heuristic is optimized for the target problem and implemented as a parameterized node scoring function learned offline via DPS. We experimentally compare OLT with pure DPS and pure LT variants on optimal control benchmark domains. The results show that the LT-based representation is a versatile way of compactly representing policies in a DPS scheme (which results in OLT being easier to tune and having lower offline complexity than pure DPS); while at the same time, DPS helps to significantly reduce the size of the look-ahead trees that are required to take high-quality decisions (which results in OLT having lower online complexity than pure LT). Moreover, OLT produces overall better performing policies than pure DPS and pure LT and also results in policies that are robust with respect to perturbations of the initial conditions. [less ▲]

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See detailL'invité - Damien Ernst - "Nous allons vers une globalisation du marché de l'électricité"
Ernst, Damien ULg

Article for general public (2014)

En décembre 2013, Damien Ernst, Professeur à l’ULG, a donné une conférence au CESW intitulée : «Vers une globalisation du marché de l’électricité. Quel rôle pour les acteurs du secteur belge de ... [more ▼]

En décembre 2013, Damien Ernst, Professeur à l’ULG, a donné une conférence au CESW intitulée : «Vers une globalisation du marché de l’électricité. Quel rôle pour les acteurs du secteur belge de l’électricité?». Damien Ernst est un observateur privilégié du secteur énergétique belge, et plus particulièrement de tout ce qui concerne le secteur de l’électricité. Auteur de nombreuses publications et études, Damien Ernst s’est notamment interrogé sur les perspectives des énergies renouvelables en Belgique. Damien Ernst est l’invité de ce numéro 120 de la revue Wallonie. Dans son interview, il nous explique pourquoi la globalisation du marché de l’électricité est inéluctable et quelles en seront les conséquences, pour les entreprises du secteur et pour la Wallonie. [less ▲]

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See detailGlobal power grids for harnessing world renewable energy
Chatzivasileiadis, Spyros; Ernst, Damien ULg; Andersson, Göran

E-print/Working paper (2014)

The Global Grid advocates the connection of all regional power systems into one electricity transmission system spanning the whole globe. Power systems are currently forming larger and larger ... [more ▼]

The Global Grid advocates the connection of all regional power systems into one electricity transmission system spanning the whole globe. Power systems are currently forming larger and larger interconnections. Environmental awareness and increased electricity consumption leads more investments towards renewable energy sources, abundant in remote locations (off-shore or in deserts). The Global Grid will facilitate the transmission of this “green” electricity to load centers, serving as backbone. This chapter elaborates on the concept presenting four stages that could gradually lead to the development of a globally interconnected power network. Quantitative analyses are carried out for all stages, demonstrating that a Global Grid is both technically feasible and economically competitive. Real price data from Europe and the USA are used to identify the potential of intercontinental electricity trade, showing that substantial profits can be generated through such interconnections. [less ▲]

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See detailOn periodic reference tracking using batch-mode reinforcement learning with application to gene regulatory network control
Sootla, Aivar; Strelkowa, Natajala; Ernst, Damien ULg et al

in Proceedings of the 52nd Annual Conference on Decision and Control (CDC 2013) (2013, December)

In this paper, we consider the periodic reference tracking problem in the framework of batch-mode reinforcement learning, which studies methods for solving optimal control problems from the sole knowledge ... [more ▼]

In this paper, we consider the periodic reference tracking problem in the framework of batch-mode reinforcement learning, which studies methods for solving optimal control problems from the sole knowledge of a set of trajectories. In particular, we extend an existing batch-mode reinforcement learning algorithm, known as Fitted Q Iteration, to the periodic reference tracking problem. The presented periodic reference tracking algorithm explicitly exploits a priori knowledge of the future values of the reference trajectory and its periodicity. We discuss the properties of our approach and illustrate it on the problem of reference tracking for a synthetic biology gene regulatory network known as the generalised repressilator. This system can produce decaying but long-lived oscillations, which makes it an interesting application for the tracking problem. [less ▲]

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See detailAn efficient algorithm for the provision of a day-ahead modulation service by a load aggregator
Mathieu, Sébastien ULg; Ernst, Damien ULg; Louveaux, Quentin ULg

in Proceedings of the 4th European Innovative Smart Grid Technologies (ISGT) (2013, October)

This article studies a decision making problem faced by an aggregator willing to offer a load modulation service to a Transmission System Operator. This service is contracted one day ahead and consists in ... [more ▼]

This article studies a decision making problem faced by an aggregator willing to offer a load modulation service to a Transmission System Operator. This service is contracted one day ahead and consists in a load modulation option, which can be called once per day. The option specifies the range of a potential modification on the demand of the loads within a certain time interval. The specific case where the loads can be modeled by a generic tank model is considered. Under this assumption, the problem of maximizing the range of the load modulation service can be formulated as a mixed integer linear programming problem. A novel heuristic-method is proposed to solve this problem in a computationally efficient manner. This method is tested on a set of problems. The results show that this approach can be orders of magnitude faster than CPLEX without significantly degrading the solution accuracy. [less ▲]

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See detailThe global grid
Chatzivasileiadis, Spyros; Ernst, Damien ULg; Andersson, Göran

in Renewable Energy : An International Journal (2013), 57

This paper puts forward the vision that a natural future stage of the electricity network could be a grid spanning the whole planet and connecting most of the large power plants in the world: this is the ... [more ▼]

This paper puts forward the vision that a natural future stage of the electricity network could be a grid spanning the whole planet and connecting most of the large power plants in the world: this is the “Global Grid”. The main driving force behind the Global Grid will be the harvesting of remote renewable sources, and its key infrastructure element will be the high capacity long transmission lines. Wind farms and solar power plants will supply load centers with green power over long distances. This paper focuses on the introduction of the concept, showing that a globally interconnected network can be technologically feasible and economically competitive. We further highlight the multiple opportunities emerging from a global electricity network such as smoothing the renewable energy supply and electricity demand, reducing the need for bulk storage, and reducing the volatility of the energy prices. We also discuss possible investment mechanisms and operating schemes. Among others, we envision in such a system a global power market and the establishment of two new coordinating bodies, the “Global Regulator” and the “Global System Operator”. [less ▲]

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See detailMonte Carlo search algorithm discovery for single-player games
Maes, Francis; Lupien St-Pierre, David ULg; Ernst, Damien ULg

in IEEE Transactions on Computational Intelligence and AI in Games (2013), 5(3), 201-213

Much current research in AI and games is being devoted to Monte Carlo search (MCS) algorithms. While the quest for a single unified MCS algorithm that would perform well on all problems is of major ... [more ▼]

Much current research in AI and games is being devoted to Monte Carlo search (MCS) algorithms. While the quest for a single unified MCS algorithm that would perform well on all problems is of major interest for AI, practitioners often know in advance the problem they want to solve, and spend plenty of time exploiting this knowledge to customize their MCS algorithm in a problem-driven way. We propose an MCS algorithm discovery scheme to perform this in an automatic and reproducible way. We first introduce a grammar over MCS algorithms that enables inducing a rich space of candidate algorithms. Afterwards, we search in this space for the algorithm that performs best on average for a given distribution of training problems. We rely on multi-armed bandits to approximately solve this optimization problem. The experiments, generated on three different domains, show that our approach enables discovering algorithms that outperform several well-known MCS algorithms such as Upper Confidence bounds applied to Trees and Nested Monte Carlo search. We also show that the discovered algorithms are generally quite robust with respect to changes in the distribution over the training problems. [less ▲]

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See detailBatch mode reinforcement learning based on the synthesis of artificial trajectories
Fonteneau, Raphaël ULg; Murphy, Susan A.; Wehenkel, Louis ULg et al

in Annals of Operations Research (2013), 208(1), 383-416

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See detailRisque majeur de blackout : que faire ?
Ernst, Damien ULg

Article for general public (2013)

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See detailQuelles perspectives pour les énergies renouvelables en Wallonie ?
Ernst, Damien ULg

in LiègeU (2013), Eté 2013

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See detailOutbound SPIT Filter with Optimal Performance Guarantees
Jung, Tobias ULg; Martin, Sylvain ULg; Nassar, Mohamed et al

in Computer Networks (2013), 57(7), 16301643

This paper presents a formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work is largely ... [more ▼]

This paper presents a formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing, our work is largely different from related previous work: our goal is to rigorously formalize the problem in terms of mathematical decision theory, find the optimal solution to the problem, and derive concrete bounds for its expected loss (number of mistakes the SPIT filter will make in the worst case). This goal is achieved by considering an abstracted scenario amenable to theoretical analysis, namely SPIT detection in an outbound scenario with pure sources. Our methodology is to first define the cost of making an error (false positive and false negative), apply Wald’s sequential probability ratio test to the individual sources, and then determine analytically error probabilities such that the resulting expected loss is minimized. The benefits of our approach are: (1) the method is optimal (in a sense defined in the paper); (2) the method does not rely on manual tuning and tweaking of parameters but is completely self-contained and mathematically justified; (3) the method is computationally simple and scalable. These are desirable features that would make our method a component of choice in larger, autonomic frameworks. [less ▲]

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See detailOptimal discovery with probabilistic expert advice: finite time analysis and macroscopic optimality
Bubeck, Sébastien; Ernst, Damien ULg; Garivier, Aurélien

in Journal of Machine Learning Research (2013), 14

We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice. We address it with an algorithm based ... [more ▼]

We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice. We address it with an algorithm based on the optimistic paradigm and on the Good-Turing missing mass estimator. We prove two different regret bounds on the performance of this algorithm under weak assumptions on the probabilistic experts. Under more restrictive hypotheses, we also prove a macroscopic optimality result, comparing the algorithm both with an oracle strategy and with uniform sampling. Finally, we provide numerical experiments illustrating these theoretical findings. [less ▲]

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See detailGénéralisation Min Max pour l'Apprentissage par Renforcement Batch et Déterministe : Relaxations pour le Cas Général T Etapes
Fonteneau, Raphaël ULg; Ernst, Damien ULg; Boigelot, Bernard ULg et al

in 8èmes Journées Francophones de Planification, Décision et Apprentissage pour la conduite de systèmes (JFPDA'13) (2013)

Cet article aborde le problème de généralisation minmax dans le cadre de l'apprentissage par renforcement batch et déterministe. Le problème a été originellement introduit par [Fonteneau, 2011], et il a ... [more ▼]

Cet article aborde le problème de généralisation minmax dans le cadre de l'apprentissage par renforcement batch et déterministe. Le problème a été originellement introduit par [Fonteneau, 2011], et il a déjà été montré qu'il est NP-dur. Deux schémas de relaxation pour le cas deux étapes ont été présentés aux JFPDA'12, et ce papier présente une généralisation de ces schémas au cas T étapes. Le premier schéma fonctionne en éliminant des contraintes afin d'obtenir un problème soluble en temps polynomial. Le deuxième schéma est une relaxation lagrangienne conduisant également à un problème soluble en temps polynomial. On montre théoriquement que ces deux schémas permettent d'obtenir de meilleurs résultats que ceux proposés par [Fonteneau, 2011]. [less ▲]

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See detailScenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
Defourny, Boris; Ernst, Damien ULg; Wehenkel, Louis ULg

in INFORMS Journal on Computing (2013), 25(3), 488-501

In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector ... [more ▼]

In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued recourse decisions optimized using scenario-tree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenario-tree solution and are selected by out-of-sample simulation given the true problem. Because the learned rules depend on the given scenario tree, we repeat the procedure for a large number of randomly generated scenario trees and then select the best solution (policy) found for the true problem. The scheme leads to an ex post selection of the scenario tree itself. Numerical tests evaluate the dependence of the approach on the machine learning aspects and show cases where one can obtain near-optimal solutions, starting with a “weak” scenario-tree generator that randomizes the branching structure of the trees. [less ▲]

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See detailMin max generalization for deterministic batch mode reinforcement learning: relaxation schemes
Fonteneau, Raphaël ULg; Ernst, Damien ULg; Boigelot, Bernard ULg et al

in SIAM Journal on Control & Optimization (2013), 51(5), 33553385

We study the min max optimization problem introduced in Fonteneau et al. [Towards min max reinforcement learning, ICAART 2010, Springer, Heidelberg, 2011, pp. 61–77] for computing policies for batch mode ... [more ▼]

We study the min max optimization problem introduced in Fonteneau et al. [Towards min max reinforcement learning, ICAART 2010, Springer, Heidelberg, 2011, pp. 61–77] for computing policies for batch mode reinforcement learning in a deterministic setting with fixed, finite time horizon. First, we show that the min part of this problem is NP-hard. We then provide two relaxation schemes. The first relaxation scheme works by dropping some constraints in order to obtain a problem that is solvable in polynomial time. The second relaxation scheme, based on a Lagrangian relaxation where all constraints are dualized, can also be solved in polynomial time. We also theoretically prove and empirically illustrate that both relaxation schemes provide better results than those given in [Fonteneau et al., 2011, as cited above]. [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 detailStratégies d'échantillonnage pour l'apprentissage par renforcement batch
Fonteneau, Raphaël ULg; Murphy, Susan A.; Wehenkel, Louis ULg et al

in Revue d'Intelligence Artificielle [=RIA] (2013), 27(2), 171-194

We propose two strategies for experiment selection in the context of batch mode reinforcement learning. The first strategy is based on the idea that the most interesting experiments to carry out at some ... [more ▼]

We propose two strategies for experiment selection in the context of batch mode reinforcement learning. The first strategy is based on the idea that the most interesting experiments to carry out at some stage are those that are the most liable to falsify the current hypothesis about the optimal control policy. We cast this idea in a context where a policy learning algorithm and a model identification method are given a priori. The second strategy exploits recently published methods for computing bounds on the return of control policies from a set of trajectories in order to sample the state-action space so as to be able to discriminate between optimal and non-optimal policies. Both strategies are experimentally validated, showing promising results. [less ▲]

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See detailMeta-learning of Exploration/Exploitation Strategies: The Multi-Armed Bandit Case
Maes, Francis; Wehenkel, Louis ULg; Ernst, Damien ULg

in Filipe, Joaquim; Fred, Ana (Eds.) Agents and Artificial Intelligence: 4th International Conference, ICAART 2012, Vilamoura, Portugal, February 6-8, 2012. Revised Selected Papers (2013)

The exploration/exploitation (E/E) dilemma arises naturally in many subfields of Science. Multi-armed bandit problems formalize this dilemma in its canonical form. Most current research in this field ... [more ▼]

The exploration/exploitation (E/E) dilemma arises naturally in many subfields of Science. Multi-armed bandit problems formalize this dilemma in its canonical form. Most current research in this field focuses on generic solutions that can be applied to a wide range of problems. However, in practice, it is often the case that a form of prior information is available about the specific class of target problems. Prior knowledge is rarely used in current solutions due to the lack of a systematic approach to incorporate it into the E/E strategy. To address a specific class of E/E problems, we propose to proceed in three steps: (i) model prior knowledge in the form of a probability distribution over the target class of E/E problems; (ii) choose a large hypothesis space of candidate E/E strategies; and (iii), solve an optimization problem to find a candidate E/E strategy of maximal average performance over a sample of problems drawn from the prior distribution. We illustrate this meta-learning approach with two different hypothesis spaces: one where E/E strategies are numerically parameterized and another where E/E strategies are represented as small symbolic formulas. We propose appropriate optimization algorithms for both cases. Our experiments, with two-armed “Bernoulli” bandit problems and various playing budgets, show that the metalearnt E/E strategies outperform generic strategies of the literature (UCB1, UCB1-T UNED, UCB-V, KL-UCB and epsilon-GREEDY); they also evaluate the robustness of the learnt E/E strategies, by tests carried out on arms whose rewards follow a truncated Gaussian distribution. [less ▲]

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