References of "Ernst, Damien"
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See detailQuestions d'argent - Damien Ernst
Sury, Caroline; Ernst, Damien ULg

Article for general public (2017)

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See detailApproximate Bayes Optimal Policy Search using Neural Networks
Castronovo, Michaël ULg; François-Lavet, Vincent ULg; Fonteneau, Raphaël ULg et al

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

Bayesian Reinforcement Learning (BRL) agents aim to maximise the expected collected rewards obtained when interacting with an unknown Markov Decision Process (MDP) while using some prior knowledge. State ... [more ▼]

Bayesian Reinforcement Learning (BRL) agents aim to maximise the expected collected rewards obtained when interacting with an unknown Markov Decision Process (MDP) while using some prior knowledge. State-of-the-art BRL agents rely on frequent updates of the belief on the MDP, as new observations of the environment are made. This offers theoretical guarantees to converge to an optimum, but is computationally intractable, even on small-scale problems. In this paper, we present a method that circumvents this issue by training a parametric policy able to recommend an action directly from raw observations. Artificial Neural Networks (ANNs) are used to represent this policy, and are trained on the trajectories sampled from the prior. The trained model is then used online, and is able to act on the real MDP at a very low computational cost. Our new algorithm shows strong empirical performance, on a wide range of test problems, and is robust to inaccuracies of the prior distribution. [less ▲]

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See detailResidential heat pump as flexible load for direct control service with parametrized duration and rebound effect
Georges, Emeline ULg; Cornélusse, Bertrand ULg; Ernst, Damien ULg et al

in Applied Energy (2017), 187

This paper addresses the problem of an aggregator controlling residential heat pumps to offer a direct control flexibility service. The service consists of a power modulation, upward or downward, that is ... [more ▼]

This paper addresses the problem of an aggregator controlling residential heat pumps to offer a direct control flexibility service. The service consists of a power modulation, upward or downward, that is activated at a given time period over a fixed number of periods. The service modulation is relative to an optimized baseline that minimizes the energy costs. The load modulation is directly followed by a constrained rebound effect, consisting of a delay time with no deviations from the baseline consumption and a payback time to return to the baseline state. The potential amount of modulation and the constrained rebound effect are computed by solving mixed integer linear problems. Within these problems, the thermal behavior of the building is modeled by an equivalent thermal network made of resistances and lumped capacitances. Simulations are performed for different sets of buildings typical of the Belgian residential building stock and are presented in terms of achievable modulation amplitude, deviations from the baseline and associated costs. A cluster of one hundred ideal buildings, corresponding to retrofitted freestanding houses, is then chosen to investigate the influence of each parameter defined within the service. Results show that with a set of one hundred heat pumps, a load aggregator could expect to harvest mean modulation amplitudes of up to 138 kW for an upward modulation and up to 51 kW for a downward modulation. The obtained values strongly depend on the proposed flexibility service. For example, they can decrease down to 2.6 kW and 0.4 kW, respectively, if no rebound effect is allowed. [less ▲]

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See detailAn App-based Algorithmic Approach for Harvesting Local and Renewable Energy Using Electric Vehicles
Dubois, Antoine; Wehenkel, Antoine; Fonteneau, Raphaël ULg 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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See detailUber-like Models for the Electrical Industry
Ernst, Damien ULg

Speech/Talk (2017)

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See detailLes batteries vont bouleverser notre quotidien
Scharff, Christine; Ernst, Damien ULg

Article for general public (2017)

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See detailGREDOR. Outcomes and recommendations
Cornélusse, Bertrand ULg; Ernst, Damien ULg

Report (2017)

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See detailOn the Dynamics of the Deployment of Renewable Energy Production Capacities
Fonteneau, Raphaël ULg; Ernst, Damien ULg

in Furze, James N.; Swing, Kelly; Gupta, Anil K. (Eds.) et al Mathematical Advances Towards Sustainable Environmental Systems (2017)

This chapter falls within the context of modeling the deployment of renewable en-ergy production capacities in the scope of the energy transition. This problem is addressed from an energy point of view, i ... [more ▼]

This chapter falls within the context of modeling the deployment of renewable en-ergy production capacities in the scope of the energy transition. This problem is addressed from an energy point of view, i.e. the deployment of technologies is seen as an energy investment under the constraint that an initial budget of non-renewable energy is provided. Using the Energy Return on Energy Investment (ERoEI) characteristics of technologies, we propose MODERN, a discrete-time formalization of the deployment of renewable energy production capacities. Be-sides showing the influence of the ERoEI parameter, the model also underlines the potential benefits of designing control strategies for optimizing the deployment of production capacities, and the necessity to increase energy efficiency. [less ▲]

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See detailDeep Reinforcement Learning Solutions for Energy Microgrids Management
François-Lavet, Vincent ULg; Taralla, David; Ernst, Damien ULg et al

in European Workshop on Reinforcement Learning (EWRL 2016) (2016, December)

This paper addresses the problem of efficiently operating the storage devices in an electricity microgrid featuring photovoltaic (PV) panels with both short- and long-term storage capacities. The problem ... [more ▼]

This paper addresses the problem of efficiently operating the storage devices in an electricity microgrid featuring photovoltaic (PV) panels with both short- and long-term storage capacities. The problem of optimally activating the storage devices is formulated as a sequential decision making problem under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. This paper proposes to address this problem using deep reinforcement learning. To this purpose, a specific deep learning architecture has been designed in order to extract knowledge from past consumption and production time series as well as any available forecasts. The approach is empirically illustrated in the case of a residential customer located in Belgium. [less ▲]

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See detailThe Green Grid Network and Trading Renewable Energy
Ernst, Damien ULg

Speech/Talk (2016)

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See detailBatteries and disrupting business models for the energy sector
Ernst, Damien ULg; Goka, Olivier

Speech/Talk (2016)

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See detailActive network management for electrical distribution systems: problem formulation, benchmark, and approximate solution
Gemine, Quentin ULg; Ernst, Damien ULg; Cornélusse, Bertrand ULg

in Optimization and Engineering (2016)

With the increasing share of renewable and distributed generation in electrical distribution systems, active network management (ANM) becomes a valuable option for a distribution system operator to ... [more ▼]

With the increasing share of renewable and distributed generation in electrical distribution systems, active network management (ANM) becomes a valuable option for a distribution system operator to operate his system in a secure and cost-effective way without relying solely on network reinforcement. ANM strategies are short-term policies that control the power injected by generators and/or taken off by loads in order to avoid congestion or voltage issues. While simple ANM strategies consist in curtailing temporary excess generation, more advanced strategies rather attempt to move the consumption of loads to anticipated periods of high renewable generation. However, such advanced strategies imply that the system operator has to solve large-scale optimal sequential decision-making problems under uncertainty. The problems are sequential for several reasons. For example, decisions taken at a given moment constrain the future decisions that can be taken, and decisions should be communicated to the actors of the system sufficiently in advance to grant them enough time for implementation. Uncertainty must be explicitly accounted for because neither demand nor generation can be accurately forecasted. We first formulate the ANM problem, which in addition to be sequential and uncertain, has a nonlinear nature stemming from the power flow equations and a discrete nature arising from the activation of power modulation signals. This ANM problem is then cast as a stochastic mixed-integer nonlinear program, as well as second-order cone and linear counterparts, for which we provide quantitative results using state of the art solvers and perform a sensitivity analysis over the size of the system, the amount of available flexibility, and the number of scenarios considered in the deterministic equivalent of the stochastic program. To foster further research on this problem, we make available at http://www.montefiore.ulg.ac.be/~anm/ three test beds based on distribution networks of 5, 33, and 77 buses. These test beds contain a simulator of the distribution system, with stochastic models for the generation and consumption devices, and callbacks to implement and test various ANM strategies. [less ▲]

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See detailAgent-based analysis of dynamic access ranges to the distribution network
Mathieu, Sébastien ULg; Ernst, Damien ULg; Cornélusse, Bertrand ULg

in Proceedings of the 6th European Innovative Smart Grid Technologies (ISGT) (2016, October)

There is a need to clearly state an interaction model that formalizes interactions between actors of the distribution system exchanging flexibility. In previous works we quantitatively evaluated the ... [more ▼]

There is a need to clearly state an interaction model that formalizes interactions between actors of the distribution system exchanging flexibility. In previous works we quantitatively evaluated the performance of five interaction models devised with industrial partners using the agent-based testbed DSIMA. Simulation results showed that these interaction models relying on active network management suffer from a lack of coordination between the distribution and the transmission system operator, activating flexibility simultaneously in opposite directions. This paper introduces a new interaction model fixing this issue based on dynamic access bounds to the network changing throughout the day and preventing the activation of flexibility leading to congestions. This new interaction model is implemented in DSIMA and compared to a model restricting the grid users to a very restrictive but safe access range. Results show that this new model allows to safely increase by 55% the amount of distributed generation in the network. [less ▲]

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See detailLa transition énergétique, l’affaire de tous
Ernst, Damien ULg

Speech/Talk (2016)

Conférence d'ouverture donnée par le Prof . Ernst lors de la soirée de lancement de la saison 2016-2017 de Liège Créative. Accéder à la vidéo de la conférence: https://www.youtube.com/watch?v=2SJ14Sj33bI

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See detailCes robots qui pourraient nous vouloir du mal...
Bouffioux, Michel; Ernst, Damien ULg

Article for general public (2016)

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See detailDirect control service from residential heat pump aggregation with specified payback
Georges, Emeline ULg; Cornélusse, Bertrand ULg; Ernst, Damien ULg et al

in Proceedings of the 19th Power Systems Computation Conference (PSCC) (2016, June)

This paper addresses the problem of an aggregator controlling residential heat pumps to offer a direct control flexibility service. The service is defined by a 15 minute power modulation, upward or ... [more ▼]

This paper addresses the problem of an aggregator controlling residential heat pumps to offer a direct control flexibility service. The service is defined by a 15 minute power modulation, upward or downward, followed by a payback of one hour and 15 minutes. The service modulation is relative to an optimized baseline that minimizes the energy costs. The potential amount of modulable power and the payback effect are computed by solving mixed integer linear problems. Within these problems, the building thermal behavior is modeled by an equivalent thermal network made of resistances and lumped capacitances whose parameters are identified from validated models. Simulations are performed on 100 freestanding houses. For an average 4.3 kW heat pump, results show a potential of 1.2 kW upward modulation with a payback of 600 Wh and 150 Wh of overconsumption. A downward modulation of 500 W per house can be achieved with a payback of 420 Wh and 120 Wh of overconsumption. [less ▲]

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See detailA Gaussian mixture approach to model stochastic processes in power systems
Gemine, Quentin ULg; Cornélusse, Bertrand ULg; Glavic, Mevludin ULg et al

in Proceedings of the 19th Power Systems Computation Conference (PSCC'16) (2016, June)

Probabilistic methods are emerging for operating electrical networks, driven by the integration of renewable generation. We present an algorithm that models a stochastic process as a Markov process using ... [more ▼]

Probabilistic methods are emerging for operating electrical networks, driven by the integration of renewable generation. We present an algorithm that models a stochastic process as a Markov process using a multivariate Gaussian Mixture Model, as well as a model selection technique to search for the adequate Markov order and number of components. The main motivation is to sample future trajectories of these processes from their last available observations (i.e. measurements). An accurate model that can generate these synthetic trajectories is critical for applications such as security analysis or decision making based on lookahead models. The proposed approach is evaluated in a lookahead security analysis framework, i.e. by estimating the probability of future system states to respect operational constraints. The evaluation is performed using a 33-bus distribution test system, for power consumption and wind speed processes. Empirical results show that the GMM approach slightly outperforms an ARMA approach. [less ▲]

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See detailBenchmarking for Bayesian Reinforcement Learning
Castronovo, Michaël ULg; Ernst, Damien ULg; Couëtoux, Adrien ULg et al

in PLoS ONE (2016)

In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise the col- lected rewards while interacting with their environment while using some prior knowledge that is accessed beforehand ... [more ▼]

In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise the col- lected rewards while interacting with their environment while using some prior knowledge that is accessed beforehand. Many BRL algorithms have already been proposed, but even though a few toy examples exist in the literature, there are still no extensive or rigorous benchmarks to compare them. The paper addresses this problem, and provides a new BRL comparison methodology along with the corresponding open source library. In this methodology, a comparison criterion that measures the performance of algorithms on large sets of Markov Decision Processes (MDPs) drawn from some probability distributions is defined. In order to enable the comparison of non-anytime algorithms, our methodology also includes a detailed analysis of the computation time requirement of each algorithm. Our library is released with all source code and documentation: it includes three test prob- lems, each of which has two different prior distributions, and seven state-of-the-art RL algorithms. Finally, our library is illustrated by comparing all the available algorithms and the results are discussed. [less ▲]

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