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A Gaussian mixture approach to model stochastic processes in power systems Gemine, Quentin ; Cornélusse, Bertrand ; Glavic, Mevludin et al in Proceedings of the 19th Power Systems Computation Conference (PSCC'16) (in press) 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 ▲] Detailed reference viewed: 57 (9 ULg)Direct control service from residential heat pump aggregation with specified payback Georges, Emeline ; Cornélusse, Bertrand ; Ernst, Damien 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 ▲] Detailed reference viewed: 35 (8 ULg)Artificial Intelligence and Energy Cornélusse, Bertrand ; Fonteneau, Raphaël Conference (2016, February 02) Detailed reference viewed: 80 (8 ULg)DSIMA: A testbed for the quantitative analysis of interaction models within distribution networks Mathieu, Sébastien ; Louveaux, Quentin ; Ernst, Damien et al in Sustainable Energy, Grids and Networks (2016), 5 This article proposes an open-source testbed to simulate interaction models governing the exchange of flexibility services located within a distribution network. The testbed is an agent-based system in ... [more ▼] This article proposes an open-source testbed to simulate interaction models governing the exchange of flexibility services located within a distribution network. The testbed is an agent-based system in which the distribution system operator, the transmission system operator, producers and retailers make their decisions based on mixed-integer linear programs. This testbed helps to highlight the characteristics of an interaction model, the benefits for the agents and eases the detection of unwanted or abusive behaviors which decreases the welfare. The testbed is implemented in Python and the optimization problems are encoded in the modeling language ZIMPL. A web interface is coupled with an instance generator using macroscopic parameters of the system such as the total power production. This testbed is, therefore, well suited to test the implemented interaction models on various scenarios and to extend the implementation to other models. Five interaction models developed with industrial partners are simulated over a year on a 75-bus test system. Simulations show that interaction models relying on active network management, as they have been developed, lead to substantial welfare even though they suffer from a lack of coordination between the DSO and the TSO. A conservative interaction model restricting grid users to an access range that is computed ahead of time to prevent any congestion, avoids shedding distributed generation but considerably restrains the amount of distributed production. [less ▲] Detailed reference viewed: 418 (35 ULg)Global capacity announcement of electrical distribution systems: A pragmatic approach Cornélusse, Bertrand ; ; Glavic, Mevludin et al in Sustainable Energy, Grids and Networks (2015), 4 We propose a pragmatic procedure to facilitate the connection process of Distributed Generation (DG) with reference to the European regulatory framework where Distribution System Operators (DSOs) are ... [more ▼] We propose a pragmatic procedure to facilitate the connection process of Distributed Generation (DG) with reference to the European regulatory framework where Distribution System Operators (DSOs) are, except in specific cases, not allowed to own their generation. The procedure is termed Global Capacity ANnouncement (GCAN) and is intended to compute the estimates of maximum generation connection amount at appropriate substations in a distribution system, to help generation connection decisions. The pragmatism of the proposed procedure stems from its reliance on the tools that are routinely used in distribution systems planning and operation, and their use such that the possibilities of network sterilization are avoided. The tools involved include: long-term load forecasting, long-term planning of network extension/reinforcement, network reconfiguration, and power flow. Network sterilizing substations are identified through repeated power flow computations. The proposed procedure is supported by results using an artificially created 5-bus test system, the IEEE 33-bus test system, and a part of real-life distribution system of ORES (a Belgian DSO serving a large portion of the Walloon region in Belgium). [less ▲] Detailed reference viewed: 69 (23 ULg)Graph matching for reconciling SCADA and GIS of a distribution network Cornélusse, Bertrand ; ; Glavic, Mevludin et al in Proceedings of the International Conference on Electricity Distribution, CIRED 2015 (2015, June) This article deals with the problem of automatically es- tablishing a correspondence between two databases popu- lated independently over the years by a distribution com- pany, for instance a SCADA system ... [more ▼] This article deals with the problem of automatically es- tablishing a correspondence between two databases popu- lated independently over the years by a distribution com- pany, for instance a SCADA system and a geographical information system. This problem is abstracted as a graph matching problem, well known in the combinatorial op- timisation community. It is then casted as an integer quadratic program. An idea of achievable results on a real system is provided, and needs for approximation or decom- position algorithms are discussed. [less ▲] Detailed reference viewed: 253 (15 ULg)A process to address electricity distribution sector challenges: the GREDOR project approach Cornélusse, Bertrand ; ; Glavic, Mevludin et al in Proceedings of the International Conference on Electricity Distribution, CIRED 2015 (2015, June) This paper presents a general process set in the GREDOR (French acronym for “Gestion des Réseaux Electriques de Distribution Ouverts aux Renouvelables”) project to address the challenges in distribution ... [more ▼] This paper presents a general process set in the GREDOR (French acronym for “Gestion des Réseaux Electriques de Distribution Ouverts aux Renouvelables”) project to address the challenges in distribution systems posed by the integration of renewable generation, changing load patterns, and the changes in the electricity market sector. A use case describing interactions among different players that fits the process is also presented. A pseudo-dynamic approach to Global Capacity Announcement as a way to increase penetration of Renewable Energy Sources in a distribution system is elaborated in more details. [less ▲] Detailed reference viewed: 184 (13 ULg)Macroscopic analysis of interaction models for the provision of flexibility in distribution systems Mathieu, Sébastien ; Ernst, Damien ; Cornélusse, Bertrand in Proceedings of the International Conference on Electricity Distribution, CIRED 2015 (2015, June) To ease the transition towards the future of distribution grid management, regulators must revise the current interaction model, that is, the set of rules guiding the interactions between all the parties ... [more ▼] To ease the transition towards the future of distribution grid management, regulators must revise the current interaction model, that is, the set of rules guiding the interactions between all the parties of the system. Five interaction models are proposed, three of them considering active network management. This paper evaluates the economic efficiency of each model using macroscopic representation of the system, by opposition to more techniques requiring a complete picture of the system. The interaction models are simulated on the horizon 2015-2030. Results show that for the first five years all the models provide similar economic efficiency. For the remaining ten years, interaction models implementing active network management provide up to a 10% higher economic efficiency. [less ▲] Detailed reference viewed: 155 (23 ULg)A quantitative analysis of the effect of flexible loads on reserve markets Mathieu, Sébastien ; Louveaux, Quentin ; Ernst, Damien et al in Proceedings of the 18th Power Systems Computation Conference (PSCC) (2014, August) We propose and analyze a day-ahead reserve market model that handles bids from flexible loads. This pool market model takes into account the fact that a load modulation in one direction must usually be ... [more ▼] We propose and analyze a day-ahead reserve market model that handles bids from flexible loads. This pool market model takes into account the fact that a load modulation in one direction must usually be compensated later by a modulation of the same magnitude in the opposite direction. Our analysis takes into account the gaming possibilities of producers and retailers, controlling load flexibility, in the day-ahead energy and reserve markets, and in imbalance settlement. This analysis is carried out by an agent-based approach where, for every round, each actor uses linear programs to maximize its profit according to forecasts of the prices. The procurement of a reserve is assumed to be determined, for each period, as a fixed percentage of the total consumption cleared in the energy market for the same period. The results show that the provision of reserves by flexible loads has a negligible impact on the energy market prices but markedly decreases the cost of reserve procurement. However, as the rate of flexible loads increases, the system operator has to rely more and more on non-contracted reserves, which may cancel out the benefits made in the procurement of reserves. [less ▲] Detailed reference viewed: 253 (39 ULg)Relaxations for multi-period optimal power flow problems with discrete decision variables Gemine, Quentin ; Ernst, Damien ; Louveaux, Quentin 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 ▲] Detailed reference viewed: 194 (39 ULg)Gestion active d’un réseau de distribution d’électricité : formulation du problème et benchmark Gemine, Quentin ; Ernst, Damien ; Cornélusse, Bertrand 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 ▲] Detailed reference viewed: 249 (49 ULg)Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution Gemine, Quentin ; Ernst, Damien ; Cornélusse, Bertrand E-print/Working paper (2014) 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 formalize the ANM problem, which in addition to be sequential and uncertain, has a non-linear 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 non-linear program, for which we provide quantitative results using state of the art open source solvers and perform a sensitivity analysis over the amount of flexibility available in the system and the number of scenarios considered in the deterministic equivalent of the stochastic program. To foster further research on this problem, we make available a test bed based on a 75-bus distribution network at http://www.montefiore.ulg.ac.be/~anm/ . This test bed contains 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 ▲] Detailed reference viewed: 182 (27 ULg)Active network management: planning under uncertainty for exploiting load modulation Gemine, Quentin ; Karangelos, Efthymios ; Ernst, Damien 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 ▲] Detailed reference viewed: 174 (52 ULg)Supervised Learning for Sequential and Uncertain Decision Making Problems - Application to Short-Term Electric Power Generation Scheduling Cornélusse, Bertrand Doctoral thesis (2010) Our work is driven by a class of practical problems of sequential decision making in the context of electric power generation under uncertainties. These problems are usually treated as receding horizon ... [more ▼] Our work is driven by a class of practical problems of sequential decision making in the context of electric power generation under uncertainties. These problems are usually treated as receding horizon deterministic optimization problems, and/or as scenario-based stochastic programs. Stochastic programming allows to compute a first stage decision that is hedged against the possible futures and -- if a possibility of recourse exists -- this decision can then be particularized to possible future scenarios thanks to the information gathered until the recourse opportunity. Although many decomposition techniques exist, stochastic programming is currently not tractable in the context of day-ahead electric power generation and furthermore does not provide an explicit recourse strategy. The latter observation also makes this approach cumbersome when one wants to evaluate its value on independent scenarios. We propose a supervised learning methodology to learn an explicit recourse strategy for a given generation schedule, from optimal adjustments of the system under simulated perturbed conditions. This methodology may thus be complementary to a stochastic programming based approach. With respect to a receding horizon optimization, it has the advantages of transferring the heavy computation offline, while providing the ability to quickly infer decisions during online exploitation of the generation system. Furthermore the learned strategy can be validated offline on an independent set of scenarios. On a realistic instance of the intra-day electricity generation rescheduling problem, we explain how to generate disturbance scenarios, how to compute adjusted schedules, how to formulate the supervised learning problem to obtain a recourse strategy, how to restore feasibility of the predicted adjustments and how to evaluate the recourse strategy on independent scenarios. We analyze different settings, namely either to predict the detailed adjustment of all the generation units, or to predict more qualitative variables that allow to speed up the adjustment computation procedure by facilitating the ``classical'' optimization problem. Our approach is intrinsically scalable to large-scale generation management problems, and may in principle handle all kinds of uncertainties and practical constraints. Our results show the feasibility of the approach and are also promising in terms of economic efficiency of the resulting strategies. The solutions of the optimization problem of generation (re)scheduling must satisfy many constraints. However, a classical learning algorithm that is (by nature) unaware of the constraints the data is subject to may indeed successfully capture the sensitivity of the solution to the model parameters. This has nevertheless raised our attention on one particular aspect of the relation between machine learning algorithms and optimization algorithms. When we apply a supervised learning algorithm to search in a hypothesis space based on data that satisfies a known set of constraints, can we guarantee that the hypothesis that we select will make predictions that satisfy the constraints? Can we at least benefit from our knowledge of the constraints to eliminate some hypotheses while learning and thus hope that the selected hypothesis has a better generalization error? In the second part of this thesis, where we try to answer these questions, we propose a generic extension of tree-based ensemble methods that allows incorporating incomplete data but also prior knowledge about the problem. The framework is based on a convex optimization problem allowing to regularize a tree-based ensemble model by adjusting either (or both) the labels attached to the leaves of an ensemble of regression trees or the outputs of the observations of the training sample. It allows to incorporate weak additional information in the form of partial information about output labels (like in censored data or semi-supervised learning) or -- more generally -- to cope with observations of varying degree of precision, or strong priors in the form of structural knowledge about the sought model. In addition to enhancing the precision by exploiting information that cannot be used by classical supervised learning algorithms, the proposed approach may be used to produce models which naturally comply with feasibility constraints that must be satisfied in many practical decision making problems, especially in contexts where the output space is of high-dimension and/or structured by invariances, symmetries and other kinds of constraints. [less ▲] Detailed reference viewed: 151 (28 ULg)Tree based ensemble models regularization by convex optimization Cornélusse, Bertrand ; Geurts, Pierre ; Wehenkel, Louis Conference (2009, December 12) Tree based ensemble methods can be seen as a way to learn a kernel from a sample of input-output pairs. This paper proposes a regularization framework to incorporate non-standard information not used in ... [more ▼] Tree based ensemble methods can be seen as a way to learn a kernel from a sample of input-output pairs. This paper proposes a regularization framework to incorporate non-standard information not used in the kernel learning algorithm, so as to take advantage of incomplete information about output values and/or of some prior information about the problem at hand. To this end a generic convex optimization problem is formulated which is first customized into a manifold regularization approach for semi-supervised learning, then as a way to exploit censored output values, and finally as a generic way to exploit prior information about the problem. [less ▲] Detailed reference viewed: 140 (45 ULg)Supervised learning of intra-daily recourse strategies for generation management under uncertainties Cornélusse, Bertrand ; ; Defourny, Boris et al in PowerTech, 2009 IEEE Bucharest (2009) The aim of this work is to design intra-daily recourse strategies which may be used by operators to decide in real-time the modifications to bring to planned generation schedules of a set of units in ... [more ▼] The aim of this work is to design intra-daily recourse strategies which may be used by operators to decide in real-time the modifications to bring to planned generation schedules of a set of units in order to respond to deviations from the forecasted operating scenario. Our aim is to design strategies that are interpretable by human operators, that comply with real-time constraints and that cover the major disturbances that may appear during the next day. To this end we propose a new framework using supervised learning to infer such recourse strategies from simulations of the system under a sample of conditions representing possible deviations from the forecast. This framework is validated on a realistic generation system of medium size. [less ▲] Detailed reference viewed: 48 (21 ULg)Automatic learning for the classification of primary frequency control behaviour Cornélusse, Bertrand ; Wehenkel, Louis ; in Power Tech, 2007 IEEE Lausanne (2007) In this paper we propose a methodology based on supervised automatic learning in order to classify the behaviour of generators in terms of their performance in providing primary frequency control ... [more ▼] In this paper we propose a methodology based on supervised automatic learning in order to classify the behaviour of generators in terms of their performance in providing primary frequency control ancillary services. The problem is posed as a time-series classification problem, and handled by using state-of- the-art supervised learning methods such as ensembles of decision trees and support-vector machines combined with several preprocessing techniques. The method was designed in the context of the Belgian system and is validated on real-life data composed of more than 600 time-series recorded on this system. [less ▲] Detailed reference viewed: 39 (8 ULg) |
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