17,331 research outputs found

    A predictive pan-European economic and production dispatch model for the energy transition in the electricity sector

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    The energy transition is well underway in most European countries. It has a growing impact on electric power systems as it dramatically modifies the way electricity is produced. To ensure a safe and smooth transition towards a pan-European electricity production dominated by renewable sources, it is of paramount importance to anticipate how production dispatches will evolve, to understand how increased fluctuations in power generations can be absorbed at the pan-European level and to evaluate where the resulting changes in power flows will require significant grid upgrades. To address these issues, we construct an aggregated model of the pan-European transmission network which we couple to an optimized, few-parameter dispatch algorithm to obtain time- and geographically-resolved production profiles. We demonstrate the validity of our dispatch algorithm by reproducing historical production time series for all power productions in fifteen different European countries. Having calibrated our model in this way, we investigate future production profiles at later stages of the energy transition - determined by planned future production capacities - and the resulting interregional power flows. We find that large power fluctuations from increasing penetrations of renewable sources can be absorbed at the pan-European level via significantly increased electricity exchanges between different countries. We identify where these increased exchanges will require additional power transfer capacities. We finally introduce a physically-based economic indicator which allows to predict future financial conditions in the electricity market. We anticipate new economic opportunities for dam hydroelectricity and pumped-storage plants.Comment: 6 pages, 8 figure

    On the Buchsbaum index of rank two vector bundles on P3

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    We classify rank two vector bundles on P3 with Buchsbaum index equal to three and also give some results on the H1-module of "negative instanton"bundles.Comment: Submitted to the proceedings of the Pau-Trieste conferences Vector bundles day

    How fast can one overcome the paradox of the energy transition? A physico-economic model for the European power grid

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    The paradox of the energy transition is that the low marginal costs of new renewable energy sources (RES) drag electricity prices down and discourage investments in flexible productions that are needed to compensate for the lack of dispatchability of the new RES. The energy transition thus discourages the investments that are required for its own harmonious expansion. To investigate how this paradox can be overcome, we argue that, under certain assumptions, future electricity prices are rather accurately modeled from the residual load obtained by subtracting non-flexible productions from the load. Armed with the resulting economic indicator, we investigate future revenues for European power plants with various degree of flexibility. We find that, if neither carbon taxes nor fuel prices change, flexible productions would be financially rewarded better and sooner if the energy transition proceeds faster but at more or less constant total production, i.e. by reducing the production of thermal power plants at the same rate as the RES production increases. Less flexible productions, on the other hand, would see their revenue grow more moderately. Our results indicate that a faster energy transition with a quicker withdrawal of thermal power plants would reward flexible productions faster.Comment: 13 pages, 11 figures and 2 table

    Epitope prediction improved by multitask support vector machines

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    Motivation: In silico methods for the prediction of antigenic peptides binding to MHC class I molecules play an increasingly important role in the identification of T-cell epitopes. Statistical and machine learning methods, in particular, are widely used to score candidate epitopes based on their similarity with known epitopes and non epitopes. The genes coding for the MHC molecules, however, are highly polymorphic, and statistical methods have difficulties to build models for alleles with few known epitopes. In this case, recent works have demonstrated the utility of leveraging information across alleles to improve the performance of the prediction. Results: We design a support vector machine algorithm that is able to learn epitope models for all alleles simultaneously, by sharing information across similar alleles. The sharing of information across alleles is controlled by a user-defined measure of similarity between alleles. We show that this similarity can be defined in terms of supertypes, or more directly by comparing key residues known to play a role in the peptide-MHC binding. We illustrate the potential of this approach on various benchmark experiments where it outperforms other state-of-the-art methods

    Kernel methods for in silico chemogenomics

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    Predicting interactions between small molecules and proteins is a crucial ingredient of the drug discovery process. In particular, accurate predictive models are increasingly used to preselect potential lead compounds from large molecule databases, or to screen for side-effects. While classical in silico approaches focus on predicting interactions with a given specific target, new chemogenomics approaches adopt cross-target views. Building on recent developments in the use of kernel methods in bio- and chemoinformatics, we present a systematic framework to screen the chemical space of small molecules for interaction with the biological space of proteins. We show that this framework allows information sharing across the targets, resulting in a dramatic improvement of ligand prediction accuracy for three important classes of drug targets: enzymes, GPCR and ion channels

    Analytical matrix elements of the Uehling potential in three-body systems, and applications to exotic molecules

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    Exact analytical expressions for the matrix elements of the Uehling potential in a basis of explicitly correlated exponential wave functions are presented. The obtained formulas are then used to compute with an improved accuracy the vacuum polarization correction to the binding energy of muonic and pionic molecules, both in a first-order perturbative treatment and in a nonperturbative approach. The first resonant states lying below the n=2 threshold are also studied, by means of the stabilization method with a real dilatation parameter
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