Dynamic Energy Storage Management for Dependable Renewable Electricity Generation

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Forecasting tools for the electrical production of winds origin : application for the optimization of the coupling of electric power distribution networks

La forte variabilité de la vitesse du vent fait que l'énergie produite par un parc éolien n'est pas constante dans le temps. Le gestionnaire ne peut donc pas dimensionner son réseau électrique en prenant intégralement ce type de production en compte. L' une des solutions préconisées pour permettre le développement de l' éolien et son intégration avec une plus grande sureté aux réseaux, est de développer et d'améliorer les outils de prévisions. Le travail de thèse consiste à améliorer les performances d'un outil de prédiction basé sur les réseaux de neurones bayesiens, permettant la prédiction de la puissance à très court terme . Le prédicteur fonctionne notamment par J'ajustement de paramètres, certain se détermine « automatiquement » via le mécanisme des réseaux de neurones bayesiens d' autres, que nous nommerons paramètres temporels, sont à l' appréciation de l'utilisateur. Le travail mené consiste à établir un protocole pour la fixation de ces paramètres tout en améliorant les performances du prédicteur . Nous avons donc décidé de conditionner leurs valeurs en fonction de la variabilité des séquences de puissance précédent l'instant de prévision. Tout d'abord nous avons classifié des séquences de puissance en fonction de leurs coefficients de variation en appliquant la méthode des C-moyennes floues. Puis, chaque classe formée a été testée sur plusieurs valeurs de paramètres, les valeurs associées aux meilleures prédictions ont été retenues. Enfin, ces résultats couplés au formalisme des Chaines de Markov, par le biais de la matrice de transition , ont perm is d'obtenir des taux d'amélioration par rapport à la persistance allant de 7,73 à 23,22 % selon l'horizon de prédiction considéréThe high variability of the wind speed has for conse quences that the energy produced by a wind farm is not constant over time. Therefore, the manager can't size the electrical network by takin g into account this type of production. One solution advocated for the development of wind energy and its integrati on with greater security at network, is to develop and improve fore casting tools. The thesi s objective is to improve the performance of a predi ction tool based on Bayesian neural networks, allowing the predi ction of wind power for short timescales. The predictor works, in part icular by the adjustment of parameters, sorne is determined "automatically" through the mechan ism of neural networks Bayesian other , which we cali temporal parameters are at the discretion of the user. The work involves establishing a protocol for the determination of these parameters and improving the performance of the predictor. So, we decided to condition their values depending on the sequence variability of wind power previous the moment of the forecast. First we classified sequences of power according to their coefficients of variation using the method of fuzzy C-means. Then, each formed class was tested for several parameters values, the values associated with the best predictions were selected. Finally , these result s coupled with the formalism of Markov chains , through the transition matrix allowed to obtain rates of improvement over the persistence ranging from 7.73 to 23.22 % depending on the prediction horizon considere

An advanced tool for wind power in network

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Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model

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Assesment of two spatio temporal forecasting technics for hourly satellite derived irradiance for a study case in the Caribbean isalnds

International audienceSolar forecasts are essential for grid-connected solar photovoltaics (PV) as penetration increases. This increase leads to increased grid variability and uncertainty that must be managed by power system operators and/or PV plant owners. Better solar forecasting tools contribute to facilitating this management. This work examines two spatio-temporal approaches for short-term forecasting of global horizontal irradiance using gridded satellite-derived irradiance as experimental support. The first approach is a spatio-temporal vector autoregressive (STVAR) model combined with a statistical process for op-timum selection of input variables. The second is an existing operational cloud motion vector (CMV) model, a deterministic approach. An evaluation of the predictive performance of these models is investigated for a case study area in the Caribbean Islands. This region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting context. Using scaled persistence as a reference, we benchmark the performance of the two spatio-temporal models over an extended 220×220 km domain, and for three specific, climatically distinct locations within this domain. We also assess the influence of intra-day solar resource variability on model performance. Exploiting this observation could lead to better forecast performance by harnessing the strengths and minimizing the weaknesses of both models for different conditions/locations. In a subsequent investigation, a blended model CMV/STVAR will be developed, by combining the strengths of a purely physical approach and those of a purely statistical approach. Operationally, such an approach would mesh with operational industry-targeted forecast services that exploit gridded satellite remote sensing resources

Preliminary assessment of two spatio-temporal forecasting technics for hourly satellite-derived irradiance in a complex meteorological context

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Assessment of nitrogen oxides and ground-level ozone behavior in a dense air quality station network: Case study in the Lesser Antilles Arc

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