Letteratura scientifica selezionata sul tema "Électricité – Production – Prévision"
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Tesi sul tema "Électricité – Production – Prévision":
Siebert, Nils Walter. "Développement de méthodes pour la prédiction de la production éolienne régionale". Paris, ENMP, 2008. http://tel.archives-ouvertes.fr/tel-00287551.
The large-scale integration of wind power can be a challenge for power system operators because, unlike conventional power sources, wind power is variable and non-dispatchable. To alleviate some of the problems posed by large-scale wind power integration, power system operators express the need for short-term (48 to 120 hours ahead) forecasts of the aggregated output of all wind farms within a specified geographical region. The aim of the thesis is to develop a framework and tools to help in the implementation of statistical regional wind power forecasting models. We first propose a framework for the characterization of the regional wind power. In this way, salient aspects of the regional wind power forecasting problem that must be taken into account when designing a regional forecasting model are identified. We then examine the regional forecasting problem from a statistical learning perspective. We define three generic approaches that can be used to combine sub-models to build regional models. The influence of these approaches on forecast accuracy is examined, as well as that of the choice of sub-models. The comparison of sub-models is made possible by the introduction of a novel forecasting model whose performance is shown to be comparable to that of other state-of-the-art models. Finally, we examine the impact of explanatory variable selection on forecast accuracy and derive general guidelines applicable in the frame of regional wind power forecasting. To ease modelling, automatic selection techniques are investigated. Two variable selection methods (a filter and a wrapper method) that exploit problem-specific characteristics are proposed. These methods are shown to compare very favourably to a generic state-of-the-art method
Cassagnole, Manon. "Analyse du lien entre la qualité des prévisions hydrologiques et leur valeur économique pour le secteur hydroélectrique". Electronic Thesis or Diss., Paris, AgroParisTech, 2020. http://www.theses.fr/2020AGPT0001.
The quality of hydrological forecasts is a widely-studied field. It has been shown that probabilistic or ensemble forecasts (i.e. with several scenarios) are often better in terms of quality and decision support than deterministic (single-valued) forecasts. Probabilistic forecasts are therefore increasingly used in operational forecasting. However, the contribution of these forecasts in terms of economic value for the users remains a subject that has so far received little attention. In the literature, the evaluation of a forecast is mainly based on its quality (comparison with an observation). However, a forecast can also be assessed with regard to its contribution (or value) to the decision-making process. In the case of reservoir operation for hydroelectric generation, studies on the value of forecasts are also less frequent, particularly when it comes to the short-term range (forecasts ranging from a few days to a few weeks).The work carried out in this thesis aims to examine the link between the quality and the economic value of a forecast for the hydropower sector. In other words, we want to know in which cases a better forecast impacts the management revenues. We investigate the value of hydrological forecasts at several forecasts ranges: short (up to seven days) and medium (up to seven months). Finally, we investigate whether short-term reservoir management can be improved when coupled with medium-term management. The last research axis of this thesis thus consists in setting up a coupled forecast-reservoir management system, where medium-term management information is used to inform short-term reservoir management.The results of this thesis contribute: (1) to shed light on the links between the quality and the value of hydrometeorological forecasts and (2) to the modelling of hydropower reservoirs for optimal management. The modelling tools established in this study allow complex hydroelectric systems to be represented in a simplified way. With their help, we have highlighted the existence of a link between the quality of hydrological forecasts and their economic value. The economic value of short-term hydrological forecasts depends on their quality: the best forecast system in terms of forecast quality corresponds to the forecast system with the best management revenue and conversely. However, this relationship also depends on how the forecast information is taken into account in the reservoir management model, and on the size of the reservoir with regard to the average inflow volumes. In the medium-term management context, the link between the quality of medium-range forecasts and their economic value may also exist, but it is less obvious. Finally, the revenue obtained from the short-term management of the reservoir can be improved by more than 10 % by taking into account long-term management information
Monjoly, Stéphanie. "Outils de prédiction pour la production d’électricité d’origine éolienne : application à l’optimisation du couplage aux réseaux de distributions d’électricité". Thesis, Antilles-Guyane, 2013. http://www.theses.fr/2013AGUY0679/document.
The 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 considered
Assoumou, Edi. "Modélisation MARKAL pour la planification énergétique long terme dans le contexte français". Phd thesis, École Nationale Supérieure des Mines de Paris, 2006. http://pastel.archives-ouvertes.fr/pastel-00002752.
Foucault, Fiona. "Optimisation de l’implantation de centrales éoliennes dans l’environnement d’un marché à prix locaux". Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEM079/document.
Electricity markets are in a period of intense change. This is notably due to liberalization efforts to increase the extent of electricity system’s management carried out through market operations. One such example is the implementation of nodal prices for network constraints. Moreover, the surge for electricity from renewable sources questions the operation of the electricity system. In this framework, the investment issue for wind producers is becoming more complex. Its income may go from a subsidy-based scheme to a full market participation in the short term, and more volatile according to time and location (in a nodal-pricing scheme). Bearing all this in mind, this PhD work first analyzes the impact of potential installation sites’ characteristics: load factor, and predictability (a site’s ability to enable reliable predictions), on investment. To this end, we carry out a statistical analysis on historical data from several markets, then we suggest an estimator of wind producers revenue, to carry out the same work with a less costly approach than exhaustive calculation. Then, in order to carry out the same kind of analysis, this time in a customizable framework, we build an algorithm to solve the problem of Optimal investment planning of wind turbines within a nodal price market environment. It takes into account the participation in the Day-ahead market as well as penalties paid for imbalances between the energy contracted and injected in real-time (due to forecasting errors). We assume renewable production is important enough to influence market prices which are also generated with our model, and we integrate scenarios for wind production and demand. Therefore we have a stochastic problem which we solve using Benders decomposition. Ultimately we analyze the impact of load factor and predictability on optimal investment according to the chosen setting for regulation cost, line capacities and wind data correlation
Kam, Ollé Michel. "Prédiction pour la gestion intelligente territorialisée de la ressource renouvelable photovoltaïque & intégration par déploiement d’un réseau de capteurs IoT LoRaWAN". Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0197.
The implementation of decentralized electrical micro-grids base on renewable energies is a challenge for the power supplies of telecommunication infrastructures. These power sources supply from off-grid photovoltaic and micro-wind systems are more suitable for remote or isolated sites like telecom antennas considering the local installation feasibilities and renewable resource availabilities. However, the intermittence and the variability of renewable sources require to ensure the balance between the production and the consumption of energies. In addition, the system sizing requires a trade-off between size, investment cost, safety and reliability of the power supply due to the intermittency. This thesis aims to develop a model characterizing the global solar irradiation variability to predict PV productions from data recorded on sites and over a long period. We validated the calibration methods of the Weibull function allowing a reliable prediction of the global irradiation in a semi-continental climate (mainland French sites). More precisely, we experimentally determine the Weibull parameters for obtaining a reliable global irradiation forecast prediction by considering the sun incident radiation during time periods. This forecast allows to estimate the yearly energy generation and its prediction during the time evolution from a PV plant in considered sites. The originality of the proposed approach is to obtain reliable predictive yearly PV energy distributions which also can be used to generate synthetic data times series of the global irradiation. The proposed model is based a parametrized mathematical formula providing reliable prediction results and which can be integrated to a real-time data acquisition system. To validate the proposed approach for the prediction of the PV power source supplying of telecommunication infrastructures, we developed an IoT sensors network for real-time acquisition to instrument a telecom antenna allowing a wide area network coverage. We demonstrate that deployed networked IoT sensors based on LoRaWAN protocol on a traditional telecommunication tower operating under real conditions communicate reliably without affecting the tower’s functions while keeping the data quality of the sensors. The proposed LoRaWAN network is used for the data acquisition of the weather parameters performing the proposed forecast of the real-time PV production. The software implementation of the proposed prediction model interfaced to weather sensors allows a real-time and intelligent management of deployed renewable energy systems. Future work is also discussed to develop renewable energies on a wide territory with a semi-continental climate
Haessig, Pierre. "Dimensionnement et gestion d’un stockage d’énergie pour l'atténuation des incertitudes de production éolienne". Thesis, Cachan, Ecole normale supérieure, 2014. http://www.theses.fr/2014DENS0030/document.
The context of this PhD thesis is the integration of wind power into the electricity grid of small islands. This work is supported by EDF SEI, the system operator for French islands. We study a wind-storage system where an energy storage is meant to help a wind farm operator fulfill a day-ahead production commitment to the grid. Within this context, we propose an approach for the optimization of the sizing and the control of the energy storage system (energy management). Because day-ahead wind power forecast errors are a major source of uncertainty, the energy management of the storage is a stochastic optimization problem (stochastic optimal control). To solve this problem, we first study the modeling of the components of the system. This include energy-based models of the storage system, with a focus on Lithium-ion and Sodium-Sulfur battery technologies. We then model the system inputs and in particular the stochastic time series like day-ahead forecast errors. We also discuss the modeling of storage aging, using a formulation which is adapted to the control optimization. Assembling all these models enables us to optimize the energy management of the storage system using the stochastic dynamic programming (SDP) method. We introduce the SDP algorithms and present our optimization results, with a special interest for the effect of the shape of the penalty function on the energy control law. We also present additional energy management applications with SDP (mitigation of wind power ramps and smoothing of ocean wave power). Having optimized the storage energy management, we address the optimization of the storage sizing (choice of the rated energy). Stochastic time series simulations show that the temporal structure (autocorrelation) of wind power forecast errors have a major impact on the need for storage capacity to reach a given performance level. Then we combine simulation results with cost parameters, including investment, losses and aging costs, to build a economic cost function for sizing. We also study storage sizing when the penalization of commitment deviations includes a tolerance threshold. We finish this manuscript with a structural study of the interaction between the optimizations of the sizing and the control of an energy storage system, because these two optimization problems are coupled
Buire, Jérôme. "Intégration des incertitudes liées aux prévisions de consommation et production à la gestion prévisionnelle d'un réseau de distribution". Thesis, Ecole centrale de Lille, 2018. http://www.theses.fr/2018ECLI0017/document.
The voltage profiles inside the network and power flows at the transport-distribution interface are modified under the massive insertion of renewable sources in distribution grids. The system’s uncertainties cannot be handled by local controllers which parameters are tuned at the actuator installation stage. A solution, widely accepted in the literature, consists of achieving a centralized optimization of the actuators references (distributed generators reactive powers, reference voltage of the On Load Tap Changer, capacitor banks reactive power). Within this framework, a supervisor computes all references at the same time and delivers the references to each actuators, which requires an efficient and reliable communication system.The main contribution of the thesis is to design an alternative approach which keeps the local control structures which settings will be updated on an hourly basis. The optimization relies on a stochastic representation of the grid that accounts for the On Load Tap Changer uncertainties and day ahead forecasts of the productions and consumptions. It is shown that every variable of the system can be represented by Gaussian or sum of truncated Gaussian variables. A stochastic optimization allows to select the controllers settings that minimize overvoltages and control efforts, without using time-consuming algorithms such as Monte-Carlo methods. This work will demonstrate that an appropriate management of uncertainties spares unnecessary and costly oversizing
Rakotoson, Vanessa. "Intégration de l'analyse de cycle de vie dans l'étude de la production électrique en milieux insulaires". Thesis, La Réunion, 2018. http://www.theses.fr/2018LARE0035/document.
Population growth, the raising of the standard of living and quality of life, and energy-intensive activities are key parameters affecting the territory energy demand, through electricity consumption. To meet this demand, reliance on fossil fuels is the main adopted solution, particularly in insular context. The downside of this method is the large amount of greenhouse gas emissions (GHG) emitted, and vulnerability of the territories. Current policies are now in favor of the energy self-sufficiency as a medium-term objective, and put in place measures to support the use of sustainable energy sources to mitigate GHG emissions. This work aims to assess environmental impact of electricity production in Reunion island, to establish a territorial diagnosis. Based on a life cycle assessment approach, according to ISO 14044 standards, varying environmental impacts have been evaluated from existing power plants. An evaluating tool has been developed to identify the most emissive life cycle stage from 1 kWh electricity produced. The obtained results serve as a reference point to develop prospective scenarios. Eight scenarios have been presented and aim to satisfy environmental, technical, social and economic constraints
Ahmidi, Amir. "Participation de parcs de production éolienne au réglage de la tension et de la puissance réactive dans les réseaux électriques". Phd thesis, Ecole Centrale de Lille, 2010. http://tel.archives-ouvertes.fr/tel-00590371.
Libri sul tema "Électricité – Production – Prévision":
Canada, Canada Industry, a cura di. Canadian electric power technology roadmap: Forecast. Ottawa: Industry Canada, 2000.
Flavin, Christopher. Powering the future: Blueprint for a sustainable electricity industry. Washington, D.C: Worldwatch Institute, 1994.
Catalão, João P. S. Electric power systems: Advanced forecasting techniques and optimal generation scheduling. Boca Raton: CRC Press, 2012.
Catalão, João P. S. Electric Power Systems: Advanced Forecasting Techniques and Optimal Generation Scheduling. Taylor & Francis Group, 2017.
Catalão, João P. S. Electric Power Systems: Advanced Forecasting Techniques and Optimal Generation Scheduling. Taylor & Francis Group, 2017.
Catalão, João P. S. Electric Power Systems: Advanced Forecasting Techniques and Optimal Generation Scheduling. Taylor & Francis Group, 2017.