242 research outputs found

    Analytical functions from a stochastic model for extractions of a hydroelectric reservoir after the rainy season

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    [ES] El presente trabajo tiene como objetivo el planteamiento de ecuaciones estocásticas para la resolución de los niveles de embalse de un sistema hidroeléctrico que opera en cascada a través de 2 presas, en los meses posteriores a la época de lluvias, específicamente, los meses de noviembre y diciembre. A partir de los modelos de control markovianos se establecen el espacio de estados, el espacio de acciones, el kernel de transición y las funciones de ganancia y costo, para luego proponer una función objetivo que maximice una ecuación de optimalidad mediante variables continuas. Como resultado se determinan ecuaciones de los beneficios esperados en unidades energéticas de GW-hora, así como un modelo de políticas óptimas basado en estados continuos. En conclusión, se obtiene un conjunto de funciones analíticas que dependen de la época del año y los volúmenes actuales de cada uno de los embalses, para así determinar la mejor política de decisión para obtener el mayor beneficio de energía sin poner en riesgo la seguridad de la población por posibles derrames y minimizando déficits.[EN] The objective of the present work is the approach of stochastic models that allow to determine the level of a hydroelectric system that operates in cascade through two reservoirs, in the months after the rainy season, specifically, the months of November and December. The components that determine the control model are the state space, the action set, the transition kernel, and the so-called reward and cost functions, and with them it is proposed to maximize the optimality equation through the objective function by continuous variables The result is the obtaining of benefit equations given in GW-hour, as well as obtaining an optimal policy model through the continuous states. It is concluded that analytical functions can be obtained that only depend on the current volumes of the reservoirs and the time of the year, and thereby establish the optimal policy to maximize energy and minimize spills avoiding situations of risk to the population and minimizing deficits.De La Cruz-Courtois, O.; Guichard, D.; Arganis, M. (2020). Funciones analíticas a partir de un modelo estocástico de las extracciones de una presa hidroeléctrica después de la temporada de lluvias. Ingeniería del agua. 24(4):235-253. https://doi.org/10.4995/ia.2020.12311OJS235253244Abolghasemi, R.H. 2008. Optimization of the Kootenay river hydroelectric system with a linear programming model. Master Thesis, University of Canada, Canada.Ailing, Li. 2004. A study on the large-scale system descomposition-coordination method used in optimal operation of a hydroelectric system. Water International, 29(2), 228-231. https://doi.org/10.1080/02508060408691772Alegría, A. 2010. Política de operación óptima del sistema de presas del Río Grijalva. Efectos de la curva guía. Tesis de Maestría, Universidad Nacional Autónoma de México, México.Arganis, M.L., Domínguez, R., Cisneros-Itube, H., Fuentes-Mariles, G. 2008. Syntetic sample generation of monthly inflows into two dams using the modified Svanidze method. Hydrological sciences journal, 53(1), 130-141. https://doi.org/10.1623/hysj.53.1.130Ahmad, A., El-Shafie, A., Razali, S.F.M., Mohamad, Z.S. 2014. Reservoir optimization water resources: a review. Water Resources Management, 28(11), 135-146. https://doi.org/10.1007/s11269-014-0700-5Barros, T.L.M, Tsai, F.T.C., Yang, S., Lopes, J.E.G., Yeh, W.W.G. 2003. Optimization of larg-scale hydropower system operations. Journal of Water Resources Planning and Management, 129(3), 178-188. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(178)Bertsekas, D.P. 1995. Dynamic Programming and Optimal Control. Prentice-Hall, Massachusetts, USA.Butcher, W.S. 1971. Stochastic dynamic programming for optimal reservoir operation. Journal of the American Water Resources Association, 71(11), 143-158. https://doi.org/10.1111/j.1752-1688.1971.tb01683.xCrichigno, J., Talavera, F. 2012. Enrutamiento multicast utilizando optimización multiobjetivo. Ingeniería y Ciencia, 4(7), 87-111.De la Cruz-Courtois, O., Guichard, R., Arganis, M. 2018. Políticas de operación óptima de presas para generación hidroeléctrica con modelos markovianos y variable continua. Pakbal, 44(3), 12-25.Domínguez, R., Arganis, M. 2001. Revisión de las políticas de operación de las presas Angostura y Malpaso en el río Grijalva. Informe de la Comisión Federal de Electricidad, CDMX, México.Domínguez, R., Arganis, M., Carrizosa, E. 2006. Determinación de avenidas de diseño y ajuste de los parámetros del modelo de optimización de las políticas de operación del sistema de presas del Río Grijalva. Informe de Comisión Federal de Electricidad, Universidad Nacional Autónoma de México, México.Eschenbach, E.A., Magee, T., Zagona, E., Goranflo, M., Shane, R. 2001. Goal programming Decision Support System for Multiobjective Operation of Reservoir Systems. Journal of Water Resources Planning and Management, 127(2), 108-120. https://doi.org/10.1061/(ASCE)0733-9496(2001)127:2(108)García, V., 1992. Aprovechamientos hidroeléctricos y de bombeo. Trillas. CDMX, México.Hernández-Lerma, O., Laserre, J. 1991. Discrete-Time Markov Control processes. Basic optimality criteria, Springer, New York, USA.Labadie, J.W. 2004. Optimal Operation on Multireservoir Systems: State-of-the-art Review. Journal of Water Resources Planning and Management, 130(2), 93-111. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:2(93)Labadie, J.W., Lee, J.H. 2007. Stochastic optimization of multireservoir systems via reinforcement learning. Water Resources Research, 43 W11408. https://doi.org/10.1029/2006WR005627Mendoza-Pérez, A., Jasso-Fuentes, H., De la Cruz-Courtois, O. 2016. Constrained Markov decision processes in Borel spaces: from discounted to average optimality. Springer Berlin Heidelberg, 84(3), 489-525. https://doi.org/10.1007/s00186-016-0551-3Quitana F, F. 1981. Aplicaciones de la Programación Dinámica a la Operación de Presas. Tesis. Universidad de Sonora, Sonora, México.Rani, D., Moreira, M.M. 2010. Simulation-Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation. Water Resources Management, 24, 1107-1138. https://doi.org/10.1007/s11269-009-9488-0Rincón, L. 2012. Introducción a los procesos estocásticos. Universidad Nacional Autónoma de México, México.Sánchez, C.E., Andreu A.J. 2002. Expansión óptima de sistemas de recursos hídricos superficiales: Aplicación a un sistema real en España. In 11 Congreso Intern. de Métodos Numéricos en Ingeniería y Ciencias Aplicadas (Vol 1).Sánchez, C.E., Wagner, G.A., 2003. Determinación de reglas de operación óptima para dos embalses, utilizando un algoritmo genético. Universidad Autónoma de Coahuila, México.Sánchez, C.E., Wagner, G.A., 2004. Modelo numérico para la operación óptima de un hidrosistema de aguas superficiales. Instituto Mexicano de Tecnología del Agua, 15(2), 23-38.Kumar, V., Yadav, S.M. 2018. Optimization of reservoir operation with a new approach in evolutionary computation using TLBO and Jaya Algorithm. 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    Notes on dark energy interacting with dark matter and unparticle in loop quantum cosmology

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    We investigate the behavior of dark energy interacting with dark matter and unparticle in the framework of loop quantum cosmology. In four toy models, we study the interaction between the cosmic components by choosing different coupling functions representing the interaction. We found that there are only two attractor solutions namely dark energy dominated and dark matter dominated Universe. The other two models are unstable, as they predict either a dark energy filled Universe or one completely devoid of it.Comment: 9 pages, 10 figures. v2: Minor revisions, matches published versio

    Effects of carbonated water injection on the pore system of a carbonate rock (coquina)

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    CO2 injection is a well-known Enhanced Oil Recovery (EOR) technique that has been used for years to improve oil extraction from carbonate rock and other oil reservoirs. Optimal functioning of CO2 injection requires a thorough understanding of how this method affects the petrophysical properties of the rocks. We evaluated pore-scale changes in these properties, notably porosity and absolute permeability, following injection of CO2-saturated water in two coquina outcrop samples from the Morro do Chaves Formation in Brazil. The coquinas are close analogues of Pre-salt oil reservoirs off the coast of southern Brazil. The effects of carbonated water injection were evaluated using a series of experimental and numerical steps before and after coreflooding: cleaning, basic petrophysics, microtomography (microCT) imaging, nuclear magnetic resonance (NMR) analyses, and pore network modeling (PNM). Our study was motivated by an earlier experiment which did not show the development of a wormhole in the center of the sample, with a concomitant increase in permeability of the coquina as often noted in the literature. We instead observed a substantial decrease in the absolute permeability (between 71 and 77%), but with little effect on the porosity and no wormhole formation. While all tests were carried out on both samples, here we present a comprehensive analysis for one of the samples to illustrate changes at the pore network level. Different techniques were used for the pore-scale analyses, including pore network modeling using PoreStudio, and software developed by the authors to enable a statistical analysis of the pore network. Results provided much insight in how injected carbonated water affects the pore network of carbonate rocks
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