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Hydropower generation in future climate scenarios

机译:未来气候情景中的水电站

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Knowledge on the effects of climate change in a system can contribute to the better management of its water and energy resources. This study evaluates the consequences of alterations in the rainfall and temperature patterns for a hydroelectric plant. The methodology adopted consists of four steps. First, a hydrological model is developed for the chosen basin following a semi-distributed and conceptual approach. The hydrological model is calibrated utilizing the optimization algorithm Shuffled Complex Evolution University of Arizona (SCE-UA) and then validated. Secondly, a hydropower model is developed fora hydroelectric plant of the chosen basin. The hydropower model is adjusted to the physical characteristics of the plant. Thirdly, future climate scenarios are extracted from the literature for the studied area. These scenarios include quantitative and seasonal climate variations, as well as different initial reservoir levels. Fourth, the hydrological-hydropower model is simulated for 52 scenarios and the impact of changes in the rainfall and temperature patterns for hydropower generation is evaluated. For each scenario, the water storage in the reservoir and energy produced by the plant are analyzed. The financial impact for extreme scenarios is presented. The methodology is applied to the Tres Marias hydroelectric plant at the upper SAo Francisco river basin (Brazil) and it can be replicated to any other hydropower system. The results show that extreme positive values predicted for rainfall will likely not cause issues to the plant, considering a moderate rise in temperature. However, negative predictions for rainfall, regardless of changes in temperature, should be an alert to the authorities responsible for water and energy resources management. (C) 2020 International Energy Initiative. Published by Elsevier Inc. All rights reserved.
机译:关于气候变化在系统中的影响的知识可以促进其水和能源的更好管理。本研究评估了水力发电降雨和温度模式的改变的后果。采用的方法包括四个步骤。首先,在半分布和概念方法之后为所选盆地开发水文模型。利用亚利桑那州的优化算法(SCE-UA)进行了优化算法校准了水文模型,然后验证。其次,开发了水电模型,开发了所选盆地的水力发电厂。水电模型调整为植物的物理特性。第三,未来的气候情景是从研究区域的文献中提取的。这些情景包括定量和季节性气候变化,以及不同的初始水库水平。第四,对水文水电模型进行了模拟52场景,评估了水电站降雨量和温度模式的影响。对于每种情况,分析了储层中的储水和由工厂产生的能量。提出了极端情景的财务影响。该方法应用于TRES Marias水电站,位于Sao Francisco河流域(巴西),可以复制到任何其他水电系统。结果表明,考虑到温度升高,降雨预测的极端正值可能不会导致植物问题。然而,无论温度变化如何,降雨的否定预测应该是对负责水和能源管理的当局的警报。 (c)2020国际能源倡议。由elsevier Inc.出版的所有权利保留。

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