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A high-resolution hydro power time-series model for energy systems analysis: Validated with Chinese hydro reservoirs

机译:用于能源系统分析的高分辨率水力发电时间序列模型:用中国水库进行了验证

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We expand the renewable technology model palette and present a validated high resolution hydro power time series model for energy systems analysis. Among the weather-based renewables, hydroelectricity shows unique storage-like flexibility, which is particularly important given the high variability of wind and solar power. Often limited by data availability or computational performance, a high resolution, globally applicable and validated hydro power time series model has not been available. For a demonstration, we focus on 41 Chinese reservoir-based hydro stations as a demo, determine their upstream basin areas, estimate their inflow based on gridded surface runoff data and validate their daily inflow time series in terms of both flow volume and potential power generation. Furthermore, we showcase an application of these time series with hydro cascades in energy system long term investment planning. Our method's novelty lies in:?it is based on highly resolved spatial-temporal datasets;?both data and algorithms used here are globally applicable;?it includes a hydro cascade model that can be integrated into energy system simulations.
机译:我们扩展了可再生技术模型的调色板,并提出了经过验证的高分辨率水电时间序列模型,用于能源系统分析。在基于天气的可再生能源中,水电显示出独特的类似于存储的灵活性,鉴于风能和太阳能的高度可变性,这一点尤为重要。通常受数据可用性或计算性能的限制,尚未提供高分辨率,全球适用且经过验证的水电时间序列模型。作为演示,我们以41个中国水库为基础的水电站作为演示,确定其上游流域面积,根据网格化地表径流量数据估算其入水量,并根据流量和潜在发电量验证其每日入水时间序列。此外,我们展示了这些时间序列与水梯级联在能源系统长期投资规划中的应用。我们的方法的新颖性在于:它基于高度解析的时空数据集;此处使用的数据和算法均在全球范围内适用;它包括可集成到能源系统仿真中的水力梯级模型。

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