Abandoned underground quarries or mines may be rehabilitated as natural reservoirs for underground pumped hydro energy storage (UPHES). In addition to the inherent modeling inaccuracies of the traditional PHES that arise from, e.g., approximating the nonlinear pump/turbine head-dependent performance curves, the optimal operation of these underground plants is also affected by endogenous model uncertainties. The latter typically arise from a limited knowledge of the physical characteristics of the system such as the geometry and hydraulic properties of the underground cavity. In this paper, chance-constrained programming is leveraged to immunize the day-ahead scheduling of an UPHES owner against both these model uncertainties and the modeling approximations. The proposed method is tested on a fictitious UPHES system using an existing underground quarry as lower reservoir. Results demonstrate that the methodology allows finding a compromise between conservativeness and economic performance, while being computationally efficient. This model may thus be integrated in the daily scheduling routine of UPHES owners, or may help regulators and system operators to better estimate the available flexibility of such resources.

中文翻译：

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存在模型不确定性的地下抽水蓄能机会约束调度

废弃的地下采石场或矿山可以恢复为地下抽水蓄能（UPHES）的天然水库。除了传统PHES固有的建模误差（例如，近似于非线性泵/涡轮机头相关性能曲线）外，这些地下电站的最佳运行还受到内源模型不确定性的影响。后者通常是由于对系统物理特性（例如地下空腔的几何形状和水力特性）的了解有限而产生的。在本文中，利用机会受限的编程来使UPHES所有者的日前调度免受这些模型不确定性和建模近似的影响。在虚拟UPHES系统上使用现有的地下采石场作为下层水库对提出的方法进行了测试。结果表明，该方法可以在保守性和经济绩效之间找到折衷方案，同时在计算上也很有效。因此，该模型可以集成到UPHES所有者的日常调度例程中，或者可以帮助监管者和系统运营商更好地估计此类资源的可用灵活性。