水下压缩空气储能(underwater compressed air energy storage, UW-CAES)采用水下柔性气囊储气，能够实现恒压储气与释气，是新能源消纳的有力工具之一。然而，目前鲜有研究能够通过参数优化，在降低UW-CAES成本的同时，有效提升电站的运行经济性。针对上述问题，提出了基于分布鲁棒机会约束(distributionally robust chance constraints, DRCC)的UW-CAES优化配置方法。首先，构建了UW-CAES系统模型，充分考虑输气管道压力损失对系统的影响；随后，构建了考虑管道压力损失的UW-CAES优化配置模型，以最小化投资成本、最大化运行收益为目标，优化系统关键参数；最后，通过DRCC将优化问题中的机会约束转换成线性约束，该方法能使优化问题便于求解，同时通过调整参数的方式实现优化结果经济性与保守性间的平衡。算例分析显示，通过所提优化配置方法求解得到的系统能够保持额定释能功率为60 MW，同时使额定储能功率变为53.2 MW，较原系统下降8.75%，提升了系统效率；敏感性分析显示，通过调整DRCC中的置信度与Wasserstein半径即可实现求解结果在经济性与保守性之间的平衡。

Underwater compressed air energy storage (UW-CAES), which utilizes flexible underwater air bags to enable constant-pressure charge and discharge, has emerged as a compelling solution for renewable energy accommodation. However, there remains a distinct lack of research focused on parameter optimization to simultaneously reduce the capital costs of UW-CAES and enhance the operational economics of the plant. To address this critical gap, this paper proposes an optimal configuration method for UW-CAES based on distributionally robust chance constraints (DRCC). First, a comprehensive UW-CAES system model is established, explicitly accounting for the impact of pipeline pressure losses on system dynamics. Subsequently, an optimal configuration framework incorporating these pressure losses is formulated to optimize key system parameters, with the dual objectives of minimizing investment costs and maximizing operational revenues. Furthermore, the DRCC approach is employed to reformulate the stochastic chance constraints into tractable linear constraints. This mathematical transformation not only ensures computational efficiency but also facilitates a flexible trade-off between economic optimality and robustness. Case studies demonstrate the efficacy of the proposed methodology: the optimized system maintains a rated discharge power of 60 MW while reducing the required rated charge power to 53.2 MW − an 8.75% decrease compared to the original baseline − thereby significantly improving overall system efficiency. Finally, sensitivity analyses reveal that systematically calibrating the confidence level and Wasserstein radius within the DRCC framework effectively navigates the equilibrium between economic performance and system conservatism.