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 UWCAES 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