【目的】 在电池储能电站实际运行中，周期性检修维护计划停运以及设备突发故障非计划停运都会引起可用容量损失，显著影响电站的经济性。现有电池储能容量配置优化方法未能充分计及这两类事件的影响，为此，提出一种考虑计划与非计划停运事件影响的电池储能容量配置优化方法。【方法】 首先，针对电池储能电站的周期性维护需求，构建基于全生命周期的计划性停运下收益动态评估模型；其次，对电池储能电站一次设备和二次设备的故障展开建模，建立非计划性停运场景下的电池储能电站收益评估方法。然后，综合考虑最大化受两类停运事件影响的综合收益与最小化储能电站综合投资成本，构建储能容量配置多目标优化模型。为自适应平衡优化模型的两个目标函数，设计基于边际改善率的求解算法实现迭代求解。最后，以IEEE 30节点系统和某省区域电网为例进行算例分析，验证所提方法的有效性。【结果】 相比传统配置方法，储能的高可用日占比提升约51.5%，验证了所提容量配置优化方法的合理性和有效性。【结论】 通过同时计及两种事件的影响和多目标优化，在模拟运行中可降低因舱室级维护和故障带来的影响，为运行调度和交易履约提供便利。

[Objective] In the practical operation of battery energy storage stations （BESS）， both planned outages caused by periodic maintenance and unplanned outages resulting from sudden equipment failures can lead to available capacity losses， significantly affecting the economic viability of the station. Existing optimization methods for battery energy storage capacity configuration have not adequately considered the impacts of these two types of outage events. To address this gap， this paper proposes an optimization method for battery energy storage capacity configuration that explicitly incorporates planned and unplanned outages. [Methods] First， addressing the periodic maintenance requirements of battery energy storage stations， a life-cycle-based dynamic revenue evaluation model is established for planned outages associated with periodic maintenance. Second， failures of primary and secondary equipment in battery energy storage stations are modeled to assess revenues under unplanned outage scenarios. On this basis， a multi-objective optimization model for energy storage capacity configuration is formulated to maximize aggregate revenues affected by both types of outage events while minimizing the total investment cost. To adaptively balance the two objective functions of the optimization model， an iterative solution algorithm based on marginal improvement rates is designed. Finally， case studies using the IEEE 30-bus system and a provincial regional power grid are conducted to verify the validity of the proposed method. [Results] Compared with traditional allocation methods， the proportion of high-availability days for energy storage is increased by approximately 51.5%， validating the rationality and effectiveness of the proposed capacity allocation optimization method. [Conclusions] By simultaneously considering the impacts of both types of outage events and employing multi-objective optimization， the proposed method can mitigate the effects of cabin-level maintenance and failures during simulated operation， thereby facilitating operational scheduling and transaction fulfillment.