发表机构
University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估长期电池储能规划中不同建模保真度对生命周期结论的影响，发现保真度需求因指标而异，需根据目标结果选择合适模型。
AI 中文摘要
长期电池储能系统（BESS）规划通常依赖简化的退化、运行和时间表示以保持计算可行性，但这些简化对生命周期结论的影响尚不明确。本文评估了规划研究中BESS设计长期生命周期评估所需的建模保真度。一个20年并网微电网使用忽略退化的规划模型进行规模确定，随后固定已安装组合，并通过顺序生命周期验证进行评估。参考表示结合了非线性日历老化和循环老化、依赖C率的效率、依赖健康状态（SOH）的性能、自放电、电池更换以及完整的8760小时时序。通过生命周期成本、更换时机、健康状态和能量充足性，比较了电池模型层级、针对性消融、线性退化替代模型、健康更新间隔、时间缩减以及组合简化。参考案例在第9年和第18年发生更换，生命周期净现值为1.1125亿美元，累计未供电能量为24.01兆瓦时。省略日历老化消除了两次更换，并将生命周期成本低估了35.1%，而单独校准的线性替代模型重现了两次更换年份，并将成本偏差限制在0.2%，尽管未供电能量仍比参考低31.8%。一个基于峰值校准的12天表示保留了更换时机，但报告未供电能量为零，而将保留的峰值日替换为最大日能量赤字日会显著高估未供电能量，因为代表日闭合改变了关键事件周围的电池状态。结果表明，建模保真度依赖于指标，应根据要保留的生命周期结果来选择。
英文摘要
Long-term battery energy storage system (BESS) planning often relies on simplified degradation, operational, and temporal representations to maintain computational tractability, yet their effects on lifecycle conclusions are not well understood. This paper assesses the modeling fidelity needed for long-term lifecycle evaluation of BESS designs used in planning studies. A 20-year grid-connected microgrid is sized using a degradation-naive planning model, after which the installed portfolio is fixed and evaluated through sequential lifecycle validation. The reference representation combines nonlinear calendar and cycle aging, C-rate-dependent efficiencies, state-of-health-dependent performance, self-discharge, battery replacement, and full 8,760-h chronology. Battery-model hierarchies, targeted ablations, linear degradation surrogates, health-update intervals, temporal reductions, and combined simplifications are compared using lifecycle cost, replacement timing, state of health, and energy adequacy. The reference case produces replacements in years 9 and 18, a $111.25 million lifecycle net present cost, and 24.01 MWh of cumulative energy not served. Omitting calendar aging eliminates both replacements and understates lifecycle cost by 35.1%, whereas a separately calibrated linear surrogate model reproduces both replacement years and limits the cost deviation to 0.2%, although energy not served remains 31.8% below the reference. A peak-informed calibrated 12-day representation preserves replacement timing but reports zero energy not served, while replacing the preserved peak day with the maximum daily-energy-deficit day substantially overstates energy not served because representative-day closure alters the battery state surrounding the critical event. The results show that modeling fidelity is metric-dependent and should be selected according to the lifecycle outcome to be preserved.
