Capacity credits, i.e., metrics that describe the contribution of different technologies in meeting the load during peak periods, are widely used in the context of long-term energy-system optimization models to ensure a pre-defined level of firm capacity. In the same vain, such capacity credits may be used in capacity markets to reflect the availability of an asset during periods of peak load. For storage technologies it seems that there is a discrepancy between the capacity credit that correctly captures the capacity contribution to the capacity target, and the capacity credit that correctly values the storage capacity. This is illustrated in a case study, which shows the differences in planning model outcomes when different capacity credit interpretations are used. Our results indicate that defining the capacity credit according to the contribution to the capacity target overvalues storage technologies, causing overinvestments. On the contrary, defining the capacity credit to reflect the value of the storage capacity, triggers the correct amount of storage investments, but underestimates the true peak reduction potential, which results in overinvestments in other firm capacity providers. In this regard, a novel iterative approach is introduced that leverages both capacity credit interpretations simultaneously to remove any bias in the solution.

容量信用（即描述不同技术在高峰时段满足负荷的作用的度量标准）广泛用于长期能源系统优化模型中，以确保企业容量达到预定水平。同样，在容量市场中可以使用这种容量信用来反映峰值负荷期间资产的可用性。对于存储技术，似乎可以正确捕获容量对容量目标的贡献的容量信用与正确评估存储容量的容量信用之间存在差异。案例研究对此进行了说明，该案例研究显示了使用不同容量信用解释时计划模型结果的差异。我们的结果表明，根据对容量目标的贡献来定义容量信用会高估存储技术，从而导致过度投资。相反，定义容量信用以反映存储容量的价值，会触发正确数量的存储投资，但会低估真正的峰值降低潜力，从而导致对其他公司容量提供商的过度投资。在这方面，引入了一种新颖的迭代方法，该方法同时利用两种容量信用解释来消除解决方案中的任何偏差。但是低估了真正的峰值降低潜力，这导致对其他公司容量提供商的过度投资。在这方面，引入了一种新颖的迭代方法，该方法同时利用两种容量信用解释来消除解决方案中的任何偏差。但是低估了真正的峰值降低潜力，这导致对其他公司容量提供商的过度投资。在这方面，引入了一种新颖的迭代方法，该方法同时利用两种容量信用解释来消除解决方案中的任何偏差。