This paper considers the representation of energy storage in electricity sector capacity planning models. The incorporation of storage in long-term systems models of this type is increasingly relevant as the costs of storage technologies, particularly batteries, and of complementary variable renewable technologies decline. To value energy storage technologies appropriately in optimization models, a representation of linkages between time periods is required, breaking classical temporal aggregation strategies that greatly improve computation time. Our paper reviews approaches to address the problem of compressing chronology for large-scale electricity planning models and provides a generalized conceptual model, conditions for lossless aggregation, and key principles to evaluate aggregation methods. We propose a novel approach, which we call the “expected value” method, to maintain key economic characteristics of energy storage, variable renewables, dispatchable generation, and other power system resources at a relatively low computational cost and conduct numerical experiments to compare its accuracy and computational performance with other temporal aggregation methods.

中文翻译：

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电力规划模型中储能的表示


本文考虑了电力部门容量规划模型中储能的表示。随着存储技术（特别是电池）和互补可变可再生技术的成本下降，将存储纳入此类长期系统模型变得越来越重要。为了在优化模型中适当评估储能技术，需要表示时间段之间的联系，这打破了大大缩短计算时间的经典时间聚合策略。我们的论文回顾了解决大规模电力规划模型压缩时间问题的方法，并提供了通用概念模型、无损聚合的条件以及评估聚合方法的关键原则。我们提出了一种新方法，称为“期望值”方法，以相对较低的计算成本维持储能、可变可再生能源、可调度发电和其他电力系统资源的关键经济特征，并进行数值实验来比较其准确性以及其他时间聚合方法的计算性能。