Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.