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 cost of storage technologies, particularly batteries, and of complementary variable renewable technologies, decline. To value storage technologies appropriately, a representation of linkages between time periods is required, breaking classical temporal aggregation strategies that greatly improve computation time. This paper appraises approaches to address this problem, highlighting a common underlying structure  and challenges of aggregation at relevant geographical scales , and investigates improvements on the literature state of the art. We also demonstrate a novel decomposition scheme to avoid temporal aggregation for applications where longer runtimes are permissible . These examples frame aspects of the problem ripe for contributions from the operations research community.
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 cost of storage technologies, particularly batteries, and of complementary variable renewable technologies, decline. To value storage technologies appropriately, a representation of linkages between time periods is required, breaking classical temporal aggregation strategies that greatly improve computation time. We appraise approaches to address this problem, highlighting a common underlying structure , conditions for lossless aggregation, and challenges of aggregation at relevant geographical scales . We then investigate solutions to the modeling problem including a decomposition scheme to avoid temporal aggregation at a parallelizable computational cost . These examples frame aspects of the problem ripe for contributions from the operational research community.
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