On representation of energy storage in electricity planning models

TL;DR

This paper examines how to accurately represent energy storage in long-term electricity planning models, emphasizing the importance of temporal linkages and proposing solutions to improve modeling without sacrificing detail.

Contribution

It analyzes existing approaches to modeling energy storage, identifies key challenges, and introduces a decomposition scheme to enhance computational efficiency in representing storage.

Findings

Identifies conditions for lossless temporal aggregation.

Highlights challenges of spatial and temporal scale integration.

Proposes a decomposition method to improve model scalability.

Abstract

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.