Work

Improved formulation for long-duration storage in capacity expansion models using representative periods

Year: 2026

Type: article

Abstract: With the increasing complexity and size of capacity expansion models, temporal aggregation has emerged as a common method to improve computational tractability. However, this approach inherently complicates the inclusion of long-duration storage (LDS) systems, whose operation involves the entire time horizon connecting all time steps. This work presents a detailed investigation of LDS modelling with temporal aggregation. We propose a novel compact formulation to reduce the number of constraints of the linear programming problem, thus enabling a reduction in runtime and memory use. Our formulation incorporates dedicated constraints to track the storage content and enforce limits on the state of charge throughout the entire time horizon, including non-representative periods. The developed method is compared with the two leading state-of-the-art formulations. We implement all three methods in the Dolphyn capacity expansion model and test them on a case study for the continental United States, considering different configurations in terms of spatial resolutions and representative periods. The performance is assessed with both the commercial solver Gurobi and the open-source solver HiGHS. Results show that our compact formulation consistently outperforms the state-of-the-art methods in terms of both runtime (30%–70% faster) and memory usage (1%–9% lower).

Source: Journal of Energy Storage

DOI: 10.1016/j.est.2026.124539

Authors: Federico Parolin, Paolo Colbertaldo, Ruaridh Macdonald

Institutions: Massachusetts Institute of Technology, Politecnico di Milano

Language: English

Topic: Integrated Energy Systems Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,560