SpineOpt: A flexible open-source energy system modelling framework. (September 2022)

• Record Type: Journal Article
• Authors: Ihlemann, Maren; Kouveliotis-Lysikatos, Iasonas; Huang, Jiangyi; Dillon, Joseph; O'Dwyer, Ciara; Rasku, Topi; Marin, Manuel; Poncelet, Kris; Kiviluoma, Juha
• Is Part Of: Energy strategy reviews. Volume 43(2022)
• Publication Date: 2022-09
• Subjects: Open source tool -- Energy system modelling -- Energy system analysis -- Integrated energy systems -- Investment planning -- Sector coupling; Energy policy -- Periodicals; 333.7905
• DOI: 10.1016/j.esr.2022.100902
• Languages: English
• ISSNs: 2211-467X

Abstract

Abstract: The transition towards more sustainable energy systems poses new requirements on energy system models. New challenges include representing more uncertainties, including short-term detail in long-term planning models, allowing for more integration across energy sectors, and dealing with increased model complexities. SpineOpt is a flexible, open-source, energy system modelling framework for performing operational and planning studies, consisting of a wide spectrum of novel tools and functionalities. The most salient features of SpineOpt include a generic data structure, flexible temporal and spatial structures, a comprehensive representation of uncertainties, and model decomposition capabilities to reduce the computational complexity. These enable the implementation of highly diverse case studies. SpineOpt's features are presented through several publicly-available applications. An illustrative case study presents the impact of different temporal resolutions and stochastic structures in a co-optimised electricity and gas network. Using a lower temporal resolution in different parts of the model leads to a lower computational time (44%–98% reductions), while the total system cost varies only slightly (-1.22–1.39%). This implies that modellers experiencing computational issues should choose a high level of temporal accuracy only when needed. Highlights: Open-source energy system model with a rich set of features suitable for modelling operational and planning optimisation problems. Novel composition of the temporal structure allowing for a trade-off between required modelling detail and complexity reduction. New flexible stochastic structure for custom branching and recombining of scenarios. Multitude of open-source published case studies for different energy sectors.