TY - JOUR
T1 - SpineOpt
T2 - A flexible open-source energy system modelling framework
AU - Ihlemann, Maren
AU - Kouveliotis-Lysikatos, Iasonas
AU - Huang, Jiangyi
AU - Dillon, Joseph
AU - O'Dwyer, Ciara
AU - Rasku, Topi
AU - Marin, Manuel
AU - Poncelet, Kris
AU - Kiviluoma, Juha
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
KW - open source tool
KW - energy system modelling
KW - energy system analysis
KW - integrated energy systems
KW - investment planning
KW - sector coupling
KW - Sector coupling
KW - Energy system analysis
KW - Open source tool
KW - Energy system modelling
KW - Integrated energy systems
KW - Investment planning
UR - http://www.scopus.com/inward/record.url?scp=85134876160&partnerID=8YFLogxK
U2 - 10.1016/j.esr.2022.100902
DO - 10.1016/j.esr.2022.100902
M3 - Article
SN - 2211-467X
VL - 43
JO - Energy Strategy Reviews
JF - Energy Strategy Reviews
M1 - 100902
ER -