Comparison of SpineOpt and PyPSA in Hydro Power System Modelling

Yi Liu; Mikael Amelin; Topi Rasku

doi:10.1109/EEM60825.2024.10608896

Comparison of SpineOpt and PyPSA in Hydro Power System Modelling

Yi Liu
, Mikael Amelin
, Topi Rasku

KTH Royal Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

Hydro power modelling is important to facilitate the integration of large amounts of variable renewable energy. However, appropriately modelling hydro power presents challenges due to its interconnections with both electricity and river systems. The aim of this paper is to investigate the performance of two selected open-source energy modelling tools for hydro power planning, SpineOpt and PyPSA, with a focus on user-friendliness, accuracy and execution time. In this study, a small river system with two hydro power plants is modelled to maximize the revenue using both tools. At the same time, this linear optimization problem is implemented by Gurobi directly, such that results and execution times from SpineOpt and PyPSA are compared with this baseline. In conclusion, both open-source tools can appropriately model the hydro power system, with SpineOpt having a unique graphical interface and PyPSA has a good performance in execution speed.

Original language	English
Title of host publication	20th International Conference on the European Energy Market, EEM 2024 - Proceedings
Publisher	IEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)	9798350381740
DOIs	https://doi.org/10.1109/EEM60825.2024.10608896
Publication status	Published - 2024
MoE publication type	A4 Article in a conference publication
Event	20th International Conference on the European Energy Market, EEM 2024 - Istanbul, Turkey Duration: 10 Jun 2024 → 12 Jun 2024

Publication series

Series	International Conference on the European Energy Market, EEM
ISSN	2165-4077

Conference

Conference	20th International Conference on the European Energy Market, EEM 2024
Country/Territory	Turkey
City	Istanbul
Period	10/06/24 → 12/06/24

Funding

This work has been supported by European Union project MOPO (2023-2026) which received funding from European Union’s HORIZON Research and Innovation Actions under grant agreement N°101095998.

Keywords

Hydro power modelling
Linear optimization
Open-source tools
PyPSA
SpineOpt

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/EEM60825.2024.10608896

Cite this

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title = "Comparison of SpineOpt and PyPSA in Hydro Power System Modelling",

abstract = "Hydro power modelling is important to facilitate the integration of large amounts of variable renewable energy. However, appropriately modelling hydro power presents challenges due to its interconnections with both electricity and river systems. The aim of this paper is to investigate the performance of two selected open-source energy modelling tools for hydro power planning, SpineOpt and PyPSA, with a focus on user-friendliness, accuracy and execution time. In this study, a small river system with two hydro power plants is modelled to maximize the revenue using both tools. At the same time, this linear optimization problem is implemented by Gurobi directly, such that results and execution times from SpineOpt and PyPSA are compared with this baseline. In conclusion, both open-source tools can appropriately model the hydro power system, with SpineOpt having a unique graphical interface and PyPSA has a good performance in execution speed.",

keywords = "Hydro power modelling, Linear optimization, Open-source tools, PyPSA, SpineOpt",

author = "Yi Liu and Mikael Amelin and Topi Rasku",

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booktitle = "20th International Conference on the European Energy Market, EEM 2024 - Proceedings",

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note = "20th International Conference on the European Energy Market, EEM 2024 ; Conference date: 10-06-2024 Through 12-06-2024",

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Liu, Y, Amelin, M & Rasku, T 2024, Comparison of SpineOpt and PyPSA in Hydro Power System Modelling. in 20th International Conference on the European Energy Market, EEM 2024 - Proceedings. IEEE Institute of Electrical and Electronic Engineers, International Conference on the European Energy Market, EEM, 20th International Conference on the European Energy Market, EEM 2024, Istanbul, Turkey, 10/06/24. https://doi.org/10.1109/EEM60825.2024.10608896

Comparison of SpineOpt and PyPSA in Hydro Power System Modelling. / Liu, Yi; Amelin, Mikael; Rasku, Topi.
20th International Conference on the European Energy Market, EEM 2024 - Proceedings. IEEE Institute of Electrical and Electronic Engineers, 2024. (International Conference on the European Energy Market, EEM).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

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AU - Amelin, Mikael

AU - Rasku, Topi

PY - 2024

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N2 - Hydro power modelling is important to facilitate the integration of large amounts of variable renewable energy. However, appropriately modelling hydro power presents challenges due to its interconnections with both electricity and river systems. The aim of this paper is to investigate the performance of two selected open-source energy modelling tools for hydro power planning, SpineOpt and PyPSA, with a focus on user-friendliness, accuracy and execution time. In this study, a small river system with two hydro power plants is modelled to maximize the revenue using both tools. At the same time, this linear optimization problem is implemented by Gurobi directly, such that results and execution times from SpineOpt and PyPSA are compared with this baseline. In conclusion, both open-source tools can appropriately model the hydro power system, with SpineOpt having a unique graphical interface and PyPSA has a good performance in execution speed.

AB - Hydro power modelling is important to facilitate the integration of large amounts of variable renewable energy. However, appropriately modelling hydro power presents challenges due to its interconnections with both electricity and river systems. The aim of this paper is to investigate the performance of two selected open-source energy modelling tools for hydro power planning, SpineOpt and PyPSA, with a focus on user-friendliness, accuracy and execution time. In this study, a small river system with two hydro power plants is modelled to maximize the revenue using both tools. At the same time, this linear optimization problem is implemented by Gurobi directly, such that results and execution times from SpineOpt and PyPSA are compared with this baseline. In conclusion, both open-source tools can appropriately model the hydro power system, with SpineOpt having a unique graphical interface and PyPSA has a good performance in execution speed.

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KW - Linear optimization

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KW - SpineOpt

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BT - 20th International Conference on the European Energy Market, EEM 2024 - Proceedings

PB - IEEE Institute of Electrical and Electronic Engineers

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Comparison of SpineOpt and PyPSA in Hydro Power System Modelling