Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term models
The study of future energy scenarios with high shares of variable renewable energy sources (VRES) requires an accurate representation of VRES variability and storage capacity. However, long-term optimal expansion models, which are typically used to prescribe the evolution of energy systems, make use...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | Energy Conversion and Management: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174522000976 |
| _version_ | 1798039611920875520 |
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| author | Riccardo Novo Paolo Marocco Giuseppe Giorgi Andrea Lanzini Massimo Santarelli Giuliana Mattiazzo |
| author_facet | Riccardo Novo Paolo Marocco Giuseppe Giorgi Andrea Lanzini Massimo Santarelli Giuliana Mattiazzo |
| author_sort | Riccardo Novo |
| collection | DOAJ |
| description | The study of future energy scenarios with high shares of variable renewable energy sources (VRES) requires an accurate representation of VRES variability and storage capacity. However, long-term optimal expansion models, which are typically used to prescribe the evolution of energy systems, make use of coarse time series to limit computational effort. This weakness can entail an incorrect sizing of VRES plants and storage facilities. In this work, a novel method is proposed to mitigate the current limitations and enable accurate long-term planning of high-VRES decarbonisation pathways. Clustering methods are applied to time series, preserving the possibility of having inter-day and intra-day energy storage. To this end, the temporal framework of an open-source energy system model, OSeMOSYS, is modified to allow the implementation of interconnected, clustered representative days. Traditional and novel approaches are compared and benchmarked for a reference case study, i.e., a remote island. The results show that time series clustering can significantly improve the evaluation of the overall system cost, leading to a relative error of −5% (novel approach) instead of −35% (traditional approach) when 24 representative days are considered. Similarly, the new approach improves the sizing of VRES and storage facilities. The new technique is found to require three orders of magnitude less computation time than the traditional technique to achieve a comparable level of accuracy. |
| first_indexed | 2024-04-11T21:56:17Z |
| format | Article |
| id | doaj.art-dc6de2d7ce68455792807e36ca681cbe |
| institution | Directory Open Access Journal |
| issn | 2590-1745 |
| language | English |
| last_indexed | 2024-04-11T21:56:17Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj.art-dc6de2d7ce68455792807e36ca681cbe2022-12-22T04:01:06ZengElsevierEnergy Conversion and Management: X2590-17452022-08-0115100274Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term modelsRiccardo Novo0Paolo Marocco1Giuseppe Giorgi2Andrea Lanzini3Massimo Santarelli4Giuliana Mattiazzo5Dipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, 10129 Torino, Italy; MOREnergy Lab, Politecnico di Torino, 10129 Torino, Italy; Energy Center Lab, Politecnico di Torino, 10129 Torino, Italy; Corresponding author at: Dipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, 10129 Torino, Italy.Dipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Torino, ItalyDipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, 10129 Torino, Italy; MOREnergy Lab, Politecnico di Torino, 10129 Torino, ItalyDipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Torino, Italy; Energy Center Lab, Politecnico di Torino, 10129 Torino, ItalyDipartimento Energia “Galileo Ferraris”, Politecnico di Torino, 10129 Torino, ItalyDipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, 10129 Torino, Italy; MOREnergy Lab, Politecnico di Torino, 10129 Torino, Italy; Energy Center Lab, Politecnico di Torino, 10129 Torino, ItalyThe study of future energy scenarios with high shares of variable renewable energy sources (VRES) requires an accurate representation of VRES variability and storage capacity. However, long-term optimal expansion models, which are typically used to prescribe the evolution of energy systems, make use of coarse time series to limit computational effort. This weakness can entail an incorrect sizing of VRES plants and storage facilities. In this work, a novel method is proposed to mitigate the current limitations and enable accurate long-term planning of high-VRES decarbonisation pathways. Clustering methods are applied to time series, preserving the possibility of having inter-day and intra-day energy storage. To this end, the temporal framework of an open-source energy system model, OSeMOSYS, is modified to allow the implementation of interconnected, clustered representative days. Traditional and novel approaches are compared and benchmarked for a reference case study, i.e., a remote island. The results show that time series clustering can significantly improve the evaluation of the overall system cost, leading to a relative error of −5% (novel approach) instead of −35% (traditional approach) when 24 representative days are considered. Similarly, the new approach improves the sizing of VRES and storage facilities. The new technique is found to require three orders of magnitude less computation time than the traditional technique to achieve a comparable level of accuracy.http://www.sciencedirect.com/science/article/pii/S2590174522000976Energy systemsRenewable energyMixed integer linear programmingEnergy modellingTime series clustering |
| spellingShingle | Riccardo Novo Paolo Marocco Giuseppe Giorgi Andrea Lanzini Massimo Santarelli Giuliana Mattiazzo Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term models Energy Conversion and Management: X Energy systems Renewable energy Mixed integer linear programming Energy modelling Time series clustering |
| title | Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term models |
| title_full | Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term models |
| title_fullStr | Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term models |
| title_full_unstemmed | Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term models |
| title_short | Planning the decarbonisation of energy systems: The importance of applying time series clustering to long-term models |
| title_sort | planning the decarbonisation of energy systems the importance of applying time series clustering to long term models |
| topic | Energy systems Renewable energy Mixed integer linear programming Energy modelling Time series clustering |
| url | http://www.sciencedirect.com/science/article/pii/S2590174522000976 |
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