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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.00227 |
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| _version_ | 1866913487878356992 |
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| author | Zhang, Hongyu Heir, Erlend Nisi, Asbjørn Tomasgard, Asgeir |
| author_facet | Zhang, Hongyu Heir, Erlend Nisi, Asbjørn Tomasgard, Asgeir |
| contents | Recent developments in decomposition methods for multi-stage stochastic programming with block separable recourse enable the solution to large-scale stochastic programs with multi-timescale uncertainty. Multi-timescale uncertainty is important in energy system planning problems. Therefore, the proposed algorithms were applied to energy system planning problems to demonstrate their performance. However, the impact of multi-timescale uncertainty on energy system planning is not sufficiently analysed. In this paper, we address this research gap by comprehensively modelling and analysing short-term and long-term uncertainty in energy system planning. We use the REORIENT model to conduct the analysis. We also propose a parallel stabilised Benders decomposition as an alternative solution method to existing methods. We analyse the multi-timescale uncertainty regarding stability, the value of the stochastic solution, the rolling horizon value of the stochastic solutions and the planning decisions. The results show that (1) including multi-timescale uncertainty yields an increase in the value of the stochastic solutions, (2) long-term uncertainty in the right-hand side parameters affects the solution structure more than cost coefficient uncertainty, (3) parallel stabilised Benders decomposition is up to 7.5 times faster than the serial version. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00227 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modelling and analysis of multi-timescale uncertainty in energy system planning Zhang, Hongyu Heir, Erlend Nisi, Asbjørn Tomasgard, Asgeir Optimization and Control Recent developments in decomposition methods for multi-stage stochastic programming with block separable recourse enable the solution to large-scale stochastic programs with multi-timescale uncertainty. Multi-timescale uncertainty is important in energy system planning problems. Therefore, the proposed algorithms were applied to energy system planning problems to demonstrate their performance. However, the impact of multi-timescale uncertainty on energy system planning is not sufficiently analysed. In this paper, we address this research gap by comprehensively modelling and analysing short-term and long-term uncertainty in energy system planning. We use the REORIENT model to conduct the analysis. We also propose a parallel stabilised Benders decomposition as an alternative solution method to existing methods. We analyse the multi-timescale uncertainty regarding stability, the value of the stochastic solution, the rolling horizon value of the stochastic solutions and the planning decisions. The results show that (1) including multi-timescale uncertainty yields an increase in the value of the stochastic solutions, (2) long-term uncertainty in the right-hand side parameters affects the solution structure more than cost coefficient uncertainty, (3) parallel stabilised Benders decomposition is up to 7.5 times faster than the serial version. |
| title | Modelling and analysis of multi-timescale uncertainty in energy system planning |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2409.00227 |