An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation
It is important to design multi-energy supply systems optimally in consideration of their operations for variations in energy demands. An approach for efficiently solving such an optimal design problem with a large number of periods for variations in energy demands is to derive an approximate optima...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1128681/full |
| _version_ | 1797783903587532800 |
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| author | Ryohei Yokoyama Yuji Shinano Tetsuya Wakui |
| author_facet | Ryohei Yokoyama Yuji Shinano Tetsuya Wakui |
| author_sort | Ryohei Yokoyama |
| collection | DOAJ |
| description | It is important to design multi-energy supply systems optimally in consideration of their operations for variations in energy demands. An approach for efficiently solving such an optimal design problem with a large number of periods for variations in energy demands is to derive an approximate optimal design solution by time series aggregation. However, such an approach does not provide any information on the accuracy for the optimal value of the objective function. In this paper, an effective approach for time series aggregation is proposed to derive an approximate optimal design solution and evaluate a proper gap between the upper and lower bounds for the optimal value of the objective function based on a mixed-integer linear model. In accordance with aggregation, energy demands are relaxed to uncertain parameters and the problem for deriving an approximate optimal design solution and evaluating it is transformed to a three-level optimization problem, and it is solved by applying both the robust and hierarchical optimization methods. A case study is conducted on a cogeneration system with a practical configuration, and it turns out that the proposed approach enables one to derive much smaller gaps as compared with those obtained by a conventional approach. |
| first_indexed | 2024-03-13T00:32:30Z |
| format | Article |
| id | doaj.art-f78d072796ac41caaa3647eaae3ddde3 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-13T00:32:30Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-f78d072796ac41caaa3647eaae3ddde32023-07-10T10:52:28ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-07-011110.3389/fenrg.2023.11286811128681An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregationRyohei Yokoyama0Yuji Shinano1Tetsuya Wakui2Department of Mechanical Engineering, Osaka Metropolitan University, Sakai, Osaka, JapanDepartment of Applied Algorithmic Intelligence Methods, Zuse Institute Berlin, Berlin, GermanyDepartment of Mechanical Engineering, Osaka Metropolitan University, Sakai, Osaka, JapanIt is important to design multi-energy supply systems optimally in consideration of their operations for variations in energy demands. An approach for efficiently solving such an optimal design problem with a large number of periods for variations in energy demands is to derive an approximate optimal design solution by time series aggregation. However, such an approach does not provide any information on the accuracy for the optimal value of the objective function. In this paper, an effective approach for time series aggregation is proposed to derive an approximate optimal design solution and evaluate a proper gap between the upper and lower bounds for the optimal value of the objective function based on a mixed-integer linear model. In accordance with aggregation, energy demands are relaxed to uncertain parameters and the problem for deriving an approximate optimal design solution and evaluating it is transformed to a three-level optimization problem, and it is solved by applying both the robust and hierarchical optimization methods. A case study is conducted on a cogeneration system with a practical configuration, and it turns out that the proposed approach enables one to derive much smaller gaps as compared with those obtained by a conventional approach.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1128681/fullenergy supplyapproximate optimal designtime series aggregationupper and lower boundsrobust optimizationhierarchical optimization |
| spellingShingle | Ryohei Yokoyama Yuji Shinano Tetsuya Wakui An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation Frontiers in Energy Research energy supply approximate optimal design time series aggregation upper and lower bounds robust optimization hierarchical optimization |
| title | An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation |
| title_full | An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation |
| title_fullStr | An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation |
| title_full_unstemmed | An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation |
| title_short | An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation |
| title_sort | effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation |
| topic | energy supply approximate optimal design time series aggregation upper and lower bounds robust optimization hierarchical optimization |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1128681/full |
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