Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method
The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to sear...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yokoyama, Ryohei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion - Solanki, Nayan ELSEVIER, 2017, the international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:228 ; year:2021 ; day:1 ; month:08 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.energy.2021.120505 |
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ELV054183774 |
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| 520 | |a The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. | ||
| 520 | |a The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. | ||
| 650 | 7 | |a Energy supply |2 Elsevier | |
| 650 | 7 | |a Time aggregation |2 Elsevier | |
| 650 | 7 | |a Hierarchical optimization |2 Elsevier | |
| 650 | 7 | |a Clustering |2 Elsevier | |
| 650 | 7 | |a Multiobjective optimal design |2 Elsevier | |
| 650 | 7 | |a Mixed-integer linear programming |2 Elsevier | |
| 700 | 1 | |a Takeuchi, Kotaro |4 oth | |
| 700 | 1 | |a Shinano, Yuji |4 oth | |
| 700 | 1 | |a Wakui, Tetsuya |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Solanki, Nayan ELSEVIER |t Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion |d 2017 |d the international journal |g Amsterdam [u.a.] |w (DE-627)ELV000529575 |
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10.1016/j.energy.2021.120505 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001405.pica (DE-627)ELV054183774 (ELSEVIER)S0360-5442(21)00754-4 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Yokoyama, Ryohei verfasserin aut Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. Energy supply Elsevier Time aggregation Elsevier Hierarchical optimization Elsevier Clustering Elsevier Multiobjective optimal design Elsevier Mixed-integer linear programming Elsevier Takeuchi, Kotaro oth Shinano, Yuji oth Wakui, Tetsuya oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:228 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.energy.2021.120505 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 228 2021 1 0801 0 |
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10.1016/j.energy.2021.120505 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001405.pica (DE-627)ELV054183774 (ELSEVIER)S0360-5442(21)00754-4 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Yokoyama, Ryohei verfasserin aut Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. Energy supply Elsevier Time aggregation Elsevier Hierarchical optimization Elsevier Clustering Elsevier Multiobjective optimal design Elsevier Mixed-integer linear programming Elsevier Takeuchi, Kotaro oth Shinano, Yuji oth Wakui, Tetsuya oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:228 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.energy.2021.120505 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 228 2021 1 0801 0 |
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10.1016/j.energy.2021.120505 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001405.pica (DE-627)ELV054183774 (ELSEVIER)S0360-5442(21)00754-4 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Yokoyama, Ryohei verfasserin aut Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. Energy supply Elsevier Time aggregation Elsevier Hierarchical optimization Elsevier Clustering Elsevier Multiobjective optimal design Elsevier Mixed-integer linear programming Elsevier Takeuchi, Kotaro oth Shinano, Yuji oth Wakui, Tetsuya oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:228 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.energy.2021.120505 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 228 2021 1 0801 0 |
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10.1016/j.energy.2021.120505 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001405.pica (DE-627)ELV054183774 (ELSEVIER)S0360-5442(21)00754-4 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Yokoyama, Ryohei verfasserin aut Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. Energy supply Elsevier Time aggregation Elsevier Hierarchical optimization Elsevier Clustering Elsevier Multiobjective optimal design Elsevier Mixed-integer linear programming Elsevier Takeuchi, Kotaro oth Shinano, Yuji oth Wakui, Tetsuya oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:228 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.energy.2021.120505 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 228 2021 1 0801 0 |
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10.1016/j.energy.2021.120505 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001405.pica (DE-627)ELV054183774 (ELSEVIER)S0360-5442(21)00754-4 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Yokoyama, Ryohei verfasserin aut Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. Energy supply Elsevier Time aggregation Elsevier Hierarchical optimization Elsevier Clustering Elsevier Multiobjective optimal design Elsevier Mixed-integer linear programming Elsevier Takeuchi, Kotaro oth Shinano, Yuji oth Wakui, Tetsuya oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:228 year:2021 day:1 month:08 pages:0 https://doi.org/10.1016/j.energy.2021.120505 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 228 2021 1 0801 0 |
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Enthalten in Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion Amsterdam [u.a.] volume:228 year:2021 day:1 month:08 pages:0 |
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Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion |
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effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical milp method |
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Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method |
| abstract |
The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. |
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The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. |
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The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k -medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k -medoids method is effective very limitedly when importance is given to minimizing the second objective function. |
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Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method |
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