Recurrent neural network for combined economic and emission dispatch
Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) un...
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Gespeichert in:
| Autor*in: |
Deng, Ting [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 48(2017), 8 vom: 23. Okt., Seite 2180-2198 |
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| Übergeordnetes Werk: |
volume:48 ; year:2017 ; number:8 ; day:23 ; month:10 ; pages:2180-2198 |
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| DOI / URN: |
10.1007/s10489-017-1072-3 |
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| 520 | |a Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently. | ||
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10.1007/s10489-017-1072-3 doi (DE-627)OLC206610485X (DE-He213)s10489-017-1072-3-p DE-627 ger DE-627 rakwb eng 004 VZ Deng, Ting verfasserin aut Recurrent neural network for combined economic and emission dispatch 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently. Combined heat and power units Combined economic and emission dispatch Recurrent neural network He, Xing (orcid)0000-0001-8652-8479 aut Zeng, Zhigang aut Enthalten in Applied intelligence Springer US, 1991 48(2017), 8 vom: 23. Okt., Seite 2180-2198 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2017 number:8 day:23 month:10 pages:2180-2198 https://doi.org/10.1007/s10489-017-1072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2017 8 23 10 2180-2198 |
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10.1007/s10489-017-1072-3 doi (DE-627)OLC206610485X (DE-He213)s10489-017-1072-3-p DE-627 ger DE-627 rakwb eng 004 VZ Deng, Ting verfasserin aut Recurrent neural network for combined economic and emission dispatch 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently. Combined heat and power units Combined economic and emission dispatch Recurrent neural network He, Xing (orcid)0000-0001-8652-8479 aut Zeng, Zhigang aut Enthalten in Applied intelligence Springer US, 1991 48(2017), 8 vom: 23. Okt., Seite 2180-2198 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2017 number:8 day:23 month:10 pages:2180-2198 https://doi.org/10.1007/s10489-017-1072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2017 8 23 10 2180-2198 |
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10.1007/s10489-017-1072-3 doi (DE-627)OLC206610485X (DE-He213)s10489-017-1072-3-p DE-627 ger DE-627 rakwb eng 004 VZ Deng, Ting verfasserin aut Recurrent neural network for combined economic and emission dispatch 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently. Combined heat and power units Combined economic and emission dispatch Recurrent neural network He, Xing (orcid)0000-0001-8652-8479 aut Zeng, Zhigang aut Enthalten in Applied intelligence Springer US, 1991 48(2017), 8 vom: 23. Okt., Seite 2180-2198 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2017 number:8 day:23 month:10 pages:2180-2198 https://doi.org/10.1007/s10489-017-1072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2017 8 23 10 2180-2198 |
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10.1007/s10489-017-1072-3 doi (DE-627)OLC206610485X (DE-He213)s10489-017-1072-3-p DE-627 ger DE-627 rakwb eng 004 VZ Deng, Ting verfasserin aut Recurrent neural network for combined economic and emission dispatch 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently. Combined heat and power units Combined economic and emission dispatch Recurrent neural network He, Xing (orcid)0000-0001-8652-8479 aut Zeng, Zhigang aut Enthalten in Applied intelligence Springer US, 1991 48(2017), 8 vom: 23. Okt., Seite 2180-2198 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2017 number:8 day:23 month:10 pages:2180-2198 https://doi.org/10.1007/s10489-017-1072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2017 8 23 10 2180-2198 |
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10.1007/s10489-017-1072-3 doi (DE-627)OLC206610485X (DE-He213)s10489-017-1072-3-p DE-627 ger DE-627 rakwb eng 004 VZ Deng, Ting verfasserin aut Recurrent neural network for combined economic and emission dispatch 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently. Combined heat and power units Combined economic and emission dispatch Recurrent neural network He, Xing (orcid)0000-0001-8652-8479 aut Zeng, Zhigang aut Enthalten in Applied intelligence Springer US, 1991 48(2017), 8 vom: 23. Okt., Seite 2180-2198 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:48 year:2017 number:8 day:23 month:10 pages:2180-2198 https://doi.org/10.1007/s10489-017-1072-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 48 2017 8 23 10 2180-2198 |
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Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently. © Springer Science+Business Media, LLC 2017 |
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Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently. © Springer Science+Business Media, LLC 2017 |
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Abstract Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently. © Springer Science+Business Media, LLC 2017 |
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Recurrent neural network for combined economic and emission dispatch |
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He, Xing Zeng, Zhigang |
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