A Multiobjective Interval Programming Model for Wind-Hydrothermal Power System Dispatching Using 2-Step Optimization Algorithm
Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear n...
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| Veröffentlicht in: | TheScientificWorld Jg. 2014; H. 2014; S. 1 - 9 |
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| Sprache: | Englisch |
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Cairo, Egypt
Hindawi Publishing Corporation
01.01.2014
John Wiley & Sons, Inc Wiley |
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| ISSN: | 2356-6140, 1537-744X, 1537-744X |
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| Abstract | Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision. |
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| AbstractList | Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision. Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision.Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision. Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision. |
| Audience | Academic |
| Author | Qu, Jihong Ren, Kun |
| AuthorAffiliation | 2 North China University of Water Resources and Electric Power, Zhengzhou 450011, China 1 Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an 710048, China |
| AuthorAffiliation_xml | – name: 2 North China University of Water Resources and Electric Power, Zhengzhou 450011, China – name: 1 Institute of Water Resources and Hydro-electric Engineering, Xi'an University of Technology, Xi'an 710048, China |
| Author_xml | – sequence: 1 fullname: Ren, Kun – sequence: 2 fullname: Qu, Jihong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24895663$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1587_transinf_2020EDP7146 crossref_primary_10_33889_IJMEMS_2020_5_1_010 |
| Cites_doi | 10.1057/palgrave.jors.2600891 10.1016/j.apenergy.2010.10.005 10.1109/TPWRS.2013.2258824 10.1002/er.2886 10.1016/j.epsr.2010.08.009 10.1002/er.3001 10.1049/iet-gtd:20060208 10.1109/59.260826 10.1016/j.energy.2010.11.024 10.1109/TPWRS.2008.919318 10.1016/j.ijepes.2010.08.014 10.1109/4235.996017 10.1016/j.amc.2008.12.035 10.1016/j.enconman.2005.12.016 10.1016/0165-0114(95)00393-2 10.1109/TPWRS.2007.907519 10.1109/TPWRS.2005.857930 10.1016/j.compstruc.2008.10.005 10.1007/s00158-010-0501-2 10.1016/S0377-2217(99)00319-7 |
| ContentType | Journal Article |
| Copyright | Copyright © 2014 Kun Ren and Qu Jihong. COPYRIGHT 2014 John Wiley & Sons, Inc. Copyright © 2014 Kun Ren and Qu Jihong. Kun Ren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2014 K. Ren and Q. Jihong. 2014 |
| Copyright_xml | – notice: Copyright © 2014 Kun Ren and Qu Jihong. – notice: COPYRIGHT 2014 John Wiley & Sons, Inc. – notice: Copyright © 2014 Kun Ren and Qu Jihong. Kun Ren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright © 2014 K. Ren and Q. Jihong. 2014 |
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| SubjectTerms | Algorithms Buildings and facilities Energy-Generating Resources Fuel cells Heuristic Hydroelectric power plants Linear programming Mathematical optimization Methods Models, Theoretical Power plants Probability distribution Production scheduling Random variables Runoff Studies Wind Wind power |
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| Title | A Multiobjective Interval Programming Model for Wind-Hydrothermal Power System Dispatching Using 2-Step Optimization Algorithm |
| URI | https://search.emarefa.net/detail/BIM-1051214 https://dx.doi.org/10.1155/2014/825216 https://www.ncbi.nlm.nih.gov/pubmed/24895663 https://www.proquest.com/docview/1547917577 https://www.proquest.com/docview/1532940409 https://www.proquest.com/docview/1566832712 https://pubmed.ncbi.nlm.nih.gov/PMC4034478 https://doaj.org/article/1986411b7d8d4cecbfc3c4d4c2fa7390 |
| Volume | 2014 |
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