Many-objective optimization for coordinated operation of integrated electricity and gas network
Abstract This paper develops a many-objective optimization model, which contains objectives representing the interests of the electricity and gas networks, as well as the distributed district heating and cooling units, to coordinate the benefits of all parties participated in the integrated energy s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017-04-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s40565-017-0279-y |
| _version_ | 1818946894244610048 |
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| author | Y. N. KOU J. H. ZHENG Zhigang LI Q. H. WU |
| author_facet | Y. N. KOU J. H. ZHENG Zhigang LI Q. H. WU |
| author_sort | Y. N. KOU |
| collection | DOAJ |
| description | Abstract This paper develops a many-objective optimization model, which contains objectives representing the interests of the electricity and gas networks, as well as the distributed district heating and cooling units, to coordinate the benefits of all parties participated in the integrated energy system (IES). In order to solve the many-objective optimization model efficiently, an improved objective reduction (IOR) approach is proposed, aiming at acquiring the smallest set of objectives. The IOR approach utilizes the Spearman’s rank correlation coefficient to measure the relationship between objectives based on the Pareto-optimal front captured by the multi-objective group search optimizer with adaptive covariance and Lévy flights algorithm, and adopts various strategies to reduce the number of objectives gradually. Simulation studies are conducted on an IES consisting of a modified IEEE 30-bus electricity network and a 15-node gas network. The results show that the many-objective optimization problem is transformed into a bi-objective formulation by the IOR. Furthermore, our approach improves the overall quality of dispatch solutions and alleviates the decision making burden. |
| first_indexed | 2024-12-20T08:22:15Z |
| format | Article |
| id | doaj.art-362670fea63848399e99ed31835497dd |
| institution | Directory Open Access Journal |
| issn | 2196-5625 2196-5420 |
| language | English |
| last_indexed | 2024-12-20T08:22:15Z |
| publishDate | 2017-04-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-362670fea63848399e99ed31835497dd2022-12-21T19:46:57ZengIEEEJournal of Modern Power Systems and Clean Energy2196-56252196-54202017-04-015335036310.1007/s40565-017-0279-yMany-objective optimization for coordinated operation of integrated electricity and gas networkY. N. KOU0J. H. ZHENG1Zhigang LI2Q. H. WU3School of Electric Power Engineering, South China University of TechnologySchool of Electric Power Engineering, South China University of TechnologySchool of Electric Power Engineering, South China University of TechnologySchool of Electric Power Engineering, South China University of TechnologyAbstract This paper develops a many-objective optimization model, which contains objectives representing the interests of the electricity and gas networks, as well as the distributed district heating and cooling units, to coordinate the benefits of all parties participated in the integrated energy system (IES). In order to solve the many-objective optimization model efficiently, an improved objective reduction (IOR) approach is proposed, aiming at acquiring the smallest set of objectives. The IOR approach utilizes the Spearman’s rank correlation coefficient to measure the relationship between objectives based on the Pareto-optimal front captured by the multi-objective group search optimizer with adaptive covariance and Lévy flights algorithm, and adopts various strategies to reduce the number of objectives gradually. Simulation studies are conducted on an IES consisting of a modified IEEE 30-bus electricity network and a 15-node gas network. The results show that the many-objective optimization problem is transformed into a bi-objective formulation by the IOR. Furthermore, our approach improves the overall quality of dispatch solutions and alleviates the decision making burden.http://link.springer.com/article/10.1007/s40565-017-0279-yIntegrated energy systemGas networkElectricity networkMany-objective optimizationObjective reduction |
| spellingShingle | Y. N. KOU J. H. ZHENG Zhigang LI Q. H. WU Many-objective optimization for coordinated operation of integrated electricity and gas network Journal of Modern Power Systems and Clean Energy Integrated energy system Gas network Electricity network Many-objective optimization Objective reduction |
| title | Many-objective optimization for coordinated operation of integrated electricity and gas network |
| title_full | Many-objective optimization for coordinated operation of integrated electricity and gas network |
| title_fullStr | Many-objective optimization for coordinated operation of integrated electricity and gas network |
| title_full_unstemmed | Many-objective optimization for coordinated operation of integrated electricity and gas network |
| title_short | Many-objective optimization for coordinated operation of integrated electricity and gas network |
| title_sort | many objective optimization for coordinated operation of integrated electricity and gas network |
| topic | Integrated energy system Gas network Electricity network Many-objective optimization Objective reduction |
| url | http://link.springer.com/article/10.1007/s40565-017-0279-y |
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