Distribution Network Planning Considering Distributed Generation by Genetic Algorithm Combined with Graph Theory
Due to requirements for improving energy efficiency and meeting renewable and clean energy targets, an increasing amount of distributed generation is being connected to the distribution network. In order to facilitate increasing levels of distributed generation, the medium-voltage distribution netwo...
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| Veröffentlicht in: | Electric power components and systems Jg. 38; H. 3; S. 325 - 339 |
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| Sprache: | Englisch |
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| Abstract | Due to requirements for improving energy efficiency and meeting renewable and clean energy targets, an increasing amount of distributed generation is being connected to the distribution network. In order to facilitate increasing levels of distributed generation, the medium-voltage distribution network planning method considering distributed generation connection is proposed in this article. In addition to constraints in the conventional medium-voltage network planning model, constraints related to distributed generation connection, such as short-circuit capacity and short-circuit ratio, are considered in the new planning model. To overcome the problems of low heritability and topological infeasibility of the existing genetic algorithm applied to the distribution network planning, this article develops the efficient genetic algorithm combined with graph theory, which avoids the generation of unfeasible configurations. The efficiency of the proposed planning method is shown using a realistic region distribution network in Shanghai. |
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| AbstractList | Due to requirements for improving energy efficiency and meeting renew- able and clean energy targets, an increasing amount of distributed generation is being connected to the distribution network. In order to facilitate increasing levels of distributed generation, the medium-voltage distribution network planning method considering distributed generation connection is proposed in this article. In addition to constraints in the conventional medium-voltage network planning model, constraints related to distributed generation connection, such as short-circuit capacity and short- circuit ratio, are considered in the new planning model. To overcome the problems of low heritability and topological infeasibility of the existing genetic algorithm applied to the distribution network planning, this article develops the efficient genetic algorithm combined with graph theory, which avoids the generation of unfeasible configurations. The efficiency of the proposed planning method is shown using a realistic region distribution network in Shanghai. [PUBLICATION ABSTRACT] Due to requirements for improving energy efficiency and meeting renewable and clean energy targets, an increasing amount of distributed generation is being connected to the distribution network. In order to facilitate increasing levels of distributed generation, the medium-voltage distribution network planning method considering distributed generation connection is proposed in this article. In addition to constraints in the conventional medium-voltage network planning model, constraints related to distributed generation connection, such as short-circuit capacity and short-circuit ratio, are considered in the new planning model. To overcome the problems of low heritability and topological infeasibility of the existing genetic algorithm applied to the distribution network planning, this article develops the efficient genetic algorithm combined with graph theory, which avoids the generation of unfeasible configurations. The efficiency of the proposed planning method is shown using a realistic region distribution network in Shanghai. |
| Author | Cheng, Haozhong Yao, Liangzhong Ouyang, Wu Bazargan, Masoud Zhang, Xiubin |
| Author_xml | – sequence: 1 givenname: Wu surname: Ouyang fullname: Ouyang, Wu organization: Department of Electrical Engineering , Shanghai Jiao Tong University – sequence: 2 givenname: Haozhong surname: Cheng fullname: Cheng, Haozhong organization: Department of Electrical Engineering , Shanghai Jiao Tong University – sequence: 3 givenname: Xiubin surname: Zhang fullname: Zhang, Xiubin organization: Department of Electrical Engineering , Shanghai Jiao Tong University – sequence: 4 givenname: Liangzhong surname: Yao fullname: Yao, Liangzhong organization: Transmission and Distribution Technology Center, AREVA – sequence: 5 givenname: Masoud surname: Bazargan fullname: Bazargan, Masoud organization: Transmission and Distribution Technology Center, AREVA |
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| Cites_doi | 10.1109/TPWRS.2007.895176 10.1109/4235.974844 10.1016/0378-7796(93)90069-Q 10.1049/ip-gtd:19970850 10.1109/PESS.2001.970074 10.1016/j.ijepes.2008.10.016 10.1109/TPWRS.2002.804998 10.1109/TPWRS.2004.841233 10.1109/TPWRS.2005.852115 |
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| SubjectTerms | connection distributed generation distribution network planning Electric power Electricity distribution Electricity generation Energy distribution Energy efficiency genetic algorithm Genetic algorithms Graph theory Joints Mathematical models Meetings Networks |
| Title | Distribution Network Planning Considering Distributed Generation by Genetic Algorithm Combined with Graph Theory |
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