Type-II Fuzzy Credibility Constraints Programming Based Distribution Network Planning Including Photovoltaic Power Generation
Renewable energy generation is an important way for human society to achieve sustainable energy and economic development. However, renewable energy with high uncertainty brings a huge challenge to the stable operation of power systems when it is connected to the grid. Accurate and effective modeling...
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| Vydáno v: | 2024 4th International Conference on Energy Engineering and Power Systems (EEPS) s. 545 - 554 |
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IEEE
09.08.2024
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| Abstract | Renewable energy generation is an important way for human society to achieve sustainable energy and economic development. However, renewable energy with high uncertainty brings a huge challenge to the stable operation of power systems when it is connected to the grid. Accurate and effective modeling of uncertainty in renewable energy is an important guarantee for achieving optimal planning and stable operation of distribution networks with high renewable energy penetration. Most existing research on uncertainty in distribution networks focuses on analyzing multiple uncertainties such as source and load, but most studies only provide a simple description of each type of uncertainty. Based on this, this article first introduces type-II fuzzy sets to better describe the uncertainty of photovoltaic (PV) under the influence of various external factors, constructs a substation location planning model based on type-II fuzzy Credibility Constrained Programming (T2FCCP). On this basis, a distribution network planning model based on type-II fuzzy Credibility Chance Constrained Programming (T2F3CP) is established. To solve the two models, Particle Swarm Optimization (PSO) and Discrete Particle Swarm Optimization (DPSO) are adopted respectively. Finally, a regional distribution network with 6 microgrids and 50 load points is used to validate the proposed model. The research results show that the T2FCCP and T2F3CP optimization models can effectively describe the uncertainty of PV in distribution network planning under high PV penetration, reduce the impact of uncertainty, and ensure the stable operation of distribution networks under high renewable energy penetration. |
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| AbstractList | Renewable energy generation is an important way for human society to achieve sustainable energy and economic development. However, renewable energy with high uncertainty brings a huge challenge to the stable operation of power systems when it is connected to the grid. Accurate and effective modeling of uncertainty in renewable energy is an important guarantee for achieving optimal planning and stable operation of distribution networks with high renewable energy penetration. Most existing research on uncertainty in distribution networks focuses on analyzing multiple uncertainties such as source and load, but most studies only provide a simple description of each type of uncertainty. Based on this, this article first introduces type-II fuzzy sets to better describe the uncertainty of photovoltaic (PV) under the influence of various external factors, constructs a substation location planning model based on type-II fuzzy Credibility Constrained Programming (T2FCCP). On this basis, a distribution network planning model based on type-II fuzzy Credibility Chance Constrained Programming (T2F3CP) is established. To solve the two models, Particle Swarm Optimization (PSO) and Discrete Particle Swarm Optimization (DPSO) are adopted respectively. Finally, a regional distribution network with 6 microgrids and 50 load points is used to validate the proposed model. The research results show that the T2FCCP and T2F3CP optimization models can effectively describe the uncertainty of PV in distribution network planning under high PV penetration, reduce the impact of uncertainty, and ensure the stable operation of distribution networks under high renewable energy penetration. |
| Author | Lu, Xinyi Lou, Jing Zheng, Lingwei Shen, Shuyi He, Yingjing Sun, Xinyu |
| Author_xml | – sequence: 1 givenname: Yingjing surname: He fullname: He, Yingjing email: heyingjing@jyy.zj.sgcc.com.cn organization: Economic Research Institute, State Grid Zhejiang Electric Power Company,Hangzhou,China – sequence: 2 givenname: Shuyi surname: Shen fullname: Shen, Shuyi email: shenshuyi@jyy.zj.sgcc.com.cn organization: Economic Research Institute, State Grid Zhejiang Electric Power Company,Hangzhou,China – sequence: 3 givenname: Jing surname: Lou fullname: Lou, Jing email: loujing@hdu.edu.cn organization: School of Automation, Hangzhou Dianzi University,Hangzhou,China – sequence: 4 givenname: Xinyi surname: Lu fullname: Lu, Xinyi email: 971058930@qq.com organization: School of Automation, Hangzhou Dianzi University,Hangzhou,China – sequence: 5 givenname: Xinyu surname: Sun fullname: Sun, Xinyu email: 17753825735@163.com organization: School of Automation, Hangzhou Dianzi University,Hangzhou,China – sequence: 6 givenname: Lingwei surname: Zheng fullname: Zheng, Lingwei email: zhenglw@hdu.edu.cn organization: School of Automation, Hangzhou Dianzi University,Hangzhou,China |
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| Snippet | Renewable energy generation is an important way for human society to achieve sustainable energy and economic development. However, renewable energy with high... |
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| SubjectTerms | Distribution network planning Distribution networks fuzzy credibility constrained programming Load modeling Optimization models Particle swarm optimization Photovoltaic systems photovoltaic uncertainty Planning Power system stability Programming Renewable energy sources type-II fuzzy sets Uncertainty |
| Title | Type-II Fuzzy Credibility Constraints Programming Based Distribution Network Planning Including Photovoltaic Power Generation |
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