Non-intrusive modeling for integrated energy system based on two-stage GAN
Generally, an accurate model can describe the operating states of a system more effectively and provide a more reliable theoretical basis for the system optimization and control. Different from the traditional intrusive modeling, a non-intrusive modeling method based on two-stage generative adversar...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2022-06-01
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| Series: | iEnergy |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/IEN.2022.0027 |
| _version_ | 1798006797022265344 |
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| author | Qiuye Sun Chengze Ren Jingwei Hu Rui Wang |
| author_facet | Qiuye Sun Chengze Ren Jingwei Hu Rui Wang |
| author_sort | Qiuye Sun |
| collection | DOAJ |
| description | Generally, an accurate model can describe the operating states of a system more effectively and provide a more reliable theoretical basis for the system optimization and control. Different from the traditional intrusive modeling, a non-intrusive modeling method based on two-stage generative adversarial network (TS-GAN) is proposed for integrated energy system (IES). By using this method, non-intrusive modeling for the IES including photovoltaic, wind power, energy storage, and energy coupling equipment can be carried out. First, the characteristics of IES are analyzed and extracted based on the meteorological data, energy output, and energy price, and then the characteristic database is established. Meanwhile, the loads are classified as uncontrollable loads and schedulable loads based on frequency domain decomposition to facilitate energy management. Furthermore, TS-GAN algorithm based on the Stackelberg game is designed. In the TS-GAN, the first-stage GAN is used to generate the operating data of each equipment identified by non-invasive monitoring, and the second-stage GAN distinguishes the accumulated data generated by first-stage GAN and further modifies the generator models of the first-stage GAN. Finally, the effectiveness and accuracy of the proposed method are verified by the simulation of an energy region. |
| first_indexed | 2024-04-11T13:00:14Z |
| format | Article |
| id | doaj.art-3e61ab34bb2e4dc2b79b9175b5060b37 |
| institution | Directory Open Access Journal |
| issn | 2771-9197 |
| language | English |
| last_indexed | 2024-04-11T13:00:14Z |
| publishDate | 2022-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | iEnergy |
| spelling | doaj.art-3e61ab34bb2e4dc2b79b9175b5060b372022-12-22T04:22:56ZengTsinghua University PressiEnergy2771-91972022-06-011225726610.23919/IEN.2022.0027Non-intrusive modeling for integrated energy system based on two-stage GANQiuye Sun0Chengze Ren1Jingwei HuRui Wang2Institute of Economic Technology, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110003, ChinaInstitute of Economic Technology, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110003, ChinaInstitute of Economic Technology, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110003, ChinaGenerally, an accurate model can describe the operating states of a system more effectively and provide a more reliable theoretical basis for the system optimization and control. Different from the traditional intrusive modeling, a non-intrusive modeling method based on two-stage generative adversarial network (TS-GAN) is proposed for integrated energy system (IES). By using this method, non-intrusive modeling for the IES including photovoltaic, wind power, energy storage, and energy coupling equipment can be carried out. First, the characteristics of IES are analyzed and extracted based on the meteorological data, energy output, and energy price, and then the characteristic database is established. Meanwhile, the loads are classified as uncontrollable loads and schedulable loads based on frequency domain decomposition to facilitate energy management. Furthermore, TS-GAN algorithm based on the Stackelberg game is designed. In the TS-GAN, the first-stage GAN is used to generate the operating data of each equipment identified by non-invasive monitoring, and the second-stage GAN distinguishes the accumulated data generated by first-stage GAN and further modifies the generator models of the first-stage GAN. Finally, the effectiveness and accuracy of the proposed method are verified by the simulation of an energy region.https://www.sciopen.com/article/10.23919/IEN.2022.0027non-intrusive monitoringsystem modelinggenerative adversarial networksintegrated energy systemstackelberg game |
| spellingShingle | Qiuye Sun Chengze Ren Jingwei Hu Rui Wang Non-intrusive modeling for integrated energy system based on two-stage GAN iEnergy non-intrusive monitoring system modeling generative adversarial networks integrated energy system stackelberg game |
| title | Non-intrusive modeling for integrated energy system based on two-stage GAN |
| title_full | Non-intrusive modeling for integrated energy system based on two-stage GAN |
| title_fullStr | Non-intrusive modeling for integrated energy system based on two-stage GAN |
| title_full_unstemmed | Non-intrusive modeling for integrated energy system based on two-stage GAN |
| title_short | Non-intrusive modeling for integrated energy system based on two-stage GAN |
| title_sort | non intrusive modeling for integrated energy system based on two stage gan |
| topic | non-intrusive monitoring system modeling generative adversarial networks integrated energy system stackelberg game |
| url | https://www.sciopen.com/article/10.23919/IEN.2022.0027 |
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