A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation Curtailment
Herein, we propose a novel method for identifying power congestion and renewable energy source (RES) power curtailment in power grids by using deep neural network (DNN)-based optimal power flow (OPF) analysis. Synthetic data for load demand and RES power generation are used to obtain the OPF solutio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9878340/ |
| _version_ | 1811183556501700608 |
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| author | Rehman Zafar Ba Hau Vu Il-Yop Chung |
| author_facet | Rehman Zafar Ba Hau Vu Il-Yop Chung |
| author_sort | Rehman Zafar |
| collection | DOAJ |
| description | Herein, we propose a novel method for identifying power congestion and renewable energy source (RES) power curtailment in power grids by using deep neural network (DNN)-based optimal power flow (OPF) analysis. Synthetic data for load demand and RES power generation are used to obtain the OPF solutions by using an OPF solver. RES locations are selected based on an analysis of the congestion cases, and the DNN is trained by using the load demand and RES power as inputs and the OPF solution as the output. Thereafter, post-processing is performed on the DNN-OPF output by using network information. The final results show the accuracies of identified values on RES curtailment, line loading, generation dispatch schedule, and total generation operating cost. The IEEE 39-bus system was adopted as a case study to validate the proposed model. The results demonstrate that the proposed scheme is much faster and more suitable for transmission systems than conventional OPF methods. Furthermore, the normalized root-mean-squared error was less than 1%, and the computational time was more than 30 times faster than conventional OPF analysis. |
| first_indexed | 2024-04-11T09:47:28Z |
| format | Article |
| id | doaj.art-7de471b8c47c4cb7bf862fc20e7d61d1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T09:47:28Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7de471b8c47c4cb7bf862fc20e7d61d12022-12-22T04:30:55ZengIEEEIEEE Access2169-35362022-01-0110956479565710.1109/ACCESS.2022.32048039878340A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation CurtailmentRehman Zafar0https://orcid.org/0000-0001-9261-3784Ba Hau Vu1https://orcid.org/0000-0001-8752-6686Il-Yop Chung2https://orcid.org/0000-0002-4617-102XSchool of Electrical Engineering, Kookmin University, Seongbuk-gu, Seoul, Republic of KoreaSchool of Electrical Engineering, Kookmin University, Seongbuk-gu, Seoul, Republic of KoreaSchool of Electrical Engineering, Kookmin University, Seongbuk-gu, Seoul, Republic of KoreaHerein, we propose a novel method for identifying power congestion and renewable energy source (RES) power curtailment in power grids by using deep neural network (DNN)-based optimal power flow (OPF) analysis. Synthetic data for load demand and RES power generation are used to obtain the OPF solutions by using an OPF solver. RES locations are selected based on an analysis of the congestion cases, and the DNN is trained by using the load demand and RES power as inputs and the OPF solution as the output. Thereafter, post-processing is performed on the DNN-OPF output by using network information. The final results show the accuracies of identified values on RES curtailment, line loading, generation dispatch schedule, and total generation operating cost. The IEEE 39-bus system was adopted as a case study to validate the proposed model. The results demonstrate that the proposed scheme is much faster and more suitable for transmission systems than conventional OPF methods. Furthermore, the normalized root-mean-squared error was less than 1%, and the computational time was more than 30 times faster than conventional OPF analysis.https://ieeexplore.ieee.org/document/9878340/Deep neural networknetwork congestionoptimal power flowpower curtailmentrenewable energy sources |
| spellingShingle | Rehman Zafar Ba Hau Vu Il-Yop Chung A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation Curtailment IEEE Access Deep neural network network congestion optimal power flow power curtailment renewable energy sources |
| title | A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation Curtailment |
| title_full | A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation Curtailment |
| title_fullStr | A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation Curtailment |
| title_full_unstemmed | A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation Curtailment |
| title_short | A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation Curtailment |
| title_sort | deep neural network based optimal power flow approach for identifying network congestion and renewable energy generation curtailment |
| topic | Deep neural network network congestion optimal power flow power curtailment renewable energy sources |
| url | https://ieeexplore.ieee.org/document/9878340/ |
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