A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach
Abstract The uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologi...
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| Format: | Article |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13096 |
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| author | Ghulam Abbas Wu Zhi Aamir Ali |
| author_facet | Ghulam Abbas Wu Zhi Aamir Ali |
| author_sort | Ghulam Abbas |
| collection | DOAJ |
| description | Abstract The uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologies have promoted the advancement of hybrid model‐data‐driven algorithms. This article proposes a two‐stage reactive power optimization method for distributed networks (DNs) based on a hybrid model‐data‐driven approach. In the first stage, based on the topology and line parameters of the DN, as well as forecasts of loads and renewable energy outputs, a mixed‐integer second‐order cone programming (MISOCP) algorithm is used to control the on‐load tap changer (OLTC) positions on an hourly day‐ahead basis. In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. Specifically, using traditional solvers, the global optimal reactive power output for PV and wind power units is determined first, corresponding to various load and renewable energy output scenarios. Then, neural networks are trained to map node power to the optimal reactive power outputs of renewable energy units, capturing the complex physical relationships. For the second stage, a transformer network framework with a self‐attention mechanism and multi‐head attention for deep learning training is applied to uncover the intrinsic and physical spatial relationships among high‐dimensional features. The proposed method is tested on a modified IEEE 33‐bus system with multiple distributed renewable energy sources. The case study results demonstrate that the proposed hybrid model‐data‐driven algorithm effectively coordinates day‐ahead and real‐time controls of various devices, achieving real‐time model‐free optimization throughout the day. Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results. |
| format | Article |
| id | doaj-art-20b605691d054d22b19ba048a1b8ffb0 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-20b605691d054d22b19ba048a1b8ffb02025-01-30T12:15:54ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118163967397910.1049/rpg2.13096A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approachGhulam Abbas0Wu Zhi1Aamir Ali2School of Electrical Engineering Southeast University Nanjing ChinaSchool of Electrical Engineering Southeast University Nanjing ChinaDepartment of Electrical Engineering Quaid‐e‐Awam University of Engineering Science and Technology Nawabshah Sindh PakistanAbstract The uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologies have promoted the advancement of hybrid model‐data‐driven algorithms. This article proposes a two‐stage reactive power optimization method for distributed networks (DNs) based on a hybrid model‐data‐driven approach. In the first stage, based on the topology and line parameters of the DN, as well as forecasts of loads and renewable energy outputs, a mixed‐integer second‐order cone programming (MISOCP) algorithm is used to control the on‐load tap changer (OLTC) positions on an hourly day‐ahead basis. In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. Specifically, using traditional solvers, the global optimal reactive power output for PV and wind power units is determined first, corresponding to various load and renewable energy output scenarios. Then, neural networks are trained to map node power to the optimal reactive power outputs of renewable energy units, capturing the complex physical relationships. For the second stage, a transformer network framework with a self‐attention mechanism and multi‐head attention for deep learning training is applied to uncover the intrinsic and physical spatial relationships among high‐dimensional features. The proposed method is tested on a modified IEEE 33‐bus system with multiple distributed renewable energy sources. The case study results demonstrate that the proposed hybrid model‐data‐driven algorithm effectively coordinates day‐ahead and real‐time controls of various devices, achieving real‐time model‐free optimization throughout the day. Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.https://doi.org/10.1049/rpg2.13096distributed power generationdistribution networksoptimisationreactive powerrenewable energy sources |
| spellingShingle | Ghulam Abbas Wu Zhi Aamir Ali A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach IET Renewable Power Generation distributed power generation distribution networks optimisation reactive power renewable energy sources |
| title | A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach |
| title_full | A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach |
| title_fullStr | A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach |
| title_full_unstemmed | A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach |
| title_short | A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach |
| title_sort | two stage reactive power optimization method for distribution networks based on a hybrid model and data driven approach |
| topic | distributed power generation distribution networks optimisation reactive power renewable energy sources |
| url | https://doi.org/10.1049/rpg2.13096 |
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