Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods
Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While convention...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/16/5974 |
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| author | Cheng Yang Yupeng Sun Yujie Zou Fei Zheng Shuangyu Liu Bochao Zhao Ming Wu Haoyang Cui |
| author_facet | Cheng Yang Yupeng Sun Yujie Zou Fei Zheng Shuangyu Liu Bochao Zhao Ming Wu Haoyang Cui |
| author_sort | Cheng Yang |
| collection | DOAJ |
| description | Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the “Dual-Carbon” target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations. |
| first_indexed | 2024-03-10T23:58:10Z |
| format | Article |
| id | doaj.art-46a86d217c854b89ab1fee791b1c7df0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T23:58:10Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-46a86d217c854b89ab1fee791b1c7df02023-11-19T00:56:54ZengMDPI AGEnergies1996-10732023-08-011616597410.3390/en16165974Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization MethodsCheng Yang0Yupeng Sun1Yujie Zou2Fei Zheng3Shuangyu Liu4Bochao Zhao5Ming Wu6Haoyang Cui7College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaShanghai Zhabei Power Plant of State Grid Corporation of China, Shanghai 200432, ChinaCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaShanghai Guoyun Information Technology Co., Ltd., Shanghai 201210, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaChina Electric Power Research Institute, State Grid Corporation of China, Beijing 100192, ChinaCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, ChinaDistributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the “Dual-Carbon” target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations.https://www.mdpi.com/1996-1073/16/16/5974optimal power flowdistributed generatorsdistribution networkmathematical optimizationartificial intelligence |
| spellingShingle | Cheng Yang Yupeng Sun Yujie Zou Fei Zheng Shuangyu Liu Bochao Zhao Ming Wu Haoyang Cui Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods Energies optimal power flow distributed generators distribution network mathematical optimization artificial intelligence |
| title | Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods |
| title_full | Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods |
| title_fullStr | Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods |
| title_full_unstemmed | Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods |
| title_short | Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods |
| title_sort | optimal power flow in distribution network a review on problem formulation and optimization methods |
| topic | optimal power flow distributed generators distribution network mathematical optimization artificial intelligence |
| url | https://www.mdpi.com/1996-1073/16/16/5974 |
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