Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives
The Optimal Power Flow (OPF) problem has become increasingly pivotal in the planning and operation of modern power systems. With the expansion of the grid scale, the advent of smart grid technologies, and the unpredictable nature of renewable energy sources (RESs), interest in OPF has surged. These...
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2025-01-01
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| author | Ahmed Babiker Sulaiman S. Ahmad Ijaz Ahmed Muhammad Khalid Mohammad A. Abido Fahad Saleh Al-Ismail |
| author_facet | Ahmed Babiker Sulaiman S. Ahmad Ijaz Ahmed Muhammad Khalid Mohammad A. Abido Fahad Saleh Al-Ismail |
| author_sort | Ahmed Babiker |
| collection | DOAJ |
| description | The Optimal Power Flow (OPF) problem has become increasingly pivotal in the planning and operation of modern power systems. With the expansion of the grid scale, the advent of smart grid technologies, and the unpredictable nature of renewable energy sources (RESs), interest in OPF has surged. These challenges with new energy storage have introduced a heightened level of uncertainty into the power system’s operation as well as planning. Because of this, OPF is seen as an important tool for achieving different goals, such as optimizing the distribution of resources, making electrical networks more efficient, and so on. However, the OPF problem is inherently difficult to solve because of its non-linear characteristics. Different constraints and limitations intrinsic to real power system grids further accentuate this complexity. Moreover, modern power systems have incorporated new constraints, which make the OPF problem more complex in terms of mathematical formulation and solution. This paper offers a comprehensive and foundational review of OPF, covering the main concept, mathematical formulation, OPF types, comprehensive OPF optimization problem concepts, and the various methods developed to solve it. Additionally, it explores the evolution of these methods from conventional approaches to advanced and recent techniques, including mathematical methods and artificial intelligence methods, which include metaheuristic (search-based) and machine learning algorithms (data-driven). The paper also discusses various types of convex relaxation methods in depth. Ultimately, the paper highlights key gaps, challenges, and opportunities for future research. |
| format | Article |
| id | doaj-art-7901a91d75d04d24aa3ea67e295d074f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7901a91d75d04d24aa3ea67e295d074f2025-08-20T03:18:26ZengIEEEIEEE Access2169-35362025-01-0113600126003910.1109/ACCESS.2025.355616810945774Optimal Power Flow: A Review of State-of-the-Art Techniques and Future PerspectivesAhmed Babiker0https://orcid.org/0009-0009-4648-2897Sulaiman S. Ahmad1https://orcid.org/0000-0001-6611-1051Ijaz Ahmed2Muhammad Khalid3https://orcid.org/0000-0001-7779-5348Mohammad A. Abido4https://orcid.org/0000-0001-5292-6938Fahad Saleh Al-Ismail5https://orcid.org/0000-0002-8743-5706Electrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaInterdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaThe Optimal Power Flow (OPF) problem has become increasingly pivotal in the planning and operation of modern power systems. With the expansion of the grid scale, the advent of smart grid technologies, and the unpredictable nature of renewable energy sources (RESs), interest in OPF has surged. These challenges with new energy storage have introduced a heightened level of uncertainty into the power system’s operation as well as planning. Because of this, OPF is seen as an important tool for achieving different goals, such as optimizing the distribution of resources, making electrical networks more efficient, and so on. However, the OPF problem is inherently difficult to solve because of its non-linear characteristics. Different constraints and limitations intrinsic to real power system grids further accentuate this complexity. Moreover, modern power systems have incorporated new constraints, which make the OPF problem more complex in terms of mathematical formulation and solution. This paper offers a comprehensive and foundational review of OPF, covering the main concept, mathematical formulation, OPF types, comprehensive OPF optimization problem concepts, and the various methods developed to solve it. Additionally, it explores the evolution of these methods from conventional approaches to advanced and recent techniques, including mathematical methods and artificial intelligence methods, which include metaheuristic (search-based) and machine learning algorithms (data-driven). The paper also discusses various types of convex relaxation methods in depth. Ultimately, the paper highlights key gaps, challenges, and opportunities for future research.https://ieeexplore.ieee.org/document/10945774/Optimal power flow (OPF)machine learningmetaheuristic optimization algorithmsOPF convex relaxationartificial intelligence methodsrenewable energy systems |
| spellingShingle | Ahmed Babiker Sulaiman S. Ahmad Ijaz Ahmed Muhammad Khalid Mohammad A. Abido Fahad Saleh Al-Ismail Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives IEEE Access Optimal power flow (OPF) machine learning metaheuristic optimization algorithms OPF convex relaxation artificial intelligence methods renewable energy systems |
| title | Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives |
| title_full | Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives |
| title_fullStr | Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives |
| title_full_unstemmed | Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives |
| title_short | Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives |
| title_sort | optimal power flow a review of state of the art techniques and future perspectives |
| topic | Optimal power flow (OPF) machine learning metaheuristic optimization algorithms OPF convex relaxation artificial intelligence methods renewable energy systems |
| url | https://ieeexplore.ieee.org/document/10945774/ |
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