Advanced Intelligent Optimization Algorithms for Multi-Objective Optimal Power Flow in Future Power Systems: A Review
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910554058129408 |
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| author | Li, Yuyan |
| author_facet | Li, Yuyan |
| contents | This review explores the application of intelligent optimization algorithms to Multi-Objective Optimal Power Flow (MOPF) in enhancing modern power systems. It delves into the challenges posed by the integration of renewables, smart grids, and increasing energy demands, focusing on evolutionary algorithms, swarm intelligence, and deep reinforcement learning. The effectiveness, scalability, and application of these algorithms are analyzed, with findings suggesting that algorithm selection is contingent on the specific MOPF problem at hand, and hybrid approaches offer significant promise. The importance of standard test systems for verifying solutions and the role of software tools in facilitating analysis are emphasized. Future research is directed towards exploiting machine learning for dynamic optimization, embracing decentralized energy systems, and adapting to evolving policy frameworks to improve power system efficiency and sustainability. This review aims to advance MOPF research by highlighting state-of-the-art methodologies and encouraging the development of innovative solutions for future energy challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advanced Intelligent Optimization Algorithms for Multi-Objective Optimal Power Flow in Future Power Systems: A Review Li, Yuyan Neural and Evolutionary Computing Systems and Control This review explores the application of intelligent optimization algorithms to Multi-Objective Optimal Power Flow (MOPF) in enhancing modern power systems. It delves into the challenges posed by the integration of renewables, smart grids, and increasing energy demands, focusing on evolutionary algorithms, swarm intelligence, and deep reinforcement learning. The effectiveness, scalability, and application of these algorithms are analyzed, with findings suggesting that algorithm selection is contingent on the specific MOPF problem at hand, and hybrid approaches offer significant promise. The importance of standard test systems for verifying solutions and the role of software tools in facilitating analysis are emphasized. Future research is directed towards exploiting machine learning for dynamic optimization, embracing decentralized energy systems, and adapting to evolving policy frameworks to improve power system efficiency and sustainability. This review aims to advance MOPF research by highlighting state-of-the-art methodologies and encouraging the development of innovative solutions for future energy challenges. |
| title | Advanced Intelligent Optimization Algorithms for Multi-Objective Optimal Power Flow in Future Power Systems: A Review |
| topic | Neural and Evolutionary Computing Systems and Control |
| url | https://arxiv.org/abs/2404.09203 |