{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T02:02:44Z","timestamp":1779328964983,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of China Southern Power Grid Corporation","award":["030000WX24210010"],"award-info":[{"award-number":["030000WX24210010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>To address the issues of instability and inefficiency that the fluctuating and uncertain characteristics of renewable energy sources impose on low-carbon microgrids, this research introduces a novel Knowledge-Data-Driven Load Frequency Control (KDD-LFC) approach. This advanced strategy seamlessly combines pre-existing knowledge frameworks with the capabilities of deep learning neural networks, enabling the adaptive management and multi-faceted optimization of microgrid functionalities, with a keen emphasis on the symmetry and equilibrium of active power. Initially, the process involves the cultivation of foundational knowledge through established methodologies to augment the reservoir of experience. Following this, a Knowledge-Aggregation-based Proximal Policy Optimization (KA-PPO) technique is employed, which proficiently acquires an understanding of the microgrid\u2019s state representations and operational tactics. This strategy meticulously navigates the delicate balance between the exploration of new strategies and the exploitation of known efficacies, ensuring the harmonization of frequency stability, precision in tracking, and the optimization of control expenditures through the strategic formulation of the reward function. The empirical validation of the KDD-LFC method\u2019s effectiveness and its superiority are demonstrated via simulation tests conducted on the load frequency control (LFC) framework of the Sansha isolated island microgrid, which is under the administration of the China Southern Grid.<\/jats:p>","DOI":"10.3390\/sym16030322","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T08:59:37Z","timestamp":1709801977000},"page":"322","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Deep Reinforcement Learning for Load Frequency Control in Isolated Microgrids: A Knowledge Aggregation Approach with Emphasis on Power Symmetry and Balance"],"prefix":"10.3390","volume":"16","author":[{"given":"Min","family":"Wu","sequence":"first","affiliation":[{"name":"Guangdong Power Grid Co., Ltd., Guangzhou 510600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dakui","family":"Ma","sequence":"additional","affiliation":[{"name":"Guangdong Power Grid Co., Ltd., Guangzhou 510600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiqing","family":"Xiong","sequence":"additional","affiliation":[{"name":"Yangjiang Power Supply Bureau, Guangdong Power Grid Co., Ltd., Yangjiang 529500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linkun","family":"Yuan","sequence":"additional","affiliation":[{"name":"Yangjiang Power Supply Bureau, Guangdong Power Grid Co., Ltd., Yangjiang 529500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pachaiyappan, R., Arasan, E., and Chandrasekaran, K. 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