{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:26:02Z","timestamp":1783182362558,"version":"3.54.6"},"reference-count":57,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Project of China Southern Power Grid","award":["YNKJXM20222166"],"award-info":[{"award-number":["YNKJXM20222166"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research.<\/jats:p>","DOI":"10.3390\/info16080662","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T10:50:21Z","timestamp":1754391021000},"page":"662","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Spatiotemporal Deep Learning Framework for Joint Load and Renewable Energy Forecasting in Stability-Constrained Power Systems"],"prefix":"10.3390","volume":"16","author":[{"given":"Min","family":"Cheng","sequence":"first","affiliation":[{"name":"Yunnan Electric Power Dispatching and Control Center, Kunming 655000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of HVDC, Guangzhou 510700, China"},{"name":"Electric Power Research Institute, Guangzhou 510700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingkang","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of HVDC, Guangzhou 510700, China"},{"name":"Electric Power Research Institute, Guangzhou 510700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihua","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of HVDC, Guangzhou 510700, China"},{"name":"Electric Power Research Institute, Guangzhou 510700, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yayao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanfu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Yunnan Electric Power Dispatching and Control Center, Kunming 655000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109985","DOI":"10.1016\/j.rser.2020.109985","article-title":"Low carbon transition pathway of power sector with high penetration of renewable energy","volume":"130","author":"Chen","year":"2020","journal-title":"Renew. 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