{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T17:56:40Z","timestamp":1791482200559,"version":"4.3.4"},"reference-count":38,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Grid Jilin Electric Power Co., Ltd.","award":["2025ZX-06"],"award-info":[{"award-number":["2025ZX-06"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Even if persistent stagnant weather does not directly damage power grid equipment, it may lead to prolonged insufficient output of renewable energy sources and further trigger power balance risks. This paper proposes a rapid risk assessment framework that comprehensively incorporates meteorological sensitivity models, time-varying component failure probabilities, a Denoising Variational Autoencoder (DVAE) for generating coupled wind-photovoltaic-temperature scenarios, and a Deep Deterministic Policy Gradient (DDPG) model embedded with the feature token attention mechanism.The 72-hour assessment task is formulated as an offline Markov Decision Process (MDP): the state space covers meteorological conditions, power sources, power grid topology, load profiles, and risk features of the previous time step; continuous actions are defined as risk indicator vectors; the reward function is designed to penalize deviations between model outputs and PSD-BPA benchmarks, temporal inconsistency, and violations of physical constraints.To distinguish the sequential reinforcement learning formulation from direct regression, a feedforward Multi-Layer Perceptron (MLP) is introduced as the non-reinforcement learning baseline. Four metrics, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coefficient of determination (R\n                    <jats:sup>2<\/jats:sup>\n                    ), and the 95th percentile of absolute error (P95AE), are adopted to evaluate all models on an independent scenario-level test set.Verified on the planned power grid of Northeast China in 2025 as the test case, the proposed DDPG-Attention achieves MAE of 0.0310, RMSE of 0.0389, R\n                    <jats:sup>2<\/jats:sup>\n                    of 0.954 and P95AE of 0.0760 on the independent test set. The average time consumption for a single risk assessment is approximately 0.12 seconds, yielding an acceleration ratio of multiple orders of magnitude compared with the traditional time-sequential Monte Carlo simulation based on PSD-BPA. This study provides an efficient and accurate novel technical paradigm for the rapid evaluation of power supply-demand imbalance risks in power systems under extreme weather conditions.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0359101","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T17:34:09Z","timestamp":1791480849000},"page":"e0359101","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A fast risk assessment method for power balance under extreme weather conditions based on deep reinforcement learning with a coupled attention mechanism"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0676-406X","authenticated-orcid":true,"given":"Wang","family":"Hang","sequence":"first","affiliation":[{"name":"Northeast Electric Power University","place":["Jilin, China"],"department":["School of Energy and Power Engineering"]},{"name":"Electric Power Research Institute of State Grid Jilin Electric Power Co., Ltd.","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun","family":"Shouheng","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute of State Grid Jilin Electric Power Co., Ltd.","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yiming","sequence":"additional","affiliation":[{"name":"State Grid Jilin Electric Power Co., Ltd.","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Baoju","sequence":"additional","affiliation":[{"name":"State Grid Jilin Electric Power Co., Ltd.","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Yu","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute of State Grid Jilin Electric Power Co., Ltd.","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Meiyue","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute of State Grid Jilin Electric Power Co., Ltd.","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"HeXiang","family":"Niu","sequence":"additional","affiliation":[{"name":"Northeast Electric Power University","place":["Jilin, China"],"department":["School of Energy and Power Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"pone.0359101.ref001","first-page":"1","volume-title":"Compilation Group of the Blue Book on New Power System Development. 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summer drought on conventional and renewable generation capacity: Methods and a case study on the Eastern U.S. power system","volume":"363","author":"H Shuai","year":"2024","journal-title":"Applied Energy"},{"issue":"6","key":"pone.0359101.ref009","first-page":"47","article-title":"Evaluation of typhoon resilience of distribution network considering grid reconstruction and disaster recovery","volume":"42","author":"B Chen","year":"2018","journal-title":"Automation of Electric Power Systems"},{"issue":"4","key":"pone.0359101.ref010","doi-asserted-by":"crossref","first-page":"2918","DOI":"10.1109\/TSG.2016.2623818","article-title":"A novel metric to quantify and enable resilient distribution system using graph theory and choquet integral","volume":"9","author":"P Bajpai","year":"2018","journal-title":"IEEE Trans Smart Grid"},{"issue":"16","key":"pone.0359101.ref011","first-page":"3","article-title":"Flexibility supply-demand balance in power system with ultra-high proportion of renewable energy","volume":"46","author":"Z Lu","year":"2022","journal-title":"Automation of Electric Power Systems"},{"issue":"1","key":"pone.0359101.ref012","first-page":"30","article-title":"Optimization approach of energy storage system locating and sizing considering distribution system resilience","volume":"39","author":"H Yang","year":"2018","journal-title":"Electric Power Construction"},{"issue":"1","key":"pone.0359101.ref013","first-page":"123","article-title":"Construction and evaluation of resilience restoration capability indices for distribution network under extreme ice and snow weather","volume":"46","author":"S Wang","year":"2020","journal-title":"High Voltage Engineering"},{"issue":"7","key":"pone.0359101.ref014","first-page":"176","article-title":"Transient stability assessment of power system based on clustering adaptive active learning","volume":"41","author":"D Lu","year":"2021","journal-title":"Electric Power Automation Equipment"},{"issue":"19","key":"pone.0359101.ref015","first-page":"7409","article-title":"An active transfer learning scheme for power system transient stability assessment adaptive to the topological variability","volume":"43","author":"H Chen","year":"2023","journal-title":"Proceedings of the CSEE"},{"issue":"9","key":"pone.0359101.ref016","first-page":"180","article-title":"A survey of deep active learning","volume":"54","author":"P Ren","year":"2022","journal-title":"ACM Computing Surveys"},{"issue":"15","key":"pone.0359101.ref017","first-page":"5732","article-title":"Active transfer learning for TTC assessment of transmission interfaces with changing operating scenarios","volume":"43","author":"Z Wang","year":"2023","journal-title":"Proceedings of the CSEE"},{"issue":"4","key":"pone.0359101.ref018","first-page":"121","article-title":"Adaptive assessment of power system transient stability based on active transfer learning","volume":"47","author":"B Li","year":"2023","journal-title":"Automation of Electric Power Systems"},{"issue":"7","key":"pone.0359101.ref019","doi-asserted-by":"crossref","first-page":"9970","DOI":"10.1109\/TNNLS.2023.3238168","article-title":"Intelligent power system stability assessment and dominant instability mode identification using integrated active deep learning","volume":"35","author":"Z Shi","year":"2024","journal-title":"IEEE Trans Neural Netw Learning Syst"},{"issue":"6","key":"pone.0359101.ref020","doi-asserted-by":"crossref","first-page":"5128","DOI":"10.1109\/TPWRS.2022.3220955","article-title":"Bidirectional active transfer learning for adaptive power system stability assessment and dominant instability mode identification","volume":"38","author":"Z Shi","year":"2023","journal-title":"IEEE Trans Power Syst"},{"key":"pone.0359101.ref021","author":"TP Lillicrap","year":"2015"},{"issue":"10","key":"pone.0359101.ref022","first-page":"2924","article-title":"Modular denoising variational autoencoder for multi source load joint scene generation considering complex meteorological coupling characteristics","volume":"39","author":"N Huang","year":"2019","journal-title":"Chinese Journal of Electrical Engineering"},{"key":"pone.0359101.ref023","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"A Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"11","key":"pone.0359101.ref024","first-page":"3503","article-title":"Analysis and assessment of operation risk for power system with large-scale wind power integration","volume":"40","author":"J Li","year":"2016","journal-title":"Power System Technology"},{"issue":"10","key":"pone.0359101.ref025","first-page":"4180","article-title":"Risk assessment of urban distribution network under extreme rainstorm disasters","volume":"47","author":"Z Liang","year":"2023","journal-title":"Power System Technology"},{"key":"pone.0359101.ref026","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.apenergy.2017.05.116","article-title":"Techno-economic optimization of hybrid photovoltaic\/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response","volume":"202","author":"MH Amrollahi","year":"2017","journal-title":"Applied Energy"},{"key":"pone.0359101.ref027","first-page":"1","article-title":"Promote energy transformation and empower green development","author":"X Zhang","year":"2024","journal-title":"State Grid News"},{"key":"pone.0359101.ref028","first-page":"231235","article-title":"Research on the impacts of climate change on the power generation of hydropower station","author":"L Yuan","year":"2020","journal-title":"China Rural Water and Hydropower"},{"issue":"12","key":"pone.0359101.ref029","first-page":"27","article-title":"Estimation of evaporation from water surface of Three Gorges Reservoir","volume":"43","author":"Y Wang","year":"2022","journal-title":"Express Water Resources & Hydropower Information"},{"issue":"5","key":"pone.0359101.ref030","first-page":"35","article-title":"Study on the calculation method of reservoir surface evaporation","volume":"29","author":"Z Qian","year":"2003","journal-title":"Water Power"},{"key":"pone.0359101.ref031","author":"T Liao","year":"2009"},{"issue":"1","key":"pone.0359101.ref032","article-title":"Incentive based emergency demand response effectively reduces peak load during heatwave without harm to vulnerable groups","volume":"14","author":"Z Wang","year":"2023","journal-title":"Nat Commun"},{"issue":"1","key":"pone.0359101.ref033","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TSG.2019.2919601","article-title":"Estimating the Profile of Incentive-Based Demand Response (IBDR) by Integrating Technical Models and Social-Behavioral Factors","volume":"11","author":"Q Shi","year":"2020","journal-title":"IEEE 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