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Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling.

, , , and . IEEE Trans. Ind. Informatics, 15 (11): 6058-6069 (2019)

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Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach., , , , , and . IAS, page 1-10. IEEE, (2019)Probabilistic Optimal Power Flow With Correlated Wind Power Uncertainty via Markov Chain Quasi-Monte-Carlo Sampling., , , and . IEEE Trans. Ind. Informatics, 15 (11): 6058-6069 (2019)Proactive Resilient Day-ahead Unit Commitment with Cloud Computing Data Centers., , , , and . IAS, page 1-8. IEEE, (2021)Deep Reinforcement Learning for Smart Grid Operations: Algorithms, Applications, and Prospects., , , , , and . Proc. IEEE, 111 (9): 1055-1096 (September 2023)A highly accurate and fast power quality disturbances classification based on dictionary learning sparse decomposition., , , , and . Trans. Inst. Meas. Control, 41 (1): 145-155 (2019)PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning., , , , and . CoRR, (2023)Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management., , , , and . CoRR, (2023)Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach., , , , and . CoRR, (2022)Advancing Attack-Resilient Scheduling of Integrated Energy Systems with Demand Response via Deep Reinforcement Learning., , , , and . CoRR, (2023)A Novel Distributed PV Data Virtual Collection With Continuous-Binary Denoising Auto-Encoders., , , , , and . IEEE Trans. Smart Grid, 15 (1): 1152-1164 (January 2024)