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Bayesian deep neural networks for spatio-temporal probabilistic optimal power flow with multi-source renewable energy. (2024). Xu, Zidong ; Yin, Linfei ; Gao, Fang.
In: Applied Energy.
RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014708.

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  1. Optimal Power Flow for High Spatial and Temporal Resolution Power Systems with High Renewable Energy Penetration Using Multi-Agent Deep Reinforcement Learning. (2025). Chen, Xin ; Xu, Hao ; Liu, Linlin ; Huo, Long ; Zhou, Liangcai.
    In: Energies.
    RePEc:gam:jeners:v:18:y:2025:i:7:p:1809-:d:1627533.

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  2. Analyzing complexities of integrating Renewable Energy Sources into Smart Grid: A comprehensive review. (2025). Sheybani, Ehsan ; Pourbehzadi, Motahareh ; Javidi, Giti ; Taabodi, M H ; Sharifhosseini, S M ; Shasadeghi, M ; Niknam, T ; Aghajari, Asadi H.
    In: Applied Energy.
    RePEc:eee:appene:v:383:y:2025:i:c:s0306261925000479.

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  3. Short-Term Output Scenario Generation of Renewable Energy Using Transformer–Wasserstein Generative Adversarial Nets-Gradient Penalty. (2024). Yu, YI ; Hua, Xiaojun ; Deng, Youhan ; Ke, Deping ; Xu, Jian ; Gu, Liuqing.
    In: Sustainability.
    RePEc:gam:jsusta:v:16:y:2024:i:24:p:10936-:d:1543102.

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  4. Enhancing electric vehicle charging efficiency at the aggregator level: A deep-weighted ensemble model for wholesale electricity price forecasting. (2024). Eichman, Josh ; Pallonetto, Fabiano ; Hussain, Zakir ; Teni, Abhishek Prasad ; Kim, Yun-Su ; Zia, Muhammad Fahad ; Alharby, Maher ; Alwayle, Ibrahim M ; Irshad, Reyazur Rashid.
    In: Energy.
    RePEc:eee:energy:v:308:y:2024:i:c:s0360544224025970.

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  5. Online probabilistic energy flow for hydrogen-power-heat system based on multi-parametric programming. (2024). Zhou, Yijia ; Yan, Mingyu ; Peng, Hongyi.
    In: Applied Energy.
    RePEc:eee:appene:v:372:y:2024:i:c:s0306261924012194.

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