Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Hou, Shengren"'
Autor:
Hou, Shengren, Gao, Shuyi, Xia, Weijie, Duque, Edgar Mauricio Salazar, Palensky, Peter, Vergara, Pedro P.
Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optima
Externí odkaz:
http://arxiv.org/abs/2408.03685
Lane-changing (LC) is a challenging scenario for connected and automated vehicles (CAVs) because of the complex dynamics and high uncertainty of the traffic environment. This challenge can be handled by deep reinforcement learning (DRL) approaches, l
Externí odkaz:
http://arxiv.org/abs/2407.02521
The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation. By exploiting the generalization capa
Externí odkaz:
http://arxiv.org/abs/2307.14304
Autor:
Xia, Weijie, Huang, Hanyue, Duque, Edgar Mauricio Salazar, Hou, Shengren, Palensky, Peter, Vergara, Pedro P.
Publikováno v:
In Sustainable Energy, Grids and Networks June 2024 38
Publikováno v:
In Applied Energy 15 May 2022 314
Publikováno v:
In Renewable and Sustainable Energy Reviews March 2021 137
Publikováno v:
In Journal of Cleaner Production 1 September 2020 266
Autor:
Zhang, Yiyi, Wang, Jiaqi, Zhang, Linmei, Liu, Jiefeng, Zheng, Hanbo, Fang, Jiake, Hou, Shengren, Chen, Shaoqing
Publikováno v:
In Applied Energy 1 August 2020 271
Publikováno v:
Proceedings of 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022
Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45184172b9aa24fa7799ccc981eb9564
https://doi.org/10.1109/isgt-europe54678.2022.9960642
https://doi.org/10.1109/isgt-europe54678.2022.9960642
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