no code implementations • 31 Mar 2025 • Yang Li, Shitu Zhang, Yuanzheng Li
In the era of Industry 4. 0, ensuring the resilience of cyber-physical systems against sophisticated cyber threats is increasingly critical.
no code implementations • 15 Oct 2024 • Yang Li, Jiankai Gao, Yuanzheng Li, Chen Chen, Sen Li, Mohammad Shahidehpour, Zhe Chen
To coordinate the interests of operator and users in a microgrid under complex and changeable operating conditions, this paper proposes a microgrid scheduling model considering the thermal flexibility of thermostatically controlled loads and demand response by leveraging physical informed-inspired deep reinforcement learning (DRL) based bi-level programming.
no code implementations • 28 Nov 2023 • Yang Li, Wenjie Ma, Yuanzheng Li, Sen Li, Zhe Chen, Mohammad Shahidehpor
The state-adversarial soft actor-critic (SA-SAC) algorithm is proposed to mitigate the impact of cyber-attacks on the scheduling strategy, integrating adversarial training into the learning process to against cyber-attacks.
no code implementations • 26 Sep 2023 • Yuanzheng Li, Xinxin Long, Yang Li, Yizhou Ding, Tao Yang, Zhigang Zeng
In this context, unreasonable profit distributions on the demand-supply side would lead to the conflict of interests and diminish the effectiveness of cooperative responses.
no code implementations • 7 Aug 2023 • Yang Li, Shitu Zhang, Yuanzheng Li, Jiting Cao, Shuyue Jia
Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems.
1 code implementation • IEEE Transactions on Power Systems 2023 • Yuanzheng Li, Fushen Zhang, Yun Liu, Huilian Liao, Hai-Tao Zhang, Chiyung Chung
Furthermore, to better address the high uncertainty of RLF, a learning scheme combing both offline and online learning is specifically developed for the regressor.
no code implementations • 29 Dec 2022 • Yuanzheng Li, Shangyang He, Yang Li, Yang Shi, Zhigang Zeng
Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents.
no code implementations • 27 Dec 2022 • Yang Li, Fanjin Bu, Yuanzheng Li, Chao Long
Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands.
no code implementations • 2 Nov 2022 • Yang Li, Ruinong Wang, Yuanzheng Li, Meng Zhang, Chao Long
To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL).
no code implementations • 17 Apr 2022 • Yuanzheng Li, Shangyang He, Yang Li, Leijiao Ge, Suhua Lou, Zhigang Zeng
This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties.
no code implementations • 24 Feb 2021 • Zhuoling Li, Haohan Wang, Tymoteusz Swistek, Weixin Chen, Yuanzheng Li, Haoqian Wang
Few-shot learning is challenging due to the limited data and labels.