Advancing Attack-Resilient Scheduling of Integrated Energy Systems with Demand Response via Deep Reinforcement Learning

no code implementations • 28 Nov 2023 • Yang Li, Wenjie Ma, Yuanzheng Li, Sen Li, Zhe Chen

Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources.

Scheduling

A Demand-Supply Cooperative Responding Strategy in Power System with High Renewable Energy Penetration

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.

PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning

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.

Data Augmentation Transfer Learning

Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management

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.

energy management Federated Learning +4

Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach

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.

Computational Efficiency Decision Making +3

Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach

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).

Federated Learning Privacy Preserving +2

Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

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.

reinforcement-learning Reinforcement Learning (RL) +2

Enabling the Network to Surf the Internet

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.

Few-Shot Learning Self-Supervised Learning