Search Results for author: Yuanzheng Li

Found 11 papers, 1 papers with code

Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling

no code implementations15 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.

AutoML Computational Efficiency +3

Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning

no code implementations28 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.

Deep Reinforcement Learning Scheduling

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

no code implementations26 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 implementations7 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 Deep Learning +1

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

no code implementations29 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.

Deep Reinforcement Learning energy management +5

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

no code implementations2 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).

Deep Reinforcement Learning Federated Learning +4

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

no code implementations17 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 +3

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