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Found 11 papers, 1 papers with code
AI-Enhanced Resilience in Power Systems: Adversarial Deep Learning for Robust Short-Term Voltage Stability Assessment under Cyber-Attacks
In the era of Industry 4. 0, ensuring the resilience of cyber-physical systems against sophisticated cyber threats is increasingly critical.
Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling
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.
Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
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.
A Demand-Supply Cooperative Responding Strategy in Power System with High Renewable Energy Penetration
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
Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems.
Residential Load Forecasting: An Online-Offline Deep Kernel Learning Method
Furthermore, to better address the high uncertainty of RLF, a learning scheme combing both offline and online learning is specifically developed for the regressor.
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management
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.
Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach
Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands.
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach
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).
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach
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.
Enabling the Network to Surf the Internet
Few-shot learning is challenging due to the limited data and labels.
