Cover Image

A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yao, Fulong, Zhao, Wanqing, Forshaw, Matthew, Song, Yang
Format:	Preprint
Published:	2024
Subjects:	Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization
by: Yao, Fulong, et al.
Published: (2025)

A Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction
by: Yao, Fulong, et al.
Published: (2024)

A Reinforcement Learning Approach for Optimal Control in Microgrids
by: Salaorni, Davide, et al.
Published: (2025)

Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling
by: Li, Yang, et al.
Published: (2024)

Cooperative Energy Scheduling of Multi-Microgrids Based on Risk-Sensitive Reinforcement Learning
by: Zhang, Rongxiang, et al.
Published: (2025)

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach
by: Wang, Yi, et al.
Published: (2025)

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management
by: Amiri, Mohammad Hossein Nejati, et al.
Published: (2025)

Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management
by: Li, Yuanzheng, et al.
Published: (2022)

Offline Reinforcement Learning for Microgrid Voltage Regulation
by: Yang, Shan, et al.
Published: (2025)

A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids
by: Maya, Neethu, et al.
Published: (2024)

Deep Learning-Enabled System Diagnosis in Microgrids: A Feature-Feedback GAN Approach
by: Kasimalla, Swetha Rani, et al.
Published: (2025)

Reinforcement Learning-Based Controlled Switching Approach for Inrush Current Minimization in Power Transformers
by: Valdivielso, Jone Ugarte, et al.
Published: (2025)

Safe Reinforcement Learning for Power System Control: A Review
by: Yu, Peipei, et al.
Published: (2024)

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach
by: Song, Fei, et al.
Published: (2024)

Deep Reinforcement Learning for Optimizing Inverter Control: Fixed and Adaptive Gain Tuning Strategies for Power System Stability
by: Das, Shuvangkar Chandra, et al.
Published: (2024)

Microgrid Operation Control with State-of-Charge- Dependent Storage Power Constraints
by: Anccas, E. D. Gomez, et al.
Published: (2025)

Adaptive Traffic Signal Control with Deep Reinforcement Learning An Exploratory Investigation
by: Muresan, Matthew, et al.
Published: (2019)

Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
by: Shi, Di, et al.
Published: (2024)

Decoupling Power Quality Issues in Grid-Microgrid Network Using Microgrid Building Blocks
by: Acharya, Samrat, et al.
Published: (2024)

Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids
by: da Silva, Caio Fabio Oliveira, et al.
Published: (2024)

Decentralized Voltage Control of AC Microgrids with Constant Power Loads using Control Barrier Functions
by: Michos, Grigoris, et al.
Published: (2025)

Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances
by: Lu, Ke, et al.
Published: (2024)

Resilient Hierarchical Power Control for Hybrid GFL/GFM Microgrids Under Mixed Cyber-Attacks and Physical Constraints
by: Ding, Lifu, et al.
Published: (2026)

Economic Dispatch and Power Flow Analysis for Microgrids
by: Putri, Saskia A., et al.
Published: (2024)

Observer-Based Discontinuous Communication in the Secondary Control of AC Microgrids
by: Najafi, Shahabeddin, et al.
Published: (2024)

LSTM-Based Net Load Forecasting for Wind and Solar Power-Equipped Microgrids
by: Silva-Rodriguez, Jesus, et al.
Published: (2024)

Model-Free Load Frequency Control of Nonlinear Power Systems Based on Deep Reinforcement Learning
by: Chen, Xiaodi, et al.
Published: (2024)

A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management
by: Liu, M. Vivienne, et al.
Published: (2023)

Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning
by: Jafari, Mohammad, et al.
Published: (2019)

Deep Reinforcement Learning-based Quadcopter Controller: A Practical Approach and Experiments
by: Do, Truong-Dong, et al.
Published: (2024)

A Novel Inverter Control Strategy with Power Decoupling for Microgrid Operations in Grid-Connected and Islanded Modes
by: Tong, Yan, et al.
Published: (2025)

Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning
by: Wang, Shuyi, et al.
Published: (2024)

Multiagent Reinforcement Learning in Enhancing Resilience of Microgrids under Extreme Weather Events
by: Wu, Yin, et al.
Published: (2026)

Hardware-Based Microgrid Coupled to Real-Time Simulated Power Grids for Evaluating New Control Strategies in Future Energy Systems
by: Kyesswa, Michael, et al.
Published: (2024)

Deep Reinforcement Learning-Based Control Strategy with Direct Gate Control for Buck Converters
by: Katayama, Noboru
Published: (2025)

Adaptive Online Optimization for Microgrids with Renewable Energy Sources
by: van Weerelt, Wouter J. A., et al.
Published: (2025)

Deep Reinforcement Learning-Aided Frequency Control of LCC-S Resonant Converters for Wireless Power Transfer Systems
by: Safari, Reza, et al.
Published: (2025)

BOOST: Microgrid Sizing using Ordinal Optimization
by: Chehade, Mohamad, et al.
Published: (2025)

Proximal Policy Optimization-Based Reinforcement Learning Approach for DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional Control Techniques
by: Saha, Utsab, et al.
Published: (2023)

Small-Signal Dynamics of Lossy Inverter-Based Microgrids for Generalized Droop Controls
by: Maruf, Abdullah Al, et al.
Published: (2024)