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Model-Free Real-Time Autonomous Control for a Residential Multi-Energy System Using Deep Reinforcement Learning

IEEE transactions on smart grid, 2020-07, Vol.11 (4), p.3068-3082

2020

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IEEE_Xplore

Deep Reinforcement Learning for Strategic Bidding in Electricity Markets

IEEE transactions on smart grid, 2020-03, Vol.11 (2), p.1343-1355

2020

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IEEE_Xplore

Multi-period and Multi-spatial Equilibrium Analysis in Imperfect Electricity Markets: A Novel Multi-Agent Deep Reinforcement Learning Approach

IEEE access, 2019-01, Vol.7, p.1-1

2019

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Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning

Energies (Basel), 2021-02, Vol.14 (3), p.531

2021

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In vitro determination of osteo-adipogenic lineage choice of bone marrow stromal/stem cells (BMSCs)

MethodsX, 2024-06, Vol.12, p.102637-102637, Article 102637

2024

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Data-driven energy management of virtual power plants: A review

Advances in applied energy, 2024-07, Vol.14, p.100170, Article 100170

2024

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Strategic retail pricing and demand bidding of retailers in electricity market: A data-driven chance-constrained programming

Advances in applied energy, 2022-09, Vol.7, p.100100, Article 100100

2022

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Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images

Sheng wu yi xue gong cheng xue za zhi, 2020-08, Vol.37 (4), p.557

2020

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Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems

Applied energy, 2022-03, Vol.310, p.118575, Article 118575

2022

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Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach

Applied energy, 2021-06, Vol.292, p.116940, Article 116940

2021

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Reinforcement learning for electric vehicle applications in power systems:A critical review

Renewable & sustainable energy reviews, 2023-03, Vol.173, p.113052, Article 113052

2023

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A Deep Reinforcement Learning Method for Pricing Electric Vehicles With Discrete Charging Levels

IEEE transactions on industry applications, 2020-09, Vol.56 (5), p.5901-5912

2020

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IEEE_Xplore

Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks

Applied energy, 2022-10, Vol.324, p.119688, Article 119688

2022

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Safe reinforcement learning for real-time automatic control in a smart energy-hub

Applied energy, 2022-03, Vol.309, p.118403, Article 118403

2022

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Exploring the effects of local energy markets on electricity retailers and customers

Electric power systems research, 2020-12, Vol.189, p.106761, Article 106761

2020

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Towards Microgrid Resilience Enhancement Via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach

IEEE transactions on power systems, 2024-01, Vol.39 (1), p.1-17

2024

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IEEE_Xplore

Hybrid Multiagent Reinforcement Learning for Electric Vehicle Resilience Control Towards a Low-Carbon Transition

IEEE transactions on industrial informatics, 2022-11, Vol.18 (11), p.8258-8269

2022

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IEEE_Xplore

Federated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance trading

Applied energy, 2023-03, Vol.333, p.120526, Article 120526

2023

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Coordinated Electric Vehicle Active and Reactive Power Control for Active Distribution Networks

IEEE transactions on industrial informatics, 2023-02, Vol.19 (2), p.1611-1622

2023

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IEEE_Xplore

Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading

IEEE transactions on power systems, 2023-09, Vol.38 (5), p.1-13

2023

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IEEE_Xplore

Graph Learning-Based Voltage Regulation in Distribution Networks with Multi-Microgrids

IEEE transactions on power systems, 2024-01, Vol.39 (1), p.1-15

2024

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IEEE_Xplore

Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience

Applied energy, 2023-04, Vol.336, p.120826, Article 120826

2023

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Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach

Applied energy, 2022-05, Vol.313, p.118790, Article 118790

2022

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Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

Applied energy, 2023-04, Vol.335, p.120759, Article 120759

2023

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Multi-agent reinforcement learning for electric vehicle decarbonized routing and scheduling

Energy (Oxford), 2023-12, Vol.284, p.129335, Article 129335

2023

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Model-Free Real-Time Autonomous Control for a Residential Multi-Energy System Using Deep Reinforcement Learning

Deep Reinforcement Learning for Strategic Bidding in Electricity Markets

Multi-period and Multi-spatial Equilibrium Analysis in Imperfect Electricity Markets: A Novel Multi-Agent Deep Reinforcement Learning Approach

Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning

In vitro determination of osteo-adipogenic lineage choice of bone marrow stromal/stem cells (BMSCs)

Data-driven energy management of virtual power plants: A review

Strategic retail pricing and demand bidding of retailers in electricity market: A data-driven chance-constrained programming

Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images

Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems

Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach

Reinforcement learning for electric vehicle applications in power systems:A critical review

A Deep Reinforcement Learning Method for Pricing Electric Vehicles With Discrete Charging Levels

Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks

Safe reinforcement learning for real-time automatic control in a smart energy-hub

Exploring the effects of local energy markets on electricity retailers and customers

Towards Microgrid Resilience Enhancement Via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach

Hybrid Multiagent Reinforcement Learning for Electric Vehicle Resilience Control Towards a Low-Carbon Transition

Federated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance trading

Coordinated Electric Vehicle Active and Reactive Power Control for Active Distribution Networks

Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading

Graph Learning-Based Voltage Regulation in Distribution Networks with Multi-Microgrids

Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience

Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach

Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

Multi-agent reinforcement learning for electric vehicle decarbonized routing and scheduling

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