Deep reinforcement learning assisted edge-terminal collaborative offloading algorithm of blockchain computing tasks for energy Internet

The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of great significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method.

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Optimal Energy Routing Design in Energy Internet with Multiple Energy Routing Centers Using Artificial Neural Network-Based Reinforcement Learning Method

Applied Sciences ◽

10.3390/app9030520 ◽

2019 ◽

Vol 9 (3) ◽

pp. 520 ◽

Cited By ~ 8

Author(s):

Dan-Lu Wang ◽

Qiu-Ye Sun ◽

Yu-Yang Li ◽

Xin-Rui Liu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Reinforcement Learning ◽

Energy Structure ◽

Energy Internet ◽

Optimal Energy ◽

Information Layer ◽

Routing Design ◽

Artificial Neural ◽

Energy Routing

In order to cope with the energy crisis, the concept of an energy internet (EI) has been proposed as a novel energy structure with high efficiency which allows full play to the advantages of multi-energy coupling. In order to adapt to the multi-energy coupled energy structure and achieve flexible conversion and interaction of multi-energy, the concept of energy routing centers (ERCs) is proposed. A two-layered structure of an ERC is established. Multi-energy conversion devices and connection ports with monitoring functions are integrated in the physical layer which allows multi-energy flow with high flexibility. As for the EI with several ERCs connected to each other, energy flows among them are managed by an energy routing controller located in the information layer. In order to improve the efficiency and reduce the operating cost and environmental cost of the proposed EI, an optimal multi-energy management-based energy routing design problem is researched. Specifically, the voltages of the ERC ports are managed to regulate the power flow on the connection lines and are restricted on account of security operations. An artificial neural network (ANN)-based reinforcement learning algorithm was proposed to manage the optimal energy routing path. Simulations were done to verify the effectiveness of the proposed method.

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Multi-objective energy management for we-energy in Energy Internet using reinforcement learning

2017 IEEE Symposium Series on Computational Intelligence (SSCI) ◽

10.1109/ssci.2017.8285243 ◽

2017 ◽

Cited By ~ 1

Author(s):

Qiuye Sun ◽

Danlu Wang ◽

Dazhong Ma ◽

Bonan Huang

Keyword(s):

Reinforcement Learning ◽

Energy Management ◽

Energy Internet ◽

Multi Objective

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Supplemental Material for Reconciling Reinforcement Learning Models With Behavioral Extinction and Renewal: Implications for Addiction, Relapse, and Problem Gambling

Psychological Review ◽

10.1037/0033-295x.114.3.784.supp ◽

2007 ◽

Cited By ~ 1

Keyword(s):

Reinforcement Learning ◽

Problem Gambling ◽

Learning Models ◽

Behavioral Extinction ◽

Reinforcement Learning Models

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Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽

10.1037/dec0000040 ◽

2016 ◽

Vol 3 (2) ◽

pp. 115-131 ◽

Cited By ~ 14

Author(s):

Helen Steingroever ◽

Ruud Wetzels ◽

Eric-Jan Wagenmakers

Keyword(s):

Reinforcement Learning ◽

Iowa Gambling Task ◽

Bayes Factors ◽

Gambling Task ◽

Learning Models ◽

Reinforcement Learning Models

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Deep reinforcement learning assisted edge-terminal collaborative offloading algorithm of blockchain computing tasks for energy Internet

Indirect Multi-energy Transactions of Energy Internet with Deep Reinforcement Learning Approach

A novel QoS-enabled load scheduling algorithm based on reinforcement learning in software-defined energy internet

Optimal Energy Management of Energy Internet: A Distributed Actor-Critic Reinforcement Learning Method

Optimal Energy Operation Strategy for We-Energy of Energy Internet Based on Hybrid Reinforcement Learning With Human-in-the-Loop

Optimal energy management strategies for energy Internet via deep reinforcement learning approach

Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

Optimal Energy Routing Design in Energy Internet with Multiple Energy Routing Centers Using Artificial Neural Network-Based Reinforcement Learning Method

Multi-objective energy management for we-energy in Energy Internet using reinforcement learning

Supplemental Material for Reconciling Reinforcement Learning Models With Behavioral Extinction and Renewal: Implications for Addiction, Relapse, and Problem Gambling

Bayes factors for reinforcement-learning models of the Iowa gambling task.

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