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

Department of Electrical Engineering, College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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Appl. Sci. 2019, 9(3), 520; https://doi.org/10.3390/app9030520
Received: 15 December 2018 / Revised: 25 January 2019 / Accepted: 29 January 2019 / Published: 3 February 2019
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Abstract

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. View Full-Text
Keywords: energy internet; energy routing center; reinforcement learning; artificial neural network; optimal energy routing design energy internet; energy routing center; reinforcement learning; artificial neural network; optimal energy routing design
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, D.-L.; Sun, Q.-Y.; Li, Y.-Y.; Liu, X.-R. Optimal Energy Routing Design in Energy Internet with Multiple Energy Routing Centers Using Artificial Neural Network-Based Reinforcement Learning Method. Appl. Sci. 2019, 9, 520.

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