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

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.

Special Issue on Applications of Artificial Neural Networks for Energy Systems

“…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. In reference [13], 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.…”

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

“…In the energy sector, Artificial Neural Networks (ANNs) are widely applied for resolving many problems and for optimization purposes. In [29] ANN-based reinforcement learning algorithm is used to manage the optimal energy routing path in energy internet (EI) concept. In order to effectively analyze the quality of power signals, research [30] proposes a method of signal feature capture and fault identification based on the ANN combined with discrete wavelet transform and Parseval's theorem.…”

Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm

“…In other words, all mobile nodes/devices in a MANET network collaborate with each other and act as a router for one another, thereby providing a robust and effective operation throughout the whole network. Mobile nodes are incorporated with routing functionality, and each node can join and/or leave the network at will depending on the capacity of its energy resources and nature of its network topology [26]. MANET is characterized by event such as constant change in network topology, which often leads to frequent link failure, degraded transmission quality, and reduced network throughput [27].…”