Abstract
Aiming at the economic benefits, load fluctuations, and carbon emissions of the microgrid (MG) group control, a method for controlling the MG group of power distribution Internet of Things (IoT) based on deep learning is proposed. Firstly, based on the cloud edge collaborative power distribution IoT architecture, combined with distributed generation, electric vehicles (EV), and load characteristics, the MG system model in the power distribution IoT is established. Then, a deep learning algorithm is used to train the features of the data model on the edge side. Finally, the group control strategy is adopted in the power distribution cloud platform to reasonably regulate the coordinated output of multiple energy sources, adjust the load state, and realize the economic operation of the power grid. Based on the MATLAB platform, a group model of MG is built and simulated. The results show the effectiveness of the proposed control method. Compared with other methods, the proposed control method has higher income and minimum carbon emission and realizes the economic and environmental protection system operation.
Highlights
With the continuous advancement of new energy power generation technology, communication technology, Internet technology, and other power industry technologies and new-generation information and communication technologies, the Internet of Things (IoT) technology and the distribution network are deeply integrated to form the Internet of distribution things and microgrids (MG)
The operating cost of a MG group in a cycle is an important factor to improve the economic benefits of users, including its initial investment cost, daily operation and maintenance costs, and load transfer compensation after users participate in the time-of-use electricity price mechanism [20, 21]
After the user load participates in the control strategy, the overall load curve changes, showing that the daytime demand power decreases, while the night time demand power increases, and the load decreases during the peak period, reducing the peak valley difference and smoothing the load curve
Summary
With the continuous advancement of new energy power generation technology, communication technology, Internet technology, and other power industry technologies and new-generation information and communication technologies, the IoT technology and the distribution network are deeply integrated to form the Internet of distribution things and microgrids (MG). The use of networked supply and intelligent management technology can play a series role between the user side and distributed energy [1, 2]. [6] proposes a “source-storage-load” coordination balance algorithm based on deep learning, which enables the system and user load to achieve Nash equilibrium without prior information, and optimizes the MG’s intelligent control capabilities. [9] studies the energy management framework of intelligent MG and analyzes the energy optimization among household load, EV, BESS, and distribution network. The uncertainty of EV and collaborative optimization of distributed energy in the MG group of power distribution IoT still need to be further studied. Under the framework of cloud edge collaborative in power distribution IoT, a MG regulation method based on deep learning is proposed. Based on the established MG system model, as well as the system optimization objectives and constraints, the edge side training learning of the deep learning algorithm is used to regulate and control the MG group
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