Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
Copyright policy )Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
A Microgrid (MG), like any other smart and interoperable power system, requires device-to-device (D2D) communication structures in order to function effectively. This communication system, however, is not immune to intentional or unintentional failures. This paper discusses the effects of communication link failures on MG control and management and proposes solutions based on enhancing message content to mitigate their detritus impact. In order to achieve this goal, generation and consumption forecasting using deep learning (DL) methods at the next time steps is used. The architecture of an energy management system (EMS) and an energy storage system (ESS) that are able to operate in coordination is introduced and evaluated by simulation tests, which show promising results and illustrate the efficacy of the proposed methods. It is important to mention that, in this paper, three dissimilar topics namely MG control/management, DL-based forecasting, and D2D communication architectures are employed and this combination is proven to be capable of achieving the aforesaid objective.
- Aalborg University Library (AUB) Denmark
- Aalborg University Denmark
- University of Southampton United Kingdom
- AALBORG UNIVERSITET Denmark
- KU Leuven Belgium
330, Chemical technology, Communication, deep learning, 600, TP1-1185, Article, 004, microgrid, Deep Learning, microgrid; machine-to-machine communication; deep learning; time series forecasting; artificial neural networks, machine-to-machine communication, time series forecasting, Computer Simulation, artificial neural networks, Forecasting
330, Chemical technology, Communication, deep learning, 600, TP1-1185, Article, 004, microgrid, Deep Learning, microgrid; machine-to-machine communication; deep learning; time series forecasting; artificial neural networks, machine-to-machine communication, time series forecasting, Computer Simulation, artificial neural networks, Forecasting
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