Implementation of EDGE Computing Platform in Feeder Terminal Unit for Smart Applications in Distribution Networks with Distributed Renewable Energies

Abstract

Under the plan of net-zero carbon emissions in 2050, the high penetration of distributed renewable energies in distribution networks will cause the operation of more complicated distribution networks. The development of edge computing platforms will help the operator to monitor and compute the system status timely and locally, and it can ensure the security operation of the system. In this paper, a novel EDGE computing platform that is implemented by a graphics processing unit in the existing feeder terminal unit (FTU) is proposed for smart applications in distribution networks with distributed renewable energies and loads. This platform makes timely forecasts of the feeder status for the next seven days in accordance with historical weather, sun, and loading data. The forecast solver uses the machine learning long short-term memory (LSTM) method. Thereafter, the power calculation analyzers transform feeder topology into the circuit model for transient-state, steady-state, and symmetrical component analyses. An important-factor explainer parses the LSTM model into the concise value of each historical datum. All information transports to remote devices via the internet for the real-time monitor feature. The software stack of the EDGE platform consists of the database archive file system, time-series forecast solver, power flow analyzers, important-factor explainer, and message queuing telemetry transport (MQTT) protocol communication. All open-source software packages, such as SQLite, LSTM, ngspyce, Shapley Additive Explanations, and Paho-MQTT, form the aforementioned function. The developed EDGE forecast and power flow computing platform are helpful for achieving FTU becoming an Internet of Things component for smart operation in active distribution networks.