This paper presents an energy management system for the microgrid present at Wroclaw University of Science and Technology. It has three components: a forecasting system, an optimizer and an optimized electrical vehicle charging station as a separate load for the system. The forecasting system is based on a deep learning model utilizing a Long Short-Term Memory (LSTM) - Autoencoder based architecture. The study provides a statistical analysis of its performance over several runs and addresses reliability and running time issues thereby building a case for its adoption. A MIDACO - MATPOWER combined optimization algorithm has been used as the optimization algorithm for energy management which intends to harness the speed of MATPOWER and the search capabilities of Mixed Integer Distributed Ant Colony Optimization (MIDACO) in finding an appropriate global minimum solution. The objective of the system is to minimize the import of power from the main grid resulting in improved self-sufficiency. Finally, an optimized electrical vehicle charging station model to maximize the renewable energy utilization within the facility is incorporated into the same.


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    Title :

    Microgrid Energy Management System with Embedded Deep Learning Forecaster and Combined Optimizer


    Contributors:

    Publication date :

    2020-01-01


    Remarks:

    Suresh , V , Janik , P , Guerrero , J M , Leonowicz , Z & Sikorski , T 2020 , ' Microgrid Energy Management System with Embedded Deep Learning Forecaster and Combined Optimizer ' , IEEE Access , vol. 8 , 9249013 , pp. 202225-202239 . https://doi.org/10.1109/ACCESS.2020.3036131



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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