Energy Management of isolated DC microgrids with hybrid batteries-hydrogen storage system using Model Predictive Control and Wavelet Neural Networks based forecasts

2019 ◽  
Author(s):  
David Morin ◽  
Yoann Stevenin ◽  
Pascal Brault
Keyword(s):  
Neural Networks ◽  
Model Predictive Control ◽  
Hydrogen Storage ◽  
Energy Management ◽  
Predictive Control ◽  
Storage System ◽  
Wavelet Neural Networks ◽  
Dc Microgrids ◽  
Hydrogen Storage System

Energy management system for PV-battery microgrid based on model predictive control

2019 ◽  
Vol 15 (1) ◽  
pp. 20 ◽  
Author(s):  
M. Reyasudin Basir Khan ◽  
Jagadeesh Pasupuleti ◽  
Jabbar Al-Fattah ◽  
Mehrdad Tahmasebi
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Management System ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Renewable Resource ◽  
Energy Management System ◽  
Solar Photovoltaic ◽  
Multiple Sources

<p>There had been increase of the usage of renewable energy sources to supply electricity in remote areas. However, microgrid consists of renewable sources such as solar Photovoltaic (PV) poses challenges for a reliable energy supply due to its intermittent nature. This paper presents a microgrid model with Energy Management System based on Model Predictive Control (MPC). The microgrid comprises of PV, and battery storage system. The goal of the EMS is to deliver a reliable and optimal generation from multiple sources in the microgrid. Moreover, the MPC will also provide controls for the battery charging for a smooth PV output. The model simulated based on actual load profile and renewable resource such as solar radiation. Several disturbances such as variation of load, generation and PV shading has been simulated to measure the performance of the EMS. The results illustrate the system ability to exhibits robust performance in variety of conditions.</p>

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