Machine learning based weighted scheduling scheme for active power control of hybrid microgrid

Photovoltaic (PV) integrated hybrid microgrid is inherently plagued by an intermittent power supply. Conventional solution is to maintain storage like batteries for grid restoration. However, collaboration development among multiple power sources is a formidable task. Grid contingencies like islanding and loading events further exacerbate the problem. In order to address these problems an efficient power management scheme is required. Machine learning based predictive tools are effective to forecast maximum available power of PV generator for any weather condition. In this study, historical climate data of Islamabad Pakistan is used to train Linear Support Vector Regression (LSVR), Matern 5/2 Gaussian Process Regression, and Rational Quadratic Gaussian Process Regression (RQGPR) based models. Root Mean Square Error (RMSE) is used as a key performance index for qualitative analysis of the trained models. RQGPR model returned lowest RMSE with slowest training time and LSVR returned vice versa. To maintain adequate battery storage level as well as grid power balance under varying climate conditions, a Power Scheduling Control (PSC) scheme aided by RQGPR controls power flow from PV. Grid frequency deviation from its nominal value of 50 Hz reflects the grid imbalance. Lastly, a set of outcomes are observed and discussed for a sample microgrid.

Automated summary

The study focuses on addressing intermittent power supply issues in PV integrated hybrid microgrids using machine learning. Historical climate data from Islamabad, Pakistan is utilized to train and control power management schemes, resulting in efficient battery storage level and grid power balance under varying climate conditions.