Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization

Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid management system (M-EMS) the decision-making centre MG. An M-EMS composed four modules which are known as forecasting, scheduling, data acquisition, human-machine interface. However, forecasting scheduling considered major from among them. Therefore, this paper proposed an advanced for grid-connected residential based on ensemble strategy grey wolf optimization (GWO) strategy. In module M-EMS, to perform short-term PV power GWO has been in minimize operating cost A experiment conducted using Raspberry Pi 3 B+ via python programming language validate effectiveness real-time historical power, demand, weather adopted inputs. performance compared with strategy-1, particle swarm artificial neural network, back-propagation network. experimental results highlight outperforms other strategies achieved lowest average value normalized root mean square error day-ahead prediction demand chosen day. Similarly, analyzed three different scenarios. Finally, prove outstanding