Energy management of a DC microgrid with hybrid energy storage system using PI and ANN based hybrid controller

Abstract

An energy management system incorporating a hybrid control scheme based on artificial neural networks (ANN)-based controller and a classical proportional–integral (PI) controller is proposed for a DC microgrid (DCMG) consisting of a fuel cell (FC) and a hybrid energy storage system (HESS) under variable load demand. The HESS incorporates a battery energy storage system (BESS) and a supercapacitor (SC) to cater high energy and high-power demands, respectively. The HESS with the proposed controller and energy management strategy (EMS) admits improved time response for sudden and slowly varying load demands, resulting in reduced battery stress with an improved battery life span. The microgrid configuration with a proposed hybrid controller is simulated on the Simulink® platform to establish its efficacy over a conventional controller. The proposed controller effectively minimises peak overshoot, settling time and deviation in DC bus voltage (DBV) , in comparison to the conventional one. Furthermore, simulation results are validated using a real-time OPAL-RT platform to ascertain effectiveness of the proposed strategy.

KEYWORDS:

• Artificial neural network
• DC microgrid
• Fuel cell
• Hybrid energy storage system
• Supercapacitor

Disclosure statement

No potential conflict of interest was reported by the author(s).