A Predictive Control-Based Power Management Strategy for Hybrid Energy Storage Systems

Abstract

The use of hybrid energy storage systems (HESS) to combine batteries with supercapacitors has emerged as one of the enablers in the process of stabilising renewable energy-based power systems under highly dynamic operating conditions. Nevertheless, effective power control is still of importance to the lack of correspondence between high energy densities of batteries and high power densities of supercapacitors that may cause rapid battery degradation and decreased system efficiency. This paper introduces a predictive power management approach based on the predictive control with Model Predictive Control (MPC) to optimise the energy allocation among HESS. The battery stress reduction and state of charge (SOC) control is built into the suggested framework through a constrained optimization formulation, allowing real time decision making under changing load conditions. The battery-supercapacitor system is modelled in detail and the MPC controller is created to allow the minimization of power variations and fluctuating stresses on the battery and also the efficient usage of the supercapacitor. The advantages of the suggested approach are tested in terms of MATLAB/Simulink simulations under various conditions of operation, both in case of load variations and intermittency of renewable power. Findings show that there is an increase in the stability of the SOC, a decrease in high-frequency power burden on the battery and an increase in the overall system efficiency than traditional rule-based control strategies. The suggested design provides a solutions that is very strong and expandable to manage advanced energy in hybrid in-storage integrated smart grid applications.