Nonlinear model predictive control of a microgrid with a variable efficiency battery storage system

This paper presents a microgrid energy flow optimization algorithm with variable battery storage efficiency in order to achieve energy savings and expand the lifespan of the components. The converter efficiency curve is deduced from converter’s datasheets and approximated with mathematical functions. The power loss on the battery internal resistance is also included in order to achieve a more accurate model of the complete storage system. The obtained nonlinear model is used in model predictive control formulation and solved by using a sequential linear program (SLP) algorithm. The SLP algorithm iteratively linearizes the model around the current solution and uses corresponding efficiencies over the prediction horizon. Simulations in MATLAB are performed for a 7-day period and compared with a conventional, constant-efficiency battery system model. The results show an improved performance regarding the charging and discharging battery power and the overall savings of 7% in comparison with the conventional model used in model predictive control.