A receding predictive horizon approach to the periodic optimization of community battery energy storage systems

Community scale battery energy storage systems can improve the utilization of network assets and increase the uptake of intermittent renewable energy sources. This paper presents an efficient algorithm for optimizing the cyclic diurnal operation of battery storages in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble a 24 hour load profile. A diurnal charge profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing future forecasts in load and PV generation.