Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load

To optimally control the energy storage system of the battery exposed to the volatile daily cycling load and electricity tariffs, a novel modification of a conventional model predictive control is proposed. The uncertainty of daily cycling load prompts the need to design a new cost function which is able to quantify the associated uncertainty. By modelling a probabilistic dependence among flow, load, and electricity tariffs, the expected cost function is obtained and used in the constrained optimization. The proposed control strategy explicitly incorporates the cycling nature of customer load. Furthermore, for daily cycling load, a fixed-end time and a fixed-end output problem are addressed. It is demonstrated that the proposed control strategy is a convex optimization problem. While stochastic and robust model predictive controllers evaluate the cost concerning model constraints and parameter variations. Also, the expected cost across the flow variations is considered. The density function of load probability improves load prediction over a progressive prediction horizon, and a nonlinear battery model is utilized.