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

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