Cost-Optimal Energy Scheduling of a Smart Home Under Uncertainty

We present a novel energy scheduling approach under uncertain data for smart homes taking into account the presence of controllable electrical loads, renewable energy sources, dispatchable energy generators, and energy storage systems. The problem is stated as a fuzzy linear programming and is aimed at minimizing energy costs. The proposed approach allows managing the use of electrical devices, plan the energy production and supplying, and program the storage charging and discharging profiles under uncertain data. The method is validated through a literature case study showing its effectiveness in exploiting the potential of local energy generation and storage and in reducing the energy consumption costs, while limiting the peak average ratio of the energy profiles and complying with the user's energy needs.