In this paper, the model predictive control (MPC) strategy is utilized in smart homes to handle the optimal operation of controllable electrical loads of residential end-users. In the proposed model, active consumers reduce their daily electricity bills by installing photovoltaic (PV) panels and battery electrical energy storage (BEES) units. The optimal control strategy will be determined by the home energy management system (HEMS), benefiting from the meteorological and electricity market data stream during the operation horizon. In this case, the optimal scheduling of home appliances is managed using the shrinking horizon MPC (SH-MPC) and the main objective is to minimize the electricity cost. To this end, the HEMS is augmented by the SH-MPC, while maintaining the desired operation time slots of controllable loads for each day. The HEMS is cast as a standard mixed-integer linear programming (MILP) model that is incorporated into the SH-MPC framework. The functionality of the proposed method is investigated under different scenarios applied to a benchmark system while both time-of-use (TOU) and real-time pricing (RTP) mechanisms have been adopted in this study. The problem is solved using six case studies. In this regard, the impact of the TOU tariff was assessed in Scenarios 1–3 while Scenarios 4–6 evaluate the problem with the RTP mechanism. By adopting the TOU tariff and without any load shifting program, the cost is $\$ $ 1.2274 while by using the load shifting program without the PV and BEES system, the cost would reduce to $\$ $ 0.8709. Furthermore, by using the SH-MPC model, PV system and the BEES system, the cost would reduce to $\$ $ -0.282713 with the TOU tariff. This issue shows that the prosumer would be able to make a profit. By adopting the RTP tariff and without any load shifting program, the cost would be $\$ $ 1.22093 without any PV and BEES systems. By using the SH-MPC model, the cost would reduce to $\$ $ 1.08383. Besides, by adopting the SH-MPC, and the PV and BEES systems, the cost would reduce to $\$ $ 0.05251 with the RTP tariff, showing the significant role of load shifting programs, local power generation, and storage systems.