Optimal Energy Management of a Residential Microgrid Considering the Range Anxiety Factor, Uncertainties, and Time-of-Use Tariff Schemes

Smart residential microgrids are one of the reliable strategies to reduce environmental issues and increase the efficiency of energy production. But the inherent intermittency of renewable energy sources (RESs) and the uncertainty of consumer behavior are the main challenges in developing these systems. For this reason, optimal energy management models are presented. In addition, different electricity pricing scenarios can also affect the optimization problem solution. This paper proposes an optimization model for energy management in a residential microgrid considering consumer behavior, RES unit and load uncertainties, and the impact of energy pricing schemes. This system consists of a solar photovoltaic (PV) system, a plug-in electric vehicle (PEV), and household loads under grid-connected operation mode. The proposed optimization problem is solved under three time-of-use (ToU) tariff scenarios, i.e., static pricing, real-time pricing, and variable peak pricing. The range anxiety factor is added to the optimization problem to model the PEV owner's behavior. Also, the optimization model is implemented on a day-ahead scheduling horizon, and optimal energy planning is obtained for all components. The results show that the variable peak pricing scheme has the lowest energy cost in the studied microgrid. Furthermore, the variable peak pricing scheme results in the highest daily utilization percentage of PEV compared to the other ToU tariff schemes.