Hybrid robust-stochastic optimal scheduling for multi-objective home energy management with the consideration of uncertainties

Home energy management systems (HEMS) have transformed the traditional structure of electricity consumption on the customer side and facilitate real-time interaction between the customers and the grid. However, the HEMS scheduling is currently challenged by a variety of uncertainties, including photovoltaic (PV) output, electric vehicle (EV) charging/discharging behavior, and real-time pricing (RTP), which can seriously impact home equipment scheduling and critical objectives like total cost and users' comfort. Therefore, a hybrid robust-stochastic (HRS) optimization approach for multi-objective home energy management with the consideration of uncertainties and users' comfort has been proposed in this paper. Firstly, a detailed classification and modeling of the loads in the household are carried out to facilitate the exchange between the user-side resources and the grid in a benchmark based on RTP. Then the stochastic charging/discharging behavior of EV is modeled by stochastic optimization (SO) methods, and uncertainties in PV production and RTP are modeled by robust optimization (RO) methods, making full use of the flexibility of various uncertainty parameters. Meanwhile, the users' requirements for comfort are considered, and a multi-objective function for the economy and comfort of the HEMS is established. Finally, the effectiveness of the proposed method has been verified by case studies. The results show that the proposed HRS is effective to deal with different levels of the uncertainty parameters and ensures the users' comfort requirements in home energy system. Moreover, the users can make trade-offs between various levels of robust decisions according to their needs of economic and comfort level, thus obtaining a variety of electricity consumption decisions.