Home energy management system (HEMS) is an important problem that has been attracting significant attentions in the recent years. However, the conventional HEMS includes several shortcomings. The conventional HEMSs mainly utilize battery energy storage system (BESS) to deal with energy uncertainties. But they only ascertain optimal charging-discharging pattern for BESS and the power and capacity of BESS are not optimally determined. Furthermore, most of the HEMSs are modeled as a mixed integer linear programming (MILP) including linearization and relaxations. Additionally, considering all possible operating conditions for home has not been adequately addressed in the existing HEMSs. The possible operating conditions are (i) receiving energy from the main grid (i.e., purchasing energy), (ii) sending energy to the utility grid (i.e., selling energy), (iii) working on standalone mode as disconnected from the network (i.e., net-zero energy building). As a result, current paper deals with these existing challenges at the same time. This paper presents HEMS including small-scale wind turbine, BESS, load curtailment option, and fuel cell vehicle. The introduced HEMS not only determines optimal chargingdischarging pattern for BESS, but also specifies optimal capacity and optimal rated power of the BESS at the same time. The proposed HEMS is expressed as a mixed integer nonlinear programming (MINLP) and solved by cultural algorithm as an effective Meta-heuristic optimization algorithm. All three operating conditions are considered for home. Output power of wind unit is modeled by Gaussian probability distribution function (PDF) and Monte-Carlo simulation (MCS) is applied to deal with uncertainties. Results emphasize on the feasibility and usefulness of the introduced HEMS.
