This paper proposes a model predictive control (MPC) framework-based distributed demand side energy management method (denoted as DMPC) for users and utilities in a smart grid. The users are equipped with renewable energy resources (RESs), energy storage system (ESSs) and different types of smart loads. With the proposed method, each user finds an optimal operation routine in response to the varying electricity prices according to his/her own preference individually, for example, the power reduction of flexible loads, the start time of shift-able loads, the operation power of schedulable loads, and the charge/discharge routine of the ESSs. Moreover, in the method a penalty term is used to avoid large fluctuation of the user's operation routines in two consecutive iteration steps. In addition, unlike traditional energy management methods which neglect the forecast errors, the proposed DMPC method can adapt the operation routine to newly updated data. The DMPC is compared with a frequently used method, namely, a day-ahead programming-based method (denoted as DDA). Simulation results demonstrate the efficiency and flexibility of the DMPC over the DDA method.To date, several effective DSM algorithms have been proposed [6,7], which range from improving energy efficiency by using better materials to coordinating operation strategies of different appliances, distributed energy resources (DERs) such as renewable energy resources (RESs) and energy storage systems (ESSs).However, existing methods, as will be reviewed in Section 2, are either not efficient or have not considered all related factors, e.g., the application of DERs, the forecast uncertainties. This study therefore proposes a model-predicative control (MPC)-based distributed DSM method (DMPC) for smart grid, aiming to handle the mentioned issue, as well as providing a more realistic energy management model.The proposed algorithm integrates newly updated forecast of RESs and loads to minimize the disturbance. Its feedback mechanism is well-suited to deal with forecast uncertainties [8,9]. Specifically, in the DMPC each user is equipped with an energy management system (EMS) to control his/her own dispatchable units (such as the ESS, power flexible loads, shift-able loads and schedulable loads), to communicate with utility companies, and to coordinate with other users so as to minimize his/her own cost and maximize the social welfare. Moreover, in the DMPC, the user needs not to broadcast his/her own energy schedule to other users like many existing studies, e.g., [10,11]. The user only needs to send his/her own load demand routine to the utility company. This can not only protect user privacy, but also reduce the communication burden. In addition, in the DMPC, a penalty term is adopted in the cost function to confine the power changes in two consecutives iterations. Furthermore, the discomfort penalty caused by curtailing the power flexible loads, delaying the shift-able loads and changing the operation schedule of schedulable loads is also considered. The user ...