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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.

A Distributed Demand Side Energy Management Algorithm for Smart Grid