Joint scheduling of large-scale appliances and batteries via distributed mixed optimization

Summary form only given. This paper investigates joint scheduling problem of large-scale smart appliances and batteries (e.g., in a smart building), to minimize electricity payment, user's dissatisfaction and battery loss under kinds of constraints. Due to the binary nature of charge and discharge states of battery, this problem is formulated as a constrained mixed-integer nonlinear program. In order to solve it efficiently, a distributed mixed optimization approach is proposed. First, Lagrangian relaxation is applied to decompose the original problem into two sets of subproblems, each of which corresponds to scheduling on appliance/battery. Then, the battery scheduling subproblem is formulated as a mixed-integer linear program and tackled by Benders decomposition. The main advantages of the proposed approach are the distributed implementation and low computational complexity, as shown by simulations.