Cooperative distributed energy scheduling for smart homes applying stochastic model predictive control

In this study, distributed energy resources scheduling problem of the set of smart homes (SHs) is investigated considering their cooperation with their neighbors and applying a stochastic model predictive control (MPC). Herein, every SH has a variety of sources and each SH is able to transact power with the local distribution company (DISCO) through the grid and with other connected SHs. The challenges of problem include modeling the technical and economic constraints of sources and dealing with the variability and uncertainties concerned with the power of photovoltaic (PV) panels that make the problem a mixed-integer nonlinear programming (MINLP), dynamic, and stochastic optimization problem. In order to handle the variability and uncertainties of problem, a stochastic MPC is applied. The numerical study demonstrates that cooperation of SHs in the energy scheduling problem has a high potential for minimizing operation cost of SHs.