  In this paper, a real time implementable load coordination strategy is
developed for the optimization of electric demands in a smart home. The
strategy minimizes the electricity cost to the home owner, while limiting the
disruptions associated with the deferring of flexible power loads. A
multi-objective nonlinear mixed integer programming is formulated as a
sequential model predictive control, which is then solved using genetic
algorithm. The load shifting benefits obtained by deploying an advanced
coordination strategy are compared against a baseline controller for various
home characteristics, such as location, size and equipment. The simulation
study shows that the deployment of the smart home energy management strategy
achieves approximately 5% reduction in grid cost compared to a baseline
strategy. This is achieved by deferring approximately 50\% of the flexible
loads, which is possible due to the use of the stationary energy storage.
