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In this paper, a real time implementable load coordination strategy isdeveloped for the optimization of electric demands in a smart home. Thestrategy minimizes the electricity cost to the home owner, while limiting thedisruptions associated with the deferring of flexible power loads. Amulti-objective nonlinear mixed integer programming is formulated as asequential model predictive control, which is then solved using geneticalgorithm. The load shifting benefits obtained by deploying an advancedcoordination strategy are compared against a baseline controller for varioushome characteristics, such as location, size and equipment. The simulationstudy shows that the deployment of the smart home energy management strategyachieves approximately 5% reduction in grid cost compared to a baselinestrategy. This is achieved by deferring approximately 50\% of the flexibleloads, which is possible due to the use of the stationary energy storage.

Development and Evaluation of an Online Home Energy Management Strategy for Load Coordination in Smart Homes with Renewable Energy Sources