In this paper, an automated dynamic energy management framework for a generic residential eco-system is presented. The proposed distributed, non-disruptive management framework is based on highly-resolved personal energy consumption models developed using a novel bottom-up approach that quantifies consumer energy use behavior. The incorporation of stochastic consumers behavior provides more accurate estimation of the actual amount of available controllable resources, hence enabling better interactions with the grid. The energy management problem is solved by use of a dynamic programming algorithm that considers household members’ behavior, as predicted by the highly-resolved stochastic personal energy consumption model, and finds the optimal schedule for all the controllable appliances and plug-in electric vehicles charging. The algorithm is flexible enough to accommodate diverse costs functions, and simulate different scenarios.

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