Power utilities issue demand response (DR) during the hours of peak load in order to reduce the demand on the network and provide congestion relief to overloaded circuits. While traditional residential DR programs are mainly one-way in the form of remote on/off control of air conditioning (A/C) units, residential customers can adopt a more proactive role through utilizing the capabilities of smart meters and home energy management systems (HEMS). HEMS can monitor energy rates and DR incentives, and accordingly change the temperature setpoint of the A/C unit and/or shift appliance loads from peak to off-peak hours in order to maximize financial benefits. All this can be achieved in an automated human-out-of-the-loop fashion. From the HEMS’ standpoint, the task can be viewed as solving an optimization problem with the goal of reducing power consumption while maximizing financial gains. However, another equally important goal would be to ensure that the comfort level of residents, if present in the building, is not compromised. This is especially crucial during periods of extreme temperatures where maintaining an acceptable indoor temperature has a direct impact on the residents’ health, especially children and the elderly. What makes this multi-objective optimization problem more challenging is the uncertain nature of some model parameters, e.g., electricity rates, building occupancy levels, and demand. This paper presents a novel solution for energy management of a smart home using DR by considering the above factors. To ensure that the solution found is feasible against all possible uncertainties, a robust model is developed and solved for a given time horizon. As shown through simulation results, considering uncertainties are necessary, since they can change the solution in a nonnegligible way.
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