Minimizing the peak power consumption of electrical appliances under delay requirements is shown to be NP-hard. To address this, we propose a “family plan” approach that partitions users into groups and schedules users’ appliances to minimize the peak power consumption of each group. Our scheme leverages the social network topology and statistical energy usage patterns of users.

To partition users into groups with the potential of reducing peak power consumption, our distributed clustering scheme seeks such a partition of users into groups that the total power consumption in each group of users achieves minimum variance. Then, given a set of jobs of users’ appliances to be scheduled in the next scheduling period, we use a distributed scheduling algorithm to minimize the peak power consumption of each group of users. Our simulation results demonstrate that our scheme achieves a significant reduction in user payments, peak power consumption, and fuel cost.

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