A privacy-friendly game-theoretic distributed scheduling system for domestic appliances

Game-theoretic Demand Side Management (DSM) systems have been investigated as a decentralized approach for the collaborative scheduling of the usage of domestic electrical appliances within a set of households. Such systems allow for the shifting of the starting time of deferrable devices according to the current energy price or power grid condition, in order to reduce the individual monthly bill or to adjust the power load experienced by the grid while meeting the users’ preferences about the time of use. The drawback of DSM distributed protocols is that they require each user to communicate his/her own energy consumption patterns to the other users, which may leak sensitive information regarding private habits. This paper proposes a distributed Privacy-Friendly DSM system which preserves users’ privacy by integrating data aggregation and perturbation techniques: users decide their schedule according to aggregated consumption measurements perturbed by means of Additive White Gaussian Noise (AWGN). We evaluate the noise power and the size of the set of users required to achieve a given privacy level, quantified by means of the Kullback-Leibler divergence. The performance of our proposed DSM system are compared to the ones obtained by a benchmark system which does not support privacy preservation in terms of social cost, peak demand and convergence time. Results show that privacy can be preserved at the cost of increasing the peak demand and the number of game iterations, whereas social cost is only marginally incremented.