This paper explores electric privacy issues that may occur along with the residential appliance demand response (DR) energy management in smart meters. Three metrics are introduced to quantitatively measure the spatial and/or temporal similarity of metered power profiles. The online stochastic optimization adopts the scenario-based approach via Monte Carlo (MC) simulation for minimizing the sum of the expected electricity payment and the weighted difference among metered power profiles for the entire day, which are measured by the three similarity metrics, in order to balance the tradeoff between the electricity payment and the electric privacy protection. In addition, batteries are employed to disguise the actual appliance power profile along with the scheduling horizon and enhance the electric privacy protection. Numerical case studies illustrate the effectiveness of the proposed approach for protecting the electric privacy in residential appliance DR energy management.
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Chen et al. (2013) studied this question.
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