Abstract
Increasing dynamics of electrical loads presents uncertainty and hence new challenges for power grid controls and optimization. In economic dispatch control (EDC) for minimizing generation cost, demand reporting by customers is a promising approach for managing the uncertainty, but it raises important privacy concerns. Adding random noise to aggregate queries of demand reports can provide differential privacy (DP) for the individual customers. But the noisy query results can adversely impact the EDC's optimality. In this paper, we analyze the privacy cost in demand reporting in terms of how DP-induced noise will increase the total generation cost. Our analysis shows that the noise amounts for different customers are intricately coupled with one another in determining the total cost. In view of the coupling, we apply the principle of Shapley value to attribute fair shares of the total cost to the power grid buses. For efficient sharing of the privacy cost, in a manner scalable to large power systems with many buses, we additionally propose heuristic algorithms to approximate the Shapley value. Trace-driven simulations based on a 5-bus power system model validate our analysis and illustrate the performance of the proposed cost sharing algorithms.