Towards efficient and secured real-time pricing in the smart grid

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources, and can effectively guide the participants to achieve individual welfare maximization. Particularly, we develop a Lagrangian- based approach that transforms the global optimization conducted by the power company to distributed optimization problems. We show that these distributed problems are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks, i.e., injecting false data into demand-users and injecting false data into supply- users. Through a combination of theoretical analysis and performance evaluation, our data shows that the proposed real-time pricing scheme can effectively guide the participants to achieve welfare maximization. Our data also shows that data integrity attacks can effectively disrupt the results of real-time pricing decisions, posing welfare reduction on participants.