Real-time electricity pricing for demand response using online convex optimization

Real-time electricity pricing strategies for demand response in smart grids are proposed. By accounting for individual consumers' responsiveness to prices, adjustments are made so as to induce desirable usage behavior and reduce peaks in load curves. An online convex optimization framework is adopted, which provides performance guarantees with minimal assumptions on the dynamics of load levels and consumer responsiveness. Two feedback structures are considered: a full information setup, where aggregate load levels as well as individual price elasticity parameters are directly available; and a partial information (bandit) case, where only the load levels are revealed. Fairness and sparsity constraints are also incorporated. Numerical tests verify the effectiveness of the proposed approach.