Real-Time Pricing for Demand Response Based on Stochastic Approximation

In this paper, we propose a new pricing algorithm to minimize the peak-to-average ratio (PAR) in aggregate load demand. The key challenge that we seek to address is the energy provider´s uncertainty about the impact of prices on users´ load profiles, in particular when users are equipped with automated energy consumption scheduling (ECS) devices. We use an iterative stochastic approximation approach to design two real-time pricing algorithms based on finite-difference and simultaneous perturbation methods, respectively. We also propose the use of a system simulator unit (SSU) that employs approximate dynamic programming to simulate the operation of the ECS devices and users´ price-responsiveness. Simulation results show that our proposed real-time pricing algorithms reduce the PAR in aggregate load and help the users to reduce their energy expenses.