A multi-timescale scheduling approach for stochastic reliability in smart grids with wind generation and opportunistic demand

In this study, we focus on the stochastic reliability of smart grids with two classes of energy users - traditional energy users and opportunistic energy users (e.g., smart appliances or electric vehicles), and investigate the procurement of energy supply from both conventional generation (base-load and fast-start) and wind generation via multi-timescale scheduling. Specifically, in day-ahead scheduling, with the distributional information of wind generation and demand, we characterize the optimal procurement of the energy supply from base-load generation and the day-ahead price; in real-time scheduling, with the realizations of wind generation and the demand of traditional energy users, we optimize real-time price to manage opportunistic demand so as to achieve system-wise reliability and efficiency. More specifically, we consider two different models for opportunistic energy users: non-persistent and persistent, and characterize the optimal scheduling and pricing decisions for both models by exploiting various computational and optimization tools. Numerical results demonstrate that the proposed scheduling and pricing schemes can effectively manage opportunistic demand and enhance system reliability, thus have the potential to improve the penetration of wind generation.