Tackling Dynamics in Grid Integration of Wind Energy: Modeling, Multi-scale Scheduling and Pricing

Friday, September 16, 2011
ENS 637

The nation's power grid is perhaps the largest and most dynamic man-made network. Thesmart grid envisaged is that many renewable energy sources (such as wind and solar) will beadded to the mega-scale smart grid. Integrating a significant amount of wind energy into thebulk power grid, however, has put forth great challenges for generation planning and systemreliability, because wind generation is highly variable and non-dispatchable. This talk takes afresh perspective to explore some of these challenges in modeling, optimization, and control, andhighlight opportunities for network research.We first study the integration of wind energy by leveraging multi-timescale dispatch andscheduling. Specifically, we consider smart grids with two classes of energy users - traditionalenergy users and opportunistic energy users (e.g., smart appliances), and investigate pricingand dispatch at two timescales, via day-ahead scheduling and real-time scheduling. In day-ahead scheduling, with statistical information on wind generation and energy demands, wecharacterize the optimal procurement of the energy supply and the day-ahead retail price for thetraditional energy users; in real-time scheduling, with the realization of wind generation andthe load of traditional energy users, we optimize real-time prices to manage the opportunisticenergy users so as to achieve system-wide reliability. Next, we investigate the supply-sideuncertainty by revisiting models for wind energy forecasting. Based on extensive measurementdata obtained from industrial wind farms, we perform a spatiotemporal analysis of the aggregatewind generation output from the wind farm. Using tools from graph theory and time-seriesanalysis, we construct a systematic procedure to characterize the wind power output, and derivea finite state Markov chain model for forecasting the wind energy while taking into account thediurnal non-stationarity and the seasonality. Building on spatio-temporal characterizations, wethen develop an optimization framework for understanding the tradeoffs between energy reserveand demand response.


Arizona State University