By Topic

Grid Integration of Intermittent Wind Generation: A Markovian Approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

8 Author(s)
Peter B. Luh ; Dept. of Electr. & Comput. Eng., Univ. of Connecticut, Storrs, CT, USA ; Yaowen Yu ; Bingjie Zhang ; Eugene. Litvinov
more authors

Although the unique characteristics of intermittent wind generation have been acknowledged and drastic impacts of sudden wind drops have been experienced, no effective integration approach has been developed. In this paper, without considering transmission capacity constraints for simplicity, aggregated wind generation is modeled as a discrete Markov process with state transition matrices established based on historical data. Wind generation is then integrated into system demand with multiple net demand levels at each hour. To accommodate the uncertain net demand, a stochastic unit commitment problem is formulated based on states instead of scenarios. The objective is to minimize the total commitment cost of conventional generators and their total expected dispatch cost while satisfying all possible net demand levels. The advantage of this formulation is that the state at a time instant summarizes the information of all previous instants in a probabilistic sense for reduced complexity. With state transition probabilities given, state probabilities calculated before optimization, and the objective function and constraints formulated in a linear manner, the problem is effectively solved by using branch-and-cut. Numerical testing shows that the new Markovian approach is effective and robust through the examined cases, resembling the sudden wind drop in Texas in February 2008.

Published in:

IEEE Transactions on Smart Grid  (Volume:5 ,  Issue: 2 )