Grid Integration of Distributed Wind Generation: Hybrid Markovian and Interval Unit Commitment


Grid integration of wind generation is challenging in view of wind uncertainties and possible transmission congestions. While not considering transmission, a stochastic unit commitment drawback was solved in our previous work by modeling aggregated wind as a Markov chain instead of eventualities for reduced complexity. With congestion, wind generation at totally different locations can not be aggregated and is modeled as a Markov chain per wind node, and also the ensuing global states are a giant number of combos of nodal states. To avoid explicitly considering all such global states, interval optimization is synergistically integrated with the Markovian approach during this paper. The key's to divide the generation level of a standard unit into a Markovian part that depends on the local state, and an interval part that manages extreme nonlocal states. With appropriate transformations, the problem is converted to a linear type and is solved by using branch-and-cut. Numerical results demonstrate that the over conservativeness of pure interval optimization is much alleviated, and also the new approach is effective in terms of computational efficiency, simulation price, and answer feasibility. In addition, solar generation shares a similar uncertain nature as wind generation, and can so be modeled and solved equally.

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