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

Abstract

Grid integration of wind generation is challenging in view of wind uncertainties and possible transmission congestions. Without considering transmission, a stochastic unit commitment problem was solved in our previous work by modeling aggregated wind as a Markov chain instead of scenarios for reduced complexity. With congestion, wind generation at different locations cannot be aggregated and is modeled as a Markov chain per wind node, and the resulting global states are a large number of combinations of nodal states. To avoid explicitly considering all such global states, interval optimization is synergistically integrated with the Markovian approach in this paper. The key is to divide the generation level of a conventional unit into a Markovian component that depends on the local state, and an interval component that manages extreme nonlocal states. With appropriate transformations, the problem is converted to a linear form and is solved by using branch-and-cut. Numerical results demonstrate that the over conservativeness of pure interval optimization is much alleviated, and the new approach is effective in terms of computational efficiency, simulation cost, and solution feasibility. In addition, solar generation shares a similar uncertain nature as wind generation, and can thus be modeled and solved similarly.