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The exceptional benefits of wind power as an environmentally responsiblerenewable energy resource have led to an increasing penetration of wind energyin today's power systems. This trend has started to reshape the paradigms ofpower system operations, as dealing with uncertainty caused by the highlyintermittent and uncertain wind power becomes a significant issue. Motivated bythis, we present a new framework using adaptive robust optimization for theeconomic dispatch of power systems with high level of wind penetration. Inparticular, we propose an adaptive robust optimization model for multi-periodeconomic dispatch, and introduce the concept of dynamic uncertainty sets andmethods to construct such sets to model temporal and spatial correlations ofuncertainty. We also develop a simulation platform which combines the proposedrobust economic dispatch model with statistical prediction tools in a rollinghorizon framework. We have conducted extensive computational experiments onthis platform using real wind data. The results are promising and demonstratethe benefits of our approach in terms of cost and reliability over existingrobust optimization models as well as recent look-ahead dispatch models.

Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind