Within the context of current day-ahead electricity markets, the presentation examines the use of two-stage adaptive robust optimization as a relevant tool to handle renewable-based uncertainty in generation scheduling. Unlike alternative approaches to deal with uncertainty, neither accurate probabilistic information nor a discrete set of uncertainty realizations are required. Rather, uncertainty is modeled by decision variables within a deterministic uncertainty set. Hence, the size of the robust models does not depend on the dimension of the space of uncertainty realizations belonging to the uncertainty set, thereby providing a computationally efficient framework. In addition, an easy control of the degree of conservativeness can be implemented. The resulting robust counterparts are instances of mixed-integer trilevel programming. Practical modeling aspects allow using effective decomposition-based techniques that guarantee finite convergence to optimality. Results from several case studies illustrate the effectiveness of the two-stage robust setting.
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PosterSeminarApril19.pdf (200 KB)