Data-Driven Risk-Averse Stochastic Program And Renewable Energy Integration

With increasing penetration of renewable energy into the power grid and its intermittent nature, it is crucial and challenging for system operators to provide reliable and cost effective daily electricity generation scheduling. In this dissertation, we present our recently developed innovative modeling and solution approaches to address this challenging problem. We start with developing several optimization-under-uncertainty models, including both stochastic and robust optimization ones, to solve reliability unit commitment problems for Independent System Operators (ISOs) so as to ensure power system cost efficiency while maintaining a high utilization of renewable energy. Then, we extend our research to study data-driven risk-averse two-stage stochastic program, for which the distribution of the random variable is within a given confidence set. By introducing a new class of probability metrics, we construct the confidence set based on historical data, and provide a framework to solve the problem for both discrete and continuous distribution cases. Our approach is guaranteed to obtain an optimal solution and in addition, we prove that our risk-averse stochastic program converges to the risk-neutral case as the size of historical data increases to infinity. Moreover, we show the “value of data” by analyzing the convergence rate of our solution approach. Finally, we illustrate examples of using this framework and discuss its application on renewable energy integration.


Dissertation, University of Florida, 08/2014



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