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Uncertainties in Optimization

Abstract : This research is motivated by the need to find out new methods to optimize a power system. In this field, traditional management and investment methods are limited in front of highly stochastic problems which occur when introducing renewable energies at a large scale. After introducing the various facets of power system optimization, we discuss the continuous black-box noisy optimization problem and then some noisy cases with extra features.Regarding the contribution to continuous black-box noisy optimization, we are interested into finding lower and upper bounds on the rate of convergence of various families of algorithms. We study the convergence of comparison-based algorithms, including Evolution Strategies, in front of different strength of noise (small, moderate and big). We also extend the convergence results in the case of value-based algorithms when dealing with small noise. Last, we propose a selection tool to choose, between several noisy optimization algorithms, the best one on a given problem.For the contribution to noisy cases with additional constraints, the delicate cases, we introduce concepts from reinforcement learning, decision theory and statistic fields. We aim to propose optimization methods closer from the reality (in terms of modelling) and more robust. We also look for less conservative power system reliability criteria.
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Submitted on : Saturday, December 24, 2016 - 3:22:44 PM
Last modification on : Thursday, July 8, 2021 - 3:49:50 AM
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  • HAL Id : tel-01422274, version 1


Marie-Liesse Cauwet. Uncertainties in Optimization. Optimization and Control [math.OC]. Université Paris Saclay (COmUE), 2016. English. ⟨NNT : 2016SACLS308⟩. ⟨tel-01422274⟩



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