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Many optimization problems incorporate uncertainty affecting their parametersand thus their objective functions and constraints. As an example, inchance-constrained optimization the constraints need to be satisfied with acertain probability. To solve these problems, scenario optimization is a wellestablished methodology that ensures feasibility of the solution by enforcingit to satisfy a given number of samples of the constraints. The maintheoretical results in scenario optimization provide the methods to determinethe necessary number of samples, or to compute the risk based on the number ofso-called support constraints. In this paper, we propose a methodology toremove constraints after observing the number of support constraints and theconsequent risk. Additionally, we show the effectiveness of the approach withan illustrative example and an application to power distribution gridmanagement when solving the optimal power flow problem. In this problem,uncertainty in the loads converts the admissible voltage limits intochance-constraints.

Sieving out Unnecessary Constraints in Scenario Optimization with an Application to Power Systems