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In order to prevent conflicting or counteracting use of flexibility options,the coordination between distribution system operator and transmission systemoperator has to be strengthened. For this purpose, methods for the standardizeddescription and identification of the aggregated flexibility potential ofdistribution grids are developed. Approaches for identifying the feasibleoperation region (FOR) of distribution grids can be categorized into two mainclasses: Random sampling/stochastic approaches and optimization-basedapproaches. While the latter have the advantage of working in real-worldscenarios where no full grid models exist, when relying on naive samplingstrategies, they suffer from poor coverage of the edges of the FOR due toconvoluted distributions. In this paper, we tackle the problem from twodifferent angles. First, we present a random sampling approach which mitigatesthe convolution problem by drawing sample values from a multivariate Dirichletdistribution. Second, we come up with a hybrid approach which solves theunderlying optimal power flow problems of the optimization-based approach bymeans of a stochastic evolutionary optimization algorithm codenamed REvol. Bymeans of synthetic feeders, we compare the two proposed FOR identificationmethods with regard to how well the FOR is covered and number of power flowcalculations required.

Comparison of Random Sampling and Heuristic Optimization-Based Methods for Determining the Flexibility Potential at Vertical System Interconnections