TY - CONF

T1 - Comparison of random sampling and heuristic optimization-based methods for determining the flexibility potential at vertical system interconnections

AU - Gerster, Johannes

AU - Sarstedt, Marcel

AU - Veith, Eric

AU - Hofmann, Lutz

AU - Lehnhoff, Sebastian

N1 - Funding Information: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 359921210.

PY - 2021

Y1 - 2021

N2 - In order to prevent conflicting or counteracting use of flexibility options, the coordination between distribution system operator and transmission system operator has to be strengthened. For this purpose, methods for the standardized description and identification of the aggregated flexibility potential of distribution grids are developed. Approaches for identifying the feasible operation region (FOR) of distribution grids can be categorized into two main classes: Random sampling/stochastic approaches and optimization-based approaches. While the former have the advantage of working in real-world scenarios where no full grid models exist, when relying on naive sampling strategies, they suffer from poor coverage of the edges of the FOR due toconvoluted distributions. In this paper, we tackle the problem from two different sides. First, we present a random sampling approach which mitigates the convolution problem by drawing sample values from a multivariate Dirichlet distribution. Second, we come up with a hybrid approach which solves the underlying optimal power flow problems of the optimization-based approachby means of a stochastic evolutionary optimization algorithm codenamed REvol. By means of synthetic feeders, we compare the two proposed FOR identification methods with regard to how well the FOR is covered and number of power flow calculations required.

AB - In order to prevent conflicting or counteracting use of flexibility options, the coordination between distribution system operator and transmission system operator has to be strengthened. For this purpose, methods for the standardized description and identification of the aggregated flexibility potential of distribution grids are developed. Approaches for identifying the feasible operation region (FOR) of distribution grids can be categorized into two main classes: Random sampling/stochastic approaches and optimization-based approaches. While the former have the advantage of working in real-world scenarios where no full grid models exist, when relying on naive sampling strategies, they suffer from poor coverage of the edges of the FOR due toconvoluted distributions. In this paper, we tackle the problem from two different sides. First, we present a random sampling approach which mitigates the convolution problem by drawing sample values from a multivariate Dirichlet distribution. Second, we come up with a hybrid approach which solves the underlying optimal power flow problems of the optimization-based approachby means of a stochastic evolutionary optimization algorithm codenamed REvol. By means of synthetic feeders, we compare the two proposed FOR identification methods with regard to how well the FOR is covered and number of power flow calculations required.

KW - convolution of probability distributions

KW - Dirichlet distribution

KW - evolutionary algorithms

KW - feasible operation region

KW - random sampling

KW - TSO/DSO-coordination

UR - http://www.scopus.com/inward/record.url?scp=85123647976&partnerID=8YFLogxK

U2 - 10.1109/ISGTEurope52324.2021.9640108

DO - 10.1109/ISGTEurope52324.2021.9640108

M3 - Paper

SP - 1

EP - 9

T2 - 11th IEEE PES Innovative Smart Grid Technologies Europe 2021

Y2 - 18 October 2021 through 21 November 2021

ER -