TY - CONF

T1 - Pointing out the Convolution Problem of Stochastic Aggregation Methods for the Determination of Flexibility Potentials at Vertical System Interconnections

AU - Gerster, Johannes

AU - Sarstedt, Marcel

AU - Veith, Eric

AU - Lehnhoff, Sebastian

AU - Hofmann, Lutz

N1 - Accepted by the The Eleventh International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies ENERGY 2021, 7 pages, 4 figures

PY - 2021/6/3

Y1 - 2021/6/3

N2 - The increase of generation capacity in the area of responsibility of the distribution system operator (DSO) requires strengthening of coordination between transmission system operator (TSO) and DSO in order to prevent conflicting or counteracting use of flexibility options. For this purpose, methods for the standardized description and identification of the aggregated flexibility potential of distribution grids (DGs) are developed. Approaches for identifying the feasible operation region (FOR) of DGs can be categorized into two main classes: Data-driven/stochastic approaches and optimization based approaches. While the latter 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. To underpin the need for improved sampling strategies for data-driven approaches, in this paper we point out and analyse the shortcomings of naive sampling strategies with focus on the problem of leptocurtic distribution of resulting interconnection power flows (IPFs). We refer to this problem as convolution problem, as it can be traced back to the fact that the probability density function (PDF) of the sum of two or more independent random variables is the convolution of their respective PDFs. To demonstrate the convolution problem, we construct a series of synthetic 0.4 kV feeders, which are characterized by an increasing number of nodes and apply a sampling strategy to them that draws set-values for the controllable distributed energy resources (DERs) from independent uniform distributions. By calculating the power flow for each sample in each feeder, we end up with a collapsing IPF point cloud clearly indicating the convolution problem.

AB - The increase of generation capacity in the area of responsibility of the distribution system operator (DSO) requires strengthening of coordination between transmission system operator (TSO) and DSO in order to prevent conflicting or counteracting use of flexibility options. For this purpose, methods for the standardized description and identification of the aggregated flexibility potential of distribution grids (DGs) are developed. Approaches for identifying the feasible operation region (FOR) of DGs can be categorized into two main classes: Data-driven/stochastic approaches and optimization based approaches. While the latter 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. To underpin the need for improved sampling strategies for data-driven approaches, in this paper we point out and analyse the shortcomings of naive sampling strategies with focus on the problem of leptocurtic distribution of resulting interconnection power flows (IPFs). We refer to this problem as convolution problem, as it can be traced back to the fact that the probability density function (PDF) of the sum of two or more independent random variables is the convolution of their respective PDFs. To demonstrate the convolution problem, we construct a series of synthetic 0.4 kV feeders, which are characterized by an increasing number of nodes and apply a sampling strategy to them that draws set-values for the controllable distributed energy resources (DERs) from independent uniform distributions. By calculating the power flow for each sample in each feeder, we end up with a collapsing IPF point cloud clearly indicating the convolution problem.

KW - eess.SY

KW - cs.SY

M3 - Paper

SP - 1

EP - 7

T2 - The Eleventh International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies - ENERGY 2021

Y2 - 30 May 2021 through 3 June 2021

ER -