Details
| Original language | English |
|---|---|
| Pages | 1-9 |
| Number of pages | 9 |
| Publication status | Published - 2021 |
| Event | 11th IEEE PES Innovative Smart Grid Technologies Europe 2021 - Espoo, Finland Duration: 18 Oct 2021 → 21 Nov 2021 https://ieee-isgt-europe.org/ |
Conference
| Conference | 11th IEEE PES Innovative Smart Grid Technologies Europe 2021 |
|---|---|
| Country/Territory | Finland |
| City | Espoo |
| Period | 18 Oct 2021 → 21 Nov 2021 |
| Internet address |
Abstract
convoluted 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 approach
by 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.
Keywords
- convolution of probability distributions, Dirichlet distribution, evolutionary algorithms, feasible operation region, random sampling, TSO/DSO-coordination
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
- Computer Science(all)
- Artificial Intelligence
- Energy(all)
- Energy Engineering and Power Technology
- Engineering(all)
- Electrical and Electronic Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Energy(all)
- Renewable Energy, Sustainability and the Environment
Sustainable Development Goals
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2021. 1-9 Paper presented at 11th IEEE PES Innovative Smart Grid Technologies Europe 2021, Espoo, Finland.
Research output: Contribution to conference › Paper › Research › peer review
}
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 -