LAPSE:2023.29366
Published Article

LAPSE:2023.29366
Survey and Comparison of Optimization-Based Aggregation Methods for the Determination of the Flexibility Potentials at Vertical System Interconnections
April 13, 2023
Abstract
The aggregation of operational active and reactive power flexibilities as the feasible operation region (FOR) is a main component of a hierarchical multi-voltage-level grid control as well as the cooperation of transmission and distribution system operators at vertical system interconnections. This article presents a new optimization-based aggregation approach, based on a modified particle swarm optimization (PSO) and compares it to non-linear and linear programming. The approach is to combine the advantages of stochastic and optimization-based methods to achieve an appropriate aggregation of flexibilities while obtaining additional meta information during the iterative solution process. The general principles for sampling an FOR are introduced in a survey of aggregation methods from the literature and the adaptation of the classic optimal power flow problem. The investigations are based on simulations of the Cigré medium voltage test system and are divided into three parts. The improvement of the classic PSO algorithm regarding the determination of the FOR are presented. The most suitable of four sampling strategies from the literature is identified and selected for the comparison of the optimization methods. The analysis of the results reveals a better performance of the modified PSO in sampling the FOR compared to the other optimization methods.
The aggregation of operational active and reactive power flexibilities as the feasible operation region (FOR) is a main component of a hierarchical multi-voltage-level grid control as well as the cooperation of transmission and distribution system operators at vertical system interconnections. This article presents a new optimization-based aggregation approach, based on a modified particle swarm optimization (PSO) and compares it to non-linear and linear programming. The approach is to combine the advantages of stochastic and optimization-based methods to achieve an appropriate aggregation of flexibilities while obtaining additional meta information during the iterative solution process. The general principles for sampling an FOR are introduced in a survey of aggregation methods from the literature and the adaptation of the classic optimal power flow problem. The investigations are based on simulations of the Cigré medium voltage test system and are divided into three parts. The improvement of the classic PSO algorithm regarding the determination of the FOR are presented. The most suitable of four sampling strategies from the literature is identified and selected for the comparison of the optimization methods. The analysis of the results reveals a better performance of the modified PSO in sampling the FOR compared to the other optimization methods.
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Keywords
active distribution grid, aggregation of flexibilities, DSO/DSO-cooperation, equivalent PQ-capability, feasible operation region, hierarchical grid control, optimization-based sampling, PQ-flexibility area, PQ-flexibility map, TSO/DSO-cooperation
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Suggested Citation
Sarstedt M, Kluß L, Gerster J, Meldau T, Hofmann L. Survey and Comparison of Optimization-Based Aggregation Methods for the Determination of the Flexibility Potentials at Vertical System Interconnections. (2023). LAPSE:2023.29366
Author Affiliations
Sarstedt M: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany [ORCID]
Kluß L: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany [ORCID]
Gerster J: Department of Computing Science, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
Meldau T: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany
Hofmann L: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany
Kluß L: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany [ORCID]
Gerster J: Department of Computing Science, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
Meldau T: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany
Hofmann L: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany
Journal Name
Energies
Volume
14
Issue
3
First Page
687
Year
2021
Publication Date
2021-01-29
ISSN
1996-1073
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Original Submission
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PII: en14030687, Publication Type: Journal Article
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