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Survey and Comparison of Optimization-Based Aggregation Methods for the Determination of the Flexibility Potentials at Vertical System Interconnections

Author

Listed:
  • Marcel Sarstedt

    (Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany)

  • Leonard Kluß

    (Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany)

  • Johannes Gerster

    (Department of Computing Science, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany)

  • Tobias Meldau

    (Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany)

  • Lutz Hofmann

    (Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany)

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.

Suggested Citation

  • Marcel Sarstedt & Leonard Kluß & Johannes Gerster & Tobias Meldau & Lutz Hofmann, 2021. "Survey and Comparison of Optimization-Based Aggregation Methods for the Determination of the Flexibility Potentials at Vertical System Interconnections," Energies, MDPI, vol. 14(3), pages 1-27, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:687-:d:489129
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    References listed on IDEAS

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    1. Stephen Frank & Steffen Rebennack, 2016. "An introduction to optimal power flow: Theory, formulation, and examples," IISE Transactions, Taylor & Francis Journals, vol. 48(12), pages 1172-1197, December.
    2. Parizad, Ali & Hatziadoniu, Konstadinos, 2020. "Security/stability-based Pareto optimal solution for distribution networks planning implementing NSGAII/FDMT," Energy, Elsevier, vol. 192(C).
    3. David Sebastian Stock & Francesco Sala & Alberto Berizzi & Lutz Hofmann, 2018. "Optimal Control of Wind Farms for Coordinated TSO-DSO Reactive Power Management," Energies, MDPI, vol. 11(1), pages 1-25, January.
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    Cited by:

    1. Đorđe Lazović & Željko Đurišić, 2023. "Advanced Flexibility Support through DSO-Coordinated Participation of DER Aggregators in the Balancing Market," Energies, MDPI, vol. 16(8), pages 1-26, April.
    2. Aguado, José A. & Paredes, Ángel, 2023. "Coordinated and decentralized trading of flexibility products in Inter-DSO Local Electricity Markets via ADMM," Applied Energy, Elsevier, vol. 337(C).
    3. Georgios Papazoglou & Pandelis Biskas, 2022. "Review of Methodologies for the Assessment of Feasible Operating Regions at the TSO–DSO Interface," Energies, MDPI, vol. 15(14), pages 1-24, July.
    4. Georgios Papazoglou & Pandelis Biskas, 2023. "Review and Comparison of Genetic Algorithm and Particle Swarm Optimization in the Optimal Power Flow Problem," Energies, MDPI, vol. 16(3), pages 1-25, January.

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