IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4319-d1155413.html
   My bibliography  Save this article

Integration of Intelligent Neighbourhood Grids to the German Distribution Grid: A Perspective

Author

Listed:
  • Rebeca Ramirez Acosta

    (OFFIS—Institute for Informatics, Escherweg 2, 26121 Oldenburg, Germany
    Department of Computer Science, Carl von Ossietzky Universität Oldenburg, 26121 Oldenburg, Germany)

  • Chathura Wanigasekara

    (Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany
    German Aerospace Center, Institute for the Protection of Maritime Infrastructures, 27572 Bremerhaven, Germany)

  • Emilie Frost

    (OFFIS—Institute for Informatics, Escherweg 2, 26121 Oldenburg, Germany)

  • Tobias Brandt

    (OFFIS—Institute for Informatics, Escherweg 2, 26121 Oldenburg, Germany)

  • Sebastian Lehnhoff

    (OFFIS—Institute for Informatics, Escherweg 2, 26121 Oldenburg, Germany
    Department of Computer Science, Carl von Ossietzky Universität Oldenburg, 26121 Oldenburg, Germany)

  • Christof Büskens

    (Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany)

Abstract

Renewable energy sources generated locally are becoming increasingly popular in order to achieve carbon neutrality in the near future. Some of these sources are being used in neighbourhood (local, or energy communities) grids to achieve high levels of self-sufficiency. However, the objectives of the local grid and the distribution grid to which it is connected are different and can sometimes conflict with each other. Although the distribution grid allows access to all variable resources, in certain circumstances, such as when its infrastructure is overloaded, redispatch measures need to be implemented. The complexity and uncertainties associated with current and future energy systems make this a challenging bi-level multi-criteria optimisation problem, with the distribution grid representing the upper level and the neighbourhood grid representing the lower level. Solving these problems numerically is not an easy task. However, there are new opportunities to solve these problems with less computational costs if we decompose the flexibility in the lower lever. Therefore, this paper presents a mathematical approach to optimise grid management systems by aggregating flexibility from neighbourhood grids. This mathematical approach can be implemented with centralised or decentralised algorithms to solve congestion problems in distribution grids.

Suggested Citation

  • Rebeca Ramirez Acosta & Chathura Wanigasekara & Emilie Frost & Tobias Brandt & Sebastian Lehnhoff & Christof Büskens, 2023. "Integration of Intelligent Neighbourhood Grids to the German Distribution Grid: A Perspective," Energies, MDPI, vol. 16(11), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4319-:d:1155413
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4319/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/11/4319/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ning, Yadong & Chen, Kunkun & Zhang, Boya & Ding, Tao & Guo, Fei & Zhang, Ming, 2020. "Energy conservation and emission reduction path selection in China: A simulation based on Bi-Level multi-objective optimization model," Energy Policy, Elsevier, vol. 137(C).
    2. Steffen Wehkamp & Lucas Schmeling & Lena Vorspel & Fabian Roelcke & Kai-Lukas Windmeier, 2020. "District Energy Systems: Challenges and New Tools for Planning and Evaluation," Energies, MDPI, vol. 13(11), pages 1-20, June.
    3. Markus Hartikainen & Kaisa Miettinen & Margaret Wiecek, 2012. "PAINT: Pareto front interpolation for nonlinear multiobjective optimization," Computational Optimization and Applications, Springer, vol. 52(3), pages 845-867, July.
    4. Jerome Bracken & James T. McGill, 1973. "Mathematical Programs with Optimization Problems in the Constraints," Operations Research, INFORMS, vol. 21(1), pages 37-44, February.
    5. Poplavskaya, Ksenia & Totschnig, Gerhard & Leimgruber, Fabian & Doorman, Gerard & Etienne, Gilles & de Vries, Laurens, 2020. "Integration of day-ahead market and redispatch to increase cross-border exchanges in the European electricity market," Applied Energy, Elsevier, vol. 278(C).
    6. Benoît Colson & Patrice Marcotte & Gilles Savard, 2007. "An overview of bilevel optimization," Annals of Operations Research, Springer, vol. 153(1), pages 235-256, September.
    7. Ricardo de Oliveira & Leonardo Willer de Oliveira & Edimar José de Oliveira, 2023. "Optimization Approach for Planning Soft Open Points in a MV-Distribution System to Maximize the Hosting Capacity," Energies, MDPI, vol. 16(3), pages 1-22, January.
    8. Matthias Ehrgott, 2005. "Multicriteria Optimization," Springer Books, Springer, edition 0, number 978-3-540-27659-3, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lei Fang & Hecheng Li, 2013. "Lower bound of cost efficiency measure in DEA with incomplete price information," Journal of Productivity Analysis, Springer, vol. 40(2), pages 219-226, October.
    2. Bo Zeng, 2020. "A Practical Scheme to Compute the Pessimistic Bilevel Optimization Problem," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1128-1142, October.
    3. Hughes, Michael S. & Lunday, Brian J., 2022. "The Weapon Target Assignment Problem: Rational Inference of Adversary Target Utility Valuations from Observed Solutions," Omega, Elsevier, vol. 107(C).
    4. Allan Peñafiel Mera & Chandra Balijepalli, 2020. "Towards improving resilience of cities: an optimisation approach to minimising vulnerability to disruption due to natural disasters under budgetary constraints," Transportation, Springer, vol. 47(4), pages 1809-1842, August.
    5. Massol, Olivier & Tchung-Ming, Stéphane & Banal-Estañol, Albert, 2015. "Joining the CCS club! The economics of CO2 pipeline projects," European Journal of Operational Research, Elsevier, vol. 247(1), pages 259-275.
    6. Mofidi, Seyed Shahab & Pazour, Jennifer A., 2019. "When is it beneficial to provide freelance suppliers with choice? A hierarchical approach for peer-to-peer logistics platforms," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 1-23.
    7. Lennard Sund & Saber Talari & Wolfgang Ketter, 2022. "Stochastic Wind Power Generation Planning in Liberalised Electricity Markets within a Heterogeneous Landscape," Energies, MDPI, vol. 15(21), pages 1-21, October.
    8. Mejía-de-Dios, Jesús-Adolfo & Mezura-Montes, Efrén & Toledo-Hernández, Porfirio, 2022. "Pseudo-feasible solutions in evolutionary bilevel optimization: Test problems and performance assessment," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    9. R. Paulavičius & C. S. Adjiman, 2020. "New bounding schemes and algorithmic options for the Branch-and-Sandwich algorithm," Journal of Global Optimization, Springer, vol. 77(2), pages 197-225, June.
    10. Gabriel Lopez Zenarosa & Oleg A. Prokopyev & Eduardo L. Pasiliao, 2021. "On exact solution approaches for bilevel quadratic 0–1 knapsack problem," Annals of Operations Research, Springer, vol. 298(1), pages 555-572, March.
    11. Lang, Magdalena A.K. & Cleophas, Catherine & Ehmke, Jan Fabian, 2021. "Multi-criteria decision making in dynamic slotting for attended home deliveries," Omega, Elsevier, vol. 102(C).
    12. Carlos Henggeler Antunes & Maria João Alves & Billur Ecer, 2020. "Bilevel optimization to deal with demand response in power grids: models, methods and challenges," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 814-842, October.
    13. Ashenafi Woldemariam & Semu Kassa, 2015. "Systematic evolutionary algorithm for general multilevel Stackelberg problems with bounded decision variables (SEAMSP)," Annals of Operations Research, Springer, vol. 229(1), pages 771-790, June.
    14. Tamás Kis & András Kovács & Csaba Mészáros, 2021. "On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management," Energies, MDPI, vol. 14(8), pages 1-22, April.
    15. Christine Tawfik & Sabine Limbourg, 2018. "Pricing Problems in Intermodal Freight Transport: Research Overview and Prospects," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    16. Casorrán, Carlos & Fortz, Bernard & Labbé, Martine & Ordóñez, Fernando, 2019. "A study of general and security Stackelberg game formulations," European Journal of Operational Research, Elsevier, vol. 278(3), pages 855-868.
    17. Oliver Cuate & Oliver Schütze, 2020. "Pareto Explorer for Finding the Knee for Many Objective Optimization Problems," Mathematics, MDPI, vol. 8(10), pages 1-24, September.
    18. Martine Labbé & Alessia Violin, 2016. "Bilevel programming and price setting problems," Annals of Operations Research, Springer, vol. 240(1), pages 141-169, May.
    19. E. A. Papa Quiroz & S. Cruzado, 2022. "An inexact scalarization proximal point method for multiobjective quasiconvex minimization," Annals of Operations Research, Springer, vol. 316(2), pages 1445-1470, September.
    20. Gang Du & Yi Xia & Roger J. Jiao & Xiaojie Liu, 2019. "Leader-follower joint optimization problems in product family design," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1387-1405, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4319-:d:1155413. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.