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Constrained Optimization as the Allocation Method in Local Flexibility Markets

Author

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  • Andreas Zeiselmair

    (Forschungsstelle für Energiewirtschaft e.V. (FfE), Am Blütenanger 71, 80995 Munich, Germany
    TUM Graduate School, Technical University of Munich, Boltzmannstr. 17, 85748 Munich, Germany)

  • Simon Köppl

    (Forschungsstelle für Energiewirtschaft e.V. (FfE), Am Blütenanger 71, 80995 Munich, Germany
    TUM Graduate School, Technical University of Munich, Boltzmannstr. 17, 85748 Munich, Germany)

Abstract

Local flexibility markets or smart markets are new tools used to harness regional flexibility for congestion management. In order to benefit from the available flexibility potential for grid-oriented or even grid-supportive applications, complex but efficient and transparent allocation is necessary. This paper proposes a constrained optimization method for matching the flexibility demand of grid operators to the flexibility supply using decentralized flexibility options located in the distribution grid. Starting with a definition of the operational and stakeholder environment of smart market design, various existing approaches are analyzed based on a literature review and a resulting meta-analysis. In the next step, a categorization of the allocation method is conducted followed by the definition of the optimization goal. The optimization problem, including all relevant input parameters, is identified and formulated by introducing the relevant boundary conditions and constraints of flexibility demand and offers. A proof of concept of the approach is presented using a case study and the Altdorfer Flexmarkt (ALF) field test within the project C/sells. In this paper, we analyze the background of the local flexibility market, provide the methodology (including publishing the code of the matching mechanism), and provide the results of the field test.

Suggested Citation

  • Andreas Zeiselmair & Simon Köppl, 2021. "Constrained Optimization as the Allocation Method in Local Flexibility Markets," Energies, MDPI, vol. 14(13), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3932-:d:586111
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    References listed on IDEAS

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    2. Daud Mustafa Minhas & Josef Meiers & Georg Frey, 2022. "Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets," Energies, MDPI, vol. 15(5), pages 1-29, February.
    3. Lynne L. Kiesling & Leonardo Meeus & Michael G. Pollitt, 2021. "Special Issue “Innovation, Policy, and Regulation in Electricity Markets”," Energies, MDPI, vol. 14(19), pages 1-4, September.
    4. Ajla Mehinovic & Matej Zajc & Nermin Suljanovic, 2023. "Interpretation and Quantification of the Flexibility Sources Location on the Flexibility Service in the Distribution Grid," Energies, MDPI, vol. 16(2), pages 1-18, January.
    5. Ádám Sleisz & Dániel Divényi & Beáta Polgári & Péter Sőrés & Dávid Raisz, 2022. "A Novel Cost Allocation Mechanism for Local Flexibility in the Power System with Partial Disintermediation," Energies, MDPI, vol. 15(22), pages 1-18, November.

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