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Resource allocation problems in decentralized energy management

Author

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  • Thijs Klauw

    (University of Twente)

  • Marco E. T. Gerards

    (University of Twente)

  • Johann L. Hurink

    (University of Twente)

Abstract

Changes in our electricity supply chain are causing a paradigm shift from centralized control towards decentralized energy management. Within the framework of decentralized energy management, devices that offer flexibility in their load profile play an important role. These devices schedule their flexible load profile based on steering signals received from centralized controllers. The problem of finding optimal device schedules based on the received steering signals falls into the framework of resource allocation problems. We study an extension of the traditional problems studied within resource allocation and prove that a divide-and-conquer strategy gives an optimal solution for the considered extension. This leads to an efficient recursive algorithm, with quadratic complexity in the practically relevant case of quadratic objective functions. Furthermore, we study discrete variants of two problems common in decentralized energy management. We show that these problems are NP-hard and formulate natural relaxations of both considered discrete problems that we solve efficiently. Finally, we show that the solutions to the natural relaxations closely resemble solutions to the original, hard problems.

Suggested Citation

  • Thijs Klauw & Marco E. T. Gerards & Johann L. Hurink, 2017. "Resource allocation problems in decentralized energy management," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 749-773, July.
    • Handle: RePEc:spr:orspec:v:39:y:2017:i:3:d:10.1007_s00291-017-0474-2
    DOI: 10.1007/s00291-017-0474-2
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    References listed on IDEAS

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    1. Claessen, F.N. & Claessens, B. & Hommelberg, M.P.F. & Molderink, A. & Bakker, V. & Toersche, H.A. & van den Broek, M.A., 2014. "Comparative analysis of tertiary control systems for smart grids using the Flex Street model," Renewable Energy, Elsevier, vol. 69(C), pages 260-270.
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    4. Patriksson, Michael & Strömberg, Christoffer, 2015. "Algorithms for the continuous nonlinear resource allocation problem—New implementations and numerical studies," European Journal of Operational Research, Elsevier, vol. 243(3), pages 703-722.
    5. Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
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    Cited by:

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    3. Martijn H. H. Schoot Uiterkamp & Marco E. T. Gerards & Johann L. Hurink, 2022. "On a Reduction for a Class of Resource Allocation Problems," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1387-1402, May.
    4. Homan, Bart & ten Kortenaar, Marnix V. & Hurink, Johann L. & Smit, Gerard J.M., 2019. "A realistic model for battery state of charge prediction in energy management simulation tools," Energy, Elsevier, vol. 171(C), pages 205-217.
    5. Schoot Uiterkamp, Martijn H.H., 2024. "A characterization of simultaneous optimization, majorization, and (bi)submodular polyhedra," Other publications TiSEM 59cc86d9-0a5d-4a7d-b47c-d, Tilburg University, School of Economics and Management.
    6. Zeyang Wu & Kameng Nip & Qie He, 2021. "A New Combinatorial Algorithm for Separable Convex Resource Allocation with Nested Bound Constraints," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1197-1212, July.

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