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Resource allocation problems in decentralized energy management

Author

Listed:
  • 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. 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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    4. Jochen Gönsch & Michael Hassler, 2016. "Sell or store? An ADP approach to marketing renewable energy," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(3), pages 633-660, July.
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    Cited by:

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    2. 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.
    3. Gijs J. H. de Goeijen & Gerard J. M. Smit & Johann L. Hurink, 2017. "Improving an Integer Linear Programming Model of an Ecovat Buffer by Adding Long-Term Planning," Energies, MDPI, vol. 10(12), pages 1-18, December.
    4. 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.
    5. 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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