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Tight Convex Relaxations for the Expansion Planning Problem

Author

Listed:
  • Ralf Lenz

    (Zuse Institute Berlin)

  • Felipe Serrano

    (Zuse Institute Berlin)

Abstract

Secure energy transport is considered as highly relevant for the basic infrastructure of nowadays society and economy. To satisfy increasing demands and to handle more diverse transport situations, operators of energy networks regularly expand the capacity of their network by building new network elements, known as the expansion planning problem. A key constraint function in expansion planning problems is a nonlinear and nonconvex potential loss function. In order to improve the algorithmic performance of state-of-the-art MINLP solvers, this paper presents an algebraic description for the convex envelope of this function. Through a thorough computational study, we show that this tighter relaxation tremendously improves the performance of the MINLP solver SCIP on a large test set of practically relevant instances for the expansion planning problem. In particular, the results show that our achievements lead to an improvement of the solver performance for a development version by up to 58%.

Suggested Citation

  • Ralf Lenz & Felipe Serrano, 2022. "Tight Convex Relaxations for the Expansion Planning Problem," Journal of Optimization Theory and Applications, Springer, vol. 194(1), pages 325-352, July.
    • Handle: RePEc:spr:joptap:v:194:y:2022:i:1:d:10.1007_s10957-022-02029-8
    DOI: 10.1007/s10957-022-02029-8
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    References listed on IDEAS

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