Journées de l'optimisation 2018

HEC Montréal, Québec, Canada, 7 — 9 mai 2018

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TA7 Advances in solving ACOPF problems

8 mai 2018 10h30 – 12h10

Salle: Groupe Cholette (35)

Présidée par Manuel Ruiz

3 présentations

  • 10h30 - 10h55

    New conic relaxation for optimal reactive power dispatch

    • Christian Bingane, prés., Polytechnique Montréal
    • Miguel F. Anjos, GERAD, Polytechnique Montréal
    • Sébastien Le Digabel, GERAD, Polytechnique Montréal

    The optimal reactive power dispatch (ORPD) problem is an alternating current optimal power flow (ACOPF) problem where discrete control devices for regulating the reactive power, such as shunt devices and load tap changers, are considered. The ORPD problem is modeled as a mixed-integer nonlinear program and its complexity is increased compared to the ACOPF problem, which is highly nonconvex and generally hard to solve. Recently, conic relaxations of the ACOPF problem have attracted a significant interest since they lead to global optimality in many cases. We propose a conic relaxation of the ORPD problem whose accuracy is corroborated by computational results on selected MATPOWER test cases.

  • 10h55 - 11h20

    Solving alternative current optimal power flow to global optimality with semi-definite programming and a branch-and-bound algorithm

    • Hadrien Godard, prés., RTE-CEDRIC-UMA
    • Sourour Elloumi, UMA-CEDRIC
    • Amélie Lambert, Cedric-Cnam
    • Jean Maeght, RTE
    • Manuel Ruiz, RTE

    Alternative Current Optimal Power Flow (ACOPF) is known as a non-convex problem. Solving ACOPF to global optimality remains a challenge when classic convex relaxations are not exact. We use Semi-Definite Programming to reformulate ACOPF and get some convexity properties. We solve the reformulation to global optimality with a branch-and-bound algorithm.

  • 11h20 - 11h45

    Application of optimization problems in complex variable with a AC-OPF modeling tool

    • Manuel Ruiz, prés., RTE
    • Julie Sliwak, RTE

    Thanks to extensive scientific research, newly developed methods are able to provide good solutions for the non-convex AC-OPF problems. Computational results can be easily reproduced on academic datasets and for some kinds of AC-OPF (minimizing losses, with or without thermal limit, unit commitment etc). In order to experiment on these methods in an industrial context, the time spent in implementing an AC-OPF needs to be reduced. The R&D department of RTE will present the key components of an AC-OPF modeler implemented in Julia, which stores the optimization problem with polynomials in complex variables while keeping information on the network structure. At the moment, the tool can build OPF problems from Matpower and the GridOptimizationCompetition input format. State-of-art relaxations (SDP, SOCP, …) or B&B methods can then be applied in a generic a way.