Journées de l'optimisation 2018
HEC Montréal, Québec, Canada, 7 — 9 mai 2018
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
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 mixedinteger 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 semidefinite programming and a branchandbound algorithm
Alternative Current Optimal Power Flow (ACOPF) is known as a nonconvex problem. Solving ACOPF to global optimality remains a challenge when classic convex relaxations are not exact. We use SemiDefinite Programming to reformulate ACOPF and get some convexity properties. We solve the reformulation to global optimality with a branchandbound algorithm.

11h20  11h45
Application of optimization problems in complex variable with a ACOPF modeling tool
Thanks to extensive scientific research, newly developed methods are able to provide good solutions for the nonconvex ACOPF problems. Computational results can be easily reproduced on academic datasets and for some kinds of ACOPF (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 ACOPF needs to be reduced. The R&D department of RTE will present the key components of an ACOPF 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. Stateofart relaxations (SDP, SOCP, …) or B&B methods can then be applied in a generic a way.