Radial distribution load flow using conic programming

By Rabih A. Jabr. IEEE Transactions on Power Systems, 2006

720 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bac44488d1da7c2cad37cf0fdae93a45d44e59b2

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In Control and Systems Engineering

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Citations in the corpus

New Formulation and Strong MISOCP Relaxations for AC Optimal Transmission Switching Problem (2015, background): “A variant of this formulation without (4i) was previously proposed in [33] and [34] for radial networks (also see [35]) while it was later adapted to general networks in [36], [37].”
Moment/Sum-of-Squares Hierarchy for Complex Polynomial Optimization (2015, background): “Since 2006, the ability of the Shor and second-order conic relaxations to find global solutions [3,7,20,34,47,52,69] has been studied.”
A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems (2017, background): “SOCP relaxations are also proposed in [27] and [28] for the model (1).”
Convex Relaxation of Optimal Power Flow—Part I: Formulations and Equivalence (2014, neutral): “5”
Convex Relaxation of Optimal Power Flow—Part II: Exactness (2014, methodology): “To the best of our knowledge solving OPF through semideﬁnite relaxation is ﬁrst proposed in [14] as a second-order cone program (SOCP) for radial (tree) networks and in [15] as a semideﬁnite program (SDP) for general networks in a bus injection model.”
A New Mathematical Model for the Restoration Problem in Balanced Radial Distribution Systems (2016, background): “Additionally, recent research has shown that several types of distribution network optimization problems that are originally mixed integer nonlinear programming problems can be transformed into convex optimization problems [24]–[26].”
Strong SOCP Relaxations for the Optimal Power Flow Problem (2015, methodology): “If the objective function C i ( p ) is linear, then we have a linear programming relaxation of the OPF problem.”
PowerModels.J1: An Open-Source Framework for Exploring Power Flow Formulations (2017, background): “For the 2https://github.com/lanl-ansi/PowerModelsAnnex.jl 3https://github.com/frederikgeth/PowerModelsReliability.jl 4https://github.com/kaarthiksundar/n-k convex QC and SOC relaxations the optimality gap is reported, that is, 100 ∗ ACP - Relaxation ACP The reported runtimes focus on the Ipopt solve”
Convex Hull of the Quadratic Branch AC Power Flow Equations and Its Application in Radial Distribution Networks (2017, background): “[5] B. Kocuk, S. Dey, and A. Sun, “Strong SOCP relaxations of the optimal power flow problem,” Oper.”
A Successive Linear Programming Approach to Solving the IV-ACOPF (2016, background): “propose strong SOCR formulations, which are an order of magnitude faster than standard SDR formulations but not as tight, and are also an order of magnitude slower yet more accurate than Jabr's original SOCR formulation [37].”
Convex relaxation of optimal power flow: A tutorial (2013, neutral): “[21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31].”
Exact Convex Relaxation of Optimal Power Flow in Radial Networks (2013, background): “It is proposed in [14], [15] to transform the nonconvex power flow constraints into linear constraints on a rank-one positive semidefinite matrix, and then remove the rank-one constraint to obtain an SDP relaxation.”
Zero Duality Gap in Optimal Power Flow Problem (2012, positive): “In an effort to convexify the OPF problem, it is justified in [13] that the load flow problem of a radial distribution system may be modeled as a convex optimization problem in the form of a conic program.”
Optimal Power Flow Using an Extended Conic Quadratic Formulation (2008, methodology): “[25] shows that the radial load flow equations can be efficiently solved using an interior-point method for convex conic quadratic programming.”
A Sufficient Condition on Convex Relaxation of AC Optimal Power Flow in Distribution Networks (2017, methodology): “The convex relaxation method for solving the AC OPF was first presented in [8] as a second-order cone programming (SOCP) for radial networks and in [9] as a semidefinite programming (SDP) for meshed networks.”
Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming (2012, background): “The result is that the rotated conic quadratic constraint in (26) will be binding at optimality, as discussed in [27].”
Local Solutions of Optimal Power Flow (2013, background): “We also point out that the SDP approach of [7], [8] can sometimes fail to recover a feasible OPF solution.”