On Computational Issues of Market-Based Optimal Power Flow

By Student Member Ieee Hongye Wang, M. I. Carlos E. Murillo-Sánchez, M. I. Ray D. Zimmerman, F. I. Robert J. Thomas. IEEE Transactions on Power Systems, 2007

381 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d28147b952548c1f3e0d309df24518ac797be543

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

Citations in the corpus

Chance Constrained Programming for Optimal Power Flow Under Uncertainty (2011, background): “Bounds of these inequality constraints are typically given by unit commitment and security analysis tools in real-time system operations [15].”
Optimal Power Flow Using an Extended Conic Quadratic Formulation (2008, methodology): “Comparisons are carried out with results published in [26], [29]–[31] and also with solutions obtained from MINOS [32] and PDIPM [17] used in MATPOWER [33].”
Experiments with the interior-point method for solving large scale optimal power flow problems (2013, methodology): “However, as the main computational burden in IPM is the factorization of a linear system of equations [12], we notice that as Matpower solvers rely on the “reduced KKT system” [10,21], the number of inequality constraints does not affect the size of this system, contrary to our implementation which ”
Physics of power networks makes hard optimization problems easy to solve (2012, neutral): “Several algorithms have been proposed for solving this highly nonconvex problem, including linear programming, quadratic programming, nonlinear programming, Lagrange relaxation, interior point methods, artificial intelligence, artificial neural network, fuzzy logic, genetic algorithm, evolutionary p”
Strong SOCP Relaxations for the Optimal Power Flow Problem (2015, methodology): “In order to solve the OPF problem, the academic literature has focused on improving non-linear optimization methods such as the interior point methods (IPM) to compute local optimal solutions, see e.g. (Wu et al. 1994, Torres and Quintana 1998, Jabr et al. 2002, Wang et al. 2007).”
Pandapower—An Open-Source Python Tool for Convenient Modeling, Analysis, and Optimization of Electric Power Systems (2017, methodology): “The interior point solver [28], [29] provided by PYPOWER is used to solve the problem, while costs, flexibilities and constraints are configured through the element-based pandapower data structure.”
A Linearized OPF Model With Reactive Power and Voltage Magnitude: A Pathway to Improve the MW-Only DC OPF (2018, methodology): “Currently, most system operators use the DC OPF method due to the concerns of computational robustness [5].”
A General Formulation of Linear Power Flow Models: Basic Theory and Error Analysis (2019, methodology): “As the representative of linear power flow models, the DC power flow model is widely used in power system operations [5], which focuses on P and θ and neglects Q and v.”
Robust Optimal Power Flow Solution Using Trust Region and Interior-Point Methods (2011, methodology): “The restructuring of the power industry has also lead to new complex OPF models [1]–[3], which require robust and reliable solution techniques.”
Probabilistic LMP Forecasting Considering Load Uncertainty (2009, background): “Optimal power flow (OPF) problem has been discussed in [12] with special attention to computational issues brought by deregulation.”