An S$$\ell _1$$ℓ1LP-active set approach for feasibility restoration in power systems

@article{Kim2014AnS\_,
  title={An S\$\$\ell \_1\$\$ℓ1LP-active set approach for feasibility restoration in power systems},
  author={Taedong Kim and Stephen J. Wright},
  journal={Optimization and Engineering},
  year={2014},
  volume={17},
  pages={385-419}
}
We consider power networks in which it is not possible to satisfy all loads at the demand nodes, due to some attack or disturbance to the network. We formulate a model, based on AC power flow equations, to restore the network to feasibility by adjusting load at demand nodes or power production at generators, but doing so in a way that minimizes a weighted measure of the total power adjustment, and affects as few nodes as possible. Besides suggesting an optimal response to a given attack, our… 

Predicting and mitigating congestion for an electric power system under load and renewable uncertainty

This work proposes an approach to estimate the probability of the occurrence of a congestion event, which is defined as the event when power flow in a transmission line exceeds critical thermal limits or voltage at a bus exits its safety limits.

Announcement: Howard Rosenbrock Prize 2016

Novel applied mathematical techniques are developed to solve the problem of minimizing the amount and number of load and generation adjustments in a power network that is affected by an attack or disturbance, using nonsmooth penalty functions, sequential linear programming techniques, and active-set heuristics.

Zero-Order Optimization-Based Iterative Learning Control

We consider an optimization-based iterative learning control (ILC) approach for nonlinear systems where the control input is obtained as the solution of a constrained nonlinear program (NLP). The NLP

A Sequential Algorithm for Solving Nonlinear Optimization Problems with Chance Constraints

An algorithm is presented for solving cardinality-constrained nonlinear optimization problems with chance constraints, i.e., those in which a constraint involving an uncertain parameter must be satisfied with at least a minimum probability.

A Feasible Sequential Linear Programming Algorithm with Application to Time-Optimal Path Planning Problems

A Feasible Sequential Linear Programming algorithm applied to time-optimal control problems (TOCP) obtained through direct multiple shooting discretization motivated by TOCP with nonlinear constraints which arise in motion planning of mechatronic systems.

TRIPODS/MOPTA 2018 special issue on energy and optimization

This Optimization and Engineering (OPTE) special issue on Energy and Optimization contains a selection of papers that were presented at the TRIPODS/MOPTA conference held at Lehigh University,

Survey of sequential convex programming and generalized Gauss-Newton methods

An overview of a class of iterative convex approximation methods for nonlinear optimization problems with convex-over-nonlinear substructure, and shows that the presented methods converge to a local minimum if and only if this local minimum is stable against a mirroring operation applied to the measurement data of the estimation problem.

Announcement: Howard Rosenbrock Prize 2017

As the editor-in-chief of Optimization and Engineering (OPTE), I am delighted to announce the 2017 Rosenbrock Prize. This prize is awarded annually to honor the authors of the best paper published in

Announcement: Howard Rosenbrock Prize 2021

Every year, Optimization and Engineering (OPTE) honors excellence in scientific research by presenting the Rosenbrock Prize to the best paper we published the previous year. The prize recognizes

References

SHOWING 1-10 OF 28 REFERENCES

Restoring solutions for unsolvable cases via minimum load shedding for a specified direction

  • L. V. BarbozaR. Salgado
  • Engineering
    PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195)
  • 2001
This paper focuses on a methodology to determine corrective adjustments on the electric power network when the steady-state power flow equations have no real solution. The proposed approach is based

Computation of a practical method to restore power flow solvability

As power systems become more heavily loaded, system operation will be increasingly constrained by contingent cases where the power flow equations have no real solutions. Since such cases often

Application of interior point methods to power flow unsolvability

This paper describes an application of an optimal power flow, solved by a direct interior point (IP) method, to restore system solvability. Using the P-Q load representation, power flow unsolvability

A Sufficient Condition for Power Flow Insolvability With Applications to Voltage Stability Margins

For the nonlinear power flow problem specified with standard PQ, PV, and slack bus equality constraints, we present a sufficient condition under which the specified set of nonlinear algebraic

MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education

The details of the network modeling and problem formulations used by MATPOWER, including its extensible OPF architecture, are presented, which are used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits.

Unsolvable power flow analysis-an approach based on interior point nonlinear optimization methods

In the proposed procedure, the load curtailment at each bus is obtained by solving a least squares problem through the nonlinear version of the predictor-corrector primal-dual interior point optimization algorithm.

A Load Flow Calculation Method for Ill-Conditioned Power Systems

A load flow calculation method for ill-conditioned power systems is developed, and it is found that the solution does not exist for the 11 and 43 bus systems though the given data are said to be operational, and also that the answer does not converge with the single precision due to the precision deficiency of the computer.

Zero Duality Gap in Optimal Power Flow Problem

The optimal power flow (OPF) problem is nonconvex and generally hard to solve. In this paper, we propose a semidefinite programming (SDP) optimization, which is the dual of an equivalent form of the

Robust Optimal Power Flow Solution Using Trust Region and Interior-Point Methods

A globally convergent optimization algorithm for solving large nonlinear optimal power flow (OPF) problems is presented. As power systems become heavily loaded, there is an increasing need for

Globally Convergent Optimal Power Flow by Trust-Region Interior-Point Methods

As power systems becomes heavily loaded there is an increasing need for globally convergent optimal power flow (OPF) algorithms. By global convergence we mean the OPF algorithm being able to find a