Abstract
This paper presents a decomposition methodology using constraint handling rules to improve the computation time of a security-constrained optimal power flow (SCOPF) problem. In order to evaluate methodology performance, tests over small (500 buses), medium (4,918 buses), and large scale (11,615 buses) transmission networks were carried out. The methodology consisted in the decomposition of the SCOPF problem into a base case problem and contingency sub-problems using constraint handling rules to solve the complete problem in an iterative fashion. The first stage involved solving an OPF problem using a base case network. The second stage dealt with the modification of the initial base case by updating some of the constraint limits according to the evaluation of potentially relevant contingencies. The entire algorithm resorted to parallel computing tools. The methodology, along with active power re-dispatch through droop control and PV/PQ switching in post-contingency scenarios, successfully solved the tested networks with the set of proposed constraints.
Highlights
The security-constrained optimal power flow (SCOPF) problem aims to find an optimal operating cost of power systems while ensuring security criteria from a plausible set of contingencies [1]–[5]
Valencia et al.: Fast Decomposition Method to Solve a SCOPF Problem Through Constraint Handling this paper presents a computationally-efficient fast decomposition strategy that relies on the constraint handling of the original, time-consuming SCOPF problem by using parallel computation tools
An algorithm based on the Matpower toolbox [40] and the Interior Point Optimizer (IPOPT [36], [41]) was proposed to solve the SCOPF problem described in Section III [32], [42]
Summary
The security-constrained optimal power flow (SCOPF) problem aims to find an optimal operating cost of power systems while ensuring security criteria (usually N − 1) from a plausible set of contingencies [1]–[5]. Many different strategies have been explored in the literature to make the SCOPF optimization problem more tractable from a computational point of view. The associate editor coordinating the review of this manuscript and approving it for publication was Zhouyang Ren. the original problem by using linearization, convexification (e.g. quadratic optimization), decomposition strategies (e.g. Alternating Direction Multipliers Method -ADMM, Augmented Lagrangian Method -ALM, Benders Decomposition -BD, etc) and the screening of most relevant contingencies, among others. The original problem by using linearization, convexification (e.g. quadratic optimization), decomposition strategies (e.g. Alternating Direction Multipliers Method -ADMM, Augmented Lagrangian Method -ALM, Benders Decomposition -BD, etc) and the screening of most relevant contingencies, among others These strategies usually yield accurate results and relatively low computational burden for electrical networks considered small, i.e. equivalent networks with few buses, branches and elements. The implementation of these strategies might oversimplify the original problem (linearization, quadratic optimization) or require time-consuming algorithms that are not suitable for fast, online applications
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