Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Multi-Solution Optimal Power Flow Problem

Optimal Power Flow (OPF) is a constrained, high-dimensional, non-convex nonlinear programming problem that typically has multiple local optimal solutions. To address the issue where most algorithms can easily trip into the local optimal solution, a novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology (CPSOATT) for Multi-Solution Optimal Power Flow Problem is proposed in this paper. By leveraging the excellent global search capability of CPSO with a novel stopping criterion, several promising sub-regions are obtained. Subsequently, the interior point method is used to find the local optima corresponding to high-quality initial points obtained from the CPSO. A deep search of these promising sub-regions is then conducted using the Trust-Tech method. This approach balances speed and accuracy to solve for multiple local optima including the global optimal solution. The effectiveness and practicality of the proposed deterministic optimization method are validated through improved IEEE 9-bus and 30-bus multi-scenario instances.