Hybrid PSO-ACO technique to solve multi-constraint economic load dispatch problems for 6-generator system

Abstract

This paper presents a new hybrid soft computing approach in solving typical economic load dispatch (ELD) problem by combining two widely used meta-heuristic techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). Though PSO has been popularly used in ELD problems for its flexibility, robustness, and fast convergence, it often produces not so good solution due to premature convergence to local optima. ACO, on the other hand, popular for its good global exploration feature, strikes a fine balance between local and global search for optimum solution when combined with PSO. This aspect has been exploited for the first time by applying a customized combination of PSO and ACO in finding solution for both convex and non-convex ELD problems with smooth and non-smooth cost functions (i.e. with and without valve-point loading) and multiple constraints like generator capacity, transmission loss, ramp rate limits, and prohibited operating zones. This paper showcases the results of different test cases for the proposed approach implemented in a simulated 6-generator thermal power system; the results validate the effectiveness and superiority of PSO–ACO when compared with few other hybrid approaches and pure PSO and ACO approaches under similar test conditions. Considering the quality of solution including convergence characteristics, power loss, and total generation cost, PSO–ACO emerges as a viable alternative for solving ELD problems in a real power generation scenario.