Planning optimal power dispatch schedule using constrained ant colony optimization

Abstract

In the modern power systems, appropriate power dispatch schedule of the online power generating units is essential for reliable and clean power supply and it is desirable to attain this at the lowest possible operating cost. Mathematical model of such an Economic Load Dispatch (ELD) problem turns out to be a complex non-convex optimization problem with practical constraints involving various factors such as, line loss, valve points effect, prohibited operating zones, ramp rate limits, system spinning reserve, multiple fuel options, etc. Nature inspired algorithms have been extensively used to solve such complex ELD problems. Through this study we propose to contribute to the available pool of efficient methodologies for solving ELD problems. Recently, Kumar et al. (2018) developed an ant colony optimization algorithm, namely ACO-LD which has shown impressive results compared to other efficient algorithms in literature when applied to a set of unconstrained optimization problems. In this study we extend ACO-LD to develop a new Constrained Ant Colony Optimization (ACO) algorithm with Adaptive Penalty (AP) method (Lemonge and Barbosa, 2014), to solve the ELD problem. The proposed algorithm is named CACO-LD-AP and is found to be quite efficient in terms of the quality of the solutions found for ELD problems of varied complexities. In order to validate the efficiency of the proposed algorithm, six power systems have been considered in this study. The performance of CACO-LD-AP is compared with various recently published state of the art algorithms for solving ELD problems. Analysis of the experimental results affirms the robustness and superiority of CACO-LD-AP over other algorithms included in this study.