Adaptive chaotic class topper optimization to solve Non-convex CHPED problems in power systems

This paper proposed a population-based meta-heuristic algorithm, an adaptive chaotic class topper optimization (AC-CTO) algorithm for solving combined heat and power economic dispatch (CHPED) problems. The proposed algorithm is an improved version of classical class topper optimization, where the desirable practical solutions are obtained based on the learning process, improving the students’ knowledge. Here, the complexity of combined heat and power is increased due to their mutual interdependence and is non-linear in nature. The main goal is to achieve the minimum fuel cost while satisfying demand, considering the practical system constraints such as valve-point effects, prohibited operating zones, and transmission loss. To evaluate the performance, the proposed algorithm is tested for two case studies, one without system losses – 4 unit system and the other with system losses – 7 unit system. The results of the study indicate that AC-CTO algorithms outperform popular optimization methods to solve the CHPED problems and obtain the lowest fuel cost.