In this article, a newly developed chaotic quasi-oppositional whale optimization algorithm (CQOWOA) is employed to analyze the combined heat and power economic dispatch (CHPED) problem for the first time. In the suggested algorithm, chaotic quasi-oppositional learning is imposed with a whale optimization algorithm (WOA) to enhance its convergence rate and reduce the generation cost. In this work, wind energy, solar energy, and electric vehicles (EVs) are scheduled with CHPED and developed in the proposed system for minimizing the expected generation cost. In the vehicle-to-grid system, EVs play a vital role, enabling to provide bidirectional power flow. The primary focus of the proposed CHPED scheduling is to optimize power generation cost by fulfilling various constraints. The presence of transmission losses and valve point effect of the thermal unit along with the uncertainty of wind, solar, and EVs introduce nonlinearity. Several optimization techniques have been studied for this proposed system to judge the effectiveness of the present optimization technique. The CQOWOA has been tested on 7-unit, 24-unit, and 48-unit CHPED systems to validate its superiority. The study of the proposed system is extended further by incorporating two wind units. In addition, the proposed algorithm is tested on a large and complicated CHPED system scheduling with two wind units, one solar unit, and one EV unit. A comparison has been made based on a convergence profile and statistical results of the CQOWOA with WOA, gravitational search algorithm, to analyze the performance of the proposed algorithm. Moreover, to validate the robustness of the suggested CQOWOA approach, 30 IEEE CEC-2020 benchmark functions are considered and the outcomes are compared with quasi-oppositional WOA (QOWOA), chaotic WOA (CWOA), WOA, and two top performing algorithms, namely, hybrid levy particle swarm variable neighborhood search optimization and improved cellular univariate marginal distribution algorithm with normal-Cauchy distribution.
