In last few decades, the emission of greenhouse gasses has exponentially increased due to large production of electric power energy from conventional fossil fuels to pose critical environmental challenges. The renewable energies (REs) are establishing themselves as key technologies for reduction of carbon emissions, in addition to low cost and high efficiency. However, the operational limits and the power generation procedures of the renewable energies invite immense challenges. The uncertainty in production with precise and error free approximation make it very complicated. Hence, an effective approach with methodical organization of the renewable energies are the need of the hour for reliable and safe system. In this study, an IEEE 30-bus hybrid power system (HPS) problem consisting of conventional thermal generators and green energies like wind generators and solar photovoltaic are considered to become environmentally and economically capable than the existing ones. Several measures like penalty cost and reserve cost have been considered in this present study for addressing the uncertainty issues underestimation and overestimation respectively. Further, three hybrid configurations such as thermal-solar (TS), thermal-wind (TW) and thermal-wind-solar (TWS) are proposed to perform the cost effective analysis. The adopted hybrid power system is extremely complex and non-linear optimization problem. Hence, a recently proposed evolutionary algorithm namely competitive swarm optimization (CSO) algorithm is implemented to discover the optimum result for the variety goals like minimum production cost, carbon emission, voltage variation and loss of the power. The performance of CSO algorithm is compared with several state-of-the-art meta-heuristic algorithms such GA, PSO, CSA, ABC, and SHADE-SF. The extraordinary outcomes achieved in this work illustrate that the CSO method can successfully be applied to handle the complex, non-convex and non-linear hybrid power system problems.