: Unit Commitment Problem (UCP) deals with the scheduling of the power-generating units to fulfill the load demand at the lowest possible cost. UCP is a nonlinear, mixed integer optimization problem solved in a constrained environment. The prime objective of the traditional UCP is to minimize the total operating cost of power generation units. The integration of the wind power generating units with thermal units has made the unit commitment more complex to solve in a constrained environment. The present work investigates the effect of wind power generation on the operating cost of thermal units using a Multipartite Adaptive binary Real Quantum-Inspired Evolutionary Algorithm (MABRQIEA). A repair-based constraint handling is also used in the suggested work to deal with the constraints of the power-generating units and the system. The suggested MABRQIEA is tested on different test systems along with wind power generation and the result shows the effectiveness of the algorithm. The test systems include the 10, 20, 40, 80, and 100-unit systems. The results obtained are found to be competitive as compared with various well-known optimization techniques.