The expansion, transformation, and upgrading of thermal power plants have significantly increased the complexity of combined heat and power (CHP) systems in terms of the number, type, and performance degradation level of units. A multi-objective optimization model based on the energy efficiency, exergy, economy and environmental factors was constructed for the unit operation planning of complex CHP systems, with comprehensive consideration of the influencing factors such as the unit commitment status and ramping rates of the units during the actual operation process. The entropy weight method was introduced to objectively allocate weights to each objective, transforming the multi-objective optimization problem into single-objective optimization problems. A dynamic programming algorithm was employed to solve the problems, ensuring the determinacy and objectivity of the optimal solution. Taking the complex CHP system of a thermal power plant as a case study, the heat and power load demand over a 24-hour period was randomly selected for operation planning and analysis. The comparison between the optimization results and the results of the original design scheme indicated that the operating cost was reduced by 79 600 yuan per day after optimization. The proposed model can effectively improve the operation economy and reliability of complex CHP systems.