Large-scale cooling energy system has developed well in the past decade. However

its optimization is still a problem to be tackled due to the nonlinearity and large scale of existing systems. Reducing the scale of problems without over-simplifying the actual system model is a big challenge nowadays. This paper proposes a dimension reduction-based many-objective optimization (DRMO) method to solve an accurate nonlinear model of a practical large-scale cooling energy system. In the first stage

many-objective and many-variable of the large system are pre-processed to reduce the overall scale of the optimization problem. The relationships between many objectives are analyzed to find a few representative objectives. Key control variables are extracted to reduce the dimension of variables and the number of equality constraints. In the second stage

the many-objective group search optimization (GSO) method is used to solve the low-dimensional nonlinear model

and a Pareto-front is obtained. In the final stage

candidate solutions along the Paretofront are graded on many-objective levels of system operators. The candidate solution with the highest average utility value is selected as the best running mode. Simulations are carried out on a 619-node-614-branch cooling system

and results show the ability of the proposed method in solving large-scale system operation problems.