人工智能的快速发展导致数据中心能源需求呈现高密度、高能耗演化趋势，为提升数据中心综合供能系统的能效与经济性，提出一种基于固体氧化物燃料电池（SOFC）的冷电联产系统“参数-容量-策略”多层次协同优化方法，对系统热力参数与容量-运行策略进行同步优化。首先构建SOFC与单效/双效/三效吸收式制冷单元仿真模型，之后采用NSGA-II对单元热力参数进行全局多目标寻优；并基于拓展能源枢纽模型构建混合整数线性规划模型，求解设备容量配置与典型日逐时运行策略，获取净电效率与全生命周期成本的Pareto最优前沿。以苏州某云计算数据中心为例，SOFC冷电联产系统净电效率和综合能效分别可达到55%和79%，SOFC发电效率提升1%可使年化成本降低527万～790万元。结果表明所提出的协同优化方法能够有效优化动力单元设计参数、容量配置与运行策略，提升系统能效与经济性。

The rapid development of artificial intelligence has led to the evolution of energy demand in data centers with high density and high energy consumption. In order to improve the comprehensive energy efficiency and economy of the energy supply system, a collaborative optimization method of 'parameter-capacity-strategy' for data center combined cooling and power generation system based on solid oxide fuel cell (SOFC) is proposed to simultaneously optimize the thermal parameters and capacity-operation strategy of the system. Firstly, the simulation model of SOFC and single-/double-/triple- effect absorption refrigeration unit is constructed, and then NSGA-II is used to optimize the thermal parameters of the unit. Based on the extended energy hub model, a mixed integer linear programming model is constructed to solve the equipment capacity configuration and typical daily hourly operation strategy, and the Pareto optimal frontier of net power efficiency and life cycle cost is obtained. In the case of a cloud computing data center in Suzhou, the net power efficiency and comprehensive energy efficiency of the co-production system can reach 55% and 79%, respectively. 1% increase in SOFC power generation efficiency can reduce the annual cost by 5.27 million to 7.9 million yuan. The results show that the proposed collaborative optimization method can effectively optimize the design parameters, capacity configuration and operation strategies of the power unit, thereby improving the system's energy efficiency and economic performance.