为提升高寒高海拔地区风光互补热电联供系统的综合性能，以系统投资成本与负荷缺失率最小、当量CO₂年排量最大为目标，建立系统容量配置的优化模型。根据西藏那区全年日照与风力情况，应用非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ，NSGA-II)对风光互补热电联供系统的容量配置开展多目标优化研究。结果表明：优化后，系统全年负荷缺失率为4.6%、设备投资成本为16.2×10⁴元、CO₂年减排量为95.4 t；系统设备中，集热器的投资约占设备总投资的50%，光伏电池与蓄电池的投资次之，风机与储热罐投资最少；系统逐时负荷缺失率主要集中在室外温度较低的时段。多目标优化设计很好地平衡了风光互补热电联供对经济性、可靠性与环境性的需求，验证了方法的有效性，为高寒高海拔地区风光互补系统的优化设计提供了理论指导。

In order to improve the comprehensive performance of wind-solar hybrid co-generation system for severely cold and high altitude regions, a capacity optimization model was established. A multi-objective optimization was performed using the Non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ) to maximize annual CO₂ emission reduction, minimize system investment cost and loss of power supply probability. According to the annual sunshine and wind conditions in that area of Tibet, the capacity optimal design for wind-solar hybrid co-generation system was carried out using the non-dominated sorting genetic algorithm-Ⅱ(NSGA-II). The results show that the loss of power supply probability, investment cost and annual CO₂ emission reduction are 4.6%, 162 000 ￥ and 95.4 t, respectively. The solar collector accounts for about 50% of the total investment, followed by the photovoltaic cell and battery. While, the investment of fan and heat storage tank is the least. Besides, the hourly loss of power supply probability is mainly concentrated in the period of low outdoor temperature. The multi-objective optimization can balance the requirements of economy, reliability and environment for wind-solar hybrid co-generation system, which verifies the effectiveness of this method. It provides theoretical guidance for the optimal design of the wind-solar hybrid co-generation system in severely cold and high altitude regions.