为了消除新能源的随机性和波动性给电网调度带来的影响﹐在新能源站侧规模化引入储能﹐文章分析了储能系统对含新能源的电力系统运行模拟的影响。结合电力系统运行过程中不同模块功能的特点，提出了含储能的新能源电力系统运行模拟框架，主要包括：在新能源出力模拟方面，利用神经网络对新能源出力进行预测；在经济调度方面，建立含储能的新能源系统经济调度模型﹐其目标函数兼顾了火电排污成本，考虑了备用容量补偿成本及风电场负效率运行惩罚成本；储能成本方面，考虑了初始投资成本和运维成本；在运行分析方面，在超短周期内，利用新能源平均功率变化率以及反调峰概率2个指标对储能的平抑功率波动能力和调峰能力进行评估，进而得到综合经济、环境、抑制波动和调峰能力的运行模拟方案。采用遗传算法以新疆电网为例进行仿真验证，结果表明，新能源站侧规模化应用储能可以有效平抑新能源输出功率的波动和反调峰现象。

The randomness and volatility of new energy bring difficulties to grid dispatching. Therefore, large-scale energy storage is introduced at the side of new energy stations, and the impact of the energy storage system on the operation simulation of power systems containing new energy is analyzed. This paper combines the characteristics of different modules with different functions during the operation of the power system, and proposes a simulation framework for the operation of new energy power systems with energy storage. It mainly includes: in the simulation of new energy output, using neural networks to predict the output of new energy; in the economy in terms of dispatching, an economic dispatch model for new energy systems with energy storage is established. Its objective function takes into account the cost of thermal power emissions, and considers the cost of reserve capacity compensation and the penalty cost of negative efficiency operation of wind farms. In terms of energy storage costs, the initial investment cost and operation and maintenance cost; in terms of operation analysis, in the ultra-short period, the two indicators of average energy change rate of new energy and peak inversion probability are used to evaluate the energy storage's ability to suppress power fluctuations and peak shaving; Operation simulation program for environment, suppression of fluctuations and peak shaving ability. Using genetic algorithm to take Xinjiang power grid as an example for simulation verification, the results show that the large-scale application of energy storage on the side of new energy stations can effectively suppress the fluctuation and reverse peaking of new energy output power.