风电场储能系统的多目标优化调度策略

为了实现风电与储能联合运行的优化调度策略，首先综合考虑风电功率预测效果、并网功率波动和储能系统的出力水平等多个目标，建立风储联合运行的多目标优化仿真模型。然后运用马尔可夫模型预测风功率，同时基于有精英策略的非支配性排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)滚动优化风储并网功率，来获得风储系统不同运行策略。并通过优选储能系统运行参数，避免储能系统的过度充放电和进入死区。最后，将决策者的偏好嵌入到多目标优化过程中，针对优化解集的分布进行了对比分析，验证了偏好情况下的储能的针对性和有效性，实现了风储系统多目标偏好下的优化调度。

Multi-objective Optimal Scheduling of Energy Storage System in Wind Farms

In order to realize the optimal dispatching strategy for the joint operation of wind power and energy storage, firstly, multiple objectives such as wind power prediction effect, grid-connected power fluctuation and energy storage system output level are comprehensively considered, and a multi-objective optimization simulation model for wind storage joint operation is established. Then the Markov model is used to predict the wind power, and at the same time, based on the non-dominated sorting genetic algorithm with elite strategy (NSGA-II), the wind storage grid-connected power is rollingly optimized to obtain different operation strategies of the wind storage system. And by optimizing the operating parameters of the energy storage system, the excessive charging and discharging of the energy storage system and entering the dead zone are avoided. Finally, the decision-maker''s preference is embedded into the multi-objective optimization process, and the distribution of optimized solution sets is analyzed comparatively. The pertinence and effectiveness of energy storage under preference are verified, and the optimal scheduling of wind storage system under multi-objective preference is realized.