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基于典型风电场景的风储协同频率支撑容量优化配置

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为了有效改善新能源渗透率下电网系统的调频能力和频率稳定性下降的问题,同时兼顾系统经济收益,文中提出了一种基于典型风电场景概率的飞轮储能与风电机组协同频率支撑容量优化配置方案.首先,利用改进的K-means聚类算法对风电年历史处理数据进行处理,得出 4 种风电典型运行场景及其概率;其次,基于风机和飞轮储能的特性建立风储联合系统运行模型和调频控制策略;最后,以年均经济收益最高为目标函数建立风储联合系统容量优化配置模型,并利用粒子群算法(PSO)对优化配置模型进行求解.通过仿真将该配置方案与只有风机的场景进行对比,结果显示所提出的配置方案在年经济收益和调频完成率上都有很大的改善,证明了该方案的合理性和有效性.
Optimization Configuration of Wind Storage Collaborative Frequency Support Capacity Based on Typical Wind Power Scenarios
For improving the issue of the frequency modulation capability and frequency stability reduction of power grid system at a high penetration rate of new energy,while taking into account the economic benefits of the system,a capacity optimized configuration scheme for frequency support of collaboration between flywheel energy storage and wind turbines based on typical wind power scenario probability is proposed in this paper.Firstly,the improved K-means clustering algorithm is used to process the annual historical processing data of wind power,and four typical operating scenarios and their probabilities of wind power are obtained.Then,the operational model and frequency regulation control strategy of the wind-storage integrated system are set up based on the characteristics of wind turbines and flywheel energy storage.Finally,a capacity optimized configuration model for wind-storage integrated system is set up with the objective function of maximizing annual economic benefits,and the optimized configuration model is solved by using the particle swarm optimization(PSO)algorithm.The configuration scheme is compared with the scenario of only having wind turbines through simulation.The results show that the proposed configuration scheme has great improvement in annual economic benefits and frequency modulation completion rate and the rationality and effectiveness of the scheme are proved.

wind-storage integratedK-means clusteringwind power scenario probabilityflywheel energy storage systemfrequency supportparticle swarm optimization(PSO)algorithm

孙广宇、胡姝博、谢赐戬、马欣彤

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国网辽宁电力科学研究院,沈阳 110006

风储协同 K-means聚类 风电场景概率 飞轮储能系统 频率支撑 粒子群优化算法

2024

电力电容器与无功补偿
西安电力电容器研究所

电力电容器与无功补偿

影响因子:0.99
ISSN:1674-1757
年,卷(期):2024.45(6)