风电储能系统优化配置及运行研究

发布时间：2018-03-14 16:32

本文选题：风力发电　切入点：混合储能系统　出处：《沈阳工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：近些年来,风电作为新型能源以清洁、环保、无污染等优点得到了大力的发展,但风力发电具有波动强、间接性大、不稳定等缺点,会使风电场发电的可靠性产生不确定性影响。同时大规模风电并网会引起电网电压和频率的剧烈震荡,严重影响电网稳定。储能系统通过功率快速充放以灵活有效的方式平抑风电功率波动,降低其对电力系统扰动性影响,提高风电并网的电能质量。风储联合系统的经济性主要体现在储能容量的有效配置上。因此对储能容量的合理配置具有重要的经济和工程现实意义。本文工作研究的重点是风储联合系统功率提取运行控制策略以及储能容量优化配置的研究。首先,通过对集中式风电功率波动特性进行分析,表明采用储能系统平抑功率波动的必要性。建立双馈型风机、蓄电池、超级电容器储能装置的数学模型,对其蓄电池及超级电容器运行互补特性行阐述分析。在MATLAB/SIMULINK仿真平台构建风储联合系统仿真模型,为后续风储联合系统功率分配以及容量配置奠定好基础。其次,提出了基于自适应卡尔曼功率分解的风电混合储能容量配置方法。该方法采用时变域递回归估算方式,将最优滤波理论与风储联合系统相结合,对风电输出功率进行卡尔曼分解,分离出并网输出功率部分和储能装置平抑部分。根据蓄电池及超级电容器运行互补特性,对吞吐部分低频和高频区分别进行平抑。同时与传统功率提取分量控制技术低通滤波器进行平滑功率输出比较,可以发现在相同条件下,卡尔曼功率分解平滑功率输出更加有效,同时,卡尔曼功率分解有效的解决了传统低通滤波器功率分解时的时间滞后性、平抑尖峰功率不敏感性等问题。有效的提高平滑功率输出的能力。最后,通过分析储能运行管理结构,建立以混合储能综合经济成本为最低的优化目标函数,满足多种约束条件下混合储能系统管理控制策略模型,以实际各储能单元运行特性及稳定性为定性指标,结合实际问题提出了基于遗传算法的混合储能容量优化配置的方法。在MATLAB平台中构建混合储能系统仿真,通过算例和优化后储能装置的荷电状态(SOC)的波动范围,验证该储能容量配置方法的有效性。
[Abstract]:In recent years, as a new type of energy, wind power has been greatly developed with the advantages of cleanliness, environmental protection and non-pollution. However, wind power generation has the disadvantages of strong fluctuation, great indirectness, instability and so on. The reliability of wind farm power generation is uncertain. Meanwhile, large-scale wind power grid connection can cause severe voltage and frequency oscillation in the power grid. The energy storage system has a flexible and effective way to suppress the fluctuation of wind power and reduce its disturbance to the power system. The economy of combined wind energy storage system is mainly reflected in the effective allocation of energy storage capacity. Therefore, it has important economic and engineering practical significance for the rational allocation of energy storage capacity. The emphasis is on the power extraction operation control strategy and the optimal configuration of energy storage capacity of the combined wind-storage system. By analyzing the characteristics of centralized wind power fluctuation, it is shown that it is necessary to use the energy storage system to stabilize the power fluctuation. The mathematical model of doubly-fed fan, storage battery and supercapacitor energy storage device is established. The complementary characteristics of storage battery and supercapacitor are described and analyzed. The simulation model of air storage joint system is built on MATLAB/SIMULINK simulation platform, which lays a good foundation for power distribution and capacity configuration of subsequent air storage joint system. Based on adaptive Kalman power decomposition, a wind power hybrid energy storage capacity allocation method is proposed, in which the optimal filtering theory is combined with the combined wind storage system by using the time-varying domain regressive estimation method. The output power of wind power is decomposed by Kalman method, and the output power part of grid-connected power is separated from that of energy storage device. According to the complementary characteristics of batteries and supercapacitors, At the same time, compared with the traditional power extraction component control technique, the smooth power output of the low-pass filter can be found under the same conditions. Kalman power decomposition smoothing power output is more efficient, meanwhile, Kalman power decomposition effectively solves the time lag of power decomposition of traditional low-pass filter. Finally, by analyzing the operation management structure of energy storage, an optimization objective function with the lowest economic cost of hybrid energy storage is established. The management and control strategy model of hybrid energy storage system under various constraints is satisfied. The operation characteristics and stability of actual energy storage units are taken as qualitative indexes. Based on the practical problems, the optimal configuration of hybrid energy storage capacity based on genetic algorithm is proposed. The simulation of hybrid energy storage system is constructed on MATLAB platform, and the fluctuating range of the charged state of the energy storage device after optimization is calculated by an example. The effectiveness of the energy storage capacity allocation method is verified.
【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM614

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