辅助风电功率爬坡控制的混合储能系统运行策略和容量配置研究

发布时间：2018-03-28 01:36

本文选题：混合储能系统　切入点：双向AC/DC变流器　出处：《上海电机学院》2017年硕士论文

【摘要】：随着大规模波动性新能源并网,潜在的风电爬坡事件影响着电力系统的安全稳定性和供电充裕性。针对复杂的风电爬坡事件,本文综合利用了混合储能技术辅助风电功率爬坡控制,并采用了粒子群算法(Particle Swarm Optimization,PSO)对混合储能系统的容量进行了优化配置。主要完成了以下工作:首先,对蓄电池和超级电容进行了特性分析和比较。采用了基于电流电压双环解耦的空间矢量脉宽调制控制策略,建立了双向AC/DC变流器的模型,完成了混合储能系统的综合等效模型的建立,并在Simulink中对其进行仿真分析。其次,针对复杂的风电爬坡事件,提出了基于储能电池SOC反馈的场景切换系统控制策略辅助风电功率爬坡控制,使复杂的风电爬坡场景得到缓解。引入场景系数k,利用模糊控制动态参数确定的方法更新k的值,通过结合弃风等手段使储能系统既满足平抑风电波动输出功率的要求,又能限制电池过放现象。并在Matlab/Simulink中搭建风储仿真模型,验证了控制策略的有效性。然后,考虑了混合储能系统的全寿命周期年均成本和辅助风电爬坡控制运行的惩罚成本,建立了储能系统经济性评估总模型。在场景切换控制策略的基础上,建立了以风电接纳能力最大和储能系统运营成本最小的多目标函数,在满足爬坡约束的前提下,运用粒子群优化算法,使系统对储能的容量需求更小,达到系统经济性和风电接纳能力最优。最后,对整个实验系统进行了设计,搭建了基于RT-LAB实时数字仿真实验平台。验证了所提出的风力发电系统和混合储能系统控制策略的有效性。
[Abstract]:With large-scale volatility of new energy connected to the grid, potential wind power climbing events affect the safety, stability and power supply adequacy of power systems. In this paper, the hybrid energy storage technology is used to assist the wind power climbing control, and particle swarm optimization algorithm (PSO) is used to optimize the capacity of the hybrid energy storage system. The characteristics of storage battery and super capacitor are analyzed and compared. The control strategy of space vector pulse width modulation based on double loop decoupling of current and voltage is adopted, and the model of bidirectional AC/DC converter is established. The integrated equivalent model of hybrid energy storage system is established and simulated in Simulink. Secondly, for the complex wind power climbing event, In this paper, a scene switching system control strategy based on SOC feedback from energy storage battery is proposed to help wind power climbing control to ease the complex wind power climbing scene. The scene coefficient k is introduced and the value of k is updated by the method of fuzzy control dynamic parameter determination. The energy storage system can not only meet the requirement of output power of wind power fluctuation but also limit the over-discharge of battery by combining with abandoned wind. The simulation model of air storage is built in Matlab/Simulink to verify the effectiveness of the control strategy. Considering the full life cycle annual cost of hybrid energy storage system and the penalty cost of auxiliary wind power climbing control operation, a general economic evaluation model of energy storage system is established. A multi-objective function with the maximum wind power acceptance capacity and the lowest operating cost of the energy storage system is established. On the premise of satisfying the climbing constraints, the particle swarm optimization algorithm is used to make the energy storage capacity of the system less demanding. The system economy and wind power acceptance capacity are optimized. Finally, the whole experimental system is designed. A real time digital simulation experiment platform based on RT-LAB is built to verify the effectiveness of the proposed control strategy for wind power system and hybrid energy storage system.
【学位授予单位】：上海电机学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM614

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