风电功率纵向时刻概率分析与风电场储能容量优化

发布时间：2018-11-05 19:06

【摘要】：随着能源、环境问题的日益突出,以及煤炭、石油等非可再生能源的日益枯竭,世界各国均已将可再生能源的发展提升到战略高度。其中,风能因其污染少、储量大、不占用耕地等优点成为最具大规模开发利用潜力的能源。近年来,随着风力发电技术的不断成熟,其规模逐年扩大,装机容量逐年增加。因此,风力发电对电网的影响也日益受到关注。风电具有波动性、间歇性等特点,这使其面临不确定性和难以准确预测等问题。风电的大规模并网给电网的安全稳定运行、电能质量等方面带来挑战。如何平抑风电的波动,成为重要研究课题。在此背景下,论文从风功率波动特性、风电场储能容量优化、风功率分级后进行储能等多方面进行研究,以期提高风电场功率输出的可靠性,提高风功率的利用率,提高风电的可调度性。论文的主要工作可以概括为以下几个部分：首先,提出一种新的分析风电功率波动特性的方法,即纵向时刻概率分析法,该方法基于实测历史数据,统计365天或更长天数内每天同一时刻的风电出力,得到96个不同时刻的概率分布结果,并通过函数拟合归纳出由分段函数表达的风电出力概率特征,在此基础上实现对风功率预测值的预评估。该方法不仅证明了纵向时刻概率分布特性是风电出力的固有属性,也为后续功率分级方法的实现提供了依据。其次,为使风电输出最大程度满足调度需求,引入储能系统,并提出考虑电池寿命和过放现象的风电场储能容量优化计算法,该方法将放电深度及过放现象等造成的寿命损伤折合为运行成本,将未满足期望输出部分的能量折合为惩罚成本,同时考虑储能设备的固有成本,以该三部分综合经济成本最小为优化目标,以功率约束、容量约束、电池寿命约束为约束条件,以遗传算法为求解方法,来求解最优的储能容量。储能系统配置这一容量后,可以从经济性、可靠性等方面最大程度减小风功率波动,满足调度需求。再次,为减小储能系统容量,降低储能成本,提高风电利用率,提出一种基于纵向时刻概率分析方法和区间估计理论的风功率分级方法,并在分级方法的基础上进行新的风电场储能容量优化。风功率分级是将风电功率分为一级出力、二级出力、三级出力,其中前两级出力可靠性较高,可直接用于风电调度,三级出力用于优化储能容量。利用三级出力求取的储能容量较小,可大大降低储能成本,一级出力、二级出力和储能后三级出力之和作为风场输出,可有效提高风场输出的稳定度和利用率。最后,在前述研究内容的基础上,以分级后加入小储能系统的风场输出作为历史数据,进行风功率预测,与不加储能时利用原始风电功率数据进行的预测相比,前者的预测精度显著提高。这种分级后储能的方法对于实现风电的可靠调度具有现实意义。
[Abstract]:With the increasingly prominent energy and environmental problems, as well as the depletion of non-renewable energy such as coal and oil, countries in the world have promoted the development of renewable energy to a strategic level. Among them, wind energy becomes the most potential energy for large-scale exploitation because of its advantages of less pollution, large reserves and no occupation of cultivated land. In recent years, with the development of wind power generation technology, its scale and installed capacity increase year by year. Therefore, the influence of wind power generation on the power grid has been paid more and more attention. Wind power has the characteristics of volatility and intermittency, which makes it face uncertainty and difficult to predict accurately. The large-scale grid connection of wind power brings challenges to the safe and stable operation of power grid and power quality. How to stabilize the fluctuation of wind power has become an important research topic. Under this background, the paper studies the wind power fluctuation characteristic, the wind farm energy storage capacity optimization, the wind power classification and so on, in order to improve the reliability of the wind farm power output and the efficiency of the wind power utilization. Improve the schedulability of wind power. The main work of this paper can be summarized as follows: firstly, a new method to analyze the fluctuation of wind power is proposed, that is, the longitudinal moment probability analysis method, which is based on the measured historical data. According to the statistics of wind power output at the same time every day for 365 days or longer days, the probability distribution results of 96 different times are obtained, and the probability characteristics of wind power output expressed by piecewise function are summed up by function fitting. On this basis, the prediction of wind power can be evaluated. This method not only proves that the probability distribution characteristic of longitudinal moment is the inherent attribute of wind power generation, but also provides the basis for the realization of subsequent power classification method. Secondly, in order to maximize the output of wind power to meet the demand of dispatching, the energy storage system is introduced, and the optimal calculation method of energy storage capacity of wind farm considering battery life and over-discharge phenomenon is proposed. In this method, the life damage caused by discharge depth and overdischarge phenomenon is reduced to the operating cost, and the energy which is not satisfied with the expected output is converted into the penalty cost, and the inherent cost of the energy storage equipment is considered at the same time. The optimal energy storage capacity is solved by taking the minimum comprehensive economic cost as the optimization objective, the power constraint, the capacity constraint, the battery life constraint as the constraint conditions and the genetic algorithm as the solution method. After the energy storage system is configured with this capacity, the fluctuation of wind power can be minimized to the greatest extent from the aspects of economy and reliability, and the dispatching demand can be satisfied. Thirdly, in order to reduce the capacity of energy storage system, reduce the cost of energy storage and improve the utilization rate of wind power, a wind power classification method based on longitudinal time probability analysis and interval estimation theory is proposed. The new wind farm energy storage capacity is optimized based on the classification method. Wind power classification is to divide wind power into first output, second output and third output, among which the first two are of high reliability and can be directly used in wind power dispatching, and the third is used to optimize energy storage capacity. The cost of energy storage can be greatly reduced by using the small storage capacity of the three-stage output, and the sum of the first-order output and the three-stage output after the storage can be taken as the output of the wind field, which can effectively improve the stability and utilization ratio of the output of the wind field. Finally, on the basis of the above research, the wind field output of the small energy storage system is used as the historical data to predict the wind power, compared with the prediction using the original wind power data without the energy storage. The prediction accuracy of the former is improved significantly. This method of energy storage after classification has practical significance for the reliable dispatching of wind power.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM614

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