基于多目标的大规模风电场并网有功动态优化调度策略研究

发布时间：2019-01-11 12:46

【摘要】：风电由于其清洁可再生性和国家政策的大力支持而得到了飞速发展。在风电系统规模日益扩大和约束条件增多的背景下,风电调度技术使风力发电的可控制以及可调度功能得到实现,合理地安排电源运行方式,最大化的利用风电资源提供优质电能,在可调度的有功功率范围之内,利用多种可调能量,尽可能提高运行的经济性。虽然包含风电场的电力系统调度有不少研究报道,但风电参与电网调度仍然处于探索的阶段。因此如何构建大规模风电场的电力系统经济调度运行机制是需要深入研究的问题,而其中的关键是提出一种新的多目标调度模型和求解该数学模型的高效率算法。大规模的风电场接入电力系统以后,需要建立以电力系统安全性评价、购电运行成本最小、大气污染排放量最小、火力发电耗能资源量最小等为目标的动态经济调度模型,构成大规模风电场并网有功动态经济调度的多目标优化问题。本文对多目标优化算法NSGA-II进行了一些改进,应用了Pareto-局部优化算法和基于PCD的精英种群更新策略,然后采用ZDT测试函数集对该算法的性能进行了测试,并根据多目标优化算法的收敛性及性能度量的方法进行了评价,验证了该算法的有效性。最后在MATLAB仿真平台上,将改进的NSGA-II优化算法对大规模风电场并网动态优化调度的问题进行了求解和验证,以IPSO算法和NSGA算法为参考,与改进的NSGA-II算法进行对比,证明了本调度方案一方面保证了系统调度运行的安全可靠,另一方面节约了燃料,减少了污染物的排放,降低了发电成本,具有更好的利用价值。
[Abstract]:Wind power has been developed rapidly because of its clean and renewable nature and the support of national policy. Under the background of increasing scale of wind power system and increasing constraints, wind power dispatching technology realizes the controllable and dispatching functions of wind power generation, and reasonably arranges the operation mode of power supply. To maximize the use of wind power resources to provide high quality electric energy, within the schedulable active power range, the use of a variety of adjustable energy, as much as possible to improve the economy of the operation. Although there are a lot of research reports on power system dispatching including wind farm, wind power dispatching is still in the exploratory stage. Therefore, how to construct the economic dispatching mechanism of large-scale wind farm is a problem that needs to be deeply studied, and the key is to propose a new multi-objective scheduling model and an efficient algorithm to solve the mathematical model. After the large-scale wind farm is connected to the power system, it is necessary to establish a dynamic economic dispatching model, which aims at the safety evaluation of the power system, the minimum operating cost of electricity purchase, the minimum emissions of air pollution and the minimum amount of energy consumption resources of thermal power generation. A multi-objective optimization problem for dynamic economic scheduling of large scale wind farms connected with active power is presented. In this paper, the multi-objective optimization algorithm NSGA-II is improved, and the Pareto- local optimization algorithm and the elite population updating strategy based on PCD are applied. Then, the performance of the algorithm is tested by using the ZDT test function set. The algorithm is evaluated according to the convergence and performance measurement of the multi-objective optimization algorithm, and the effectiveness of the algorithm is verified. Finally, on the MATLAB simulation platform, the improved NSGA-II optimization algorithm is used to solve and verify the problem of dynamic optimal scheduling of large-scale wind farms connected to the grid. The IPSO algorithm and NSGA algorithm are taken as reference, and compared with the improved NSGA-II algorithm. It is proved that this scheduling scheme can ensure the safety and reliability of the system on the one hand, save fuel on the other hand, reduce the emission of pollutants, reduce the cost of power generation, and have better utilization value.
【学位授予单位】：兰州理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM614;TM73

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