考虑风电接入和负调峰能力的机组组合优化研究

发布时间：2018-06-30 18:54

本文选题：机组组合 + 负调峰能力　；参考：《西南交通大学》2017年硕士论文

【摘要】：近年来,生态环境的日益恶化以及能源危机等问题,促使新能源发电得到了大力的发展,其中风力发电已拥有了较为成熟的技术,并在全世界范围内得到了广泛的应用。但受制于风资源自身的特性,风电具有非常明显的随机性、间歇性。当风电大规模接入后,电力系统在安全稳定、电能质量、调峰调频等方面将面临巨大的挑战。为保证安全可靠地运行,电力系统需要预留更多的旋转备用容量,以应对风电不确定性和反调峰特性。系统常规机组在负荷低谷时刻的可调出力,在很大程度上决定了系统的风电接纳能力。因此,风电的大规模消纳,最关键的是需要解决系统调峰所面临问题,这不仅取决于现有的系统结构,还需要制定合理的机组组合方案。为了能够使系统经济、可靠地运行,研究了一种综合考虑系统运行成本以及常规机组低谷时刻负调峰能力的多目标机组组合优化方法。本文首先分析了常规机组的负调峰特性,并以此建立了考虑低谷时段的负调峰能力模型。为了能在机组组合问题中体现风电的波动性,实现了一种基于非参数估计的风功率区间预测方法。建立了以运行费用和负调峰能力为目标的多目标机组组合模型,并提出了一种考虑风电不确定性的两阶段优化方法,在第一阶段使用确定性风电预测值参与机组组合多目标优化,得到Pareto最优解集,第二阶段将所得的Pareto解集与风电预测区间结合,对不同的机组组合方案进行评估,最终可得到兼顾系统经济性和可靠性的机组组合方案。针对所建立的模型,研究了两种求解方法,分别是基于离散粒子群和预测校正内点法(DPSO-PCIPM)的层级优化方法以及基于归一化法线约束法(NNC)的优化方法,通过算例分析,对比发现NNC法要优于DPSO-PCIPM法。因此,后续选用NNC法在10机系统及其扩展系统(20机)进行了仿真分析,仿真结果验证了本文方法的可行性,能够为决策者提供切合实际的机组组合优化方案。
[Abstract]:In recent years, the deterioration of ecological environment and energy crisis have promoted the development of new energy power generation, among which wind power generation has a more mature technology, and has been widely used in the world. However, due to the characteristics of wind resources, wind power has a very obvious randomness and intermittence. When wind power is connected on a large scale, the power system will face great challenges in safety and stability, power quality, peak regulation and frequency modulation. In order to ensure the safe and reliable operation of the power system, it is necessary to reserve more rotating reserve capacity to deal with the uncertainty of wind power and the anti-peak-shaving characteristics. To a great extent, the adjustable output force of the system conventional units at the low load moment determines the wind power acceptance capacity of the system to a great extent. Therefore, the key to large-scale wind power absorption is to solve the problem of peak shaving system, which not only depends on the existing system structure, but also needs to formulate a reasonable unit combination scheme. In order to make the system run economically and reliably, a multi-objective unit combination optimization method considering the system operating cost and negative peak-shaving capacity at the low moment of the conventional unit is studied. In this paper, the negative peak-shaving characteristics of conventional units are analyzed, and the model of negative peak-shaving capacity considering the low period is established. In order to reflect the fluctuation of wind power in the unit commitment problem, a wind power interval prediction method based on nonparametric estimation is implemented. A multi-objective unit combination model with the aim of operating cost and negative peak-shaving ability is established, and a two-stage optimization method considering the uncertainty of wind power is proposed. In the first stage, the deterministic wind power prediction value is used to participate in the multi-objective optimization of the unit combination, and the Pareto optimal solution set is obtained. In the second stage, the Pareto solution set is combined with the wind power prediction interval to evaluate the different unit combination schemes. Finally, the unit combination scheme which takes into account the economy and reliability of the system can be obtained. According to the established model, two methods are studied, one is hierarchical optimization based on discrete particle swarm optimization and the other is based on DPSO-PCIPM, and the other is based on normalized normal constraint method (NNC). It is found that NNC method is better than DPSO-PCIPM method. Therefore, the NNC method is used to simulate the 10-machine system and its extended system (20 machines). The simulation results verify the feasibility of this method and can provide a practical optimization scheme for the decision makers.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM614;TM73

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