火电调峰机组运行经济性研究

发布时间：2018-03-22 05:04

本文选题：火力发电机组　切入点：调峰运行　出处：《河北科技大学》2017年硕士论文　论文类型：学位论文

【摘要】：火电厂的调峰机组的运行经济性直接影响着整个电网的运行经济性,所以为了能够尽量减少火电厂调峰运行机组的成本费用,需要对调峰机组的运行方式进行正确的选择。本课题针对低负荷调峰运行方式与两班制调峰运行方式进行了研究,并对两种调峰运行方式的运行安全性、机动性、调峰幅度、故障率以及经济性等方面进行了分析。根据火力发电机组耗能的非线性特性,考虑机组所带负荷的高峰与低谷情况和发电机组在不同调峰运行方式下的不同耗能情况,最终确定机组调峰运行方式的临界时间,以确定不同时间段的最佳调峰运行方式。本课题通过对火力发电机组低负荷运行方式的负荷分配方法进行了详细的对比分析,给出了热特性曲线连续和不连续时基于等微增量原理的负荷分配方法;并提出了基于遗传算法与粒子群算法来进行机组负荷分配的模型,以机组运行经济性作为调峰目标,以发电机组输出有功功率作为优化变量,对遗传算法与粒子群算法进行了仿真研究。在进行负荷分配的过程中,发电机组根据自身的耗能特性,经过不断的迭代,通过对比遗传算法与粒子群算法的收敛性能及最优解的质量,最终确定最优的负荷分配方案。最后本课题利用实例,对火电厂的三台火力发电机组,进行了最佳调峰运行方式的确定,并利用粒子群优化算法将发电机组的负荷进行了最优分配,使火电厂的调峰机组实现最优经济运行。
[Abstract]:The operation economy of peak-shaving units in thermal power plants has a direct impact on the operation economy of the whole power network, so in order to reduce the cost of peak-shaving units in thermal power plants as far as possible, It is necessary to select the correct operation mode of peak-shaving unit. This paper studies the operation mode of low-load peak-shaving and two-shift peak-shaving, and the operation safety, maneuverability, peak shaving range of two peak-shaving operation modes. The failure rate and economy are analyzed. According to the nonlinear characteristics of thermal power generation units, the peak and low load of the units and the different energy consumption of the units under different peak-shaving operation modes are considered. Finally, the critical time of peak-shaving operation mode is determined in order to determine the best peak-shaving operation mode in different time periods. In this paper, the load distribution method of low-load operation mode of thermal power generating set is compared and analyzed in detail. The load distribution method based on the principle of equal micro-increment is given when the thermal characteristic curve is continuous or discontinuous, and the model of unit load distribution based on genetic algorithm and particle swarm optimization algorithm is proposed, which takes the unit operation economy as the peak shaving target. The genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are simulated with the active power output of the generator set as the optimization variable. In the process of load distribution, the generator sets are iterated according to their own energy dissipation characteristics. By comparing the convergence performance of genetic algorithm and particle swarm optimization algorithm and the quality of optimal solution, the optimal load distribution scheme is finally determined. The optimal peak-shaving operation mode is determined, and the load of generator set is allocated optimally by using particle swarm optimization (PSO) algorithm, which makes the peak-shaving unit of thermal power plant realize the optimal economic operation.
【学位授予单位】：河北科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM621;TM73

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