风光互补蓄能系统优化算法研究及应用

发布时间：2018-02-14 21:34

本文关键词： 抽水蓄能光伏发电风力发电免疫粒子群算法经济效益功率波动　出处：《华北电力大学》2014年硕士论文　论文类型：学位论文

【摘要】：目前,为解决化石能源短缺和能源浪费问题,新能源的开发利用就显得尤为重要,集合风、光优势互补的一种新的多能互补开发方式具有较好的经济意义和可持续发展意义。风力发电和太阳能光伏发电具有无法准确预测、随机性以及不稳定性的特点,导致风力和光伏发电无法被有效地利用并引起功率输出的波动。将风力发电、光伏发电与抽水蓄能组成风光水联合发电系统,很好的解决了这些问题,不仅可平滑风电和光伏发电的功率输出,而且随着电力市场的改革,实行了峰谷电价,也可以达到充分利用风电和光伏发电的目的。本文首先分析了风电场、太阳能光伏电站和抽水蓄能电站的相关知识,风光发电的互补特性。提出在风光发电互补的基础上结合抽水蓄能电站,构建了风光水联合系统的发电运行模型。针对联合系统的特点,对系统中抽水蓄能电站的容量进行计算,达到优化配置的目的,最终起到了降低功率波动、减少弃能的效果。为使风光水联合发电系统达到经济效益最大化优化调度并且平抑功率波动的目的,本文将以功率波动最小为目标的函数引入到经济效益最大化模型中。本文在粒子群算法的基础上,由于其易早熟、后期搜索速度慢而且精度较低的特点,提出一种动态调整学习因子的免疫粒子群算法。该算法通过对算法速度公式中的学习因子进行改进,采用非对称线性动态调整学习因子的方法,增强前期的全局搜索能力以及后期的局部搜索能力,快速得到最优解。该算法在该多目标联合优化调度系统的求解中显著提高了搜索精度,表明了模型和算法的有效性。本文研究了基于改进免疫粒子群算法在风光水联合运行系统中的应用,结果表明,这是合理利用风能和太阳能资源的有效途径,不但提高了使用新能源的效益,同时达到了降低风电场、光伏电站输出功率波动的目的,具有可观的经济效益和社会效益。
[Abstract]:At present, in order to solve the problems of fossil energy shortage and energy waste, the development and utilization of new energy is particularly important. A new multi-energy complementary development method with complementary optical advantages has better economic and sustainable development significance. Wind power generation and solar photovoltaic power generation have the characteristics of uncertainty, randomness and instability. Wind and photovoltaic power generation can not be effectively used and cause the fluctuation of power output. Wind power generation, photovoltaic power generation and pumped storage constitute the wind water combined power generation system, which solves these problems very well. Not only the power output of wind power and photovoltaic generation can be smoothed, but also with the reform of power market, peak-valley electricity price has been implemented, and the purpose of making full use of wind power and photovoltaic power generation can also be achieved. This paper first analyzes the knowledge of wind farm, solar photovoltaic power station and pumped storage power station, and the complementary characteristics of wind power generation. According to the characteristics of the combined system, the capacity of the pumped-storage power station in the system is calculated to achieve the purpose of optimizing the configuration, and finally to reduce the power fluctuation. In order to maximize the economic benefit of the combined generation system and to stabilize the power fluctuation, In this paper, the function of minimum power fluctuation is introduced into the economic benefit maximization model. On the basis of particle swarm optimization algorithm, due to its precocity, slow search speed and low precision, This paper presents an immune particle swarm optimization algorithm which dynamically adjusts the learning factor. By improving the learning factor in the speed formula of the algorithm, an asymmetric linear dynamic adjustment method is used to adjust the learning factor. The global search ability in the early stage and the local search ability in the later stage are enhanced, and the optimal solution is obtained quickly. The algorithm improves the search accuracy significantly in the solution of the multi-objective joint optimal scheduling system, and shows the validity of the model and the algorithm. In this paper, the application of improved immune particle swarm optimization algorithm in wind and water combined operation system is studied. The results show that it is an effective way to utilize wind and solar energy resources reasonably, and not only improves the efficiency of using new energy. At the same time, it can reduce the fluctuation of output power of wind farm and photovoltaic power station, and has considerable economic and social benefits.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM61;TP18

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