包含储能电池的并网光伏电站的功率预测与实时能量管理研究

发布时间：2018-07-09 23:49

本文选题：光伏电站 + Elman神经网络　；参考：《河北工业大学》2015年硕士论文

【摘要】：由于光伏发电受地理位置、天气状况和外界环境等条件的影响巨大,导致光伏电站的电能输出具有明显的间歇性、随机性,也就导致了光伏电站内部的光伏组件和储能系统、光伏电站与大电网之间的功率交换过程复杂化。光伏电站可以从电力公司购电,也可以售电给电力公司,有偿的为电网提供“削峰填谷”和紧急功率支持等服务。本文以青海某1MW光伏电站为研究对象,对其能量管理的基本理论和管理策略进行了探究,其中包括光伏发电功率短期预测和实时能量管理。准确的预知光伏组件在未来某段时间内的发电功率,对光伏电站内的光伏组件和储能系统的最优配合、经济调度、最优潮流等具有着深远意义。为此,本文基于Elman神经网络理论研究光伏发电功率短期预测模型。首先,研究了气象因素(如太阳辐照度、温度等)与光伏发电功率的相关性;其次,搭建了基于Elman神经网络的光伏发电功率短期预测模型,确定模型的输入层、隐含层、输出层和承接层的神经元数目,并定量评估了不同天气类型下的预测模型的预测精度。与BP神经网络算法和NSET算法作对比分析研究,验证本文所采用的预测模型算法比这两种算法的预测精度都高。光伏发电的出力波动剧烈,不宜独立向负荷供电,需要同其它储能装置配合使用。此外,光伏电站并网运行改变了系统中的潮流分布,所以需要对光伏单元、储能系统和大电网之间的能量进行管理,实现光伏电站稳定并网、高效经济运行。针对光伏单元在并网运行中面临的能量管理问题,本文建立了一种并网光伏电站实时能量管理模型。首先,从凌晨0:00到24:00划分为峰、平、谷三个时段;然后随时跟踪储能蓄电池的荷电状态SOC,根据当前时刻所处在的不同时段和蓄电池的SOC情况采用不同的能量调度模型;同时需要考虑储能蓄电池及其配套装置成本等;最后通过算例验证了本文所提出的方法不仅可以实现光伏电站的经济运行,还可以辅助大电网进行“削峰填谷”。
[Abstract]:Because photovoltaic power generation is greatly affected by geographical location, weather conditions and external environment, the power output of photovoltaic power station has obvious intermittence and randomness, which leads to the photovoltaic module and energy storage system inside the photovoltaic power station. The process of power exchange between photovoltaic power plants and large power grids is complicated. Photovoltaic power stations can buy electricity from power companies or sell electricity to power companies, providing services such as "peak cutting and valley filling" and emergency power support for the grid. In this paper, a 1MW photovoltaic power plant in Qinghai Province is studied. The basic theory and management strategy of energy management are discussed, including short-term prediction of photovoltaic power generation and real-time energy management. Accurately predicting the generation power of PV module in a certain period of time is of great significance to the optimal coordination, economic dispatch and optimal power flow of PV module and energy storage system in photovoltaic power plant. Therefore, based on Elman neural network theory, this paper studies the short-term prediction model of photovoltaic power generation. Firstly, the correlation between meteorological factors (such as solar irradiance, temperature, etc.) and photovoltaic power generation is studied. Secondly, the short-term prediction model of photovoltaic power generation based on Elman neural network is built to determine the input layer and hidden layer of the model. The number of neurons in the output layer and the receiving layer and the prediction accuracy of the prediction models under different weather types are quantitatively evaluated. Compared with BP neural network algorithm and NSET algorithm, it is verified that the prediction model algorithm used in this paper is more accurate than these two algorithms. The output force of photovoltaic power generation fluctuates sharply, so it is not suitable to supply power to load independently, so it is necessary to cooperate with other energy storage devices. In addition, the grid-connected operation of photovoltaic power station changes the distribution of power flow in the system, so it is necessary to manage the energy between photovoltaic unit, energy storage system and large power grid to realize stable grid connection and efficient and economical operation of photovoltaic power station. Aiming at the problem of energy management in grid-connected operation of photovoltaic unit, a real-time energy management model of grid-connected photovoltaic power station is established in this paper. First of all, from 0:00 to 24:00, it is divided into three periods: peak, level and valley, and then it tracks the state of the storage battery at any time, and adopts different energy scheduling models according to the different periods of time and the SOC of the battery at present. At the same time, it is necessary to consider the cost of energy storage battery and its supporting equipment. Finally, it is verified that the proposed method can not only realize the economic operation of photovoltaic power station, but also assist the large power network to "cut the peak and fill the valley".
【学位授予单位】：河北工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM615

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