并网型光伏电站发电功率预测方法与系统
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摘要
光伏发电功率出力具有明显的间歇波动特性,大规模光伏发电接入给电网调度管理带来巨大挑战。如何在满足安全稳定约束的前提下,最大限度地消纳这些可再生能源,成为新能源电力系统的研究热点。光伏发电功率预测是解决此问题的关键技术之一,开展光伏电站发电功率预测方法与系统研究具有重要的学术与应用价值。
光伏发电功率预测模型需要拟合的映射关系在不同的天气状态下存在明显差异,采用单一模型实现多种不同天气状态下发电功率的准确预测非常困难。此外,光伏电站不同天气状态对应历史数据的分布并不均衡,由不均衡数据训练得到的单一预测模型对多变天气状态的适应性无法保证,预测精度难以满足要求。为此,本文在研究不同天气状态下地表与地外辐照度关系变化规律基础上,从辐照度数据中提取了反映天气状态的特征参数,利用支持向量机建立了天气状态模式识别模型,实现了缺失天气类型信息历史数据的辨识恢复,确保了历史数据的完整性和可用性,对比了功率预测不同的实现方式,提出了光伏发电功率分类分步预测方法,并给出了实现的总体框图和具体的技术路线图。
作为光伏发电功率预测的前提基础,辐照度预测对于功率预测至关重要。目前采用神经网络进行辐照度预测取得了较好效果,但现有预测模型存在输入空间维数较高、模型结构复杂等不足,且其输出的预测值也未经修正。为此,本文从充分利用已有数据、尽量减小信息冗余、控制输入变量维数三个方面对辐照度神经网络预测模型进行了改进；并根据不同天气状态的特点,按照设定的可变尺度参数,从时间周期性和临近相似性两个维度,利用历史运行数据和类型内相似性度量参数生成修正参考值和权重系数,提出了基于时间周期性和临近相似性的辐照度预测值修正方法。仿真结果表明上述改进措施和修正方法对辐照度预测精度的提高具有明显作用。
作为光伏发电功率预测的关键环节,光伏电站发电功率出力特性模型对于预测精度有着重要影响。现有发电功率出力特性模型存在实施步骤繁杂、参数优化困难、适应能力不强等问题。为此,本文利用通径分析对比了各气象影响因子对发电功率的直接影响及通过其它因子的间接影响,利用选取的主要气象影响因子构建了光伏电站的运行状态空间,利用实际运行数据建立了发电功率出力特性的关联数据模型,并根据影响因子与发电功率的相关程度定义了运行状态加权距离,提出了基于关联数据模型的发电功率映射预测方法。
最后,以本文上述研究为基础,针对电网调度管理和电站优化运行的要求,参照光伏发电功率预测的有关标准和技术规范,开发了光伏电站发电功率预测系统并投入应用,实际运行取得了良好效果,验证了本文提出有关方法和模型的有效性。
Due to the intermittent fluctuations characteristic of photovoltaic (PV) power generation, the grid-connection of large scale PV plants will bring great challenges and difficulties to power system dispatching and management. How to consume these renewable energies by the greatest degree under the security and stability constraints become one of the research focuses in the area of new energy power system. Power forecasting is one of the key technologies to solve this problem, the research on power forecasting approach and system of grid-connected PV plant has very important academic and applied value.
There are significant differences between the map relations fitted through PV power forecast model under different weather statuses, and it is very difficult to forecast the PV power accurately in variational weather conditions only use one single model. Moreover, the corresponding historical data distributions of different weather statuses are imbalance, so the applicability of the single forecast model trained by these imbalance data could not be guaranteed. That means the prediction accuracy could not meet the requirements. For this reason, based on the study of the variation relation between surface and extraterrestrial solar irradiance, the feature parameters reflecting weather status characteristic are extracted from solar irradiance data sequence, pattern recognition model for weather statuses based on support vector machine is constructed, the type label of those historical data missing weather type information could be identified, the integrity and availability of historical data are guaranteed. Classification step-wise forecast approach is put forward after the comparison of different power forecast realization modes. The overall block diagram and specific technology roadmap guiding the realization work of the proposed classification PV power forecast approach are also illustrated in this paper.
As the basis of PV power forecasting, the accuracy of solar irradiance forecasting is most important for power forecasting. Although artificial neural network (ANN) based solar irradiance forecast models have good performance, there are still some deficiencies exist in the current ANN forecast models, such as high input dimensions, complex model structures, and its output values also hasn't been modified reasonably. For this reason, the ANN based solar irradiance forecast models are improved from three aspects including take full use of available data, minimize information redundancy and control the input dimensions. According to the setup parameters of variable scale modification, weight coefficient and reference value generated from historical data and similarity metrics within weather type, time periodicity and neighboring similarity based two dimensions variable scale modification method for solar irradiance forecast values is presented. Simulation results show that the precision of solar irradiance prediction has been improved visibly by the above measures.
As the key step of PV power forecasting, output characteristic model of PV power generation has great influence to the accuracy of power forecasting. The implement processes of current characteristic model of PV power generation are very complicated, the parameter optimization is quite difficult, and its applicability is also not very satisfactory. For this reason, the direct effect and indirect effect through other factors to PV power of each meteorological influence factor are analyzed, the running state space is constructed by the selected main meteorological influence factors. The relevant data model reflecting the output characteristic of PV power generation is established using actual operating data. Finally, the power mapping forecast method based on running state weighted distance defined according to the relevance between PV power and influence factors is presented to obtain the forecast value from the relevant data model.
At last, considering the requirements of power grid dispatching and power plant optimal operation, referring to the relevant standards and technical specifications of PV power forecasting, a PV power forecast system based on the results of this study is developed and put into application. The actual operation achieved good results and verifies the validity of the approaches and models presented in the paper.
光伏发电功率预测模型需要拟合的映射关系在不同的天气状态下存在明显差异,采用单一模型实现多种不同天气状态下发电功率的准确预测非常困难。此外,光伏电站不同天气状态对应历史数据的分布并不均衡,由不均衡数据训练得到的单一预测模型对多变天气状态的适应性无法保证,预测精度难以满足要求。为此,本文在研究不同天气状态下地表与地外辐照度关系变化规律基础上,从辐照度数据中提取了反映天气状态的特征参数,利用支持向量机建立了天气状态模式识别模型,实现了缺失天气类型信息历史数据的辨识恢复,确保了历史数据的完整性和可用性,对比了功率预测不同的实现方式,提出了光伏发电功率分类分步预测方法,并给出了实现的总体框图和具体的技术路线图。
作为光伏发电功率预测的前提基础,辐照度预测对于功率预测至关重要。目前采用神经网络进行辐照度预测取得了较好效果,但现有预测模型存在输入空间维数较高、模型结构复杂等不足,且其输出的预测值也未经修正。为此,本文从充分利用已有数据、尽量减小信息冗余、控制输入变量维数三个方面对辐照度神经网络预测模型进行了改进；并根据不同天气状态的特点,按照设定的可变尺度参数,从时间周期性和临近相似性两个维度,利用历史运行数据和类型内相似性度量参数生成修正参考值和权重系数,提出了基于时间周期性和临近相似性的辐照度预测值修正方法。仿真结果表明上述改进措施和修正方法对辐照度预测精度的提高具有明显作用。
作为光伏发电功率预测的关键环节,光伏电站发电功率出力特性模型对于预测精度有着重要影响。现有发电功率出力特性模型存在实施步骤繁杂、参数优化困难、适应能力不强等问题。为此,本文利用通径分析对比了各气象影响因子对发电功率的直接影响及通过其它因子的间接影响,利用选取的主要气象影响因子构建了光伏电站的运行状态空间,利用实际运行数据建立了发电功率出力特性的关联数据模型,并根据影响因子与发电功率的相关程度定义了运行状态加权距离,提出了基于关联数据模型的发电功率映射预测方法。
最后,以本文上述研究为基础,针对电网调度管理和电站优化运行的要求,参照光伏发电功率预测的有关标准和技术规范,开发了光伏电站发电功率预测系统并投入应用,实际运行取得了良好效果,验证了本文提出有关方法和模型的有效性。
Due to the intermittent fluctuations characteristic of photovoltaic (PV) power generation, the grid-connection of large scale PV plants will bring great challenges and difficulties to power system dispatching and management. How to consume these renewable energies by the greatest degree under the security and stability constraints become one of the research focuses in the area of new energy power system. Power forecasting is one of the key technologies to solve this problem, the research on power forecasting approach and system of grid-connected PV plant has very important academic and applied value.
There are significant differences between the map relations fitted through PV power forecast model under different weather statuses, and it is very difficult to forecast the PV power accurately in variational weather conditions only use one single model. Moreover, the corresponding historical data distributions of different weather statuses are imbalance, so the applicability of the single forecast model trained by these imbalance data could not be guaranteed. That means the prediction accuracy could not meet the requirements. For this reason, based on the study of the variation relation between surface and extraterrestrial solar irradiance, the feature parameters reflecting weather status characteristic are extracted from solar irradiance data sequence, pattern recognition model for weather statuses based on support vector machine is constructed, the type label of those historical data missing weather type information could be identified, the integrity and availability of historical data are guaranteed. Classification step-wise forecast approach is put forward after the comparison of different power forecast realization modes. The overall block diagram and specific technology roadmap guiding the realization work of the proposed classification PV power forecast approach are also illustrated in this paper.
As the basis of PV power forecasting, the accuracy of solar irradiance forecasting is most important for power forecasting. Although artificial neural network (ANN) based solar irradiance forecast models have good performance, there are still some deficiencies exist in the current ANN forecast models, such as high input dimensions, complex model structures, and its output values also hasn't been modified reasonably. For this reason, the ANN based solar irradiance forecast models are improved from three aspects including take full use of available data, minimize information redundancy and control the input dimensions. According to the setup parameters of variable scale modification, weight coefficient and reference value generated from historical data and similarity metrics within weather type, time periodicity and neighboring similarity based two dimensions variable scale modification method for solar irradiance forecast values is presented. Simulation results show that the precision of solar irradiance prediction has been improved visibly by the above measures.
As the key step of PV power forecasting, output characteristic model of PV power generation has great influence to the accuracy of power forecasting. The implement processes of current characteristic model of PV power generation are very complicated, the parameter optimization is quite difficult, and its applicability is also not very satisfactory. For this reason, the direct effect and indirect effect through other factors to PV power of each meteorological influence factor are analyzed, the running state space is constructed by the selected main meteorological influence factors. The relevant data model reflecting the output characteristic of PV power generation is established using actual operating data. Finally, the power mapping forecast method based on running state weighted distance defined according to the relevance between PV power and influence factors is presented to obtain the forecast value from the relevant data model.
At last, considering the requirements of power grid dispatching and power plant optimal operation, referring to the relevant standards and technical specifications of PV power forecasting, a PV power forecast system based on the results of this study is developed and put into application. The actual operation achieved good results and verifies the validity of the approaches and models presented in the paper.
引文
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