Abstract

The construction and operation of large solar power plants (SPPs) and the dependence of their production on light and other meteorological factors leads to a strong dependence of the operation modes of the Republic of Crimea and Sevastopol power system on meteorological factors. Today, given that the share of solar power plants is about 30% of the total installed capacity, it is necessary to solve the problems that have a great impact on the power system operating modes. With large output capacity of the solar power plant, the operator has to give commands to turn off the generating equipment of thermal power plants. In power systems with a large share of solar generation, it is necessary to solve this problem by improving the generated power predicting methods, as it will reduce the dependence of operating modes on weather factors and increase the reliability of the power system. The paper discusses the use of hybrid predicting methods that imply taking into account the possibility of the weather scenarios simulation, advanced cloud-based image processing technology, and close-to-real-time cloud motion surveillance cameras. There was an experimental software created that selects coefficients of set configuration time series. In combination with the conservative methods, it makes predicting the SPP Perovo output more accurate. Taken together, the chosen methods of predicting solar power generation capacity in the power system of the Republic of Crimea and Sevastopol ensure not only stability of the power system as a whole, but also the maximum efficiency of power plants, allow to accelerate the integration of solar power plants into the power system, and have positive effects on the environment.

中文翻译：

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太阳能发电量预测方法的改进：以克里米亚共和国和塞瓦斯托波尔市的电力系统为例

摘要

大型太阳能发电厂（SPPs）的建设和运营以及其生产对光和其他气象因素的依赖导致克里米亚共和国和塞瓦斯托波尔电力系统的运行模式对气象因素的强烈依赖。如今，鉴于太阳能发电厂的份额约为总装机容量的30％，有必要解决对电力系统运行模式产生重大影响的问题。由于太阳能发电厂的输出功率较大，因此操作员必须发出命令以关闭火电厂的发电设备。在太阳能发电量很大的电力系统中，有必要通过改进发电量预测方法来解决此问题，因为它将减少操作模式对天气因素的依赖性，并提高电力系统的可靠性。本文讨论了混合预测方法的使用，这些方法隐含了天气情景模拟，先进的基于云的图像处理技术以及接近实时的云运动监控摄像机的可能性。创建了一个实验软件，可以选择设置配置时间序列的系数。结合保守方法，可以更准确地预测SPP Perovo的产量。综合起来，所选择的预测克里米亚共和国和塞瓦斯托波尔共和国电力系统中太阳能发电能力的方法不仅可以确保整个电力系统的稳定性，还可以确保发电厂的最大效率，