## 1 Introduction  
Statistics show that photovoltaic power plants (PVPP) demonstrate the highest dynamics of installed capacity growth among renewable-based power plants worldwide [1]. However, given the climatic and geographical characteristics of Russian Federation territory, for the Unified Power System of Russia, as a whole, there is no considerable impact of stochastic renewable generation on the power system operation modes, but for the regional interconnected power systems (IPS) of the South and the Urals, a relatively high share of the installed capacity of PVPP is already observed. An increase of such power plants' share in the total power generation fleet leads to an even greater increase in their influence on the power balance [2], frequency [3,4], electric energy quality [5,6], static and dynamic stability [7, 8, 9], voltage levels [10,11] and electrical energy losses [12,13]. The first response to the PVPP installed capacity growth should be the development of forecasting systems, allowing reliable planning of power system operation modes. In general terms power system planning is understood as an action schedule aimed at ensuring the balance of power consumption and generation, reliability and efficiency of the entire technological chain: power generation, transmission, distribution and consumption [14,15]. Short-term planning is typically carried out for the day-ahead perspective by the dispatch control centers. Operational planning is also carried out by dispatch centers, but within the operational day in order to sustain power balance and power supply reliability online. When analyzing the renewable energy sources' (RES) influence on the power system operation mode, it is necessary to take into account the stochastic nature of weather conditions, especially for the PVPPs and wind power plants, power generation of which largely depends on meteorological factors [16,17]. This kind of uncertainty can be taken into account in their forecasting models both from the point of view of the probability theory [18] and the possibility theory [19]. When using the possibility theory, both qualitative and quantitative methods can be applied [20]. The PVPP generation forecasting is one of the most effective and least capital-intensive measures that allow the integration of stochastic generation sources into the power system and reduce the negative impact on the power system's operation mode. In this regard, the issue of day-ahead PVPP forecasting becomes a task with increasing priority in many countries [21]. Methods for PVPP generation forecasting can be divided into four main classes [22]: statistical, physical, intelligent and hybrid. Statistical models use statistical analysis to describe the relations between weather conditions and time series of solar irradiance or power generation of PVPPs, using retrospective data, as, for example, in [23, 24, 25, 26]. Numerical weather forecast and satellite images, as suggested in the studies [27, 28, 29, 30], form the basis for compiling PVPP generation forecasts for physical models. Intelligent models use artificial intelligence and machine learning methods to obtain forecasts of solar irradiance or PVPP power generation, mainly based on neural networks, as presented in [31, 32, 33, 34]. Hybrid models for PVPP generation forecasting typically combine either physical or statistical or intelligent models. Examples of such models are presented in [35, 36, 37, 38]. The authors of the article have previously developed their own step-by-step approach of PVPP generation short-term forecasting (STF), which is presented in detail in [39,40] and schematically given in Figure 1.  
The major advantage of the proposed approach is characterized by its flexibility in terms of accounting for the external factors, including the operational state of the power generation equipment, switchgear composition, operation modes of the adjacent power system, etc. due to the effective  
Figure 1: Flowchart of the photovoltaic power plants (PVPP) generation short-term forecasting algorithm.