## 1 Introduction  
Geopolitical situations, such as the war in Ukraine, have had a significant impact on global energy prices. [PERSON] (2022) and [PERSON] and [PERSON] (2023) point out that sanctions against Russia and restrictions on gas and oil supplies have caused a sharp increase in electricity prices in Europe. Additionally, climate extremes such as heatwaves and droughts have affected renewable energy production, further contributing to price volatility [1, 2]. The production of electricity from renewable sources, especially by photovoltaic (PV) power plants, has experienced significant growth over the past decades. This trend is driven by global efforts to reduce greenhouse gas emissions, decrease dependency on fossil fuels, and ensure energy security. PV technologies have become an integral part of the energy mixes of many countries, with their share in total electricity production continuously increasing [1]. In addition to technological advancements, political and economic factors also play a crucial role. Many governments worldwide have implemented supportive policies and incentives to promote the installation of PV systems. These measures include subsidies, tax credits, guaranteed purchase prices for electricity generated from renewable sources (known as feed-in tariffs), and various forms of support for research and development in the field of renewable energy. In recent years, a significant number of hours with negative electricity prices have been recorded, which has had a considerable impact on the profitability and economic efficiency of electricity sources. In [3], it was reported that in Australia, 6886 instances of negative electricity prices were observed from 2016 to 2020, primarily due to the penetration of PV and wind power plants [4]. However, numerous publications highlight the impact of various types of electricity sources on electricity prices. In [5; 6; 7; 8; 9], the authors point out that not only renewable energy sources increase the volatility and price of electricity in the market; for example, the construction of small hydropower plants can also reduce the price of electricity. Many publications address the issue of creating different models for predicting electricity prices. The article [10] examines the use of Gradient Boosting Machine (GBM) models for predicting electricity prices in the Irish market. It shows that GBM models, specifically XGBM and LGBM, can predict electricity prices with better accuracy, thereby extending forecast horizons. In [11], the authors investigated the use of linear regression for predicting electricity price trends, albeit with not very high accuracy. The most commonly used methods for predicting electricity prices are stochastic time series, causal models, machine learning, and artificial intelligence-based models [12; 13; 14; 15; 16; 17; 18; 19]. Producers and buyers meet on the exchange, where prices are determined for each hour a day in advance. It is possible to predict the price of electricity with a certain probability, but prediction is still not entirely accurate. However, what we do know is that once the price is set on the exchange, sellers know how much they will earn for each MWh produced. Given that a single price is set for each hour, not every producer has the same profit per MWh produced. This is due to different operating costs. PV and wind energy sources have the lowest operating costs. Additionally, the generated electricity must be delivered to the grid. Given that electricity prices have been highly volatile in recent years, it is not possible to predict in advance the profit that a power plant will generate. However, there are indicators that can give us a clear idea of this--CPS and CRS. These indicators are relevant for several reasons:  
1. The cost of a project is significantly influenced by its procurement price, as this determines its financial value through its ability to generate revenue. 2. Capture Price defines the value of specific technologies within a particular geographical location. 3.