## 1 Introduction
\begin{tabular}{c c c c c c c} \hline  & **Germany** & **France** & **Poland** & **Slovakia** & **Czech Republic** & **Estonia** \\ \hline
2015 & 32.3 & 15.7 & 9.4 & 18.6 & 12.6 & 14.8 \\
2016 & 32.4 & 17 & 10.2 & 20.1 & 11.9 & 11.8 \\
2017 & 36.9 & 16.2 & 11.7 & 19.1 & 11.4 & 12.8 \\
2018 & 38.9 & 19.1 & 10.4 & 17.9 & 11.6 & 13 \\
2019 & 44.5 & 19.6 & 12.5 & 22.4 & 11.7 & 23.6 \\
2020 & 49.2 & 23.4 & 14.8 & 23 & 13 & 38.1 \\
2021 & 45 & 21.5 & 14.7 & 21.2 & 13 & 28.6 \\
2022 & 48.8 & 23.9 & 19.3 & 20.6 & 12.7 & 35.6 \\
2023 & 59.7 & 26.2 & 26 & 21.8 & 14.9 & 44.7 \\
2024 & 62.3 & 26.5 & 29 & 23 & 17.5 & 54.1 \\
2025 & 55.5 & 25.9 & 28 & 16.5 & 16.1 & 52.4 \\ \hline \end{tabular}  The main drivers of increasing volatility include the following:  
1. Fluctuations in RES generation, especially sharp changes in solar radiation and wind;
2. Insufficient capacity for energy storage, with excess production pushing prices down (into negative territory);
3. Rigidity of consumption—if demand does not match supply, the market reacts with extreme prices;
4. Lack of network flexibility, especially at times of high load or transmission constraints. These phenomena are not just a technical problem of the market—they have major implications for different actors:  
1. Producers face increased profit uncertainty;
2. Network operators need to address deviations and congestion more frequently;
3. Customers are experiencing price shocks that can be reflected in end prices. Scientific volatility analysis is therefore a key prerequisite for modern energy management. An accurate understanding of how volatility changes over time, what causes it and how it relates to renewables provides important inputs for the following:  
1. Design of market mechanisms;
2. Planning of storage capacities;
3. Development of demand management systems. Energy markets are undergoing a fundamental transformation as a result of the development of renewables, and the importance of accurate analysis of electricity price developments is increasing. The academic literature relies heavily on aggregated indicators, particularly for long-term average prices, monthly or annual production averages, and captured prices for different types of resources (e.g., ’mean capture price’ for solar). While these indicators provide a general overview, they fail to capture short-term market fluctuations, temporal patterns of market behaviour, or the growing impact of extreme events such as negative prices or excessive volatility. This type of analytical approach therefore has several limitations: [1,7]  
1. It distorts the perception of market variability as the average value masks the extent of fluctuations. 2. It masks the occurrence of critical market moments, such as negative prices, which have a major impact on the strategy of both producers and network operators. 3. It ignores temporal dimensions such as daily production and consumption profiles, which are increasingly important for a low-carbon system. This thesis aims to bridge this research gap. The aim is to analyse disaggregated data with high temporal resolution (15 min to hourly), with an emphasis on the following:  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Germany** & **France** & **Poland** & **Slovakia** & **Czech Republic** & **Estonia** \\ \hline
2015 & 32.3 & 15.7 & 9.4 & 18.6 & 12.6 & 14.8 \\
2016 & 32.4 & 17 & 10.2 & 20.1 & 11.9 & 11.8 \\
2017 & 36.9 & 16.2 & 11.7 & 19.1 & 11.4 & 12.8 \\
2018 & 38.9 & 19.1 & 10.4 & 17.9 & 11.6 & 13 \\
2019 & 44.5 & 19.6 & 12.5 & 22.4 &1. Price volatility dynamics;
2. Correlations between price and renewable generation;
3. Occurrence and timing of negative prices;
4. Development of seasonal or diurnal patterns. This approach can provide a deeper understanding of market mechanisms and provide the basis for more effective instruments of flexibility, accumulation, and regulation in energy transition.