This research paper analyses the evolution of electricity price volatility in six European countries between 2015 and 2025, focusing on the relationship between the increasing penetration of renewable energy sources (RES) and short-term price fluctuations. Based on high-frequency data (at 15 min to hourly resolution) on electricity prices, solar and wind generation, and residual load, both year-on-year and structural changes in volatility are quantified. The results show a significant increase in volatility after 2021, with outliers appearing particularly during the 2022 energy crisis, most notably in countries with a high share of RES and limited system flexibility. The analysis identifies non-linear relationships between RES generation and the occurrence of negative prices, with country-specific threshold levels. Annual regression models show that the predictive power of these relationships is time-varying and influenced by externalities. The correlation matrices confirm regional differences in the impact of RES on price dynamics. The results support the design of rules for forecasting risk periods and point to the need for market mechanisms increasing flexibility, including accumulation, demand management, and cross-border integration.

Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics.

Electricity markets in Europe have undergone significant transformation due to the rapid integration of renewable energy sources, particularly wind and solar. While previous research has extensively analys sed photovoltaic capture rates, less attention has been paid to the economic performance of wind generation under volatile market conditions. This study investigates the capture price (CP) and capture rate (CR) for wind power in selected European countries over the period 2015–2025. Using hourly day-ahead electricity prices and generation data from energy-charts.info, the analysis evaluates the relationship between market dynamics and the profitability of wind power plants. These findings highlight the need for improved market design mechanisms and flexible technologies such as energy storage and cross-border balancing—to sustain wind energy profitability. The research outcomes provide valuable insights for policymakers and investors into the evolving competitiveness of wind power across European markets and its implications for achieving long-term decarbonisation goals.

The rapid global expansion of solar photovoltaic (PV) capacity has increased the operational need for accurate generation forecasting to support grid balancing, dispatch, and market participation. Artificial intelligence (AI) and machine learning (ML) methods now dominate this research area, but the resulting literature is large and methodologically fragmented, making it difficult to establish which methods are used, what data they require, and where the principal gaps lie. This paper combines a bibliometric analysis of 3111 records retrieved from the Web of Science Core Collection (2010–2026) with a technical synthesis of 27 highly cited studies published from 2022 onward, combining the most highly cited works with targeted additions from 2024–2025 covering specific methodological gaps. The bibliometric analysis shows exponential growth in annual output, from three publications in 2010 to 609 in 2025, with keyword evolution tracing a clear methodological trajectory from classical and fuzzy-logic approaches, through shallow and deep neural networks, to transformer- and attention-based architectures since 2023. The technical synthesis finds that classical machine learning remains competitive for day-ahead forecasting with well-structured numerical weather prediction inputs, that convolutional neural network–long short-term memory (CNN-LSTM) hybrids dominate the deep-learning literature, and that graph-based and transformer architectures address multi-site and multi-horizon forecasting, respectively. A comparison of reported results shows that absolute error metrics are not directly comparable across studies due to heterogeneous datasets, metrics, temporal resolutions, and climates, although relative improvements within controlled comparisons are directionally consistent. Seven research gaps are identified, including the absence of standardized benchmarks, limited public dataset availability, weak cross-region generalization, and underdeveloped uncertainty quantification.

This study employs the General Unary Hypotheses Automaton (GUHA) based approach, implemented via the CleverMiner tool, to analyze the association between solar and wind power generation and day-ahead electricity prices in Germany from 2015 to 2024. Utilizing hourly generation and market price data from the Energy-Charts portal, enhanced association rule mining reveals statistically significant and interpretable patterns linking renewable generation levels with electricity price fluctuations. The findings highlight time-specific and generation-dependent price behaviors, providing valuable insights for market participants and policy makers on renewable integration effects in the electricity market. The study demonstrates the utility of GUHA as a robust exploratory data mining method for complex energy market datasets.