The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations. Accurate and reliable electricity price forecasting is crucial for effective market participation, where price dynamics can be significantly more challenging to predict. Probabilistic forecasting, through prediction intervals, efficiently quantifies the inherent uncertainties in electricity prices, supporting better decision-making for market participants. This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques, specifically Ensemble Batch Prediction Intervals and Sequential Predictive Conformal Inference. These methods provide precise and reliable prediction intervals, outperforming traditional models in validity metrics. We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques. This ensemble delivers both narrow prediction intervals and high coverage, leading to more reliable and accurate forecasts. We further evaluate the practical implications of CP techniques through a simulated trading algorithm applied to a battery storage system. The ensemble approach demonstrates improved financial returns in energy trading in both the Day-Ahead and Balancing Markets, highlighting its practical benefits for market participants.

Efficiently integrating renewable resources into electricity markets is vital for addressing the challenges of matching real-time supply and demand while reducing the significant energy wastage resulting from curtailments. To address this challenge effectively, the incorporation of storage devices can enhance the reliability and efficiency of the grid, improving market liquidity and reducing price volatility. In short-term electricity markets, participants navigate numerous options, each presenting unique challenges and opportunities, underscoring the critical role of the trading strategy in maximizing profits. This study delves into the optimization of day-ahead and balancing market trading, leveraging quantile-based forecasts. Employing three trading approaches with practical constraints, our research enhances forecast assessment, increases trading frequency, and employs flexible timestamp orders. Our findings underscore the profit potential of simultaneous participation in both day-ahead and balancing markets, especially with larger battery storage systems; despite increased costs and narrower profit margins associated with higher-volume trading, the implementation of high-frequency strategies plays a significant role in maximizing profits and addressing market challenges. Finally, we modelled four commercial battery storage systems and evaluated their economic viability through a scenario analysis, with larger batteries showing a shorter return on investment.

The continuing deployment of renewables and battery energy storage systems is likely to lead to increased price volatility Martinez-Anido et al. (2016); Eurostat (2022). The Balancing Market (BM) is the last stage for trading electric energy, exhibiting far higher volatility compared to both the Day-Ahead Market (DAM) and Intra Day Market (IDM). It plays an essential role (in particular in regions where storage of large quantities of electric energy is not economically convenient Mazzi & Pinson (2017)) as production and consumption levels must match during the operation of electric power systems. The growing importance of accurate forecasts of BM prices to participants is outlined in Ortner & Totschnig (2019), where forecast errors of variable renewable electricity will drive demand for BM participation. Historically, the focus on the DAM is intuitive, given that it is a cornerstone of the European electricity market. In addition, the datasets required for forecasting the DAM are widely available. The lack of analysis of the BM is likely the result of a combination of factors including not all jurisdictions having a BM, the rules governing it can differ from region to region and the identification and acquisition of the relevant datasets can be complicated and expensive (with no open access dataset). In recent years, given access to additional datasets and increasing GPU speeds, the application of Deep Learning (DL) models has become an attractive option.