Final colloquium Luuk Bartels

Optimal trading strategy for solar PV in the day-ahead electricity market considering uncertain imbalance prices

Supervisor: prof.dr.ir. Tamas Keviczky

Abstract: In recent years, the share of electricity generated from renewable sources such as solar and wind has increased significantly, reaching 42% in 2023 and projected to rise to 70% by 2030. However, production from these sources is highly weather-dependent, unpredictable, and often misaligned with electricity demand. This mismatch leads to price volatility in electricity markets, emphasizing the importance of short-term portfolio management and asset flexibility. Solar panels are partially flexible assets, as their production can be curtailed. However, their output is weather-dependent and cannot be predicted with complete accuracy. This creates a challenge in determining the optimal trade volume in the day-ahead market. Trading conservatively in the day-ahead market reduces revenue but minimizes imbalance volumes through the possibility of curtailment. On the other hand, trading too much can result in unavoidable imbalances when actual production falls short. Existing literature focuses on minimizing imbalance volumes as the imbalance market is characterized by extreme prices, high volatility and lower average prices than day-ahead prices. Additionally, the imbalance price is unknown before trading, making it difficult to make optimal curtailment decisions. In this research, however, real-time imbalance price predictions are available, enabling optimal decision-making in real-time. This provides the opportunity to profit from high imbalance prices while avoiding negative prices. In this study, a coordinated bidding strategy is developed to optimize day-ahead bids, balancing revenue maximization and risk minimization. The primary objective is to explore methods for incorporating uncertain imbalance prices into day-ahead optimization. Various methods from the literature are compared, and a novel decision-focused approach is introduced. In combination with solar generation forecasts, state-of-the-art day-ahead price predictions, and optimization models, monthly revenues are simulated using Energy Management System (EMS) software. Results show that modeling uncertain imbalance prices using historical scenarios achieves the highest and most consistent revenues, especially when combined with risk-seeking optimization. The novel decision-focused approach also performs among the best models, delivering consistently high revenues. Adding battery storage to the solar panels yields similar results, and further revenue increases are possible with improved solar forecasting. This highlights an important direction for future research.