The global push for renewable energy faces challenges due to the unpredictable and inconsistent nature of wind and solar sources. These inherent characteristics of renewable energy sources add volatility to the electricity markets. In response, electrical energy storage (EES) emerges as a solution for maintaining grid flexibility, stability, and reliability. Therefore, it is important to understand the potential interdependence of the wholesale electricity markets and the EESs.
This thesis focuses on short-term EES scheduling, comparing pumped hydropower storage (PHS), compressed air energy storage (CAES), and battery energy storage systems (BESS). This thesis aims to optimize EES scheduling, which includes charging and discharging actions, in the intraday electricity market, considering market price uncertainties. Storage decisions are optimized for one day (24 hours) from the perspective of the storage owner, and its objective is to maximize its profit through market operations. The research introduces a two-stage stochastic programming approach with a rolling horizon method (SORH) to adapt to changing conditions of the intraday market throughout the day.
The results of SORH, its deterministic counterpart (DORH), and simple deterministic optimization (DO) are compared by implementing a case study organized in four typical days based on trading data from the German electricity market. SORH consistently outperforms DORH and DO and is a suitable optimization strategy. SORH reaches on average 72 percentage of the theoretical optimum, where all prices are known in advance. Moreover, SORH offers opportunities for speculative trading using the storage as an option rather than only physically operating the storage. For a practical application of the model, future research could explore methods to match the current day with representative typical days to construct relevant price scenarios.