Short-Term Electricity Price Forecasting for Flexibility Trading

To meet greenhouse gas reduction targets under the Paris Agreement, the European Union promotes large-scale electrification and prioritizes the integration of variable renewable energy sources, particularly wind and solar energy.
Flexible assets--such as heat pumps, electric vehicles, and battery energy storage systems--support this integration by responding to grid or market signals.

Short-term electricity markets--especially the day-ahead and continuous intraday markets--facilitate integration by enabling near real-time electricity trading.
Market participants use them to manage expected imbalances--deviations between forecasted and actual consumption or production--or to exploit price spreads by adjusting flexible asset schedules.
However, the high volatility and complex structure of the continuous intraday market pose challenges to effective trading and scheduling.

This thesis addresses these challenges through two research streams.
The first--short-term electricity price forecasting--addresses market volatility by developing directional price forecasts that estimate the direction of price movements.
This approach improves on traditional methods, which often ignore intraday dynamics by focusing solely on indices or distributions.
The second stream--flexible asset trading optimization--uses these forecasts to develop trading strategies, examining the impact of uncertainty from both price forecasts and user behavior, such as that of electric vehicle users.

In practical terms, this thesis advances the automation of flexibility optimization and trading in short-term electricity markets.
Therefore, it is directly relevant to stakeholders in the industry engaged in electricity trading and owners of flexible assets.