Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market
This work addresses the need for accurate price forecasting to reduce uncertainty and maximize profits for virtual bidders in electricity markets, particularly with increasing renewable energy volatility, but it is incremental as it applies a known deep learning method to a specific domain.
This study tackled the problem of forecasting electricity price spreads for virtual bidding in wholesale markets, using a Transformer-based deep learning model on ERCOT data, and found that a trading strategy focusing on peak hours with over 50% precision yielded nearly consistent profits.
Virtual bidding plays an important role in two-settlement electric power markets, as it can reduce discrepancies between day-ahead and real-time markets. Renewable energy penetration increases volatility in electricity prices, making accurate forecasting critical for virtual bidders, reducing uncertainty and maximizing profits. This study presents a Transformer-based deep learning model to forecast the price spread between real-time and day-ahead electricity prices in the ERCOT (Electric Reliability Council of Texas) market. The proposed model leverages various time-series features, including load forecasts, solar and wind generation forecasts, and temporal attributes. The model is trained under realistic constraints and validated using a walk-forward approach by updating the model every week. Based on the price spread prediction results, several trading strategies are proposed and the most effective strategy for maximizing cumulative profit under realistic market conditions is identified through backtesting. The results show that the strategy of trading only at the peak hour with a precision score of over 50% produces nearly consistent profit over the test period. The proposed method underscores the importance of an accurate electricity price forecasting model and introduces a new method of evaluating the price forecast model from a virtual bidder's perspective, providing valuable insights for future research.