Optimized Electricity Price Prediction for Battery Energy Storage System (BESS) Scheduling

With the growing integration of renewable energy sources and the deregulation of electricity markets, electricity prices have become increasingly volatile, complicating accurate price forecasting. This volatility is especially significant for Battery Energy Storage Systems (BESS), which rely on price predictions to schedule their charging and discharging cycles and maximize market arbitrage opportunities. Traditional prediction models often focus on minimizing the prediction error, which quantifies the difference between predicted and actual prices. However, this approach does not necessarily lead to optimal decision-making in BESS operations. Instead of focusing solely on prediction accuracy, the BESS's objective is to optimize its charging/discharging schedule to maximize benefits from price fluctuations, which involves minimizing the decision error—the gap between the optimal decisions based on predicted prices and those based on true market prices. This paper proposes a “predict, then optimize” framework, where accurate price predictions are integrated into a BESS scheduling model. The framework aims to reduce decision errors and enhance the arbitrage potential for BESS, contributing to more efficient energy storage management and better market performance. The research discusses various price prediction models and highlights the importance of aligning prediction strategies with the actual objectives of BESS operations to achieve superior financial outcomes.