Scripts and Data for "AI-Powered Risk Management in Energy Markets:Hedging Contracts and BESS Arbitrage Strategies"

The deregulation of electricity markets has introduced significant price volatility, exposing consumers and retailers to substantial financial risks. Traditional pricing methods, such as real-time pricing and set tariffs, fail to establish an appropriate balance between economic efficiency and risk protection. To address this issue, this paper proposes an AI-based architecture that combines customized financial risk hedging contracts with deep learning-based forecasts. A CNN-LSTM model creates 24-hour predictions of electricity prices and customer demand, allowing for exact hedging contract design based on individual consumer risk preferences. While the retailer simultaneously runs a Battery Energy Storage System (BESS) to add liquidity and improve financial resilience, customers choose insurance-style strike prices and pay premiums. The risk theory in finance is used to determine the premiums for selected strike prices. The simulation results using real-world Nord Pool spot market data demonstrate that the proposed approach achieves electricity bill reductions of 9.21\%, 3.34\%, and 4.92\% for three representative consumers. Simultaneously, the total revenue of the retailer increased by 60.96\% compared to the baseline scenario without hedging and BESS integration. The proposed AI-based mechanism ensures consumer cost stability and retailer profit optimization, offering a robust solution for managing financial risks in electricity price markets.