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.

电力市场自由化改革引发了显著的电价波动，使得消费者与电力零售商面临巨额金融风险。传统定价模式（如实时电价与固定电价）难以在经济效率与风险保障之间建立合理平衡。为解决这一问题，本文提出一种基于人工智能的架构，将定制化金融风险对冲合约与深度学习预测相结合。卷积长短期记忆网络（CNN-LSTM）模型可实现24小时的电价与用户需求预测，从而能够基于个体消费者的风险偏好精准设计对冲合约。与此同时，电力零售商可通过运营电池储能系统（BESS）来提升流动性与财务韧性；消费者则可选择保险式执行价格并支付保费。本文借助金融风险理论确定选定执行价格对应的保费金额。基于真实北欧电力交易所（Nord Pool）现货市场数据开展的仿真结果表明，所提方案可为三类典型消费者分别实现9.21%、3.34%与4.92%的电费减免。与此同时，相较于未引入对冲机制与电池储能系统集成的基准场景，电力零售商的总营收提升了60.96%。所提的人工智能驱动机制可保障消费者的电费成本稳定，并实现零售商的利润优化，为电价市场中的金融风险管理提供了可靠解决方案。