This paper proposes a risk-averse approach to energy storage price arbitrage, leveraging conformal uncertainty quantification for electricity price predictions. The method addresses the significant challenges posed by the inherent volatility and uncertainty of real-time electricity prices, which create substantial risks of financial losses for energy storage participants relying on future price forecasts to plan their operations. The framework comprises a two-layer prediction model to quantify real-time price uncertainty confidence intervals with high coverage. The framework is distribution-free and can work with any underlying point prediction model. We evaluate the quantification effectiveness through storage price arbitrage application by managing the risk of participating in the real-time market. We design a risk-averse policy for profit-maximization of energy storage arbitrage to find the safest storage schedule with very minimal losses. Using historical data from New York State and synthetic price predictions, our evaluations demonstrate that this framework can achieve good profit margins with less than $35\%$ purchases.

翻译：本文提出了一种风险规避的储能电价套利方法，该方法利用保形不确定性量化技术来处理电价预测问题。实时电价固有的波动性和不确定性带来了重大挑战，给依赖未来价格预测来规划运营的储能参与者造成了巨大的财务损失风险，本方法旨在应对这些挑战。该框架包含一个双层预测模型，用于量化具有高覆盖率的实时电价不确定性置信区间。该框架无需预设分布，可与任何基础点预测模型协同工作。我们通过管理参与实时市场的风险，在储能价格套利应用中评估了量化效果。我们设计了一种风险规避策略，以实现储能套利的利润最大化，从而找到损失极小、最安全的储能调度方案。利用纽约州的历史数据和合成价格预测，我们的评估表明，该框架能以低于$35\%$的购电比例实现良好的利润率。