Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy
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日前电价概率预测、电池交易策略与预测精度的经济评价
- 研究问题
- 电价概率预测的统计精度改善何时能转化为电池储能决策的经济价值，交易策略又能否可靠地评价预测模型？
- 方法与思路
- 作者分析分位数交易策略的激励和跨期依赖问题，以完整概率预测驱动随机规划，并在风险中性和风险厌恶情形下评价决策质量。
- 材料陈述的贡献
- 摘要指出分位数策略不能激励诚实概率预测且忽略跨期依赖，并通过理论讨论及德国电力市场案例说明交易评价中的陷阱。
- 阅读关注点
- 应核对诚实预测的激励定义、联合预测分布如何进入优化、风险偏好和电池约束的设定，以及统计评分与决策收益的联系。
- 阅读边界
- 这是 arXiv 预印本，同行评审和期刊接收均未核实。摘要未提供模型设置及收益数值，德国案例不能证明某类预测方法普遍更优。
原文摘要
Electricity price forecasting supports decision-making in energy markets and asset operation. Probabilistic forecasts are increasingly adopted to explicitly quantify uncertainty, typically issued as quantile predictions or ensembles of the full predictive distribution. However, how improvements in statistical forecast quality translate into economic value remains unclear. Battery storage arbitrage in day-ahead markets is a popular application-based benchmark for this purpose. We analyze quantile-based trading strategies (QBTS) and identify two critical flaws: they do not incentivize honest probabilistic forecasting and they ignore the intertemporal dependence structure of electricity prices. We therefore frame battery optimization as a stochastic program based on fully probabilistic forecasts and examine decision quality measurement for risk-neutral and risk-averse settings under different uncertainty models. Our discussion touches both sides of the coin: How reliable is the economic evaluation of forecasting models though (simplified) application studies - and how do improvements in statistical forecast quality for stochastic programs relate to the decision-quality and economic performance? We provide theoretical justification and empirical evidence from a case study on the German electricity market. Our results highlight the pitfalls of ranking forecasting models through battery trading strategies. We conclude with implications for evaluation practice and directions for future research in application-based forecast assessment.