用深度学习优化绿电采购协议对冲，降低电价与天气风险。
Deep Hedging of Green PPAs in Electricity Markets
- 采用机器学习构建动态对冲策略，应对非交易性天气风险。
- 在多种风险度量下，表现优于静态和传统动态策略。
- 适合电力市场从业者与绿色能源投资机构参考。
在电力市场中，绿电购电协议（Green PPAs）已成为推动从化石燃料向风能、太阳能等可再生能源转型的重要合同工具。交易绿电PPA会面临电价风险与天气风险。此外，成熟电力市场存在‘自残效应’：大规模可再生能源注入导致电价低迷，反之亦然。由于天气无法交易，如何在此高度不完全的市场中进行风险对冲成为关键问题。本文提出一种‘深度对冲’框架，利用机器学习方法构建对冲策略。结果表明，该策略在不同风险度量下均优于静态及动态基准策略。
原文摘要 · Abstract (English)
In power markets, Green Power Purchase Agreements have become an important contractual tool of the energy transition from fossil fuels to renewable sources such as wind or solar radiation. Trading Green PPAs exposes agents to price risks and weather risks. Also, developed electricity markets feature the so-called cannibalisation effect : large infeeds induce low prices and vice versa. As weather is a non-tradable entity the question arises how to hedge and risk-manage in this highly incom-plete setting. We propose a ''deep hedging'' framework utilising machine learning methods to construct hedging strategies. The resulting strategies outperform static and dynamic benchmark strategies with respect to different risk measures.
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