🤖 AI Summary
Green PPAs face three interrelated challenges in evolving electricity markets: exposure to volatile electricity prices, non-tradable weather-related risks, and market incompleteness exacerbated by the “cannibalization effect.” This paper introduces the first systematic application of deep hedging to Green PPA risk management, proposing a dynamic hedging framework that integrates deep reinforcement learning with stochastic control to end-to-end optimize risk-sensitive hedging policies. The model jointly leverages historical electricity prices, multi-source meteorological data, and market supply-demand features to train a differentiable hedging policy network. Evaluated under multiple risk metrics—including CVaR, MSE, and maximum drawdown—the approach significantly outperforms conventional static and dynamic hedging benchmarks: it reduces hedging costs by 18–32%, compresses risk exposure by over 40%, and overcomes the fundamental hedging bottleneck imposed by non-tradable weather factors.
📝 Abstract
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.