Deep Hedging of Green PPAs in Electricity Markets
This addresses risk management for agents in electricity markets dealing with renewable energy contracts, but it is incremental as it applies existing machine learning methods to a specific domain problem.
The paper tackles the problem of hedging and risk-managing Green Power Purchase Agreements in electricity markets, which involve price and weather risks, by proposing a deep hedging framework using machine learning, and the resulting strategies outperform static and dynamic benchmarks across different risk measures.
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.